Posted: August 2nd, 2022

The Role of Health Information Systems

Understanding the role information systems play in health care is essential to the success of health care professionals. Data and information share a common thread; they work together to facilitate decisions. In health care, they can mean the difference between life and death. Both data and information assist in providing quality health care.

  • Evaluate the different types of data and/or information consumed by health care organizations as it relates to patient health technology.
  • Compare and contrast the role of data and information in planning, include how data and information are implemented, managed, and accessed.
  • Assess an issue related to health care data that illustrates why data quality is important

Chapter 3
Health Care Information Systems
Learning Objectives
To be able to identify the major types of administrative and clinical information systems used in
health care.
To be able to give a brief explanation of the history and evolution of health care information
systems.
To be able to discuss the key functions and capabilities of electronic health record systems and
current adoption rates in hospitals, physician practices, and other settings.
To be able to describe the use and adoption of personal health records and patient portals.
To be able to discuss current issues pertaining to the use of HCIS systems including
interoperability, usability, and health IT safety.

Review of Key Terms
An information system (IS) is an arrangement of data (information), processes, people, and
information technology that interact to collect, process, store, and provide as output the
information needed to support the organization (Whitten & Bentley, 2007). Note that information
technology is a component of every information system. Information technology (IT) is a
contemporary term that describes the combination of computer technology (hardware and
software) with data and telecommunications technology (data, image, and voice networks).
Often in current management literature the terms information system (IS) and information
technology (IT) are used interchangeably.

Within the health care sector, health care IS and IT include a broad range of applications and
products and are used by a wide range of constituent groups such as payers, government, life
sciences, and patients, as well as providers and provider organizations. For our purpose,
however, we have chosen to focus on health care information systems from the provider
organization’s perspective. The provider organization is the hospital, health system, physician
practice, integrated delivery system, nursing home, or rural health clinic. That is, it is any setting
where health-related services are delivered. The organization (namely, the capacity, decisions
about how health IT is applied, and incentives) and the external environment (regulations and
public opinion) are important elements in how systems are used by clinicians and other users
(IOM, 2011). We also examine the use of patient engagement tools such as PHRs and secure
patient portals. Yet our focus is from an organization or provider perspective.

Major Health Care Information Systems
There are two primary categories of health care information systems: administrative and clinical.
A simple way to distinguish them is by purpose and the type of data they contain. An
administrative information system (or an administrative application) contains primarily
administrative or financial data and is generally used to support the management functions and
general operations of the health care organization. For example, an administrative information
system might contain information used to manage personnel, finances, materials, supplies, or
equipment. It might be a system for human resource management, materials management,

patient accounting or billing, or staff scheduling. Revenue cycle management is increasingly
important to health care organizations and generally includes the following:

Charge capture
Coding and documentation review
Managed care contracting
Denial management of claims
Payment posting
Accounts receivable follow-up
Patient collections
Reporting and benchmarking
By contrast, a clinical information system (or clinical application) contains clinical or
health-related information used by providers in diagnosing and treating a patient and monitoring
that patient’s care. Clinical information systems may be departmental systems—such as
radiology, pharmacy, or laboratory systems—or clinical decision support, medication
administration, computerized provider order entry, or EHR systems, to name a few. They may
be limited in their scope to a single area of clinical information (for example, radiology,
pharmacy, or laboratory), or they may be comprehensive and cover virtually all aspects of
patient care (as an EHR system does, for example). Table 3.1 lists common types of clinical
and administrative health care information systems.
Table 3.1. Common types of administrative and clinical information systems

Administrative Applications Clinical Applications
Patient administration systems
Admission, discharge, transfer (ADT) tracks the patient’s movement of care in an inpatient
setting Ancillary information systems
Laboratory information supports collection, verification, and reporting of laboratory tests
Registration may be coupled with ADT system; includes patient demographic and insurance
information as well as date of visit(s), provider information
Scheduling aids in the scheduling of patient visits; includes information on patients, providers,
date and time of visit, rooms, equipment, other resources
Patient billing or accounts receivable includes all information needed to submit claims and
monitor submission and reimbursement status
Utilization management tracks use and appropriateness of care
Other administrative and financial systems
Accounts payable monitors money owed to other organizations for purchased products and
services
General ledger monitors general financial management and reporting Radiology information
supports digital image generation (picture archiving and communication systems [PACS]),
image analysis, image management
Pharmacy information supports medication ordering, dispensing, and inventory control; drug
compatibility checks; allergy screening; medication administration
Other clinical information systems

Nursing documentation facilitates nursing documentation from assessment to evaluation, patient
care decision support (care planning, assessment, flow-sheet charting, patient acuity, patient
education)
Electronic health record (EHR) facilitates electronic capture and reporting of patient’s health
history, problem lists, treatment and outcomes; allows clinicians to document clinical findings,
progress notes, and other patient information; provides decision-support tools and reminders
and alerts
Personnel management manages human resource information for staff, including salaries,
benefits, education, and training
Materials management monitors ordering and inventory of supplies, equipment needs, and
maintenance
Payroll manages information about staff salaries, payroll deductions, tax withholding, and pay
status
Staff scheduling assists in scheduling and monitoring staffing needs
Staff time and attendance tracks employee work schedules and attendance
Revenue cycle management monitors the entire flow of revenue generation from charge capture
to patient collection; generally relies on integration of a host of administrative and financial
applications Computerized provider order entry (CPOE) enables clinicians to directly enter
orders electronically and access decision-support tools and clinical care guidelines and
protocols
Telemedicine and telehealth supports remote delivery of care; common features include image
capture and transmission, voice and video conferencing, text messaging
Rehabilitation service documentation supports the capturing and reporting of occupational
therapy, physical therapy, and speech pathology services
Medication administration is typically used by nurses to document medication given, dose, and
time
Health care organizations, particularly those that have implemented EHR systems, generally
provide patients with access to their information electronically through a patient portal. A patient
portal is a secure website through which patients may communicate with their provider, request
refill on prescriptions, schedule appointments, review test results, or pay bills (Emont, 2011).
Another term that is frequently used is personal health record (PHR). Different from an EHR or
patient portal, which is managed by the provider or health care organization, the PHR is
managed by the consumer. It may include health information and wellness information, such as
an individual’s exercise and diet. The consumer decides who has access to the information and
controls the content of the record. The adoption and use of patient portals and PHRs are
discussed further on in this chapter. For now, we begin with a brief historical overview of how
these various clinical and administrative systems evolved in health care.

History and Evolution
Since the 1960s, the development and use of health care information systems has changed
dramatically with advances in technology and the impact of environmental influences and
payment reform (see Figure 3.1). In the 1960s to 1970s, health care executives invested
primarily in administrative and financial information systems that could automate the patient
billing process and facilitate accurate Medicare cost reporting. The administrative applications

that were used were generally found in large hospitals, such as those affiliated with academic
medical centers. These larger health care organizations were often the only ones with the
resources and staff available to develop, implement, and support such systems. It was
common for these facilities to develop their own administrative and financial applications
in-house in what were then known as “data processing” departments. The systems themselves
ran on large mainframe computers, which had to be housed in large, environmentally controlled
settings. Recognizing that small, community-based hospitals could not bear the cost of an
in-house, mainframe system, leading vendors began to offer shared systems, so called
because they enabled hospitals to share the use of a mainframe with other hospitals. Vendors
typically charged participating hospitals for computer time and storage, for the number of
terminal connects, and for reports.

Figure 3.1 History and evolution of health care information systems (1960s to today)

By the 1970s, departmental systems such as clinical laboratory or pharmacy systems began to
be developed, coinciding with the advent of minicomputers. Minicomputers were smaller and
more powerful than some of the mainframe computers and available at a cost that could be
justified by revenue-generating departments. Clinical applications including departmental
systems such as laboratory, pharmacy, and radiology systems became more commonplace.
Most systems were stand-alone and did not interface well with other clinical and administrative
systems in the organization.

The 1980s brought a significant turning point in the use of health care information systems
primarily because of the development of the microcomputer, also known as the personal
computer (PC). Sweeping changes in reimbursement practices designed to rein in high costs of
health care also had a significant impact. In 1982, Medicare shifted from a cost-based
reimbursement system to a prospective payment system based on diagnosis related groups
(DRGs). This new payment system had a profound effect on hospital billing practices.
Reimbursement amounts were now dependent on the accuracy of the patient’s diagnosis and
procedures(s) and other information contained in the patient’s record. With hospital
reimbursement changes occurring, the advent of the microcomputer could not have been more
timely. The microcomputer was smaller, often as or more powerful, and far more affordable than
a mainframe computer. Additionally, the microcomputer was not confined to large hospitals. It
brought computing capabilities to a host of smaller organizations including small community
hospitals, physician practices, and other care delivery settings. Sharing information among
microcomputers also became possible with the development of local area networks. The notion
of best of breed systems was also common; individual clinical departments would select the
best application or system for meeting their unique unit’s needs and attempt to get the “systems
to talk to each other” using interface engines.

Rapid technological advances continued into the 1990s, with the most profound being the
evolution and widespread use of the Internet and electronic mail (e-mail). The Internet provided
health care consumers, patients, providers, and industries with access to the World Wide Web
and new and innovative opportunities to access care, promote services, and share information.

Concurrently, the Institute of Medicine (IOM, 1991) published its first landmark report The
Computer-Based Patient Record: An Essential Technology for Health Care, which called for the
widespread adoption of computerized patient records (CPRs) as the standard by the year 2001.
CPRs were the precursor to what we refer to today as EHR systems. Numerous studies had
revealed the problems with paper-based medical records (Burnum, 1989; Hershey, McAloon, &
Bertram, 1989; IOM, 1991). Records are often illegible, incomplete, or unavailable when and
where they are needed. They lack any type of active decision-support capability and make data
collection and analysis very cumbersome. This passive role for the medical record was no
longer sufficient. Health care providers needed access to active tools that afforded them clinical
decision-support capabilities and access to the latest relevant research findings, reminders,
alerts, and other knowledge aids. Along with patients, they needed access to systems that
would support the integration of care across the continuum.

By the start of the new millennium, health care quality and patient safety emerged as top
priorities. In 2000, the IOM published the report To Err Is Human: Building a Safer Health Care
System, which brought national attention to research estimating that 44,000 to 98,000 patients
die each year to medical errors. Since then, additional reports have indicated that these figures
are grossly underestimated and the incidents of medical errors are much higher (Classen et al.,
2011; James, 2013; Makary & Daniel, 2016;). A subsequent report, Patient Safety: Achieving a
New Standard of Care (2004), called for health care providers to adopt information technology to
help prevent and reduce errors because of illegible prescriptions, drug-to-drug interactions, and
lost medical records, for example.

By 2009, the US government launched an “unprecedented effort to reengineer” the way we
capture, store, and use health information (Blumenthal, 2011, p. 2323). This effort was realized
in the Health Information Technology for Economic and Clinical Health (HITECH) Act. Nearly
$30 billion was set aside over a ten-year period to support the adoption and Meaningful Use of
EHRs and other types of health information technology with the goal of improving health and
health care. Rarely, if ever, have we seen public investments in the advancement of health
information technology of this magnitude (Blumenthal, 2011). Interest also grew in engaging
patients more fully in providing access to their EHR through patient portals or the concept of a
PHR. We have also seen significant advances in telemedicine and telehealth, cloud computing,
and mobile applications that monitor and track a wide range of health data.

Electronic Health Records
Features and Functions
Let’s first examine the features and functions of an EHR because it is core to patient care. An
EHR can electronically collect and store patient data, supply that information to providers on
request, permit clinicians to enter orders directly into a computerized provider order entry
(CPOE) system, and advise health care practitioners by providing decision-support tools such
as reminders, alerts, and access to the latest research findings or appropriate evidence-based
guidelines. CPOE at its most basic level is a computer application that accepts provider orders
electronically, replacing handwritten or verbal orders and prescriptions. Most CPOE systems
provide physicians and other providers with decision-support capabilities at the point of ordering.

For example, an order for a laboratory test might trigger an alert to the provider that the test has
already been ordered and the results are pending. An order for a drug to which the patient is
allergic might trigger an alert warning to the provider of an alternative drug. These
decision-support capabilities make the EHR far more robust than a digital version of the paper
medical record.

Figure 3.2 illustrates an EHR alert reminding the clinician that the patient is allergic to certain
medication or that two medications should not be taken in combination with each other.
Reminders might also show that the patient is due for a health maintenance test such as a
mammography or a cholesterol test or for an influenza vaccine (Figure 3.2).

Figure 3.2 Sample drug alert screen

Source: Epic. Used with permission.

Up until the passage of the HITECH Act of 2009, EHR adoption and use was fairly low. HITECH
made available incentive money through the Medicare and Medicaid EHR Incentive Programs
for eligible professionals and hospitals to adopt and become “meaningful users” of EHR. As
mentioned in Chapter One, the Meaningful Use criteria were established and rolled out in three
phases. Each phase built on the previous phase in an effort to further the advancement and use
of EHR technology as a strategy to improve the nation’s health outcome policy priorities:

Improve health care quality, safety, and efficiency and reduce health disparities.
Engage patients and families in their health care.
Improve care coordination.
Improve population and public health.
Ensure adequate privacy and security of personal health information.
To accomplish these goals and facilitate patient engagement in managing their health and care,
health care organizations provide patients with access to their records typically through a patient
portal. A patient portal is a secure website through which patients can electronically access their
medical records. Portals often also enable users to complete forms online, schedule
appointments, communicate with providers, request refills on prescriptions, review test results,
or pay bills (Emont, 2011) (see Figure 3.3). Some providers offer patients the opportunity to
schedule e-visits for a limited number of nonurgent medical conditions such as allergic skin
reactions, colds, and nosebleeds.
Figure 3.3 Sample patient portal

Source: Epic.

EHR Adoption Rates in US Hospitals
As of 2015, nearly 84 percent of US nonfederal acute care hospitals had adopted basic EHR
systems representing a nine-fold increase from 2008 (Henry, Pylypchuck, Searcy, & Patel,
2016) (see Figure 3.4). Table 3.2 lists the difference functionality between a basic system and a
fully functional system (DesRoches et al., 2008). A key distinguishing characteristic is fully

functional EHRs provide order entry capabilities (beyond ordering medications) and
decision-support capabilities.

The Veterans Administration (VA) has used an EHR system for years, enabling any veteran
treated at any VA hospital to have electronic access to his or her EHR. Likewise, the US
Department of Defense is under contract with Cerner to replace its EHR system. EHR adoption
among specialty hospitals such as children’s (55 percent) and psychiatric hospitals (15 percent)
is significantly lower than general medicine hospitals because these types of hospitals were not
eligible for HITECH incentive payments. Small, rural, and critical access hospitals that have
historically lagged behind in EHR adoption are now closing the gap with general acute care
hospitals (Henry et al., 2016).

Figure 3.4 Percent of non-federal acute care hospitals with adoption of at least a basic EHR with
notes system and position of a certified EHR: 2008–2015

Note: Basic EHR adoption requires the EHR system to have a set of EHR functions defined in
Table 3.2. A certified EHR is EHR technology that meets the technological capability,
functionality, and security requirements adopted by the Department of Health and Human
Services. Possession means that the hospital has a legal agreement with the EHR vendor but is
not equivalent to adoption. *Significantly different from previous year (p<0.05).

Source: ONC (2015a).

EHR Adoption in Office-Based Physician Practices
In addition to EHR use in hospitals, we have also seen significant increases in the adoption and
use of EHR systems among office-based physician practices. By 2014, 79 percent of primary
care physicians had adopted a certified EHR system and 70 percent of medical and surgical
specialties had as well (Heisey-Grove & Patel, 2015) (see Figure 3.5).

Ninety-eight percent of physicians in community health centers had adopted an EHR,
three-quarters of them using a certified EHR. Not surprisingly, physicians in solo and small
group practices were less likely to have adopted EHR systems (Heisey-Grove & Patel, 2015).

EHR Adoption in Other Settings
Less is known nationally about EHR adoption rates in settings other than hospitals and
physician practices. Among home health and hospice agencies, the latest national estimates
based on data from the 2007 National Home and Hospice Care survey indicate that 44 percent
of home health and hospice agencies have adopted EHR systems (16 percent EHRs only and
28 percent EHRs and mobile technologies such as tablets or hand-held devices used to gather
information at the point of care) (Bercovitz, Park-Lee, & Jamoom, 2013).
Table 3.2 Functions defining the use of EHRs

Basic System Fully Functional System

Health Information Data
Patient demographics X X
Patient problem lists X X
Electronic lists of medications taken by patients X X
Clinical notes X X
Notes including medical history and follow-up X
Order Entry Management
Orders for prescriptions X X
Orders for laboratory tests X
Orders for radiology tests X
Prescriptions sent electronically X
Orders sent electronically X
Results Management
Viewing laboratory results X X
Viewing imaging results X X
Electronic images returned X
Clinical Decision Support
Warnings of drug interactions or contraindications provided X
Out-of-range test levels highlighted X
Reminders regarding guidelines-based interventions or screening X

Figure 3.5. Office-based physician practice EHR adoption since 2004

Source: ONC (2015a).

Some states, such as New York, have attempted to assess EHR adoption in long-term care
facilities such as nursing homes. One study found that among 473 nursing homes in New York,
56.3 percent had implemented an EHR system (Abramson, Edwards, Silver, & Kaushal, 2014).
Among the nursing homes that did not have EHRs, the majority had plans to implement one
within two years. One-fifth had plans to implement one in more than two years, and 11.7 percent
had no EHR implementation plans (Abramson et al., 2014). The majority of nursing homes
indicated the biggest barriers to health IT investment were the initial cost, a lack of IT staff
members, and the lack of fiscal incentives. National estimates on EHR adoption in long-term
care are nearly nonexistent. Most are qualitative studies examining the experiences of early
adopters (Cherry, Ford, & Peterson, 2011).
Impact of EHR Systems
Numerous studies over the years have demonstrated the value of using EHR systems and
other types of clinical applications within health care organizations. The majority of benefits fall
into three broad categories: (1) quality, outcomes, and safety; (2) efficiency, improved revenues,
and cost reduction; and (3) provider and patient satisfaction. Following is a brief discussion of
these major categories, along with several recent examples and reports illustrating the value of
EHRs to the health care process. It is important to note, however, that despite the benefits,
some studies have found mixed results or negative consequences.

Quality, outcomes, and safety. EHR systems can have a significant impact on patient quality,
outcomes, and safety. Three major effects on quality are increased adherence to
evidence-based care, enhanced surveillance and monitoring, and decreased medication errors.
Banger and Graber (2015) recently conducted a review of the literature on the impact of health
IT (including EHR systems) on patient quality and safety and found four major systematic
reviews had been conducted from 2006 through 2014 each using a consistent methodology
(Buntin, Burke, Hoaglin, & Blumenthal, 2011; Chaudhry et al., 2006; Goldzweig, Towfigh,
Maglione, & Shekelle, 2009; Jones, Rudin, Perry, & Shekelle, 2014). Two of the reviews were
published before the HITECH Act and two afterward. Collectively, 59 percent of the studies
examined demonstrated positive effects on quality and safety, 25 percent had mixed-positive
outcomes, 9 percent were neutral, and 8 percent were negative (Banger & Graber, 2015).
Limitations of most of the earlier studies were based on the fact that they did not include many
commercially available EHR systems. Since then, more than half of EHR evaluation studies
involved commercially available EHR systems (Jones et al., 2014). Findings from the most
recent systematic review conclude that CPOE effectively decreases medication errors. Hydari,
Telang, and Marella (2014) studied the incidence of adverse patient safety events reported from
231 Pennsylvania hospitals from 2005 to 2012 in relation to their level of health IT use. After
controlling for several possibly confounding factors, the authors found that hospitals adopting
advanced EHRs (as defined by HIMSS) experienced a 27 percent overall reduction in reported
patient safety events. Using advanced EHRs was associated with a 30 percent decline in
medication errors and a 25 percent decline in procedure-related errors (Hydari et al., 2014).
Efficiency, improved revenue, and cost reduction. In addition to improving quality and safety,
some studies have shown that the EHR can improve efficiency, increase revenues, and lead to
cost reductions (Barlow, Johnson, & Steck, 2004; Grieger, Cohen, & Krusch, 2007). A fairly
recent study by Howley, Chou, Hansen, and Dalrymple (2014) examined the financial impact of
EHRs on ambulatory practices by tracking the productivity (e.g., the number of patient visits)
and reimbursement of thirty practices for two years after EHR implementation. They found that
practice revenues increased during EHR implementation despite seeing fewer patients. Another
study looked at seventeen primary care clinics that used EHR systems and found that the
clinics recovered their EHR investments within an average period of ten months (95 percent CI
6.2–17.4 months), seeing more patients with an average increase of 27 percent in the
active-patients-to-clinicians full-time equivalent ratio, and an increase in the clinic net revenue
(p<.001) (Jang, Lortie, & Sanche, 2014). Provider and patient satisfaction. Provider and patient satisfaction are common factors to assess when implementing EHR systems. Results from satisfaction surveys are often mixed. In a 2008 national survey of physicians, 90 percent of providers using EHRs reported they were satisfied or very satisfied with them and a large majority could point to specific quality benefits (DesRoches et al., 2008). Those who had systems in place for two or more years were more likely to be satisfied (Menachemi, Powers, Au, & Brooks, 2010). A study that examined EHR satisfaction among obstetrics/gynecology (OB/GYN) physicians found that 63 percent reported being satisfied with their EHR system, and nearly 31 percent were not satisfied (Raglan, Margolis, Paulus, & Schulkin, 2014). Among study participants, younger OB/GYN physicians were more satisfied with their EHR than older physicians. A study by Rand (in collaboration with the AMA) found that although many physicians approved of EHRs in concept (for example, they

appreciated the fact that they could remotely access patient information and provide improved
patient care), they expressed frustrations with usability and work flow (Friedberg et al., 2013).
The time-consuming nature of data entry, interference with face-to-face patient care, inefficiency,
and the inability to exchange health information between EHR products led to dissatisfaction.
Physicians across the full range of specialties and practice models also described other
concerns regarding the degradation of clinical documentation.
Among US hospitals, a 2011 national study found that those with EHRs had significantly higher
patient satisfaction scores on items such as “staff always giving patients information about what
to do for the recovery at home,” “patients rating the hospital as a 9 or 10 overall,” and “patients
would definitely recommend the hospital to others” than hospitals that did not (Kazley, Diana,
Ford, & Menachemi, 2011, p. 26). Yet the same study found that the EHR use was not
statistically associated with other patient satisfaction measures (such as having clean rooms)
that one would not expect to be affected by EHR use. A more recent study by Jarvis and
colleagues (2013) assessed the impact of using advanced EHRs (as defined as Stages 6 or 7
on the HIMSS Analytics EMR Adoption Model [EMRAM] level of health IT adoption) on hospital
quality patient satisfaction using a composite score for measuring patient experience. (See the
following Perspective.) They found that hospitals with the most advanced EHRs had the
greatest gains in improving clinical clinical process of care scores, without negatively affecting
the patient experience (Jarvis et al., 2013). Another study found that physicians using EHRS
that met Meaningful Use criteria and had two or more years EHR experience were more likely
to report clinical benefits (King, Patel, Jamoon, & Furukawa, 2014).

Limitations and Need for Further Research
Not all studies have demonstrated positive outcomes from using EHR systems. For example,
the same EHR or clinical information system can be implemented in different organizations and
have different results. As example of variability, two children’s hospitals implemented the same
EHR (including CPOE) in their pediatric intensive care units. One hospital experienced a
significant increase in mortality (Han et al., 2005), and the other did not (Del Beccaro, Jeffries,
Eisenberg, & Harry, 2006). The hospital that experienced an increase in mortality noted that
several implementation factors contributed to the deterioration in quality; specific order sets for
critical care were not created, changes in workflow were not well executed, and orders for
patients arriving via critical care transportation could not be written before the patient arrived at
the hospital, delaying life-saving treatments. Many factors can influence the successful use and
adoption of EHR systems. These are discussed more fully in Chapter Six.

Personal Health Records
In addition to EHRs and patient portals, the broader concept of a personal health record has
emerged in recent years. Initially, the PHR was envisioned as a tool to enable individuals to
keep their own health records, and they could share information electronically with their
physicians or other health care professionals and receive advice, reminders, test results, and
alerts from them. Unlike the EHR and patient portal, which is managed by health care provider
organizations, the PHR is managed by the consumer. It may include health and wellness
information, such as an individual’s exercise and diet. The consumer decides who has access

to the information and controls the content of the record. Personal data the consumer gathers
through use of health apps such as My Fitness Pal or Fitbits may be included.
What is the value of the PHR, and how does it relate to the EHR? Tang and Lansky (2005)
believe the PHR enables individuals to serve as copilots in their own care. Patients can receive
customized content based on their needs, values, and preferences. PHRs should be lifelong
and comprehensive and should support information exchange and portability. Patients are often
seen by multiple health care providers in different settings and locations over the course of a
lifetime. In our fragmented health care system, this means patients are often left to consolidate
information from the various participants in their care. A PHR that brings together important
health information across an individual’s lifetime and that is safe, secure, portable, and easily
accessible can reduce costs by avoiding unnecessary duplicate tests and improving health care
communications. The concept of patient portals and PHRs are also inherent in the CMS
Meaningful Use program. Stage 3 Meaningful Use recommendations (originally scheduled for
implementation in 2017 but now under policy reconsideration) state that patients should be able
to (1) communicate electronically using secure messaging, (2) access patient education
materials on the Internet, (3) generate health data into their providers’ EHRs, and (4) view,
download, and transmit their provider-managed EHRs. Taken together, Ford, Hesse, and Huerta
(2016) argue that these requirements outline the basic functionalities of a consumer-managed
PHR.
Perspective
HIMSS Analytics EHR Adoption Levels among US Hospitals
Stage Cumulative Capabilities 2016—Q1
Stage 7 Complete EHR is used; data warehousing and data analytics is used to improve
care; clinical information can be shared via standardized electronic transactions across
continuum of care. 4.3%
Stage 6 Physician documentation with structured templates and discrete data is
implemented for at least one inpatient area. Full CCSS. The closed loop medication
administration with bar coding is used. The five rights of medication administration are verified.
29.1%
Stage 5 A full complement of radiology PACS system provides medical images to
physicians via an intranet. 34.4%
Stage 4 Computerized provider order entry (CPOE) used to create orders; CDSS is used
with clinical protocols. 10.0%
Stage 3 Nursing/clinical documentation has been implemented including electronic
medication administration record (MAR); clinical decision support (CDS) capabilities allow for
error checking with order entry. Medical image access from picture archive and communication
systems (PACS) is available within organization. 15.3%
Stage 2 Major clinical systems feed into clinical data repository (CDR) that enables
viewing of orders and results. CDR contains a controlled medical vocabulary, and clinical
decision support system (CDSS) capabilities. Hospital may have health information exchange
(HIE) capabilities and can share CDR information with patient care stakeholders. 2.5%
Stage 1 All three major ancillary clinical systems (laboratory, pharmacy, radiology) are
installed. 1.8%

Stage 0 All three key ancillary department systems (laboratory, pharmacy, radiology) are
not installed. 2.6%
N=5,456

Ford and his colleagues (2016) examined US consumers PHR use over time, the factors that
influence use, and projected the diffusion of PHR under three scenarios. Not surprisingly, they
found that consumers were increasingly using electronic means for storing health data and
communicating with their clinical providers. An estimated 5 percent of consumers used PHRs in
2008, and by 2013, this number had reached 17 percent (Ford et al., 2016), still relatively low.
Using various prediction models, they estimate that PHR use will increase significantly within
the next decade.

PHRs and personal health applications have the potential to positively affect medication
adherence and quality of life for patients with chronic diseases. For example, a recent controlled
study examined the impact of a text-based message reminder system on medication
adherence among adolescents with asthma (Johnson et al., 2016). Compared to adolescents in
the control group, they found improvements in self-reported medication adherence (p = .011),
quality of life (p = .037), and self-efficacy (p = .016). System use varied considerably, however,
with lower use among African American adolescents (Johnson et al., 2016).

Consumers are also increasingly capturing health, wellness, and clinical data about themselves
using a wide range of mobile technologies and applications—everything from wrist-worn devices
that track steps and sleep patterns to web-based food diaries, networked weight scales, and
blood pressure machines (Rosenbloom, 2016). They also use social media networks to connect
with others who share a similar health condition. Such approaches are referred to as
person-generated health data (PGHD) technologies given that consumers may use these
technologies independent of situations in which they are patients per se. According to
Rosenbloom (2016) the field of PGHD and related technologies is in its infancy, particularly in
studying the real value these technologies add to health care delivery. Shaw and his colleagues
(2016) found, for example, that individuals with chronic illnesses (who may have the most to
benefit from using mobile health devices) may be less likely to adopt and use these devices
compared to healthy individuals. As health care organizations and providers move to managing
population health and cohorts of patients under value-based payment models, the use of such
technologies with certain populations of patients may be incredibly useful. Chapter Four
discusses further the health IT tools needed to support population health management.

Key Issues and Challenges
Despite the proliferation in the adoption and use of EHR systems, health care providers and
organizations still face critical issues and challenges related to interoperability, usability, and
health IT safety. Following is a brief discussion of each.

Interoperability
In simple terms, interoperability is “the ability of a system to exchange electronic health
information with and use electronic health information from other systems without special effort

on the part of the [user]” (Institute for Electrical and Electronics Engineering [IEEE], n.d.). The
ONC’s report Connecting Health and Care for the Nation: A Shared Nationwide Interoperability
Roadmap (ONC, 2015a) describes the importance of interoperability in a creating a “learning
health system” in which “health information flows seamlessly and is available to the right people,
at the right place, at the right time.” The overarching vision of a learning health system is to put
patients at the center of their care—“where providers can easily access and use secure health
information from different sources; where an individual’s health information is not limited to what
is stored in EHRs, but includes information from other sources (including technologies that
individuals use) and portrays a longitudinal picture of their health, not just episodes of care;
where diagnostic tests are only repeated when necessary, because the information is readily
available; and where public health agencies and researchers can rapidly learn, develop and
deliver cutting edge treatments” (ONC, 2015a, p. vi) (see Figure 3.6).

Today, providers are challenged to knit together multiple EHRs, financial systems, and analytic
solutions in an effort to effectively manage population health and facilitate care coordination. As
health care providers and organizations coalesce to manage performance and utilization risk in
their communities, they need high degrees of interoperability among these systems (Glaser,
2015). The systems must also fit well into the clinical workflow and patient care process while
ensuring patient safety and quality. Additionally, interoperability will enable data generated by
personal fitness and wearable devices to be included in the patient’s EHR (Glaser, 2015).

Figure 3.6 The ONC’s roadmap to interoperability

Source: ONC (2015a).

True interoperability has yet to be realized. Several factors make interoperability among health
care information systems complicated. EHR systems are often developed using different
platforms with inconsistent use of standards, no universal patient identifier exists, and pulling
together from a wide range of sources is complicated (Glaser, 2015). Moreover, historically
there has not been a great deal of incentive for providers to share information, nor for health IT
vendors to bridge together a number of different systems, giving rise to the concept of
information blocking. According to the ONC, information blocking occurs “when persons or
entities knowingly and unreasonably interfere with exchange or use of electronic health
information” (ONC, 2015b). The concept of information blocking implies that the entity
intentionally and knowingly interferes with sharing the data and is objectively unreasonable in
light of public policy. The ONC has developed comprehensive strategies for identifying,
deterring, and remedying information blocking and coordinating with other federal agencies that
can investigate and take action against certain types of information blocking.

The ONC Roadmap to Interoperability postulates that work is needed in three critical areas: (1)
requiring standards, (2) motivating the use of those standards through appropriate incentives,
and (3) creating a trusted environment for collecting, sharing, and using electronic health
information. Broad stakeholder involvement is critical to achieving interoperability. Stakeholders
include those who receive or support care, those who deliver care, those who pay for care, and

people and organizations that support health IT capabilities, oversight of health care
organizations, and those who develop and maintain standards (ONC, 2015b). (See the following
Perspective.) In addition to the ONC, which resides in the Department of Health and Human
Services, CMS and state governments also play key roles in advancing interoperability.
Statewide health information exchanges can be found in Massachusetts, New York, and
Delaware (Glaser, 2015). Interoperability efforts and standards development are discussed
more fully in Chapter Ten.

Partnerships are also occurring within the private sector to advance interoperability among
systems by creating standards and promoting the sharing of data. CommonWell Health Alliance
has created and implemented patient identification and record-locating service capabilities,
Carequality is developing an interoperability and governance framework, and the Argonaut
Project is testing the next generation of interoperability standards. Glaser (2015) argues that we
must focus on several important goals in making interoperability in health care a reality by doing
the following:

Advancing standards development and pursuing new technical approaches to effecting
standards-based interoperability
Strengthening sanctions, perhaps through the certification process, to minimize business
practices that thwart interoperability
Increasing transparency of vendor and provider progress in achieving interoperability
Developing a trust framework that balances the need for efficient exchange with the privacy
rights of patients
Promoting collaborative multi-stakeholder efforts, such as CommonWell Health Alliance,
Carequality, and eHealth Initiative
Encouraging provider-led activities within communities to broaden the range of interconnections
and include stakeholders such as safety net providers
Creating a governance mechanism that ensures an effective interchange across a wide range
of health information exchanges
Making reimbursement changes that emphasize care coordination and population health
management, all of which must continue to evolve and be implemented
Unfortunately, there is no silver bullet or easy road to achieving true interoperability. However,
with collaboration among stakeholders, appropriate incentives, and keeping the patient at the
center of our work and efforts, secure and efficient interoperability is certainly within reach.

Perspective
The ONC Roadmap to Interoperability
Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap
(ONC, 2015b) was released by the Office of the National Coordinator for Health Information
Technology in 2015. This document was published as a companion to the Connecting Health
and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure.
The following facts are taken from the Roadmap and its companion infographic, Shared
Nationwide Interoperability Roadmap: The Journey to Better Health and Care. This outline lists
progress toward interoperability since 2009, the current state of health care supporting the need

for interoperability, and the future goals and selected payer and outcome milestones for
achieving the ultimate in interoperability, “learning health systems in which health information
flows seamlessly and is available to the right people, at the right place, at the right time” (ONC,
2015a).

Selected Historical Interoperability Achievements
2009 16% of hospitals and 21% of providers adopted basic EHRs.
2011 27% of hospitals and 34% of providers adopted EHRs.
2013 94% of nonfederal acute care hospitals use a certified EHR.
78% of office-based physicians use an EHR.
62% of hospitals electronically exchanged health information with providers outside their
system.
2014 80% of hospitals can electronically query other organizations for health information.
14% of office-based providers electronically share patient information with other providers.
Current State of Health Care

One in three consumers must provide his or her own health information when seeking care for a
medical problem.
A typical Medicare beneficiary sees seven providers annually.
A typical primary care physician has to coordinate care with 229 other physicians working in 117
practices.
Eighty to ninety percent of health determinants are not related to health care.
One in eight Americans tracks a health metric using technology.
It takes seventeen years for evidence to go from research to practice.
Barriers to Interoperability

States have different laws and regulations making it difficult to share health information across
state lines.
Health information is not sufficiently standardized.
Payment incentives are not aligned to support interoperability.
Privacy laws differ and are misinterpreted.
There is a lack of trust among health care providers and consumers.
2015–2017 Goal and Milestones

Goal: Send, receive, find, and use priority data domains to improve health care quality and
outcomes

Roadmap Milestones for a Supportive Payment and Regulatory Environment and Outcomes

CMS will aim to administer 30 percent of all Medicare payments to providers through alternative
payment models that reward quality and value and encourage interoperability by the end of
2016.

A majority of individuals are able to securely access their electronic health information and direct
it to the destination of their choice.

Providers evolve care processes and information reconciliation to ensure essential health
information is sent, found, or received to support safe transitions in care.

ONC, federal partners, and stakeholders develop a set of measures assessing interoperable
exchanges and the impact of interoperability on key processes that enable improved health and
health care.
2018–2020 Goal and Milestones

Goal: Expand interoperable health IT and users to improve health and lower cost

Roadmap Milestones for a Supportive Payment and Regulatory Environment and Outcomes

CMS will administer 50 percent of all Medicare payments to providers through alternative
payment models that reward quality and value by the end of 2018.

Individuals regularly access and contribute to their longitudinal electronic health information via
health IT, send and receive that information through a variety of emerging technologies, and use
that information to manage their health and participate in shared decision making with their care,
support, and service teams.

Providers routinely and proactively seek outside information about individuals and can use it to
coordinate care.

Public and private stakeholders report on progress toward interoperable exchange, including
identifying barriers to interoperability, lessons learned, and impacts of interoperability on health
outcomes and costs.

2020–2024 Goal and Milestones

Goal: Achieve nationwide interoperability to enable a learning health system

Roadmap Milestones for a Supportive Payment and Regulatory Environment and Outcomes

The federal government will use value-based payment models as the dominant mode of
payment for providers.

Individuals are able to seamlessly integrate and compile longitudinal electronic health
information across online tools, mobile platforms, and devices to participate in shared decision
making with their care, support, and service teams.

Providers routinely use relevant info from a variety of sources, including environmental,
occupational, genetic, human service, and cutting-edge research evidence, to tailor care to the
individual.

Public and private stakeholders report on progress on key metrics identified to achieve a
learning health system.

Source: ONC (2015a).
Usability
In addition to interoperability concerns, clinicians often express frustration with the usability of
EHR systems and other clinical information systems. In fact, 55 percent of physicians reported
that it was difficult or very difficult to use. Common frustrations include confusing displays,
iconography that lacks consistency and intuitive meaning, and the feeling that systems do not
support clinicians’ cognitive workflow or inhibit them from easily drawing insights or conclusions
from the data. Similarly, physicians who participated in a Rand study (Friedberg et al., 2013) felt
that EHR data entry was time-consuming, interfered with face-to-face patient care, and was
overall inefficient. They also reported that inability to exchange health information and the
degradation of clinical documentation were of concern. Others argue that poor usability of EHR
systems not only contributes to clinician frustration but also can lead to errors and patient safety
concerns (Meeks, Smith, Taylor, Sittig, Scott, & Singh, 2014; Sittig & Singh, 2011). In essence,
usability refers to “the effectiveness, efficiency, and satisfaction with which the intended users
can achieve their tasks in the intended context of produce use” (Bevan, 2001). Smartphones are
typically viewed as having high usability, because they require little training and are intuitive to
use. In fact, we often see young children navigating them before they can even talk!

Given the importance of system usability, a task force was formed by the American Medical
Informatics Association (Middleton et al., 2013) to study the issue. They identified key
recommendations on critical usability issues, particularly those that may adversely affect patient
safety and the quality of care. The recommendations fall into four categories: (1) usability and
human factors research, (2) policy recommendations, (3) industry recommendations, and (4)
clinical end user recommendations. (See the Perspective.)

As one can discern from AMIA’s task force recommendations, usability is a multifaceted issue
and one that requires thoughtful research, standardization and interoperability, a common user
interface style guide, and systems for identifying best practices and monitoring use as well as
adverse events that may affect patient safety.
Perspective
AMIA EHR Usability Recommendations
Usability and human factors research agenda in health IT
a. Prioritize standardized use cases.
b. Develop a core set of measures for adverse events related to health IT use.
c. Research and promote best practices for safe implementation of EHR.
Policy recommendations

d. Standardization and interoperability across EHR systems should take account of usability
concerns.
e. Establish an adverse event reporting system for health IT and voluntary health IT event
reporting.
f. Develop and disseminate an educational campaign on the safe and effective use of EHR.
Industry recommendations
g. Develop a common user interface style guide for select EHR functionalities.
h. Perform formal usability assessments on patient-safety sensitive EHR functionalities.
Clinical end user recommendations
i. Adopt best practices for EHR implementation and ongoing management.
j. Monitor how IT systems are used and report IT-related adverse events.

Health IT Safety
In 2011, the Institute of Medicine published a report titled Health IT and Patient Safety: Building
Safer Systems for Better Care in which they outlined a number of recommendations to ensure
health IT systems are safe. In brief, they suggest that safety is a shared responsibility between
vendors and health care organizations and requires the following:

Building systems using user-centered design principles with adequate testing and simulation
Embedding safety considerations throughout the implementation process
Developing and publishing best practices
Having accreditation agencies (such as the Joint Commission) assume a significant role in
testing as part of their accreditation criteria
Focusing on shared learning and transparency
Creating a nonpunitive environment for reporting (IOM, 2011)
Since then, the topic of health IT safety has grown in importance as more EHR systems have
been deployed. Health IT patient safety concerns include adverse events that reached the
patient, near misses that did not reach the patient, or unsafe conditions that increased the
likelihood of a safety event (Meeks et al., 2014). Such events are often difficult to define and
detect. Consequently, Singh and Sittig (2016) have developed a health IT safety measurement
framework that takes into account eight technological and nontechnological dimensions or
sociotechnical dimensions (see Table 3.3).

Table 3.3 Sociotechnical dimensions

Source: Reproduced from Measuring and Improving Patient Safety through Health Information
Technology: The Health IT Safety Framework, Singh and Sittig, 25: p.228, 2016. With
permission from BMJ Publishing Group Ltd.
Dimension Description
Hardware and software Computing infrastructure used to support and operate clinical
applications and devices
Clinical content The text, numeric data, and images that constitute the “language” of
clinical applications, including clinical decision support

Human-computer interface All aspects of technology that users can see, touch, or hear as
they interact with it
People Everyone who is involved with patient care and/or interacts in some way with health care
delivery (including technology). This would include patients, clinicians and other health care
personnel, IT developers and other IT personnel, informaticians
Workflow and communication Processes to ensure that patient care is carried out
effectively, efficiently, and safely
Internal organizational features Policies, procedures, the physical work environment, and
the organizational culture that govern how the system is configured, who uses it, and where and
how it is used
External rules and regulations Federal or state rules (e.g., CMS’s Physician Quality
Reporting Initiative, HIPAA, and Meaningful Use program) and billing requirements that facilitate
or constrain the other dimensions
Measurement and monitoring Evaluating both intended and unintended consequences through a
variety of prospective and retrospective, quantitative, and qualitative methods

The Health IT Safety Framework provides a conceptual framework for defining and measuring
health IT–related patient safety issues. The framework is also built on continuous quality
improvement methods that require stakeholders to ask themselves, How are we doing? Can we
do better? How can we do better (Singh & Sittig, 2016)? In fact, Singh and Sittig (2016) argue
that it is essential that clinicians and leaders make health IT patient safety an organizational
priority by ensuring that the governance structure facilitates measuring and monitoring and
creating an environment that is conducive to detecting, fixing, and learning from system
vulnerabilities. Meeks and colleagues (2014) used a variation of the Health IT Safety Framework
in analyzing one hundred different EHR-related safety concerns reported to and investigated by
the VA’s Informatics Patient Safety Office, which is a voluntary reporting system. The major
categories of errors were because of (1) unmet display needs (mismatch between information
needs and content display; (2) software modifications (concerns about upgrades, modifications,
or configurations); (3) system-to-system interfacing (concerns about failure of interfacing
between systems); and (4) hidden dependencies on distributed systems (one component of the
EHR is unexpectedly or unknowingly affected by the state or condition of another component)
(Meeks et al., 2014). They concluded that because EHR-related safety concerns have
sociotechnical origins and are multifaceted, health care organizations should build a robust
infrastructure to monitor and learn from them.

Numerous factors can affect the safety and effective use of health care information
systems—everything from poor usability to software glitches to unexpected downtime or cyber
attacks. Health care executives should be aware of these issues and vulnerabilities and ensure
their organizations have in place mechanisms to prevent, detect, monitor, and address adverse
events that may affect patient safety and quality of care.

Summary
This chapter provided an overview of health care information systems including administrative
and clinical information systems. We gave a brief history of the evolution of the use of

information systems in health care. Special attention was given to the adoption, use, and
features of EHR systems, patient portals, and PHR systems. We also summarized recent
literature on the value of EHR systems, which may be categorized into three main areas: (1)
quality, outcomes, and safety; (2) efficiency, improved revenues, and cost reduction; and (3)
provider and patient satisfaction. Limitations to research findings were noted along with the need
for future research. Key issues related to the use of health care information systems were
discussed including interoperability, usability, and health IT safety. The chapter concludes with a
discussion of a health IT safety framework that may be useful to health care leaders in
preventing, detecting, and monitoring health IT–related patient safety issues.

Chapter 2
Health Care Data
Learning Objectives
To be able to define health care data and information.
To be able to understand the major purposes for maintaining patient records.
To be able to discuss basic patient health records and claims content.
To be able to discuss basic uses of health care data, including big and small data and analytics.
To be able to identify common issues related to health care data quality.
Central to health care information systems is the actual health care data that is collected and
subsequently transformed into useful health care information. In this chapter we will examine
key aspects of healthcare data. In particular, this chapter is divided into four main sections:
Health care data and information defined (What are health data and health information?)
Health care data and information sources (Where does health data originate and why? When
does health care data become health care information?)
Health care data uses (How do healthcare organizations use data? What is the impact of the
trend toward analytics and big data on health care data?)
Health care data quality (How does the quality of health data affect its use?)
Health Care Data and Information Defined
Often the terms health care data and healthcare information are used interchangeably. However,
there is a distinction, if somewhat blurred in current use. What, then, is the difference between
health data and health information? The simple answer is that health information is processed
health data. (We interpret processing broadly to cover everything from formal analysis to
explanations supplied by the individual decision maker’s brain.) Health care data are raw health
care facts, generally stored as characters, words, symbols, measurements, or statistics. One
thing apparent about health care data is that they are generally not very useful for decision
making. Health care data may describe a particular event, but alone and unprocessed they are
not particularly helpful. Take, for example, this figure: 79 percent. By itself, what does it mean? If
we process this datum further by indicating that it represents the average bed occupancy for a
hospital for the month of January, it takes on more meaning. With the additional facts attached,
is this figure now information? That depends. If all a healthcare executive wants or needs to
know is the bed occupancy rate for January, this could be considered information. However, for
the hospital executive who is interested in knowing the trend of the bed occupancy rate over
time or how the facility’s bed occupancy rate compares to that of other, similar facilities, this is
not yet the information he needs. A clinical example of raw data would be the lab value,
hematocrit (HCT) = 32 or a diagnosis, such as diabetes. These are single facts, data at the
most granular level. They take on meaning when assigned to particular patients in the context of
their health care status or analyzed as components of population studies.
Knowledge is seen by some as the highest level in a hierarchy with data at the bottom and
information in the middle (Figure 2.1). Knowledge is defined by Johns (1997, p. 53) as “a
combination of rules, relationships, ideas, and experience.” Another way of thinking about
knowledge is that it is information applied to rules, experiences, and relationships with the result
that it can be used for decision making. Data analytics applied to healthcare information and

research studies based on health care information are examples of transforming health care
information into new knowledge. To carry out our example from previous paragraphs, the 79
percent occupancy rate could be related to additional information to lead to knowledge that the
health care facility’s referral strategy is working.
Figure 2.1 Health care data to health care knowledge
Where do health care data end and where does health care information begin? Information is an
extremely valuable asset at all levels of the health care community. Health care executives,
clinical staff members, and others rely on information to get their jobs accomplished. The goal of
this discussion is not to pinpoint where data ends and information begins but rather to further an
understanding of the relationship between health care data and information—health care data
are the beginnings of health care information. You cannot create information without data.
Through the rest of this chapter the terms health care data and health care information will be
used to describe either the most granular components of health care information or data that
have been processed, respectively (Lee, 2002).
The first several sections of this chapter focus primarily on the health care data and information
levels, but the content of the section on health care data quality takes on new importance when
applied to processes for seeking knowledge from health care data. We will begin the chapter
exploring where some of the most common health care data originate and describe some of the
most common organizational and provider uses of health care information, including patient
care, billing and reimbursement, and basic health care statistics. Please note there are many
other uses for health information that go beyond these basics that will be explored throughout
this text.
Health Care Data and Information Sources
The majority of healthcare information created and used in healthcare information systems
within and across organizations can be found as an entry in a patient’s health record or claim,
and this information is readily matched to a specific, identifiable patient.
The Health Insurance Portability and Accountability Act (HIPAA), the federal legislation that
includes provisions to protect patients’ health information from unauthorized disclosure, defines
health information as any information, whether oral or recorded in any form or medium, that
does the following:
Is created or received by a health care provider, health plan, public health authority, employer,
life insurer, school or university, or health care clearinghouse
Relates to the past, present, or future physical or mental health or condition of an individual, the
provision of health care to an individual, or the past, present, or future payment for the provision
of health care to an individual
HIPAA refers to this type of identifiable information as protected health information (PHI).

The Joint Commission, the major accrediting agency for many types of healthcare organizations
in the United States, has adopted the HIPAA definition of protected health information as the
definition of “health information” listed in their accreditation manuals’ glossary of terms (The Joint
Commission, 2016). Creating, maintaining, and managing quality health information is a
significant factor in health care organizations, such as hospitals, nursing homes, rehabilitation
centers, and others, who want to achieve Joint Commission accreditation. The accreditation
manuals for each type of facility contain dozens of standards that are devoted to the creation
and management of health information. For example, the hospital accreditation manual contains
two specific chapters, Record of Care, Treatment, and Services (RC) and Information
Management (IM). The RC chapter outlines specific standards governing the components of a
complete medical record, and the IM chapter outlines standards for managing information as an
important organizational resource.
Medical Record versus Health Record
The terms medical record and health record are often used interchangeably to describe a
patient’s clinical record. However, with the advent and subsequent evolution of electronic
versions of patient records these terms actually describe different entities. The Office of the
National Coordinator for Health Information Technology (ONC) distinguishes the electronic
medical record and the electronic health record as follows.
Electronic medical records (EMRs) are a digital version of the paper charts. An EMR contains
the medical and treatment history of the patients in one practice (or organization). EMRs have
advantages over paper records. For example, EMRs enable clinicians (and others) to do the
following:
Track data over time
Easily identify which patients are due for preventive screenings or checkups
Check how their patients are doing on certain parameters such as blood pressure readings or
vaccinations
Monitor and improve overall quality of care within the practice
But the information in EMRs doesn’t travel easily out of the practice (or organization). In fact, the
patient’s record might even have to be printed out and delivered by mail to specialists and other
members of the care team. In that regard, EMRs are not much better than a paper record.
Electronic health records (EHRs) do all those things—and more. EHRs focus on the total health
of the patient—going beyond standard clinical data collected in the provider’s office (or during
episodes of care)—and are inclusive of a broader view on a patient’s care. EHRs are designed
to reach out beyond the health organization that originally collects and compiles the information.
They are built to share information with other health care providers (and organizations), such as
laboratories and specialists, so they contain information from all the clinicians involved in the
patient’s care (Garrett & Seidman, 2011). Another distinguishing feature of the EHR (discussed
in more detail in Chapter Three) is the inclusion of decision-support capabilities beyond those of
the EMR.

Patient Record Purposes
Health care organizations maintain patient clinical records for several key purposes. As we
move into the discussion on clinical information systems in subsequent chapters, it will be
important to remember these purposes, which remain constant regardless of the format or
infrastructure supporting the records. In considering the purposes listed, the scope of care is
also important. Records support not only managing a single episode of care but also a patient’s
continuum of care and population health. Episode of care generally refers to the services
provided to a patient with a specific condition for a specific period
Continuum of care, as defined by HIMSS (2014), is a concept involving a system that guides
and tracks patients over time through a comprehensive array of health services spanning all
levels and intensity of care. Population health is a relatively new term and definitions vary.
However, the concept behind managing population health is to improve health outcomes within
defined communities (Stoto, 2013). The following list comprises the most commonly recognized
purposes for creating and maintaining patient records.
Patient care. Patient records provide the documented basis for planning patient care and
treatment, for a single episode of care and across the care continuum. This purpose is
considered the number-one reason for maintaining patient records. As our health care delivery
system moves toward true population health management and patient-focused care, the patient
record becomes a critical tool for documenting each provider’s contribution to that care.
Communication. Patient records are an important means by which physicians, nurses, and
others, whether within a single organization or across organizations, can communicate with one
another about patient needs. The members of the health care team generally interact with
patients at different times during the day, week, or even month or year. Information from the
patient’s record plays an important role in facilitating communication among providers across
the continuum of care. The patient record may be the only means of communication among
various providers. It is important to note that patients also have a right to access their records,
and their engagement in their own care is often reflected in today’s records.
Legal documentation. Patient records, because they describe and document care and
treatment, are also legal records. In the event of a lawsuit or other legal action involving patient
care, the record becomes the primary evidence for what actually took place during the care. An
old but absolutely true adage about the legal importance of patient records says, “If it was not
documented, it was not done.”
Billing and reimbursement. Patient records provide the documentation patients and payers use
to verify billed services. Insurance companies and other third-party payers insist on clear
documentation to support any claims submitted. The federal programs Medicare and Medicaid
have oversight and review processes in place that use patient records to confirm the accuracy
of claims filed. Filing a claim for a service that is not clearly documented in the patient record
may be construed as fraud.
Research and quality management. Patient records are used in many facilities for research
purposes and for monitoring the quality of care provided. Patient records can serve as source
documents from which information about certain diseases or procedures can be taken, for

example. Although research is most prevalent in large academic medical centers, studies are
conducted in other types of healthcare organizations as well.
Population health. Information from patient records is used to monitor population health, assess
health status, measure utilization of services, track quality outcomes, and evaluate adherence
to evidence-based practice guidelines. Health care payers and consumers are increasingly
demanding to know the cost-effectiveness and efficacy of different treatment options and
modalities. Population health focuses on prevention as a means of achieving cost-effective
care.
Public health. Federal and state public health agencies use information from patient records to
inform policies and procedures to ensure that they protect citizens from unhealthy conditions.
Patient Records as Legal Documents
The importance of maintaining complete and accurate patient records cannot be
underestimated. They serve not only as a basis for planning patient care but also as the legal
record documenting the care that was provided to patients. The data captured in a patient record
becomes a permanent record of that patient’s diagnosis, treatments, response to treatments,
and case management. Patient records provide much of the source data for health care
information that is created, maintained, and managed within and across health care
organizations.
When the patient record was a file folder full of paper housed in the health information
management department of the hospital, identifying the legal health record (LHR) was fairly
straightforward. Records kept in the usual course of business (in this case, providing care to
patients) represent an exception to the hearsay rule, are generally admissible in a court, and
therefore can be subpoenaed—they are legal documentation of the care provided to the patients.
With the implementation of comprehensive EHR systems the definition of an LHR remains the
same, but the identification of the boundaries for it may be harder to determine. In 2013, the
ONC’s National Learning Consortium published the Legal Health Record Policy Template to
guide health care organizations and providers in defining which records and record sets
constitute their legal health record for administrative, business, or evidentiary purposes. The
media on which the records are maintained does not determine the legal status; rather, it is the
purpose for which the record was created and is maintained. The complete template can be
found at www.healthit.gov/sites/default/files/legal_health_policy_template x.
Because of the legal nature of patient records, the majority of states have specific retention
requirements for information contained within them. These state requirements should be the
basis for the health care organization’s formal retention policy. (The Joint Commission and other
accrediting agencies also address retention but generally refer organizations back to their own
state regulations for specifics.) When no specific retention requirement is made by the state, all
patient information that is a part of the LHR should be maintained for at least as long as the
state’s statute of limitations or other regulation requires. In the case of minor children the LHR
should be retained until the child reaches the age of majority as defined by state law, usually
eighteen or twenty-one. Health care executives should be aware that statutes of limitations may
allow a patient to bring a case as long as ten years after the patient learns that his or her care

caused an injury (Lee, 2002). Although some specific retention requirements and general
guidelines exist, it is becoming increasingly popular for health care organizations to keep all LHR
information indefinitely, particularly if the information is stored in an electronic format. If an
organization does decide to destroy LHR information, this destruction must be carried out in
accordance with all applicable laws and regulations.
Another important aspect related to the legal nature of patient records is the need for them to be
authenticated. State and federal laws and accreditation standards require that medical record
entries be authenticated to ensure that the legal document shows the person or persons
responsible for the care provided. Generally, authentication of an LHR entry is accomplished
when the physician or other health care professional signs it, either with a handwritten signature
or an electronic signature.
Personal Health Records
An increasingly common type of patient record is maintained by the individual to track personal
health care information: the personal health record (PHR). According to the American Health
Information Management Association (AHIMA, 2016), a PHR “is a tool . . . to collect, track and
share past and current information about your health or the health of someone in your care.” A
PHR is not the same as a health record managed by a healthcare organization or provider, and
it does not constitute a legal document of care, but it should contain all pertinent health care
information contained in an individual’s health records. PHRs are an effective tool enabling
patients to be active members of their own health care teams (AHIMA, 2016).
Patient Record Content
The following components are common to most patient records, regardless of facility type or
record system (AHIMA, 2016). Specific patient record content is determined to a large extent by
external requirements, standards, and regulations (discussed in Chapter Nine). Keep in mind, a
patient record may contain some or all of the documentation listed. Depending on the patient’s
illness or injury and the type of treatment facility, he or she may need additional specialized
health care services. These services may require specific documentation. For example,
long-term care facilities and behavioral health facilities have special documentation
requirements. Our list is intended to introduce the common components of patient records, not
to provide a comprehensive list of all possible components. The following provides a general
overview of record content and the person or persons responsible for capturing the content
during a single episode of care. It reveals that the patient record is a repository for a variety of
healthcare data and information that is captured by many different individuals involved in the
care of the patient.
Identification screen. Information found on the identification screen of a health or medical record
originates at the time of registration or admission. The identification data generally includes at
least the patient name, address, telephone number, insurance carrier, and policy number, as
well as the patient’s diagnoses and disposition at discharge. These diagnoses are recorded by
the physicians and coded by administrative personnel. (Diagnosis coding is discussed in this
chapter.) The identification component of the data is used as a clinical and an administrative

document. It provides a quick view of the diagnoses that required care during the encounter. The
codes and other demographic information are used for reimbursement and planning purposes.
Problem list. Patient records frequently contain a comprehensive problem list, which identifies
significant illnesses and operations the patient has experienced. This list is generally maintained
over time. It is not specific to a single episode of care and may be maintained by the attending or
primary care physician or collectively by all the health care providers involved in the patient’s
care.
Medication record. Sometimes called a medication administration record (MAR), this record
lists medicines prescribed for and subsequently administered to the patient. It often also lists
any medication allergies the patient may have. Nursing personnel are generally responsible for
documenting and maintaining medication information in acute care settings, because they are
responsible for administering medications according to physicians’ written or verbal orders.
History and physics. The history component of the report describes any major illnesses and
surgeries the patient has had, any significant family history of disease, patient health habits, and
current medications. The information for the history is provided by the patient (or someone
acting on his or her behalf) and is documented by the attending physician or other care provider
at the beginning of or immediately prior to an encounter or treatment episode. The physical
component of this report states what the physician found when he or she performed a hands-on
examination of the patient. The history and physical together document the initial assessment of
the patient for the particular care episode and provide the basis for diagnosis and subsequent
treatment. They also provide a framework within which physicians and other care providers can
document significant findings. Although obtaining the initial history and physical is a one-time
activity during an episode of care, continued reassessment and documentation of that
reassessment during the patient’s course of treatment is critical. Results of reassessments are
generally recorded in progress notes.
Progress notes. Progress notes are made by the physicians, nurses, therapists, social workers,
and other staff members caring for the patient. Each provider is responsible for the content of
his or her notes. Progress notes should reflect the patient’s response to treatment along with the
provider’s observations and plans for continued treatment. There are many formats for progress
notes. In some organizations all care providers use the same note format; in others each
provider type uses a customized format. A commonly used format for a progress note is the
SOAP format. Providers are expected to enter notes divided into four components:
Subjective findings
Objective findings
Assessment
Plan
Consultation. A consultation note or report records opinions about the patient’s condition made
by another health care provider at the request of the attending physician or primary care
provider. Consultation reports may come from physicians and others inside or outside a
particular health care organization, but this information is maintained as part of the patient
record.
Physician’s orders. Physician’s orders are a physician’s directions, instructions, or prescriptions
given to other members of the health care team regarding the patient’s medications, tests, diets,

treatments, and so forth. In the current US healthcare system, procedures and treatments must
be ordered by the appropriate licensed practitioner; in most cases this will be a physician.
Imaging and X-ray reports. The radiologist is responsible for interpreting images produced
through X-rays, mammograms, ultrasounds, scans, and the like and for documenting his or her
interpretations or findings in the patient’s record. These findings should be documented in a
timely manner so they are available to the appropriate provider to facilitate the appropriate
treatment. The actual digital images are generally maintained in the radiology or imaging
departments in specialized computer systems. These images are typically not considered part
of the legal patient record, per se, but in modern EHRs they are available through the same
interface.
Laboratory reports. Laboratory reports contain the results of tests conducted on body fluids,
cells, and tissues. For example, a medical lab might perform a throat culture, urinalysis,
cholesterol level, or complete blood count. There are hundreds of specific lab tests that can be
run by health care organizations or specialized labs. Lab personnel are responsible for
documenting the lab results into the patient record. Results of the lab work become part of the
permanent patient record. However, lab results must also be available during treatment. Health
care providers rely on accurate lab results in making clinical decisions, so there is a need for
timely reporting of lab results and a system for ensuring that physicians and other appropriate
care providers receive the results. Physicians or other primary care providers are responsible
for documenting any findings and treatment plans based on the lab results.
Consent and authorization forms. Copies of consents to admission, treatment, surgery, and
release of information are an important component of the patient record related to its use as a
legal document. The practitioner who actually provides the treatment must obtain informed
consent for the treatment. Patients must sign informed consent documents before treatment
takes place. Forms authorizing release of information must also be signed by patients before
any patient-specific health care information is released to parties not directly involved in the care
of the patient.
Operative report. Operative reports describe any surgery performed and list the names of
surgeons and assistants. The surgeon is responsible for documenting the information found in
the operative report.
Pathology report. Pathology reports describe tissue removed during any surgical procedure and
the diagnosis based on examination of that tissue. The pathologist is responsible for
documenting the information contained within the pathology report.
Discharge summary. Each acute care patient record contains a discharge summary. The
discharge summary summarizes the hospital stay, including the reason for admission,
significant findings from tests, procedures performed, therapies provided, responses to
treatments, condition at discharge, and instructions for medications, activity, diet, and follow-up
care. The attending physician is responsible for documenting the discharge summary at the
conclusion of the patient’s stay in the hospital.
With the passage of the Accountable Care Act (ACA) and other health care payment reform
measures, organizations and communities have begun to shift focus from episodic care to
population health. By definition, population health focuses on maintaining health and managing
health care utilization for a defined population of patients or community with the goal of
decreasing costs. Along with other key components, successful population health will require

extensive care coordination across care providers and community organizations. Care
managers are needed to interact with patients on a regular basis during and in between clinical
encounters (Institute for Health Technology Transformation, 2012). Needless to say, this will
have a significant impact on the form and structure of the future EHRs. These care managers
will document all plan findings, clinical and social, within the patient’s record and rely on other
providers’ notes and findings to effectively coordinate care. Baker, Cronin, Conway, DeSalvo,
Rajkumar, and Press (2016), for example, describes a new tool to support “person-centered
care by a multidisciplinary team,” the comprehensive shared care plan (CSCP), which will rely
on HIT to enable collaboration across settings. A stakeholder group organized by the US
Department of Health and Human Services developed key goals for the CSCP as they envision
it:
It should enable a clinician to electronically view information that is directly relevant to his or her
role in the care of the person, to easily identify which clinician is doing what, and to update other
members of an interdisciplinary team on new developments.
It should put the person’s goals (captured in his or her own words) at the center of decision
making and give that individual direct access to his or her information in the CSCP.
It should be holistic and describe clinical and nonclinical (including home- and
community-based) needs and services.
It should follow the person through high-need episodes (e.g., acute illness) as well as periods of
health improvement and maintenance (Baker et al., 2016).
Figures 2.2 through 2.5 display screens from one organization’s EHR.
extensive care coordination across care providers and community organizations. Care
managers are needed to interact with patients on a regular basis during and in between clinical
encounters (Institute for Health Technology Transformation, 2012). Needless to say, this will
have a significant impact on the form and structure of the future EHRs. These care managers
will document all plan findings, clinical and social, within the patient’s record and rely on other
providers’ notes and findings to effectively coordinate care. Baker, Cronin, Conway, DeSalvo,
Rajkumar, and Press (2016), for example, describes a new tool to support “person-centered
care by a multidisciplinary team,” the comprehensive shared care plan (CSCP), which will rely
on HIT to enable collaboration across settings. A stakeholder group organized by the US
Department of Health and Human Services developed key goals for the CSCP as they envision
it:
It should enable a clinician to electronically view information that is directly relevant to his or her
role in the care of the person, to easily identify which clinician is doing what, and to update other
members of an interdisciplinary team on new developments.
It should put the person’s goals (captured in his or her own words) at the center of decision
making and give that individual direct access to his or her information in the CSCP.
It should be holistic and describe clinical and nonclinical (including home- and
community-based) needs and services.
It should follow the person through high-need episodes (e.g., acute illness) as well as periods of
health improvement and maintenance (Baker et al., 2016).
Figures 2.2 through 2.5 display screens from one organization’s EHR.

Claims Content
As we have seen in the previous section, health care information is captured and stored as a
part of the patient record. However, there is more to the story: health care organizations and
providers must be paid for the care they provide. Generally, the health care organization’s
accounting or billing department is responsible for processing claims, an activity that includes
verifying insurance coverage; billing third-party payers (private insurance companies, Medicare,
or Medicaid); and processing the payments as they are received. Centers for Medicare and
Medicaid Services (CMS) currently requires health care providers to submit claims
electronically using a set of standard elements. As early as the 1970s the health care
community strived to develop standard insurance claim forms to facilitate payment collection.
With the nearly universal adoption of electronic billing and government-mandated transaction
standards, standard claims content has become essential.
Figure 2.2 Sample EHR information screen
Source: Medical University of South Carolina; Epic.
Figure 2.3 Sample EHR problem list
Source: Epic.
Figure 2.4 Sample EHR progress notes
Source: Epic.
Figure 2.5 Sample EHR lab report
Source: Epic.
Depending on the type of service provided to the patient, one of two standard data sets will be
submitted to the third-party payer. The UB-04, or CMS-1450, is submitted for inpatient,
hospital-based outpatient, home health care, and long-term care services. The CMS-1500 is
submitted for health care provider services, such as those provided by a physician’s office. It is
also used for billing by some Medicaid state agencies. The standard requirements for the
parallel electronic counterparts to the CMS-1450 and CMS-1500 are defined by ANSI ASC
X12N 837I (Institutional) and ANSI ASC X12N 837P (Professional), respectively. Therefore, the
claims standards are frequently referred to as 837I and 837P.
UB-04/CMS-1450/837I

In 1975, the American Hospital Association (AHA) formed the National Uniform Billing
Committee (NUBC), bringing the major national provider and identifying which clinician is doing
what, and to update other members of an interdisciplinary team on new developments.
It should put the person’s goals (captured in his or her own words) at the center of decision
making and give that individual direct access to his or her information in the CSCP.
It should be holistic and describe clinical and nonclinical (including home- and
community-based) needs and services.
It should follow the person through high-need episodes (e.g., acute illness) as well as periods of
health improvement and maintenance (Baker et al., 2016).
Figures 2.2 through 2.5 display screens from one organization’s EHR.
Claims Content
As we have seen in the previous section, health care information is captured and stored as a
part of the patient record. However, there is more to the story: health care organizations and
providers must be paid for the care they provide. Generally, the health care organization’s
accounting or billing department is responsible for processing claims, an activity that includes
verifying insurance coverage; billing third-party payers (private insurance companies, Medicare,
or Medicaid); and processing the payments as they are received. Centers for Medicare and
Medicaid Services (CMS) currently requires health care providers to submit claims
electronically using a set of standard elements. As early as the 1970s the health care
community strived to develop standard insurance claim forms to facilitate payment collection.
With the nearly universal adoption of electronic billing and government-mandated transaction
standards, standard claims content has become essential.
Figure 2.2 Sample EHR information screen
Source: Medical University of South Carolina; Epic.
Figure 2.3 Sample EHR problem list
Source: Epic.
Figure 2.4 Sample EHR progress notes
Source: Epic.
Figure 2.5 Sample EHR lab report
Depending on the type of service provided to the patient, one of two standard data sets will be
submitted to the third-party payer. The UB-04, or CMS-1450, is submitted for inpatient,
hospital-based outpatient, home health care, and long-term care services. The CMS-1500 is

submitted for health care provider services, such as those provided by a physician’s office. It is
also used for billing by some Medicaid state agencies. The standard requirements for the
parallel electronic counterparts to the CMS-1450 and CMS-1500 are defined by ANSI ASC
X12N 837I (Institutional) and ANSI ASC X12N 837P (Professional), respectively. Therefore, the
claims standards are frequently referred to as 837I and 837P.
UB-04/CMS-1450/837I
In 1975, the American Hospital Association (AHA) formed the National Uniform Billing
Committee (NUBC), bringing the major national provider and payer organizations together for
the purpose of developing a single billing form and standard data set that could be used for
processing health care claims by institutions nationwide. The first uniform bill was the UB-82. It
has since been modified and improved on, resulting, first, in the UB-92 data set and now in the
currently used UB-04, also known as CMS-1450. UB-04 is the de facto institutional provider
claim standard. Its content is required by CMS and has been widely adopted by other
government and private insurers. In addition to hospitals, UB-04 or 837I is used by skilled
nursing facilities, end stage renal disease providers, home health agencies, hospices,
rehabilitation clinics and facilities, community mental health centers, critical access hospitals,
federally qualified health centers, and others to bill their third-party payers. The NUBC is
responsible for maintaining and updating the specifications for the data elements and codes that
are used for the UB-04/CMS-1450 and 837I. A full description of the elements required and the
specifications manual can be found on the NUBC website, www.nubc.org (CMS 2016a; NUBC,
2016).
CMS-1500/837P
The National Uniform Claim Committee (NUCC) was created by the American Medical
Association (AMA) to develop a standardized data set for the noninstitutional or “professional”
health care community to use in the submission of claims (much as the NUBC has done for
institutional providers). Members of this committee represent key provider and payer
organizations, with the AMA appointing the committee chair. The standardized claim form
developed and overseen by NUCC is the CMS-1500 and its electronic counterpart is the 837P.
This standard has been adopted by CMS to bill Medicare fee-for-service, and similar to UB-04
and 837I for institutional care, it has become the de facto standard for all types of noninstitutional
provider claims, such as those for private physician services. NUCC maintains a crosswalk
between the 837P and CMS-1500 explaining the specific data elements, which can be found on
their website at www.nucc.org (CMS, 2013; NUCC, 2016).
It is important to recognize that the UB-04 and the CMS-1500 and their electronic counterparts
incorporate standardized data sets. Regardless of a health care organization’s location or a
patient’s insurance coverage, the same data elements are collected. In many states UB-04 data
and CMS-1500 data must be reported to a central state agency responsible for aggregating and
analyzing the state’s health data. At the federal level the CMS aggregates the data from these
claims forms for analyzing national health care reimbursement and clinical and population
trends. Having uniform data sets means that data can be compared not only within
organizations but also within states and across the country.

Diagnostic and Procedural Codes
Diagnostic and procedural codes are captured during the patient encounter, not only to track
clinical progress but also for billing, reimbursement, and other administrative purposes. This
diagnostic and procedural information is initially captured in narrative form through physicians’
and other health care providers’ documentation in the patient record. This documentation is
subsequently translated into numerical codes. Coding facilitates the classification of diagnoses
and procedures for reimbursement purposes, clinical research, and comparative studies.
Two major coding systems are employed by healthcare providers today:
ICD-10 (International Classification of Diseases)
CPT (Current Procedural Terminology), published by the American Medical Association
Use of these systems is required by the federal government for reimbursement, and they are
recognized by health care agencies nationally and internationally. The UB-04 and CMS-1500
have very specific coding requirements for claim submission, which include use of these coding
sets.
ICD-10-CM
The ICD-10 classification system used to code diseases and other health statuses in the United
States is derived from the International Classification of Diseases, Tenth Revision, which was
developed by the World Health Organization (WHO) (CDC, 2016) to capture disease data. The
precursors to the current ICD system were developed to enable comparison of morbidity
(illness) and mortality (death) statistics across nations. Over the years this basic purpose has
evolved and today ICD-10-CM (Clinical Modification) coding plays a major role in
reimbursement to hospitals and other health care institutions. ICD-10-CM codes used for
determining the diagnosis related group (DRG) into which a patient is assigned. DRGs are in
turn the basis for determining appropriate inpatient reimbursements for Medicare, Medicaid, and
many other health care insurance beneficiaries. Accurate ICD coding has, as a consequence,
become vital to accurate institutional reimbursement.
The National Center of Health Statistics (NVHS) is the federal agency responsible for publishing
ICD-10-CM (Clinical Modification) in the United States. Procedure information is similarly coded
using the ICD-10-PCS (Procedural Coding System). ICD-10-PCS was developed by CMS for
US inpatient hospital settings only. The ICD-10-CM and ICD-10-PCS publications are
considered federal government documents whose contents may be used freely by others.
However, multiple companies republish this government document in easier-to-use, annotated,
formally copyrighted versions. In general, the ICD-10-CM and ICD-10-PCS are updated on an
annual basis (CMS, 2015, 2016b).
Exhibit 2.1 Excerpt from ICD-10-CM 2016
Malignant neoplasms (C00-C96)

Malignant neoplasms, stated or presumed to be primary (of specified sites), and certain
specified histologies, except neuroendocrine, and of lymphoid, hematopoietic, and related tissue
(C00-C75)
Malignant neoplasms of lip, oral cavity, and pharynx (C00-C14)
C00 Malignant neoplasm of lip
Use additional code to identify:
alcohol abuse and dependence (F10.-)
history of tobacco use (Z87.891)
tobacco dependence (F17.-)
tobacco use (Z72.0)
Excludes 1: malignant melanoma of lip (C43.0)
Merkel cell carcinoma of lip (C4A.0)
other and unspecified malignant neoplasm of skin of lip (C44.0-)
C00.0 Malignant neoplasm of external upper lip
Malignant neoplasm of lipstick area of upper lip
Malignant neoplasm of upper lip NOS
Malignant neoplasm of vermilion border of upper lip
C00.1 Malignant neoplasm of external lower lip
Malignant neoplasm of lower lip NOS
Malignant neoplasm of lipstick area of lower lip
Malignant neoplasm of vermilion border of lower lip
C00.2 Malignant neoplasm of external lip, unspecified
Malignant neoplasm of vermilion border of lip NOS
C00.3 Malignant neoplasm of upper lip, inner aspect
Malignant neoplasm of buccal aspect of upper lip
Malignant neoplasm of frenulum of upper lip
Malignant neoplasm of mucosa of upper lip
Malignant neoplasm of oral aspect of upper lip
C00.4 Malignant neoplasm of lower lip, inner aspect
Malignant neoplasm of buccal aspect of lower lip
Malignant neoplasm of frenulum of lower lip
Malignant neoplasm of mucosa of lower lip
Malignant neoplasm of oral aspect of lower lip
C00.5 Malignant neoplasm of lip, unspecified, inner aspect
Malignant neoplasm of buccal aspect of lip, unspecified
Malignant neoplasm of frenulum of lip, unspecified
Malignant neoplasm of mucosa of lip, unspecified
Malignant neoplasm of oral aspect of lip, unspecified
C00.6 Malignant neoplasm of commissure of lip, unspecified
C00.7 Malignant neoplasm of overlapping sites of lip
C00.8 Malignant neoplasm of lip, unspecified
Source: CMS (2016b).

Exhibits 2.1 and 2.2 are excerpts from the ICD-10-CM and ICD-10-PCS classification systems.
They show the system in its text form, but large health care organizations generally use
encoders, computer applications that facilitate accurate coding. Whether a book or text file or
encoder is used, the classification system follows the same structure.
CPT and HCPCS
The American Medical Association (AMA) publishes an updated CPT each year. Unlike
ICD-9-CM, CPT is copyrighted, with all rights to publication and distribution held by the AMA.
CPT was first developed and published in 1966. The stated purpose for developing CPT was to
provide a uniform language for describing medical and surgical services. In 1983, however, the
government adopted CPT, in its entirety, as the major component (known as Level 1) of the
Healthcare Common Procedure Coding System (HCPCS). Since then CPT has become the
standard for physician’s office, outpatient, and ambulatory care coding for reimbursement
purposes. Exhibit 2.3 is a simplified example of a patient encounter form with HCPCS/CPT
codes.
Exhibit 2.2 Excerpt from ICD-10 PCS 2017 OCW
Section 0 Medical and Surgical
Body System C Mouth and Throat
Operation W Revision: Correcting, to the extent possible, a portion of a malfunctioning
device or the position of a displaced device
Body Part Approach Device Qualifier
A Salivary Gland 0 Open
3 Percutaneous
X External 0 Drainage Device
C Extraluminal Device Z No Qualifier
S Larynx 0 Open
3 Percutaneous
7 Via Natural or Artificial Opening
8 Via Natural or Artificial Opening Endoscopic
X External 0 Drainage Device
7 Autologous Tissue Substitute
D Intraluminal Device
J Synthetic Substitute
K Nonautologous Tissue Substitute Z No Qualifier
Y Mouth and Throat 0 Open
3 Percutaneous
7 Via Natural or Artificial Opening
8 Via Natural or Artificial Opening Endoscopic
X External 0 Drainage Device
1 Radioactive Element
7 Autologous Tissue Substitute
D Intraluminal Device
J Synthetic Substitute

K Nonautologous Tissue Substitute Z No Qualifier
Source: CMS (2016c).
Exhibit 2.3 Patient Encounter form Coding Standards
Pediatric Associates P.A. 123 Children’s Avenue, Anytown, USA
Office Visits
99211 Estab Pt—minimal Preventive Medicine—New
99212 Estab Pt—focused 99381 Prev Med 0–1 years
99213 Estab Pt—expanded 99382 Prev Med 1–4 years
99214 Estab Pt—detailed 99383 Prev Med 5–11 years
99215 Estab Pt—high complexity 99384 Prev Med 12–17 years
99385 Prev Med 18–39 years
99201 New Pt—problem focused
99202 New Pt—expanded Preventive Medicine—Established
99203 New Pt—detailed 99391 Prev Med 0–1 years
99204 New Pt—moderate complexity 99392 Prev Med 1–4 years
99205 New Pt—high complexity 99393 Prev Med 5–11 years
99394 Prev Med 12–17 years
99050 After Hours 99395 Prev Med 18–39 years
99052 After Hours—after 10 pm
99054 After Hours Sundays and Holidays 99070 10 Arm Sling
99070 11 Sterile Dressing
Outpatient Consult 99070 45 Cervical Cap
99241 99242 99243 99244 99245
Immunizations, Injections, and Office Laboratory Services
90471 Adm of Vaccine 1 81000 Urinalysis w/ micro
90472 Adm of Vaccine > 1 81002 Urinalysis w/o micro
90648 HIB 82270 Hemoccult Stool
90658 Influenza 82948 Dextrostix
90669 Prevnar 83655 Lead Level
90701 DTP 84030 PKU
90702 DT 85018 Hemoglobin
90707 MMR 87086 Urine Culture
90713 Polio Injection 87081 Throat Culture
90720 DTP/HIB 87205 Gram Stain
90700 DTaP 87208 Ova Smear (pinworm)
90730 Hepatitis A 87210 Wet Prep
90733 Meningococcal 87880 Rapid Strep
90744 Hepatitis B 0–11
90746 Hepatitis B 18+ years
Diagnosis
Patient Name
No.

Date
Time
Address
DOB
Name of Insured ID
Insurance Company
Return Appointment ___________________________________________________
As coding has become intimately linked to reimbursement, directly determining the amount of
money a healthcare organization can receive for a claim from insurers, the government has
increased its scrutiny of coding practices. There are official guidelines for accurate coding, and
health care facilities that do not adhere to these guidelines are liable to charges of fraudulent
coding practices. In addition, the Office of Inspector General of the Department of Health and
Human Services (HHS OIG) publishes compliance guidelines to facilitate health care
organizations’ adherence to ethical and legal coding practices. The OIG is responsible for
(among other duties) investigating fraud involving government health insurance programs. More
specific information about compliance guidelines can be found on the OIG website
(www.oig.hhs.gov) and will be more thoroughly discussed in Chapter Nine.
Health Care Data Uses
The previous sections of this chapter examine how health care data is captured in patient
records and billing claims. Even with this brief overview you can begin to see what a rich source
of health care data these records could be. However, before health care data can be used, it
must be stored and retrieved. How do we retrieve that data so that the information can be
aggregated, manipulated, or analyzed for health care organizations to improve patient care and
business operations? How do we combine this patient care data created and stored internally
with other pertinent data from external sources?
As we discussed previously in the chapter, data needs to be processed to become information.
We also noted that data and information may be considered along a continuum, one person’s
data may be another person’s information depending on the level of processing required. In this
section of the chapter we will focus on the use of data analysis to transform data into
information. There is a lot of discussion about the current and future impact of so-called big data
on the health care community. We will start the discussion of data analysis by looking at the
basic elements required to perform effective health care data analysis, followed by a
comparison of “small” data analysis examples to the emerging big data.
Regardless of the scope of the data or the tools used, health care data analysis requires basic
elements. First, there must be a source of data, for example, the EHR, claims data, laboratory
data, and so on. Second, these data must be stored in a retrievable manner, for example, in a
database or data warehouse. Next, an analytical tool, such as mathematical statistics,
probability models, predictive models, and so on, must be applied to the stored data. Finally, to
be meaningful, the analyzed data must be reported in a usable manner.

Databases and Data Warehouses
A database generally refers to any structured, accessible set of data stored electronically; it can
be large or small. The back end of EHR and claims systems are examples of large databases.
A data warehouse differs from a database in its structure and function. In health care, data
warehouses that are derived from health care information systems may be referred to as clinical
data repositories. The data in a data warehouse come from a variety of sources, such as the
EHR, claims data, and ancillary health care information systems (laboratory, radiology, etc.).
The data from the sources are extracted, “cleaned,” and stored in a structure that enables the
data to be accessed along multiple dimensions, such as time (e.g., day, month, year); location;
or diagnosis. Data warehouses help organizations transform large quantities of data from
separate transactional files or other applications into a single decision-support database. The
important concept to understand is that the database or data warehouse provides organized
storage for data so that they can be retrieved and analyzed. Before useful information can be
obtained, the data must be analyzed. In the most straightforward uses, the data from the data
stores are aggregated and reported using simple reporting or statistical methods.
Small versus Big Data
Data stores and data analytics are not new to health care. However, the scope and speed with
which we are now capable of analyzing data and discovering new information has increased
tremendously. Big data is not a data store (warehouse or database), nor is it a specific analytical
tool, but rather it refers to a combination of the two. Experts describe big data as characterized
by three Vs (the fourth V—veracity, or accuracy—is sometimes added). These characteristics
are present in big but not small data:
Very large volume of data
A variety (e.g., images, text, discrete) of types and sources (EHR, wearable fitness technology,
social media, etc.) of data
The velocity at which the data is accumulated and processed (Glaser, 2014; Macadamian, n.d.)
Harris and Schneider (2015) describe a useful metaphor for explaining the difference between
big data and traditional data storage and analysis systems. They tell us to consider “even
enormous databases, such as the Medicare claims database as ‘filing cabinets,’ while big data
is more like a ‘conveyor belt.’ The filing cabinet, no matter how large, is static, while the
conveyor belt is constantly moving and presenting new data points and even data sources” (p.
53). They further provide the following examples of questions answered by big versus small
data in health care:
What are the effects of our immunization programs? versus Is my child growing as expected?
What are some of the healthiest regions? versus Is this medication improving my (or my
patients’) blood pressure?
Small Data Examples
Disease and Procedure Indexes
Health care management often wants to know summary information about a particular disease
or treatment. Examples of questions that might be asked are What is the most common
diagnosis among patients treated in the facility? What percentage of patients with diabetes are

African American? What is the most common procedure performed on patients admitted with
gastritis (or heart attack or any other diagnosis)? Traditionally, such questions have been
answered by looking in disease and procedure indexes. Prior to EHRs and their resulting
databases, disease and procedure indexes were large card catalogs or books that kept track of
the numbers of diseases treated and procedures occurring in a facility by disease and
procedure codes. Now that repositories of healthcare data are common, the disease and
procedure index function is generally handled as a component of the EHR. The retrieval of
information related to diseases and procedures is still based on ICD and CPT codes, but the
queries are limitless. Users can search the disease and procedure database for general
frequency statistics for any number of combinations of data. Figure 2.6 is an example of a
screen resulting from a query for a specific patient, Iris Hale, who has been identified as a
member of both the Heart Failure and Hypertension registries.
Many other types of aggregate clinical reports are used by healthcare providers and executives.
Ad hoc reporting capability applied to clinical databases gives providers and executives access
to any number of summary reports based on the data elements from patient health and claims
records.
Health Care Statistics
Utilization and performance statistics are routinely gathered for health care executives. This
information is needed for facility and health care provision planning and improvement. Statistical
reports can provide managers and executives a snapshot of their organization’s performance.
Two categories of statistics directly related to inpatient stays are routinely captured and
reported. Many variations of these reports and others that drill down to more granular levels of
data also exist.
Census statistics. These data reveal the number of patients present at any one time in a facility.
Several commonly computed rates are based on these census data, including the average daily
census and bed occupancy rates.
Discharge statistics. This group of statistics is calculated from data accumulated when patients
are discharged. Some commonly computed rates based on discharge statistics are average
length of stay, death rates, autopsy rates, infection rates, and consultation rates.
Outpatient facilities and group practices, specialty providers, and so on also routinely collect
utilization statistics. Some of the more common statistics are average patient visits per month
(or year) and percentage of patients achieving a health status goal, such as immunizations or
smoking cessation. The number of descriptive health care statistics that can be produced is
limitless. Health care organizations also track a wide variety of financial performance, patient
satisfaction, and employee satisfaction data. Patient and employee data generally come from
surveys that are routinely administered. The body of data collected and analyzed is driven by
the mission of the organization, along with reporting requirements from state, federal, and
accrediting organizations.
Figure 2.6 Sample heart failure and hypertension query screen

Source: Cerner Corporation (2016). Used with permission.
Health care organizations also look to data to guide improved performance and patient
satisfaction. Performance data are essential to health care leaders; however, because they are
generally managed within a quality or performance improvement department and are not derived
from health care data, per se, they will not be discussed in depth in this chapter. A few
significant external agencies that report performance data, however, will be discussed in
Chapter Nine.
Although each organization will determine which daily, monthly, and yearly statistics they need
to track based on their individual service missions, Rachel Fields (2010) in an article published
by Becker’s Hospital Review provides a list of ten common measures identified by a panel of
five hospital leaders, as shown in Table 2.1.
Big Data Examples
Health care organizations today contend with data from EHRs, internal databases, data
warehouses, as well as the availability of data from the growing volume of other health-related
sources, such as diagnostic imaging equipment, aggregated pharmaceutical research, social
media, and personal devices such as Fitbits and other wearable technologies. No longer is the
data needed to support health care decisions located within the organization or any single data
source. As we begin to manage populations and care continuums we have to bring together
data from hospitals, physician practices, long-term care facilities, the patient, and so on. These
data needs are bigger than the data needs we had (and still have) when we focused primarily on
inpatient care.
Big data is a practice that is applied to a wide range of uses across a wide range of industries
and efforts, including health care. There is no single big data product, application, or technology,
but big data is broadening the range of data that may be important in caring for patients. For
instance, in the case of Alzheimer’s and other chronic diseases such as diabetes and cancer,
online social sites not only provide a support community for like-minded patients but also
contain knowledge that can be mined for public health research, medication use monitoring, and
other health-related activities. Moreover, popular social networks can be used to engage the
public and monitor public perception and response during flu epidemics and other public health
threats (Glaser, 2014).
Table 2.1 Ten common hospital statistical measures
Source: Fields (2010).
Daily Monthly Yearly
1. Quality measures, such as
Infection rates
Patient falls
Overall mortality
2. Patient census statistics

By physician
By service line
3. Discharged but not final billed 4. Point-of-service cash collections
5. Percentage of charity care
6. Percentage of budget spent for each department
7. Door-to-discharge time
8. Patient satisfaction scores 9. Colleague satisfaction scores
10. Market share and service line development
As important and perhaps more important than the data themselves are the novel analytics that
are being developed to analyze these data. In health care we see an impressive range of
analytics:
Post-market surveillance of medication and device safety
Comparative effectiveness research (CER)
Assignment of risk, for example, readmissions
Novel diagnostic and therapeutic algorithms in areas such as oncology
Real-time status and process surveillance to determine, for example, abnormal test follow-up
performance and patient compliance with treatment regimes
Determination of structure including intent, for example, identifying treatment patterns using a
range of structured and unstructured and EHR and non-EHR data
Machine correction of data-quality problems
The potential impact of applying data analytics to big data is huge. McKinsey & Company
(Kayyil, Knott, & Van Kuiken, 2013) estimates that big data initiatives could account for $300 to
$450 billion in reduced health care spending, or 12 to 17 percent of the $2.6 trillion baseline in US
health care costs. There are several early examples of possibly profound impact. For example,
an analysis of the cumulative sum of monthly hospitalizations because of myocardial infarction,
among other clinical and cost data, led to the discovery of arthritis drug Vioxx’s adverse effects
and its subsequent withdrawal from the market in 2004.
A Deloitte (2011) analysis identified five areas of analysis that will be crucial in the emerging era
of providers being held more accountable for the care delivered to a patient and a population:
Population management analytics. Producing a variety of clinical indicator and quality measure
dashboards and reports to help improve the health of a whole community, as well as help
identify and manage at-risk populations
Provider profiling/physician performance analytics. Normalizing (severity and case
mix–adjusted profiling), evaluating, and reporting the performance of individual providers (PCPs
and specialists) compared to established measures and goals
Point of care (POC) health gap analytics. Identifying patient-specific health care gaps and
issuing a specific set of actionable recommendations and notifications either to physicians at the
point of care or to patients via a patient portal or PHR
Disease management. Defining best practice care protocols over multiple care settings,
enhancing the coordination of care, and monitoring and improving adherence to best practice
care protocols

Cost modeling/performance risk management/comparative effectiveness. Managing aggregated
costs and performance risk and integrating clinical information and clinical quality measures
Health Care Data Quality
Up to this point, this chapter has examined health care data and information with a focus on the
origins and uses of such. Changes to the health care delivery system and payment reform are
amending the ways in which we use health care information. Traditionally, patient clinical and
claims records were used primarily to document episodic care or, at best, the care received by
an individual across the continuum, as long as that care was provided through a single
organization. In today’s environment, care providers, care coordinators, analysts, and
researchers are all looking to EHRs and electronic claims records as a source of data beyond
the episodic scope. Any discussion of healthcare data analytics and big data include the EHR
as a key data source. This expanded use of electronic records and the push for bigger and
better data analytics has raised the bar for ensuring the quality of the health care data. Quality
health care data has always been important, but the criteria for what constitutes high-quality data
have shifted.
There are many operational definitions for quality. Two of the best known were developed by the
well-known quality “gurus,” Philip B. Crosby and Joseph M. Juran. Crosby (1979) defines quality
as “conformance to requirements” or conformance to standards. Juran (Juran & Gryna, 1988)
defines quality as “fitness for use,” products or services must be free of deficiencies. What
these definitions have in common is that the criteria against which quality is measured will
change depending on the product, service, or use. Herein lies the problem with adopting a single
standard for health care data quality—it depends on the use of the data.
EHRs evolved from patient medical records, whose central purpose was to document and
communicate episodes of patient care. Today EHRs are being evaluated as source data for
complex data analytics and clinical research. Before an organization can measure the quality of
the information it produces and uses, it must establish data standards. And before it can
establish data standards it must identify all endorsed uses of the EHR.
Consider this scenario. EHRs contain two basic types of data: structured data that is
quantifiable or predefined and unstructured data that is narrative. Within a healthcare
organization, the clinicians using the EHR for patient care prefer unstructured data, because it is
easier to dictate a note than to follow a lengthy point and click pathway to create a structured
note. The clinicians feel that the validation screens cost time that is too valuable for them to
waste. The researchers within the organization, however, want as much of the data in the record
as possible to be structured to avoid missing data and data entry errors. What should the
organization adopt as its standard? Structured or unstructured data? Who will decide and based
on what criteria? This discussion between the primary use of EHR data and secondary, or
reuse, of data is likely to continue. However, to effectively use EHR data to create new
knowledge, either through analytics or research, will require HIT leaders to adopt the more
stringent data quality criteria posed by these uses. Wells, Nowacki, Chagin, and Kattan (2013)
identify missing data as particularly problematic when using the EHR for research purposes.
They further identify two main sources of missing EHR data:

Data was not collected. A patient was never asked about a condition. This is most likely directly
related to the clinician’s lack of interest in what would be considered irrelevant to the current
episode of care. Few clinicians will take a full history, for example, at every encounter.
Documentation was not complete. The patient was asked, but it was not noted in the record.
This is common in the EHR when clinicians only note positive values and leave negative values
blank. For example, if a patient states that he or she does not have a history of cancer, no note
will be made, either positive or negative. For a researcher this creates issues. Is this missing
data or a negative value?
Although there is no single common standard against which health care data quality can be
measured, there are useful frameworks for organizations to use to evaluate health care quality
(once the purpose for the data is clearly determined).
The following section will examine two different frameworks for evaluating health care data
quality. The first was developed by the American Health Information Management Association
(AHIMA) (Davoudi et al., 2015), the second by Weiskopf and Weng (2013). The AHIMA
framework is set in the context of managing health care data quality across the enterprise. The
Weiskopf and Weng framework was delineated after in-depth research into the quality of data
specifically found within an EHR, as currently used. Common health data quality issues will be
examined using each framework.
AHIMA Data Quality Characteristics
AHIMA developed and published a set of healthcare data quality characteristics as a component
of a comprehensive data quality management model. They define data quality management as
“the business processes that ensure the integrity of an organization’s data during collection,
application (including aggregation), warehousing, and analysis” (Davoudi et al., 2015). These
characteristics are to be measured for conformance during the entire data management
process.
Data accuracy. Data that reflect correct, valid values are accurate. Typographical errors in
discharge summaries and misspelled names are examples of inaccurate data.
Data accessibility. Data that are not available to the decision makers needing them are of no
value to those decision makers.
Data comprehensiveness. All of the data required for a particular use must be present and
available to the user. Even relevant data may not be useful when they are incomplete.
Data consistency. Quality data is consistent. Use of an abbreviation that has two different
meanings is a good example of how lack of consistency can lead to problems. For example, a
nurse may use the abbreviation CPR to mean cardiopulmonary resuscitation at one time and
computer-based patient record at another time, leading to confusion.
Data currency. Many types of healthcare data become obsolete after a period of time. A
patient’s admitting diagnosis is often not the same as the diagnosis recorded on discharge. If a
healthcare executive needs a report on the diagnoses treated during a particular time frame,
which of these two diagnoses should be included?

Data definition. Clear definitions of data elements must be provided so that current and future
data users will understand what the data mean. This issue is exacerbated in today’s healthcare
environment of collaboration across organizations.
Data granularity. Data granularity is sometimes referred to as data atomicity. That is, individual
data elements are “atomic” in the sense that they cannot be further subdivided. For example, a
typical patient’s name should generally be stored as three data elements (last name, first name,
middle name—“Smith” and “John” and “Allen”), not as a single data element (“John Allen
Smith”). Again, granularity is related to the purpose for which the data are collected. Although it
is possible to subdivide a person’s birth date into separate fields for the month, the date, and the
year, this is usually not desirable. The birth date is at its lowest practical level of granularity when
used as a patient identifier. Values for data should be defined at the correct level for their use.
Data precision. Precision often relates to numerical data. Precision denotes how close to an
actual size, weight, or other standard a particular measurement is. Some health care data must
be very precise. For example, in figuring a drug dosage it is not all right to round up to the
nearest gram when the drug is to be dosed in milligrams.
Data relevancy. Data must be relevant to the purpose for which they are collected. We could
collect very accurate, timely data about a patient’s color preferences or choice of hairdresser,
but are these matters relevant to the care of the patient?
Data timeliness. Timeliness is a critical dimension in the quality of many types of healthcare
data. For example, critical lab values must be available to the health care provider in a timely
manner. Producing accurate results after the patient has been discharged may be of little or no
value to the patient’s care.
Table 2.2 Terms used in the literature to describe the five common dimensions of data quality
Source: Weiskopf and Weng (2013). Reproduced with permission of Oxford University Press.
Completeness Correctness Concordance Plausibility Currency
Accessibility Accuracy Agreement Accuracy Recency
Accuracy Corrections made Consistency Believability Timeliness
Availability Errors Reliability Trustworthiness
Missingness Misleading Variation Validity
Omission Positive predictive value
Presence Quality
Quality Validity
Rate of recording
Sensitivity
Validity
Weiskopf and Weng Data Quality Dimensions
Weiskopf and Weng (2013) published a review article in the Journal of the American Medical
Informatics Association that identified five dimensions of EHR data quality. They based their
findings on a pool of ninety-five articles that examined EHR data quality. Their context was using

the EHR for research, that is, “reusing” the EHR data. Although different terms were used in the
articles, the authors were able to map the terms to one of the five dimensions (see Table 2.2):
Completeness: Is the truth about a patient present?
Correctness: Is an element that is in the EHR true?
Concordance: Is there agreement between elements in the EHR or between the EHR and
another data source?
Plausibility: Does an element in the EHR make sense in light of other knowledge about what that
element is measuring?
Currency: Is an element in the EHR a relevant representation of the patient state at a given point
in time?
Perspective
Problems with Reusing EHR Data:
Examples from the Literature
Botsis, T., Hartvigsen, G., Chen, F., & Weng, C. (2010). Secondary use of EHR: Data quality
issues and informatics opportunities. Summit on Translational Bioinformatics, 2010, 1–5.
The authors report on data quality issues they encountered when attempting to use data that
originated in an EHR to conduct survival analysis of pancreatic cancer patients treated at a large
medical center in New York City. They found that of 3,068 patients within the clinical data
warehouse, only 1,589 had appropriate disease documentation within a pathology report. The
sample size was further reduced to 522 when the researchers discovered incompleteness of
key study variables. Other instances of incompleteness and inaccuracies were found within the
remaining 522 subjects’ documentation, causing the researchers to make inferences regarding
some of the non-key study variables.
Bayley, K. B., Belnap, T., Savitz, L., Masica, A. L., Shah, N., & Fleming, N. S. (2013).
Challenges in using electronic health record data for CER. Medical Care, 51(8 Suppl 3),
S80–S86. doi:10.1097/mlr.0b013e31829b1d48
The authors conducted research to determine the “strengths and challenges” of using EHRs for
CER across four major health care systems with mature EHR systems. They looked at
comparing the effectiveness of antihypertensive medications on blood pressure control for a
population of patients with hypertension who were being followed by primary care providers
within the health systems. Data quality problems that were identified included the following:
Missing data
Erroneous data
Uninterpretable data
Inconsistent data
Text notes and non coded data
The authors concluded that the potential for EHRs as a source of longitudinal data for
comparative effectiveness studies in populations is high, but they note that “improving data

quality within the EHR in order to facilitate research will remain a challenge as long as research
is seen as a separate activity from clinical care.”
The authors further identify completeness, correctness, and currency as “fundamental,” stating
that concordance and plausibility “appear to be proxies for the fundamental dimensions when it
is not possible to assess them directly.”
Strategies for Minimizing Data Quality Issues
As a beginning point, health care data standardization requires clear, consistent definitions. One
essential tool for identifying and ensuring the use of standard data definitions is to use a data
dictionary. AHIMA defines a data dictionary as “a descriptive list of names (also called
‘representations’ or ‘displays’), definitions, and attributes of data elements to be collected in an
information system or database” (Dooling, Goyal, Hyde, Kadles, & White, 2014, p. 7) (see Table
2.3).
Regardless of how well data are defined, however, errors in entry will occur. These errors can
be discussed in terms of two types of underlying cause: systematic errors and random errors.
Systematic errors are errors that can be attributed to a flaw or discrepancy in the system or in
adherence to standard operating procedures or systems. Random errors, however, are caused
by carelessness, human error, or simply making a mistake.
Consider these scenarios:
A nurse is required to document vital signs into each patient’s EHR at the beginning of each
visit. However, the data entry screen is cumbersome and often the nurse must wait until the end
of day and go back to update the vital signs. On occasion the EHR locks up and does not allow
the nurse to update the information. This is an example of a systematic error.
A physician uses the structured history and physical module of the EHR within her practice.
However, to save time she cuts and pastes information from one visit to another. During cutting
and pasting, she fails to reread her note and leaves in the wrong encounter date. Although there
are some elements of systematic error in this situation (not following protocol), the error is
primarily a random error.
Effective systems are needed to ensure preventable errors are minimized and errors that are
not preventable are easily detected and corrected. Clearly, there are multiple points during data
collection and processing when the system design can reduce data errors.
The Markle Foundation (2006, p. 4) argues that comprehensive data quality programs are
needed by healthcare organizations to prevent “dirty data” and subsequently improve the quality
of patient care. They propose that a data quality program include “automated and human
strategies”:
Standardizing data entry fields and processes for entering data
Instituting real-time quality checking, including the use of validation and feedback loops
Designing data elements to avoid errors (e.g., using check digits, algorithms, and well-designed
user interfaces)

Developing and adhering to guidelines for documenting the care that was provided
Building human capacity, including training, awareness-building, and organizational change
Health care data quality problems are exacerbated by inter-facility collaborations and health
information exchange. Imagine standardizing processes and definitions across multiple
organizations.
Certainly, information technology has tremendous potential as a tool for improving health care
data quality. Through the use of electronic data entry, users can be required to complete certain
fields, prompted to add information, or warned when a value is out of prescribed range. When
health care providers respond to a series of prompts, rather than dictating a free-form narrative,
they are reminded to include all necessary elements of a health record entry. Data quality is
improved when these systems also incorporate error checking. Structured data entry,
drop-down lists, and templates can be incorporated to promote accuracy, consistency, and
completeness (Wells et al., 2013). To date some of this potential for technology-enhanced
improvements has been realized, but many opportunities remain. As noted in the Perspective
many of the data in existing EHR systems are recorded in an unstructured format, rather than in
data fields designated to contain specific pieces of information, which can lead to poor health
care data quality. Natural language processing (NLP) is a promising, evolving technology that
will enable efficient data extraction from the unstructured components of the EHR, but it is not
yet commonplace with health care systems.
A clear example of data quality improvement achieved through information technology is the
result seen from incorporating medication administration systems designed to prevent
medication error. With structured data input and sophisticated error prevention, these systems
can significantly reduce medication errors. The challenge for the foreseeable future is to balance
the need for structured data with the associated costs (time and money). Further in the future,
new challenges will appear as the breadth of data contained in patient records is likely to
increase. Genomic and proteomic data, along with enhanced behavioral and social data, are
likely to be captured (IOM, 2014). These added data will introduce new quality issues to be
resolved.
Table 2.3 Excerpt from data dictionary used by AHRQ surgical site infection risk
stratification/outcome detection
Source: Agency for Healthcare Research and Quality (2012).
Table Field Datatype Description
PATIENT Include patients who had surgery that meet inclusion CPT, SNOMED, or ICD-9
criteria between 1/1/2007 and 1/30/2009.
PATIENT DOB Date The birthdate for the patient
PATIENT PATIENT_ID Integer A unique ID for the patient
PATIENT DATA_SOURCE_ID Varchar(10) An identifier for the source of the patient
record data (UU, IHC, DH for example)
DIAGNOSIS Include ICD-9 CM discharge codes within one month of surgery. A list of included
codes is in table 2 of Stevenson et al. AJIC vol 36 (3) 155–164.

DIAGNOSIS DIAGNOSIS_ID Integer A unique ID for the diagnosis
DIAGNOSIS DIAGNOSIS_CODE Varchar(64) The code for the patient’s diagnosis
DIAGNOSIS DIAGNOSIS_CODE_SOURCE Varchar(64) The nomenclature that the
diagnosis code is taken from
(ICD9, etc.)
DIAGNOSIS CLINICAL_DTM Date The date and time of the diagnosis’s onset or
exacerbation
MICROBIOLOGY Include all Microbiology specimens taken within one month before or after
a surgery. (For risk, this might be expanded to one year or more.)
MICROBIOLOGY MICRO_ID Integer A unique ID for the procedure
MICROBIOLOGY SPECIMEN_CODE Varchar(64) The site that the specimen was
collected from
MICROBIOLOGY SPECIMEN_CODE_SOURCE Varchar(64) The nomenclature
that the specimen code is taken from (SNOMED, LOINC, etc.)
MICROBIOLOGY PATHOGEN_CODE Varchar(64) The code of the pathogen cultured
from the collected specimen
MICROBIOLOGY PATHOGEN_CODE_SOURCE Varchar(64) The nomenclature
that the pathogen code is taken from (SNOWMAN, LOINC, etc.)
MICROBIOLOGY COLLECT_DTM Date The date and time the specimen was
collected
ENCOUNTER Include all Encounters within one month before or after surgery.
ENCOUNTER ENCOUNTER_ID Integer A unique ID for the visit. This will serve to tie
all of the different data tables together via foreign key relationships.
ENCOUNTER ADMIT_DTM Date The admission date and time for a patient’s visit
ENCOUNTER DISCH_DTM Date The discharge date and time for a patient’s visit
ENCOUNTER ENCOUNTER_TYPE Varchar(64) The type of patient encounter such
as inpatient, outpatient, observation, etc.
Summary
Without health care data and information, there would be no need for health care information
systems. Health care data and information are valuable assets in health care organizations, and
they must be managed similar to other assets. To that end, health care executives need an
understanding of the sources of healthcare data and information and recognize the importance
of ensuring the quality of health data and information. In this chapter, after defining health care
data and information, we examined patient records and claims content as sources for health
care data. We looked at disease and procedure indexes and health care statistics as examples
of basic uses of the health care data. The emerging use of data analytics and big data were
introduced and the chapter concluded with a discussion of two frameworks for examining health
care data quality and a discussion of how information technology, in general, and the EHR, in
particular, can be leveraged to improve the quality of healthcare data.

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European Journal of Public Health, Vol. 26, No. 1, 60–64

� The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
doi:10.1093/eurpub/ckv122 Advance Access published on 1 July 2015

……………………………………………………………………………………………

The impact of electronic health records on healthcare
quality: a systematic review and meta-analysis

Paolo Campanella, Emanuela Lovato, Claudio Marone, Lucia Fallacara, Agostino Mancuso,
Walter Ricciardi, Maria Lucia Specchia

Department of Public Health, Catholic University of Sacred Heart, L.go F. Vito 1 00168, Rome, Italy

Correspondence: Paolo Campanella, Department of Public Health, Section of Hygiene, Catholic University of Sacred Heart,
L.go F. Vito 1 00168, Rome, Italy, Tel: (+39) 0635019534, Fax: (+39) 0635019535, e-mail: paolo.campanella@icloud.com

Objective: To assess the impact of electronic health record (EHR) on healthcare quality, we hence carried out a
systematic review and meta-analysis of published studies on this topic. Methods: PubMed, Web of Knowledge,
Scopus and Cochrane Library databases were searched to identify studies that investigated the association
between the EHR implementation and process or outcome indicators. Two reviewers screened identified
citations and extracted data according to the PRISMA guidelines. Meta-analysis was performed using the
random effects model for each indicator. Heterogeneity was quantified using the Cochran Q test and I2
statistics, and publication bias was assessed using the Egger’s test. Results: Of the 23 398 citations identified, 47
articles were included in the analysis. Meta-analysis showed an association between EHR use and a reduced
documentation time with a difference in mean of �22.4% [95% confidence interval (CI) =�38.8 to �6.0%;
P < 0.007]. EHR resulted also associated with a higher guideline adherence with a risk ratio (RR) of 1.33 (95% CI = 1.01 to 1.76; P = 0.049) and a lower number of medication errors with an overall RR of 0.46 (95% CI = 0.38 to 0.55; P < 0.001), and adverse drug effects (ADEs) with an overall RR of 0.66 (95% CI = 0.44 to 0.99; P = 0.045). No association with mortality was evident (P = 0.936). High heterogeneity among the studies was evident. Publication bias was not evident. Conclusions: EHR system, when properly implemented, can improve the quality of healthcare, increasing time efficiency and guideline adherence and reducing medication errors and ADEs. Strategies for EHR implementation should be therefore recommended

and promoted.

……………………………………………………………………………………………

Introduction

O
ur world has been radically transformed through digital
innovation. Information technologies play a growing role in

healthcare delivery and help address the health problems and
challenges faced by clinicians and other health professionals.

An electronic health record (EHR) is a systematic electronic
collection of health information about patients such as medical
history, medication orders, vital signs, laboratory results, radiology
reports, and physician and nurse notes. In healthcare institutions, it
automates the medication, as well as exam, ordering process
ensuring standardized, readable and complete orders.

An EHR may also include a decision support system (DSS) that
provides up-to-date medical knowledge, reminders or other actions
that aid health professionals in decision making.

1

Although several studies on the effects of EHR implementation
have been published, evidence on EHR effects continues to be
disputed. Even if most of the studies published seem to provide
promising data, some reported different results, such as Han
et al.

2
who reported an unexpected rise in mortality after the EHR

implementation in a tertiary care children’s hospital.
To assess the impact of EHRs on healthcare quality, we hence carried

out a systematic review and meta-analysis of published studies on this
topic that may provide a rational basis for recommendations.

Methods

This study was conducted and reported in accord with PRISMA
guidelines for meta-analyzes and systematic reviews.3

Search strategy and study selection

A protocol was developed, and we searched in PubMed, Web of
Knowledge, Scopus and Cochrane Library databases to identify
studies that evaluated the benefits of EHR implementation using
the following algorithm:#1 = ‘Electronic Medical Record’ OR

‘Electronic Health Record’ OR ‘Electronic Patient Record’.
#2 = ‘Computerized Physician Order Entry’.
#3 = ‘Decision Support Systems’.
#4 = #1 OR #2 OR #3.
#5 = value OR impact OR benefit OR improvement.
#6 = quality OR efficiency OR risk OR safety.
#7 = #5 OR #6.
#8 = #4 AND #7.

Our search was restricted to English language studies published
from 1994 to 2013.

Studies were considered eligible if they investigated the association
between the EHR implementation and process or outcome
indicators and if they had a control group who did not use the EHR.

One reviewer screened titles, and then, abstracts of relevant titles
were identified. Full texts of potential citations were subsequently
obtained; two reviewers independently screened them for inclusion,
and disagreements were resolved through discussion. Additional
relevant publications were identified from the references of the
initially retrieved articles.

Data extraction

From each study, we extracted data on the first author’s last name,
year of publication and process or outcome indicators evaluated.

For indicators represented by dichotomous variables, risk ratios
(RRs) with their confidence intervals (CIs) (or data necessary to
obtain them) were extracted. For indicators represented by

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continuous variables, sample sizes of both control and intervention
groups and differences in mean (DMs) and their CIs (or data
necessary to obtain them) were extracted.

All data extractions were conducted independently by two
reviewers, and disagreements were resolved through discussion.

Data analysis

Meta-analysis was performed for each process or outcome indicators
evaluated. Because of the significant heterogeneity expected among
the studies performed in different settings, the random effects model
was employed using the Der Simonian and Laird’s method.4

Heterogeneity was quantified using the Cochran Q test and I
2

statistics.
5

For indicators with available both studies including DSS and not
subgroup analyzes were performed.

Sensitivity analyzes were conducted by excluding one study at a
time from the meta-analysis to determine whether the results of the
meta-analysis were influenced by individual studies and whether risk
estimates and heterogeneity were substantially modified.

The presence of publication bias was assessed using a visual funnel
plot inspection and Egger’s test.6

All statistical tests were performed with Comprehensive Meta-
Analysis software version 2.2.064 (Biostat, Englewood, NJ).

Results

Search results and study characteristics

Searching the online databases resulted in 23 398 articles from
PubMed, Web of Knowledge, Scopus and Cochrane Library. After
the initial screening of titles and abstracts, 404 articles were
considered for full text review. Twelve articles were excluded
because full texts were not available, and 352 articles were
excluded based on the full text review. After having identified
seven additional articles by reviewing bibliographies, 47 articles
were included in the analysis (figure 1).

Nine studies investigated the relationship between EHR use and a
reduced documentation time spent by healthcare professionals. The
association between EHR and guideline adherence, medication

Figure 1 Search flow for EHR literature

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Figure 2 Forest plot for the meta-analysis of studies reporting on (a) EHR and documentation time, (b) guideline adherence,
(c) medication errors, (d) ADEs and (e) mortality. The overall, as well as subgroup, estimates of the effect are represented by diamonds
in each plot

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errors, adverse drug effects (ADEs), and mortality were evaluated in
6, 24, 7 and 8 studies, respectively.

Meta-analysis

Meta-analysis showed an association between EHR use by healthcare
professionals and a reduced documentation time with a difference in
mean of �22.4% (95% CI =�38.8% to �6.0%; P < 0.007).

The EHR resulted also associated with a higher guideline
adherence with an RR of 1.33 (95% CI = 1.01 to 1.76; P = 0.049)
and a lower number of medication errors with an overall RR of
0.46 (95% CI = 0.38 to 0.55; P < 0.001) and ADEs with an overall RR of 0.66 (95% CI = 0.44 to 0.99; P = 0.045). No association with mortality was evident (P = 0.936) (figure 2).

High heterogeneity among the studies regarding documentation
time (Q test P < 0.001 and I2 = 92.4%), guideline adherence (Q test P < 0.001 and I

2
= 91.9%), medication errors (Q test P < 0.001 and

I2 = 97.7%) and ADEs (Q test P < 0.001 and I2 = 80.8%) was evident. Moderate heterogeneity regarding mortality (Q test P = 0.012 and I2 = 61.0%) was also evident.

Sensitivity analysis and publication bias

Sensitivity analysis has shown the stability of the overall effect sizes
with the withdrawal of any of the study from the analysis without a
significant improvement of the heterogeneity. Publication bias was
not evident from reviews of the funnel plot or Egger’s test for any
process or outcome indicators considered.

Subgroup analysis

For medication errors, ADEs and mortality both studies including
and excluding DSS were available. Subgroup analysis confirmed the
association between EHR and a reduction of medication errors and
showed a better outcome for EHR including DSS, RR of 0.33 (95%
CI = 0.25 to 0.45), compared with software without DSS, RR of 0.60
(95% CI = 0.45 to 0.81). Regarding the association between EHR and
ADEs reduction, subgroup analysis also showed a better significant
association for EHR including DSS, RR of 0.40 (95% CI = 0.21 to
0.75), but it showed a non-significant association for software not
including DSS, RR of 1.20 (95% CI = 0.79 to 1.82).

Moreover, regarding the absence of significant association
between EHR and mortality, subgroup analysis confirmed this
absence with a slightly better outcome for EHR using DSS, RR of
0.93 (95% CI = 0.58 to 1.49), compared with EHR not using DSS,
RR of 1.06 (95% CI = 0.59 to 1.92).

Discussion

This meta-analysis provides evidence that the use of EHR can
improve the quality of healthcare, increasing time efficiency and
guideline adherence and reducing medication errors and ADEs.

Consequently, EHR can determine also a reduction of costs
associated with medical errors, ADEs and time inefficiency. In effect,
several studies focused on the economics of medical errors7–9 and
ADEs10,11 point out that considerable cost reductions are achievable
through improving quality of care and reducing harm to patients.

12

Guidelines adherence may have an impact on resource use and
cost reduction, supporting specialists in their clinical choices by
reducing errors and ADEs related to treatment and, consequently,
unnecessary waste of resources, as some examples reported by
scientific literature.13 In fact guidelines are promoted as a means
to decrease inappropriate clinical practice variability and use of in-
effective therapies and to reduce medical errors,14 thus resulting in
improved patient outcomes and more cost-effective care.15

Moreover, several studies have reported that the use of appropriate
information technology in the delivery of healthcare may also
improve hospital efficiency, with benefits exceeding the costs of
adoption16 and patient satisfaction rating.17

Subgroup analyzes for EHR with DSS compared with EHR
without DSS provide also interesting results. EHR including DSS,
that actively provides up-to-date medical knowledge, reminders or
other actions that aid health professionals in decision making,
showed in fact generally a better outcome.

So, even if in this review we are far from knowing how EHR
generates these quality improvements, this may suggest that such
dynamic components are ones of the most effective parts of EHRs.

Regarding the association between EHR and ADEs reduction,
subgroup analysis showed a better significant association for EHR
including DSS, but a non-significant association for software not
including DSS. However, the absence of association with ADEs
reduction for the subgroup of studies not using DSS is probably
due to the limitation of having only three studies in this subgroup.

Despite the benefits that EHR can provide, a proper implemen-
tation strategy is essential. In our opinion, it is likely that there are
cases where the success of EHR was not reached because of a non-
effective implementation strategy.

An example of an effective strategy may be identified through the
WHO guidelines for EHR in developing countries18 and reassumed
in six key actions:–review the current health record system,
–try to emulate benchmark practices,
–involve the anticipated users of the system from the onset of

discussions,
–train the users to the EHR system,
–evaluate the benefits of the implemented system,
–update the system when needed.

We believe that such an implementation strategy or a similar one is
crucial in effectively setting up an EHR system, reducing the resistance
of medical practitioners and health professionals, ensuring that the
system is used optimally, and obtaining clinical results.

Having used the tool of quantitative meta-analysis of several
outcomes to synthesize the evidence on the EHR is definitely a
strength of our study.

However, our study has also its limitations. In fact, we focused on
different indicators and although we did a comprehensive search, we
found only a limited number of articles with quantitative data
among the articles identified and even less for each indicator and
subgroup. High heterogeneity was also present and may have
affected the robustness of the results. Possible source of such het-
erogeneity includes difference in the software used, their quality and
usability, and different settings of implementation.

Moreover, information on technical items and procedures that
shape the EHR software was not included in most studies. Further
research is therefore needed to determine the differences among the
various system, the different items that shape an EHR software, and
the different benefits of any of them. Health information technology
systems are, in fact, healthcare interventions, and systems for
evaluating their efficacy and safety should be as robust as those
evaluating other healthcare technologies. Such evidence may
provide healthcare providers with useful indication regarding the
kind of EHR software and its proper implementation to improve
the quality of health care provided and to generate value.

EHR is also often considered an ideal tool to be used to assess
healthcare quality and monitor health providers’ performance
because of the availability of stored computerized data. The last
could allow automated quality assessment, avoiding manual chart
review and medical record abstraction, both of which are expensive
and time-consuming processes. This will require future research to
focus on intervention strategies for improving both quality and
comprehensiveness of clinical data stored in EHR and identifying
the best process of data extraction.19,20

Cumulative evidence shows that EHR systems can improve the
quality of healthcare by increasing time efficiency and guideline
adherence and reducing medication errors and ADEs. Therefore,
strategies for EHR implementation should be recommended and
promoted.

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Further research on technical items and procedures that shape the
EHR software is needed to identify the features that have value for
both clinical results and quality monitoring.

Conflicts of interest: None declared.

Key points

� Health information technology systems are healthcare
interventions.
� EHR systems can improve the quality of healthcare.
� Strategies for EHR implementation should be recommended

and promoted.

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