JMIR MEDICAL INFORMATICS

Yen et alViewpoint

Health Information Technology (HIT) Adaptation: Refocusing on
the Journey to Successful HIT Implementation
Po-Yin Yen1,2, RN, PhD; Ann Scheck McAlearney3, ScD; Cynthia J Sieck3, PhD; Jennifer L Hefner3, PhD; Timothy
R Huerta3, PhD
1

Washington University in St Louis, Institute for Informatics, St Louis, MO, United States2

Goldfarb School of Nursing, BJC Healthcare, St Louis, MO, United States

3

The Ohio State University, Department of Family Medicine, Columbus, OH, United States

Corresponding Author:
Po-Yin Yen, RN, PhD
Washington University in St Louis
Institute for Informatics
4444 Forest Park Ave, Suite 6318
St Louis, MO,
United States
Phone: 1 314 273 2213
Email: yenp@wustl.edu

Abstract
In past years, policies and regulations required hospitals to implement advanced capabilities of certified electronic health records(EHRs) in order to receive financial incentives. This has led to accelerated implementation of health information technologies(HIT) in health care settings. However, measures commonly used to evaluate the success of HIT implementation, such as HIT
adoption, technology acceptance, and clinical quality, fail to account for complex sociotechnical variability across contexts andthe different trajectories within organizations because of different implementation plans and timelines. We propose a new focus,
HIT adaptation, to illuminate factors that facilitate or hinder the connection between use of the EHR and improved quality ofcare as well as to explore the trajectory of changes in the HIT implementation journey as it is impacted by frequent system
upgrades and optimizations. Future research should develop instruments to evaluate the progress of HIT adaptation in both its
longitudinal design and its focus on adaptation progress rather than on one cross-sectional outcome, allowing for moregeneralizability and knowledge transfer.
(JMIR Med Inform 2017;5(3):e28) doi: 10.2196/medinform.7476
KEYWORDS
health information technology; adaptation; adoption; acceptanceIntroduction
Health information technology (HIT) is defined as “theapplication of information processing involving both computer
hardware and software that deals with the storage, retrieval,sharing, and use of health care information, data, and knowledge
for communication and decision making” [1]. During the past
10 years in the United States, several policies, such as the Health
Information Technology for Economic and Clinical Health Act,
have led to accelerated HIT adoption and implementation in
health care settings, especially implementation of electronic
health record (EHR) systems [2,3]. In addition, the Centers for
Medicare and Medicaid Services established the EHR incentive
program to promote the development of a robust HIT
infrastructure, and as part of that effort, released Meaningful
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implement advanced capabilities of certified EHRs by certain
dates in order to receive financial incentives. Other efforts
focused on the creation of regional extension centers to facilitate
the transition to EHR use through training. MU criteria consistof 3 stages [4]: stage 1, begun in 2011, has a focus on data
capture and sharing; stage 2, begun in 2014, aims to improve
clinical processes with health information exchange,ePrescription, and patient access; and stage 3, in 2017, wasrecently replaced by Advanced Care Information [5,6] due tocriticism of the MU program [7,8].
Hospitals have been rapidly responding to these new policies
and incentives with large-scale implementations of EHRs during
the past few years. Adopting new technology requires theredesign of individual and collective workflows and results in
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changes in both organizational structure and process [9-13]. Yet
rapid adoption may hinder the interoperability of the EHR
system [14,15]. To facilitate appropriate adoption and use,
upgrades, redesign, and optimization are needed, including bothminor and major changes in EHR infrastructures, functions,interfaces, and workflows. Further, recent studies have shownthat there is a close relationship between the speed of adoption
and patient safety concerns of clinicians, both across facilities
and within different units [16-18]. EHR implementation could
be a distraction from patient care with negative impact on patient
outcomes [19] and has mixed association with quality
improvement [20,21].
At the same time, studies suggest that unsuccessful
implementation of HIT systems could be due to poorly designed
HIT, poor use of HIT by clinicians, or socioorganizational
factors such as goal conflicts, lack of time, or lack of support
from colleagues [22]. However, these studies lack clarity intheir measures [23]. This lack of differentiation between
technological and human factors thus limits the ability to apply
research findings to practice in technology implementation [24].
Given MU regulations, MU requirements have commonly beenused as a means to assess HIT implementation success in orderto promote essential HIT functionalities [4]. For example, MU
stage 2 requires providers to have certain HIT functionalities(eg, computerized provider order entry, personal health record,
medication reconciliation) in order to continue to participate in
the EHR incentive programs [25]. However, this approach also
creates a ceiling effect, hindering the advancement of innovative
utilities. While the MU program may accelerate development
and implementation of certain key functions, it also slows downother functionalities [26,27]. By focusing on achieving MU, werisk missing the big picture of health care system changes.
Therefore, we propose that there is a need to improve ourunderstanding of how to appropriately assess the performanceand success of HIT implementation over time to allow us togeneralize to other HIT implementation contexts.

Measuring Health Information Technology
Implementation Success
Successful HIT implementation is commonly evaluated using
measures such as HIT adoption, technology acceptance, andclinical quality. Yet this disparate array of measures fails toaccount for complex sociotechnical interactions, variability
across contexts, and the different trajectories withinorganizations that exist because of different implementationplans and timelines. Appropriate measurement of HIT
implementation thus needs to take into account this variability
across organizations and over time but at the same time enable
us to generalize the variation across HIT implementation studies
in order to inform practice. As a result, the issue of consistentmeasurement becomes increasingly significant.
Current measures that exist in the literature include HIT
adoption, HIT acceptance, and clinical quality measures(CQMs). The first common measure, HIT adoption, is definedby the EHR MU stages outlined by the Office of the National
Coordinator and measures the rate of health care systems having
chosen to invest resources toward EHR implementation. It is
commonly reported as an adoption rate to reflect the percentage
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Yen et alof health care organizations with specific EHR functionalities
or capabilities that are meaningful for patient care. In 2013,
59% of hospitals reported at least a basic EHR system, but only
5.1% could meet the MU stage 2 criteria [2]. The expectationis that more meaningful use of an EHR system will ultimately
result in improved care and more empowered clinicians. In
addition, the Healthcare Information and Management Systems
Society (HIMSS) measures EHR adoption through the Electronic
Medical Record Adoption Model (EMRAM), which categorizes
EHR capabilities into an 8-stage scale from stage 0 to stage 7[28]. In 2015, HIMSS Analytics’ Annual Study reported that
27% of hospitals are at stage 6 or above. Although it is helpful
to recognize the EHR capabilities across organizations in thenation, it is unclear whether those functions are fully used byclinicians.
The second approach to measuring implementation successinvolves HIT acceptance, the extent of individual commitment
to use the technology [29-33]. When assessing individual user
acceptance, the technology acceptance model (TAM) [34,35]
is a commonly applied and useful model, albeit with limitations[36]. TAM’s predictive power in health care is lower than what
has been found in other domains [24], and some recommendthat the TAM should be integrated with other adoption theories[36], particularly those that include variables related to bothhuman and social change processes [24].
CQMs [37] are another common metric used to assess the
success of HIT [38]. However, HIT implementation appears tohave little impact on care quality whether measured by patientmortality, adverse drug events, or readmission rates [39].
Although CQMs are helpful for assessing the extent to which
HIT can be used to monitor the quality of health care services
provided, this approach to measurement does not take into
account organizational or human factors that could impact HIT
implementation.
Measuring HIT adoption and acceptance alone provides only alimited understanding of HIT success. Both HIT adoption rates
and TAM are helpful to understand the status of HIT
implementation and acceptance, but they do not inform a
strategic plan for promoting successful HIT implementation ina health care organization. CQM as a proxy for HIT successalso fails to take into account the organizational context of
implementation. In short, as HIT implementation is a process,not an outcome, understanding implementation success requiresconsideration of the sociotechnical environment in which it
takes place.

Sociotechnical Theory: Improving Our Understandingof Health Information Technology Implementation
Sociotechnical theory positions people-focused (socio) elements,organizational and human, and information technology elements(technical) as interdependent parts of a system that cannot be
studied in isolation and therefore must be evaluated together[40]. Sociotechnical theory has been discussed as a theoreticalframework that is responsive to the tenets of complex adaptive
systems (CAS) [41-44]. When viewed in concert, these 2
theoretical approaches support that interdependent interactions
between people and technology within the workplace have both
direct impacts, in the classical cause and effect sense, andJMIR Med Inform 2017 | vol. 5 | iss. 3 | e28 | p. 2
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impacts through feedback, where these same people andtechnology attenuate, strengthen, distort, halt, or change valenceover time [41,43,45].
Current sociotechnical evaluations involve assessing both thetechnology and the social contexts where the technology isimplemented. A systematic review conducted on EHR
implementations revealed that sociotechnical factors complicate
HIT deployments [46]. Technical features of HIT interact withthe social features of a health care work environment. Further,it has been demonstrated that the quality of the implementation
process is just as important as the features and capabilities ofthe system being implemented [47-49].
We suggest grounding the theoretical framing of CAS that refers
to adaptiveness as “the ability of components of a CAS tochange their behavior as a result of interactions with the othercomponents and the surroundings” [41]. In shifting the conceptof adoption to adaptation, we frame sociotechnological change
as occurring over time with system response characterized as
the adaptiveness of a health care organization in the context ofchanges to HIT implementation [42,44]. For example, technicalfeatures are not static; rather they frequently change over time
as new versions of the software are promulgated. As such,

Yen et aladoption is not an end state; it is the application of an arbitraryend point to facilitate our understanding. From that perspective,
understanding the adaptiveness, or HIT adaptation in thisprocess, is thus significant in our understanding of HIT
implementation success [50].

Health Information Technology Adaptation
Although sociotechnical theory and CAS have been used to
explain complexity in health care [51], little has been discussed
that uses adaptation as a measure to evaluate the success of HIT
implementation over time. We thus propose a new focus:
adaptation. Adaptation is conceptualized as “a process ofmodifying existing conditions in an effort to achieve alignment”[52] involving workflow redesign, user trainings, and technology
maintenance [53]. In the context of HIT implementation,
refocusing from adoption and acceptance to adaptation
illuminates factors that facilitate or hinder the connection
between use of the EHR and improved quality of care. Further,by shifting to adaptation, we refocus the question of HIT
adoption to one that explores the trajectory of change as an
explicit component of the way we measure these issues. Table
1 presents the definitions of adoption, adaptation, and acceptanceas differentiated by Cooper and Zmud [53].

Table 1. Definitions of adoption, adaptation, and acceptance [54].
Concept

Definition

AdoptionA decision is reached to invest resources to accommodate the implementation effort.

Adaptation

The innovation is developed, installed, and maintained. Procedures are developed and revised. Members are trained bothin the new procedures and in the innovation.

Acceptance

Organizational members are induced to commit to the innovation’s usage.

MU criteria [54-56] and CQMs can be seen as verification andvalidation steps, respectively, for HIT implementation. In
product or system design, evaluation is commonly done via
verification and validation. Verification serves as quality control
to assess whether a system is in compliance with regulations
and specifications. On the other hand, validation is a quality
assurance process that establishes evidence to ensure a system
accomplishes what was intended. However, no measures havebeen proposed to assess HIT implementation performance
between the steps of system verification and validation. We
suggest that this period encompasses the HIT adaptation process,requiring its own measurement approach.
In Figure 1, we illustrate the current knowledge gap between
meeting the MU criteria and achieving CQMs, linking this

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conceptuallytoDonabedian’s
well-known
structure-process-outcomes model, a quality assessment model
presented to evaluate health services outcomes [57]. Considering
HIT implementation in the context of the Donabedian model,
structure refers to HIT resources, which are determined by MU
criteria; process refers to clinicians’ use or adaptation of HIT
for their use; and outcomes refer to the effects of using HIT forthe delivery of health care, as measured by CQMs. In practice,
the HIT implementation journey will be impacted by frequentsystem upgrades and optimizations, leading to performancevariability throughout the process. However, by including
considerations of sociotechnical factors such as technology
acceptance, communication and collaboration, work
productivity, training and competency, leadership, and policy,
the progress of HIT adaptation could be appropriately assessed.

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Yen et al

Figure 1. Health information technology adaptation measures as the process evaluation.

Theoretical Frameworks to Study Health Information
Technology Adaptation
Two theoretical frameworks provide guidance for HIT
adaptation research: the information technology (IT)implementation framework [58] and a new sociotechnical model[42]. First, the IT implementation framework [58] suggests that
(1) IT use is complex, multidimensional, and influenced by avariety of factors at individual and organizational levels and (2)success in achieving change is enhanced by active participationof members from the target user group [58]. The new

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adaptive health care systems and suggests investigating 8
dimensions: (1) hardware and software computing infrastructure;(2) clinical content; (3) human-computer interface; (4) people;(5) workflow and communication; (6) internal organizational
policies, procedures, and culture; (7) external rules, regulations,and pressures; and (8) system measurement and monitoring[42]. Figure 2 illustrates our adapted model from the newsociotechnical model [42]. We do not include the seventh
dimension, “external rules, regulations, and pressures,” as we
focus on factors within the organization.

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Yen et al

Figure 2. Adapted from the sociotechnical model [43].

Recommendations for Future Health Information
Technology Adaptation Research
We propose that HIT adaptation research should deploy
multilevel and multidimensional evaluation to understand the
HIT adaptation progress, drawing from both of thesefoundational theories. Specifically, HIT adaptation researchshould focus on developing fundamental and multidimensional
facts that can inform the progress of HIT adaptation. Below we
describe 4 directions that can drive future HIT adaptation
research.

Develop Appropriate Process Measures
While the outcome measures (HIT adoption rate, acceptance,
and CQMs) have been established, there is a need to develop
process measures from individual and organizational
perspectives and include multidimensional measures ofadaptation to EHRs. These measures will need to incorporate
factors such as communication channels, cultural conflict,interdisciplinary team dynamics, user satisfaction, work
productivity, cost, and quality [38,59,60].

Consider the Culture and Context in Which Health
Information Technology Is Implemented
Most HIT adoption or acceptance studies have used individuals
or hospitals as the unit of analysis [39,61,62]. These findings
are informative for identifying associated individual perceptions
and experiences as well as hospital demographics. However,additional factors such as the culture of a discipline or a
department, the interprofessional or multidisciplinary
communication within or across departments, the training
received, and workflow at the department level have not been
discussed. In particular, while social support has been identified
as one of the key factors for acceptance [63,64], no studies have
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been conducted at the department or unit level to study thisfactor.

Standardize the Definition and Methods forSociotechnical Studies
Implementing a new technology into a complex environmentis often disruptive, particularly in health care. Sociotechnicalevaluations of HIT implementations are supported in both theory
and empirically; however, little guidance exists in terms of how
to conduct a sociotechnical evaluation [65]. Challenges in
conducting sociotechnical evaluations include a lack ofagreement on the components of the sociotechnical system,
possible study designs, and data analysis strategies which may
give light to both practical and conceptual challenges [65].

Study Adaptation Longitudinally and Multidimensionally
Processes are more important to study than outcomes because
studying processes allows for generalizability and knowledgetransfer beyond the clinical setting where the research was
conducted [65]. Future studies need to employ longitudinal
study designs with multiple data time periods to establish causal
relationships [32,66,67]. In addition, the HIT evaluation toolkit
proposed by the Agency for Healthcare Research and Qualityemphasizes the advantages of conducting mixed methods studies
to provide important dimensions in an evaluation study [68].
Thus, future HIT research studies should be designed as mixed
methods sociotechnical evaluations focused on exploring the
dynamic relationship between technology and social factors
over time [65].

Conclusion
Measuring HIT adaptation can provide a more thoroughunderstanding of the connection between HIT use and healthcare outcomes. Our ability to advance our understanding is
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predicated on good evaluation models, notably in the area of ahealth organization’s overall performance. As the sociotechnicalenvironment remains a confounding problem influencing ourunderstanding of the generalizability of research findings about
HIT implementation success, there is a need to integrate issues
exacerbated by workarounds, poorly designed interfaces,
suboptimal functionality, and the sheer complexity of systems
that contribute to HIT adoption issues as well as consider the

Yen et alidiosyncrasies across contexts. However, existing evaluation
models are not supportive of a greater understanding of thephenomenon itself. This paper is therefore presented to provide
a new perspective to shift the focus from adoption to adaptation.
Future research should develop instruments to evaluate theprogress of HIT adaptation in both its longitudinal design andits focus on adaptation progress rather than on a single outcome,allowing for more generalizability and knowledge transfer.

Acknowledgments
This work was supported by the Agency for Healthcare Research and Quality (R21HS024767).

Conflicts of Interest
None declared.

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Yen et al

Abbreviations
CAS: complex adaptive system
CQM: clinical quality measure
EMRAM: electronic medical record adoption model
HER: electronic health record
HIMSS: Healthcare Information and Management Systems Society
HIT: health information technology
IT: information technology
MU: Meaningful Use
TAM: technology acceptance model

Edited by G Eysenbach; submitted 08.02.17; peer-reviewed by M Pino, J Kim; comments to author 22.07.17; revised version received
04.08.17; accepted 04.08.17; published 07.09.17
Please cite as:
Yen PY, McAlearney AS, Sieck CJ, Hefner JL, Huerta TR
Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation
JMIR Med Inform 2017;5(3):e28
URL: http://medinform.jmir.org/2017/3/e28/
doi: 10.2196/medinform.7476
PMID: 28882812©Po-Yin Yen, Ann Scheck McAlearney, Cynthia J Sieck, Jennifer L Hefner, Timothy R Huerta. Originally published in JMIR
Medical Informatics (http://medinform.jmir.org), 07.09.2017. This is an open-access article distributed under the terms of theCreative 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 JMIR Medical Informatics, is properly cited. The
complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright andlicense information must be included.

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