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Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful Use Era

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The U.S. health system has recently achieved widespread adoption of electronic health record (EHR) systems, primarily driven by financial incentives provided by the Meaningful Use program. Although successful in promoting EHR adoption and use, the program, and other contributing factors, also produced important unintended consequences (UCs) with far-reaching implications for the U.S. health system. Based on our own experiences from large health information technology (HIT) adoption projects and a collection of key studies in HIT evaluation, we discuss the most prominent UCs of Meaningful Use: failed expectations; EHR market saturation; innovation vacuum; physician burnout, and data obfuscation. We identify challenges resulting from these UCs and provide recommendations for future research in order to empower the broader medical and informatics communities to realize the full potential of a now digitized health system. We believe that fixing these unanticipated effects will demand efforts from diverse players such as health care providers, administrators, HIT vendors, policy makers, informatics researchers, funding agencies, and outside developers; promotion of new business models; collaboration between academic medical centers and informatics research departments; and improved methods for evaluations of HIT.
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Unintended Consequences of Nationwide Electronic Health Record
Adoption: Challenges and Opportunities in the Post-Meaningful
Use Era
Tiago K Colicchio1, PhD, MBA; James J Cimino1, MD; Guilherme Del Fiol2, MD, PhD
1Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, United States
2Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
Corresponding Author:
Tiago K Colicchio, PhD, MBA
Informatics Institute
University of Alabama at Birmingham
1900 University Boulevard
Birmingham, AL, 35294
United States
Phone: 1 2059960735
Email: tcolicchio@uabmc.edu
Abstract
The US health system has recently achieved widespread adoption of electronic health record (EHR) systems, primarily driven
by financial incentives provided by the Meaningful Use (MU) program. Although successful in promoting EHR adoption and
use, the program, and other contributing factors, also produced important unintended consequences (UCs) with far-reaching
implications for the US health system. Based on our own experiences from large health information technology (HIT) adoption
projects and a collection of key studies in HIT evaluation, we discuss the most prominent UCs of MU: failed expectations, EHR
market saturation, innovation vacuum, physician burnout, and data obfuscation. We identify challenges resulting from these UCs
and provide recommendations for future research to empower the broader medical and informatics communities to realize the
full potential of a now digitized health system. We believe that fixing these unanticipated effects will demand efforts from diverse
players such as health care providers, administrators, HIT vendors, policy makers, informatics researchers, funding agencies, and
outside developers; promotion of new business models; collaboration between academic medical centers and informatics research
departments; and improved methods for evaluations of HIT.
(J Med Internet Res 2019;21(5):e13313) doi:10.2196/13313
KEYWORDS
meaningful use; medical informatics applications; adoption
Introduction
When humans created the cities to enable surplus food, labor
division, and trade, the city itself generated new modalities of
problems such as disease and violence. The American
sociologist Robert K. Merton (1910-2013) coined the term
unintended consequences (UCs) to describe these antagonistic
elements inherent in any human endeavor [1]. The health care
industry, which in the United States has reached near universal
adoption of electronic health record (EHR) systems, is no
exception.
Calls for nationwide adoption of EHRs [2] finally came to
fruition when the US Congress passed the Health Information
Technology for Economic and Clinical Health (HITECH) Act
into law in 2009 [3], establishing the Meaningful Use (MU)
program. As a result of MU, EHR adoption among US hospitals
increased an impressive 8-fold in 6 years, and today, 9 in 10
hospitals use a government-certified EHR, and adoption among
office-based physicians is above 80% [4]. However, although
successful in promoting its intended consequences (EHR
adoption and use), the program, and other contributing factors,
also produced important UCs, with effects that range from the
health system level all the way to the point of care level. Many
recent publications have criticized MU and particularly EHRs;
however, little attention has been dedicated to promoting
effective solutions. Although previous articles have elicited
emerging health information technology (HIT) UCs such as
decreased patient-provider interaction, security breaches, and
overdependence on technology [5] and proposed a research
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agenda to fixing the EHR [6], such reports were produced during
the MU implementation, and therefore, their conclusions were
made before the US health system had been exposed to the
effects of nationwide EHR adoption. On the basis of our own
experiences from large-scale HIT adoption projects and a
collection of key studies in HIT evaluation, we discuss the most
prominent UCs of MU (Figure 1) and provide recommendations
for future research to empower the broader medical and
informatics communities to realize the full potential of a now
digitized health system.
Figure 1. Unintended consequences of Meaningful Use, their contributing factors, and opportunities for future research from the broadest to the most
specific level. ARRA: American Recovery and Reinvestment Act; EHR: electronic health record; HIT: health information technology; UC: unintended
consequence.
Unintended Consequence 1: Failed Expectations
Recent systematic reviews have found that most HIT evaluations
published before MU reported predominantly positive outcomes
[7,8]. These outcomes served as the foundation for the MU
program and have produced a hype around HIT. Such a hype
led to a nationwide adoption of commercial EHRs with high
expectations for improving the US health care cost and quality
[9]. However, after 4 years of nationwide EHR adoption, health
care in the United States is still the most expensive and lags
behind in some quality outcomes when compared with other
developed countries [10], which indicates that the expected
benefits of a digital health system have not yet materialized
[11-14]. As the adoption of commercial EHRs increased, new,
unanticipated modalities of problems emerged [5]. The first
systematic review of HIT impact published after MU continued
to find mostly positive results; however, it also reported that
19% of the studies found no significant HIT impact, and the
lack of negative outcomes is likely explained by publication
bias [15].
The same systematic reviews that have reported positive findings
have also reported several mixed results, which leaves
unanswered questions as to the impact of HIT on quality,
productivity, and safety. Furthermore, studies from other
industries demonstrate that IT adoption rarely produces positive
results if not accompanied by complementary factors or
investments [16]. Several internal and external factors have
been identified as potentially affecting care outcomes during
HIT interventions [17], which suggests that previous studies
may have been subjected to similar context-dependent factors,
as they are common to HIT interventions [18,19]. Pre-MU
studies are being criticized for relying on weak research designs
such as short-term pretest-posttests and for the use of a small
set of nonconsensus measurements [8,12,20]. The latter is an
important barrier to the reproducibility of studies [21] and to
the comparison of outcomes across studies [20], which prevents
more comprehensive assessments of HIT impact and produces
questions regarding the strength of the evidence supporting HIT
effectiveness [22]. The lack of consistent evidence resulting
from the use of poorly designed studies indicates that what
others have called positive outcomes [7,8] are in fact putative
outcomes. It has been estimated that without improved research
methods, around 100 hypotheses per year will continue to be
tested without providing any valuable knowledge [23].
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With insufficient evidence to support the hype around HIT and
generalizable effects of HIT across care outcomes, settings, and
EHR systems, an important question remains unanswered: was
the over 20-billion-dollar investment in HIT from the America
Recovery and Reinvestment Act (ARRA) worth it?
The Path Forward
Implementation of a new EHR will inevitably add to the
complexity of the several aspects of care, and as users adapt to
the system, they demand new customizations [24]. These
customizations are often added to updated EHR versions that
demand extensive local testing and an implementation process
almost as complex, risky, and labor intensive as the
implementation of a newly adopted EHR. In such a scenario,
simple pretest-posttest designs are ineffective [25]. A paradigm
shift on the choice of research designs for HIT studies is needed
to produce more longitudinal evaluations able to detect
time-sensitive effects common to HIT interventions [26] and
to assess a large set of measures capable of detecting the diverse
effects of such interventions [11,12]. Furthermore, as HIT
interventions are subject to context-dependent factors,
assessment of potential covariates is of paramount importance,
as demonstrated elsewhere [17]. A better understanding of the
full impact of HIT on the US health system will demand more
comprehensive evaluations that assess a large sample of
agreed-upon measures shared across researchers to allow
comparison of outcomes across studies by future systematic
reviews—and potential meta-analyses. In addition to increasing
our understating of HIT impact on a national scale, such an
approach has the potential to produce compelling evidence to
the need for improving HIT effectiveness and can lead us to a
more realistic assessment of the real value of the ARRA
investment in HIT.
Unintended Consequence 2: Electronic Health Record
Market Saturation
The time frame to implement MU’s certification criteria was
constrained, and the larger EHR vendors more rapidly complied
with the criteria, contributing to an increased adoption of
systems with established market share [27]. In 2017, the top 3
US HIT vendors shared 66% of the EHR market for acute care
hospitals, which includes most large academic medical centers
[28,29]. Given the complexity and high cost involved in
implementing a commercial EHR, health care organizations are
unlikely to change an EHR vendor anytime soon, causing a
saturation of the US EHR market.
The Path Forward
As new, expensive EHR implementations become rarer, EHR
vendors will be forced to find new business models to remain
profitable. This path is evolving through initiatives such as the
Substitutable Medical Applications & Reusable Technologies
(SMART), which coupled with data standards, such as Fast
Healthcare Interoperability Resources (FHIR), is enabling
development of third-party applications seamlessly connected
to commercial EHRs. Such applications have the potential to
replace or augment commercial EHRs’ functionality, in a model
similar to the mobile phone industry [30]. To providers, such
an approach represents an interesting opportunity to expand,
customize, or replace EHR functionality as needed; to EHR
vendors, it represents an opportunity to diversify their products,
solutions, and sources of income. However, the saturation of
the national market has produced a situation analogous to an
oligopoly, and the path to producing new business models is
unclear. Although some vendors seem to be open to the idea of
having external applications connected to their EHR, others
intend to charge providers per FHIR transaction, which will
eventually hamper use of external applications. In addition, the
2 leading US EHR vendors are increasing their global presence
[31], which may help to keep them financially sustainable and
postpone the development of new business models. With an
increased bargaining power of these vendors, the success of
initiatives such as SMART on FHIR may emerge from the
tension between providers’ needs and vendors’ desire to keep
control over their products [19].
Some researchers have suggested that the use of similar systems
across the country will create opportunities for human factors
researchers by facilitating comparison of similar functionality
[5]; however, such opportunities may not reach fruition because
of local configurations that allow the same product to be
implemented in completely different ways across clients [32].
Overcoming the vendor oligopoly will demand development of
informatics solutions proved to be more effective than current
systems’ functionality, which leads us to the next UC: innovation
vacuum.
Unintended Consequence 3: Innovation Vacuum
As EHR adoption has primarily been achieved through financial
incentives, the cycle of technological innovation typical of other
industries has not been observed in the US HIT sector. As a
result, commercial EHRs were adopted before fixing widely
known problems such as poor usability [33], which has been
associated to patient harm [34,35], and suboptimal clinical
decision support (CDS) systems [36] such as excessive,
overzealous alerts frequently ignored by providers [37]. In
addition, a recent evaluation of EHR certification criteria
concluded that the certification process is not designed to
prevent patient harm [38]. Specifically, the report found that
the usability testing required does not include a representative
sample, does not include real clinical scenarios, and does not
simulate changes added through system configuration by local
clients.
The accelerated adoption also affected benchmarking
organizations such as Intermountain Healthcare, Partners
Healthcare, and the Veterans Health Administration that have
traditionally promoted most HIT innovations [39]. These
organizations decided to replace their systems with commercial
EHRs, putting an end to the homegrown systems’ era. As a
result, some of these organizations decided to dissolve their
informatics departments [40,41], decreasing their investment
in informatics innovation.
With widespread adoption of suboptimal and poorly tested
systems, along with traditional innovators stepping aside, fixing
the EHR now is a bit like fixing an airplane midflight, and
without a pilot.
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The Path Forward
At least 2 panels at recent American Medical Informatics
Association annual symposia have presented informatics
innovations in the post-MU era with clients of 1 large HIT
vendor, and most innovations included SMART on FHIR apps
[42,43]. Panelists have pointed out that as commercial EHRs
can properly handle capabilities such as billing, data storage,
and privacy regulations, informatics innovators tend to be freer
to innovate in the post-MU era. However, as previously
mentioned, most HIT vendors are not yet fully open to seamless
interface with external apps. In addition, FHIR is a standard
under development, and a substitute for the traditional innovators
is yet to be found. To aggravate the problem, most contracts
signed between providers and HIT vendors include clauses that
hamper transparency by preventing providers from sharing
usability and safety issues that could otherwise advance EHR
design [44].
There was a natural reason for having most HIT innovations
coming from benchmarking organizations: neither HIT vendors
nor academic departments have seamless access to clinicians
at the point of care, where informatics applications are put to
the test. In naturalistic settings, iterations between clinicians
and informaticists facilitate an understanding of users’ needs
to inform EHR development. Academic informatics departments
could serve as a natural replacement for the traditional
innovators by promoting cutting-edge research toward fixing
the EHR, coupled with more robust HIT evaluations. However,
this replacement will demand a closer relationship between
academic departments and their medical centers. In US
universities, these departments tend to function as independent
organizations, which hampers researchers’ access to HIT
resources and clinicians at the point of care. Work in such a
direction has started [45-47] and serves as example of the path
needed to design new business models, fostering innovation
and transparency, and fixing the EHR.
Unintended Consequence 4: Physician Burnout
The accelerated adoption of commercial EHRs coincided (and
likely was programmed to coincide) with the implementation
of the Affordable Care Act (ACA). The slow, but steady,
implementation of pay-for-performance payment models has
given rise to the EHR-based quality measurement [48]. The
push for reporting clinical performance generates an increased
demand for capturing accurate, structured data [5], and the use
of suboptimal EHRs in these tasks has contributed to the so
called EHR-associated physician burnout [49]. The use of
clinical documentation for nonclinical purposes is increasing
and is source of frustration among physicians [50,51]. This is
reinforced by the fact that electronic clinical notes generated in
the United States are significantly longer than similar
documentation in other developed countries [52]. Recent studies
have found that in the post-MU and ACA era, for every hour
of patient contact time, physicians may spend up to 2 hours on
electronic documentation [53,54]. The documentation burden
has been so intense that in some cases, physicians intentionally
close slots in their agenda to complete electronic documentation
of previous patients [17].
The Path Forward
In addition to simplifying billing requirements [6] and
developing informatics solutions to extract quality indicators
from clinical documentation [5], a fundamental redesign of the
EHR to improve data entry and retrieval is needed. The
structured and static format of current EHR interfaces force
physicians to record clinical data through predefined and strict
functionality dependent on the current desktop kit (pointer +
keyboard + monitor with a cluttered EHR interface). For
physicians to keep the richer narrative of their clinical
assessments while decreasing the documentation burden, EHRs
must demand less typing and clicking [55]. New technologies
such as conversational speech recognition (CSR) have recently
achieved human parity with regards to transcription error rate
[56] and have tremendous potential for substantially decreasing
typing and clicking. However, CSR solutions may be
compromised by the fact that clinicians may make conscious
decisions about what information to communicate to patients
and to document in the EHR [57]. Therefore, there are
opportunities for research exploring what information clinicians
document (or not) in the EHR and what information they do
not communicate verbally to the patient but document in their
clinical notes [58]; such findings will inform development of
CSR and other data-entry solutions capable of handling such
situations. Regarding data retrieval, EHR content retrieved by
physicians is influenced by their tasks or information goals
[59,60]; however, such stimuli are not captured by current EHRs.
Future research should investigate how EHRs can support data
retrieval with intelligent stimulus- or goal-oriented functionality
that allows a holistic view of the patient and flexible navigation
across the record [58] to hopefully decrease the documentation
burden and its contribution to the next UC: data obfuscation.
Unintended Consequence 5: Data Obfuscation
Physicians frequently create their clinical notes by using the
patient’s previous note, a practice known as copy-and-paste.
[61] As a result, they often produce (and later deal with)
uninformative, bloated notes that often contain redundant
information and errors [62,63]. In addition, these notes do not
provide the data in a way that increases clinicians’ situational
awareness (ie, the perception and comprehension of relevant
information necessary to take action) [64], and in some cases
may never be read [65]. The problem is aggravated by
overwhelming CDS alerts and reminders; many clinicians
complain that such alerts make them vulnerable to information
overload, which might lead them to miss important information
[66]. The obfuscation of relevant data resulting from bloated
records has been reported [67,68], associated with potential
safety hazards [69] and with delayed or incorrect decisions at
the point of care [70].
The Path Forward
Some proposed solutions to highlighting relevant data include
tailoring physicians’ use of EHRs to document what they are
thinking about the patient’s situation [64], transferring some
data entry to patients [6], or new policies to facilitate health
information exchange (HIE) [5,6,71]. Such proposals are
unlikely to succeed in isolation as they require clinicians to enter
or import even more information into already bloated records.
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In addition, the effectiveness of HIE seems to be understudied
[72]; although some studies report HIE-associated improvements
[73], others report the opposite [74].
Concise documentation that highlights relevant data will come
from smarter EHRs that actively participate in patient care [75];
however, to be smarter, EHRs must be able to capture and
process more information about the patient’s context and
clinicians’ reasoning. Previous studies suggest that clinicians
seem to always know something that is only partially represented
in or is missing entirely from the EHR [37,76]. For example,
EHRs are incapable of understanding why clinicians order what
they order, or how current symptoms are related to previous
problems. Although most EHRs allow medical records to be
structured on a problem-oriented basis, such structure does not
capture the reasoning behind the relationship between problems
and other clinical concepts. For example, a medication can be
linked to a problem, indicating that it was ordered to treat a
particular problem, but the reasoning (why) behind the choice
for this particular medication is not captured by the EHR. If
such data were captured, several opportunities for informatics
research would emerge to apply (and improve) computational
methods (eg, machine learning, natural language processing,
and text generation methods) to empower the EHR to use
patient’s care context data. Context-rich data could be used to
facilitate note creation, to create automatic notes ready for
review, and to increase the accuracy of CDS, potentially
mitigating the already infamous alert fatigue [37]. However, 2
major challenges remain: (1) A formal representation of the
semantic relationships between clinical concepts (eg, symptoms,
findings, problems, diagnoses, and treatments) does not exist
and (2) Effective methods for capturing and representing
clinicians’ reasoning need to be developed [58]. EHR vendors
have avoided this path to avert coliability for medical errors
when eventual system failures lead to misleading
recommendations [77,78]. What vendors have avoided translates
into several opportunities for informatics researchers. The
development of a formal representation of clinicians reasoning
seems to be a promising alternative to empower EHRs to
represent patients’ situation [79]. However, the application of
such a representation into actual patient data will demand new,
more effective data-entry approaches [58], improvements to
data visualization [80], and computational methods [55].
On balance, despite the unexpected effects and challenges of
nationwide EHR adoption, several opportunities for developing
more effective EHRs and evaluation methods are likely to
emerge from the forces promoting progress. The UCs here
discussed do not intend to be exhaustive; other consequences
may be revealed as new, more robust HIT evaluations are
reported. We hypothesize that overcoming these UCs will likely
require a path reverse to the one that produced them. By creating
smarter clinical information systems with more intuitive
navigation and data entry functionality, clinicians could save
time searching, synthesizing, and documenting data in the EHR,
which would contribute to alleviate data obfuscation and
mitigate burnout. Such systems will likely come from external
applications developed through cutting-edge research conducted
in academic medical centers that tend to be a natural replacement
for earlier informatics innovators. These applications, if
successfully implemented and evaluated, may back providers
up on their demands to have most large EHR vendors opening
their platforms, which would facilitate the development of new
business models and decrease market oligopoly. Finally, by
accumulating evidence of the effectiveness of these applications,
in isolation and in conjunction with commercial EHRs, a better
understanding of the true positive effects of HIT can be obtained
by future systematic reviews and meta-analyses.
The multiple efforts proposed here will demand collaboration
between diverse players such as health care providers,
administrators, HIT vendors, policy makers, informatics
researchers, funding agencies, and outside developers toward
a single goal: to realize the full potential of a digitized health
system.
Acknowledgments
This work was supported by research funds from the Informatics Institute of the University of Alabama at Birmingham.
Conflicts of Interest
None declared.
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Abbreviations
ACA: Affordable Care Act
ARRA: America Recovery and Reinvestment Act
CDS: clinical decision support
CSR: conversational speech recognition
EHR: electronic health record
FHIR: Fast Healthcare Interoperability Resources
HIE: health information exchange
HIT: health information technology
MU: Meaningful Use
SMART: Substitutable Medical Applications & Reusable Technologies
UCs: unintended consequences
Edited by G Eysenbach; submitted 07.01.19; peer-reviewed by A Martínez-García, N Delvaux, B Vaes; comments to author 28.03.19;
revised version received 09.04.19; accepted 26.04.19; published 18.05.19
Please cite as:
Colicchio TK, Cimino JJ, Del Fiol G
Unintended Consequences of Nationwide Electronic Health Record Adoption: Challenges and Opportunities in the Post-Meaningful
Use Era
J Med Internet Res 2019;21(5):e13313
URL: http://www.jmir.org/2019/5/e13313/
doi:10.2196/13313
PMID:
©Tiago K Colicchio, James J Cimino, Guilherme Del Fiol. Originally published in the Journal of Medical Internet Research
(http://www.jmir.org), 18.05.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution
License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete
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