Difference between revisions of "Journal:Factors associated with adoption of health information technology: A conceptual model based on a systematic review"

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|title_full  = Factors associated with adoption of health information technology: A conceptual model based on a systematic review
|title_full  = Factors associated with adoption of health information technology: A conceptual model based on a systematic review
|journal      = ''JMIR Medical Informatics''
|journal      = ''JMIR Medical Informatics''
|authors      = Kruse1, Clemens Scott; DeShazo, Jonathan; Forest, Kim; Fulton, Lawrence
|authors      = Kruse, Clemens Scott; DeShazo, Jonathan; Forest, Kim; Fulton, Lawrence
|affiliations = Texas State University, Virginia Commonwealth University, Baylor University
|affiliations = Texas State University, Virginia Commonwealth University, Baylor University
|contact      = Email: scottkruse@txstate.edu; Phone: 1.210.355.4742
|contact      = Email: scottkruse@txstate.edu; Phone: 1.210.355.4742
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|download    = [http://medinform.jmir.org/article/download/medinform_v2i1e9/2 http://medinform.jmir.org/article/download/medinform_v2i1e9/2] (PDF)
|download    = [http://medinform.jmir.org/article/download/medinform_v2i1e9/2 http://medinform.jmir.org/article/download/medinform_v2i1e9/2] (PDF)
}}
}}
 
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==Abstract==
==Abstract==
'''Background:''' The [[Health Information Technology for Economic and Clinical Health Act]] (HITECH) allocated $19.2 billion to incentivize adoption of the [[electronic health record]] (EHR). Since 2009, Meaningful Use Criteria have dominated information technology (IT) strategy. Health care organizations have struggled to meet expectations and avoid penalties to reimbursements from the [[Centers for Medicare and Medicaid Services|Center for Medicare and Medicaid Services]] (CMS). Organizational theories attempt to explain factors that influence organizational change, and many theories address changes in organizational strategy. However, due to the complexities of the health care industry, existing organizational theories fall short of demonstrating association with significant health care IT implementations. There is no organizational theory for health care that identifies, groups, and analyzes both internal and external factors of influence for large health care IT implementations like adoption of the EHR.
'''Background:''' The [[Health Information Technology for Economic and Clinical Health Act]] (HITECH) allocated $19.2 billion to incentivize adoption of the [[electronic health record]] (EHR). Since 2009, Meaningful Use Criteria have dominated information technology (IT) strategy. Health care organizations have struggled to meet expectations and avoid penalties to reimbursements from the [[Centers for Medicare and Medicaid Services|Center for Medicare and Medicaid Services]] (CMS). Organizational theories attempt to explain factors that influence organizational change, and many theories address changes in organizational strategy. However, due to the complexities of the health care industry, existing organizational theories fall short of demonstrating association with significant health care IT implementations. There is no organizational theory for health care that identifies, groups, and analyzes both internal and external factors of influence for large health care IT implementations like adoption of the EHR.
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There has been a tremendous amount of research dedicated to the study of acceptance of technology, specifically the Technology Acceptance Model (TAM).<ref name="DavisPerc">{{cite journal |title=Perceived usefulness, perceived ease of use, and user acceptance of information technology |journal=MIS Quarterly |author=Davis, F. |volume=13 |issue=3 |pages=319–340 |year=1989 |doi=10.2307/249008}}</ref> More recent work has suggested modifications to the TAM that explain a perception of usefulness and intentions from the aspect of social influence and the cognitive instrumental process.<ref name="VenkateshasATheo">{{cite journal |title=A theoretical extension of the Technology Acceptance Model: four longitudinal field studies |journal=Management Science |author=Venkatesh, V; Davis, F.D. |volume=46 |issue=2 |pages=186–204 |year=2000 |doi=10.1287/mnsc.46.2.186.11926}}</ref><ref name="VenkateshasUser">{{cite journal |title=User acceptance of information technology: Toward a unified view |journal=MIS Quarterly |author=Venkatesh, V; Morris, M.; Davis, G.; Davis, F. |volume=27 |issue=3 |pages=425–478 |year=2003 |doi=10.2307/30036540}}</ref> Several organizational theories have been developed. These focus on the sources of influence and the reason for their existence.
There has been a tremendous amount of research dedicated to the study of acceptance of technology, specifically the Technology Acceptance Model (TAM).<ref name="DavisPerc">{{cite journal |title=Perceived usefulness, perceived ease of use, and user acceptance of information technology |journal=MIS Quarterly |author=Davis, F. |volume=13 |issue=3 |pages=319–340 |year=1989 |doi=10.2307/249008}}</ref> More recent work has suggested modifications to the TAM that explain a perception of usefulness and intentions from the aspect of social influence and the cognitive instrumental process.<ref name="VenkateshasATheo">{{cite journal |title=A theoretical extension of the Technology Acceptance Model: four longitudinal field studies |journal=Management Science |author=Venkatesh, V; Davis, F.D. |volume=46 |issue=2 |pages=186–204 |year=2000 |doi=10.1287/mnsc.46.2.186.11926}}</ref><ref name="VenkateshasUser">{{cite journal |title=User acceptance of information technology: Toward a unified view |journal=MIS Quarterly |author=Venkatesh, V; Morris, M.; Davis, G.; Davis, F. |volume=27 |issue=3 |pages=425–478 |year=2003 |doi=10.2307/30036540}}</ref> Several organizational theories have been developed. These focus on the sources of influence and the reason for their existence.
===Organizational theories===
Organizational theories address influence, but none adequately addresses the complexity of the health care organization. Payers, providers, and patients all control resources that exert influence. The nature of the competitive environment will also exert influence on decisions. External influence from those who control resources can be explained through resource dependence theory.<ref name="AldrichEnv">{{cite journal |title=Environments of organizations |journal=Annual Review of Sociology |author=Aldrich, H.E.; Pfeffer, J. |volume=2 |issue=1 |pages=79–105 |year=1976 |doi=10.1146/annurev.so.02.080176.000455}}</ref><ref name="PfefferTheEx1">{{cite book |title=The External Control of Organizations: A Resource Dependence Perspective |author=Pfeffer, J.; Salancik, G. |location=New York, NY |publisher=Harper & Row |year=1978}}</ref> Internal and external influences can be explained by the Diffusion of Innovation Theory through its introduction of compatibility, complexity, trialability, observability, and relative advantage.<ref name="ScholzApp">{{cite web |url=http://mspace.lib.umanitoba.ca/xmlui/handle/1993/2732 |title=Applying Rogers' theory of diffusion of innovations to examine older females' perceptions of size labels for apparel |author=Scholz, Carolyn Elaine |publisher=University of Manitoba |date=01 May 2001}}</ref><ref name="RogersDiff4">{{cite book |title=Diffusion of Innovations |author=Rogers, E.M. |location=New York, NY |publisher=Free Press |edition=4th |year=1995 |isbn=9780029266717}}</ref><ref name="RogersDiff5">{{cite book |title=Diffusion of Innovations |author=Rogers, E.M. |location=New York, NY |publisher=Free Press |edition=5th |year=2003 |isbn=9780743222099}}</ref><ref name="PfefferTheEx2">{{cite book |title=The External Control of Organizations: A Resource Dependence Perspective |author=Pfeffer, J.; Salancik, G. |location=Stanford, CA |publisher=Stanford Business Books |year=2003 |isbn=9780804747899}}</ref>
According to resource dependence theory, health care organizations with the greatest level of dependence on other organizations that control the resources will feel the greatest level of environmental influence on its decisions.<ref name="PfefferTheEx2" /> The Resource Dependence Theory describes an external interdependence of organizations. External Control of Organizations,<ref name="PfefferTheEx2" /> which is an adaptation of Resource Dependence Theory, provides good insight for this study. The authors’ premise is that the external environment creates a social context and plays an important role in how organizational decisions are made. The lack of absolute independence requires some degree of interorganizational exchange of goods or services.<ref name="PfefferTheEx2" /> As organizations build and negotiate relationships with each other in the exchange of resources, positions of power are established. No one organization can provide all of its own resources, so each organization becomes dependent on the other organizations that control the resources.
Similar to Resource Dependence, the Diffusion of Innovation Theory describes a social system that influences through communication channels.<ref name="ScholzApp" /><ref name="RogersDiff4" /><ref name="RogersDiff5" /> Diffusion of Innovation attempts to explain how “an innovation, is communicated through channels over time among members of a social system”.<ref name="RogersDiff5" /> This theory accounts for 49-97% of variance in the rate of adoption of innovation through five factors: compatibility, complexity, trialability, observability, and relative advantage. These factors are sorted into three categories of a predictive model for EHR adoption: innovation determinants, organizational determinants, and environmental determinants.<ref name="VenkateshasUser" /> The next several paragraphs exercise the five factors to this study.
The concept of compatibility<ref name="RogersDiff5" /> goes beyond answering the question, “is a product/service right for a market?” It also asks, “is the market ready for the product/service?” For instance, the Chevy Nova failed in Spanish-speaking markets because in Spanish the word “Nova” means “does not go.” Promotion of conservation techniques to farmers in the United States initially failed because farmers associated conservation with lower crop yield. Boiling water to sanitize it makes perfect sense to a market that is familiar with germ theory, but primitive tribes in Peru only heated water for sicker, weaker members; as a result, the concept failed when initially introduced and dysentery continued to flourish. In relation to this study, the concept of compatibility might ask, “is the market ready for the EHR?”
The concept of complexity<ref name="RogersDiff5" /> is appropriate to this study because innovation can be a double-edged sword. On one hand, it is new and may offer some improvement to a product or service. However, it might also be perceived as too complex; and perception can be a powerful force. If the Baby Boomer generation perceives computers to be too complex, and this perception causes computer anxiety, its users may reject its adoption and use.<ref name="CzajaFact">{{cite journal |title=Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE) |journal=Psychology and Aging |author=Czaja, S.J.; Charness, N.; Fisk, A.D.; Hertzog, C.; Nair, S.N.; Rogers, W.A.; Sharit, J. |volume=21 |issue=2 |pages=333–352 |year=2006 |doi=10.1037/0882-7974.21.2.333 |pmid=16768579 |pmc=PMC1524856}}</ref> The older physicians in a hospital have greater seniority, and are therefore, more influential in the hospital’s decision to adopt the EHR. Would this same generation of providers influence the health care organization considering EHR adoption?
The concept of trialability<ref name="RogersDiff5" /> applies more to the early-adopter group than the other groups: innovators, early-majority, late-majority, and laggards. In the early phase of promotion for a new product or service, the vendor might lower the risk of adoption by offering free trials or samples to potential users. Once the user is confident of the new item’s efficacy, then he/she is more likely to pay full price for its use. When a new producer of an EHR enters the marketplace, it must incentivize the use of its product because it is not known in the industry. The user accepts a risk by trying the new EHR, but the risk is overcome by the incentive. Once the new EHR gains momentum in the industry, adoption enters the early-majority phase. The new EHR has already gained momentum in the industry, and the producer does not need to incentivize its use.
The concept of observability<ref name="RogersDiff5" /> is also highly applicable to this study. Decision makers in a hospital that has not yet adopted an EHR will observe the experiences of other hospitals that have adopted it. Vendors will promote or advertise specifically to the nonadopters and help them observe how the EHR can benefit its organization. External players in the health care organization’s competitive environment will provide some level of observability.
Relative advantage is a multifaceted concept for this study. In health care, the most important factor is provision of health, as well as the treatment and prevention of disease. If adoption of the EHR speaks directly to the health care organization’s primary purpose, then it might provide relative advantage over competitors that have not adopted it. Another concept is that of social prestige.<ref name="RogersDiff5" /> Unless a health care organization can serve as an example to other health care organizations (observability), there may not be a sufficient level of relative advantage to be considered.
===Strategy and decision making===
Strategy can be a multifaceted concept, and organizations around the world hire strategy experts to help identify and focus on a market forces. An operational definition of strategy is borrowed from education<ref name="FumasoliPatt">{{cite journal |title=Patterns of strategies in Swiss higher education institutions |journal=Higher Education |author=Fumasoli, T.; Lepori, B. |volume=61 |issue=2 |pages=157–178 |year=2011 |doi=10.1007/s10734-010-9330-x}}</ref> and is adapted to health care: strategy is defined as instruments by which health care organizations manage their organizational processes and deal with their environments in order to select a portfolio of activities and find appropriate position in the health care industry (italics indicate a change in wording from the authors’ definition). It follows that adoption of an EHR would alter how a health care organization manages its organizational processes, so this definition of strategy is a good fit for the health care industry. However, two significant considerations in the health care environment are the level of local competiveness, and how health care organizations compete.<ref name="HendersonHealth">{{cite book |title=Health Economics and Policy (with Economic Applications) |author=Henderson, J. |publisher=South-Western College Pub |location=Mason, OH |edition=2nd |isbn=9780538481175}}</ref>
Studies have shown that decision making in the health care industry is often based on how the organization competes, whether in a single-market or multimarket environment.<ref name="SikkaTheEff">{{cite journal |title=The efficiency of hospital-based clusters: evaluating system performance using data envelopment analysis |journal=Health Care Management Review |author=Sikka, V.; Luke, R.D.; Ozcan, Y.A. |volume=34 |issue=3 |pages=251–261 |year=2009 |doi=10.1097/HMR.0b013e3181a16ba7 |pmid=19625830}}</ref> In either environment, decision-making varies on competition, and the health care industry competes in clusters.<ref name="SikkaTheEff" /> The way health care organizations compete will also affect its organizational structure. A four-cluster solution was identified as a reliable, internally valid, and stable model for health networks and a five-cluster solution for health systems.<ref name="BazzoliATax">{{cite journal |title=A taxonomy of health networks and systems: Bringing order out of chaos |journal=Health Services Research Journal |author=Bazzoli, G.J.; Shortell, S.M.; Dubbs, N.; Chan, C.; Kralovec, P. |volume=33 |issue=6 |pages=1683–1717 |year=1999 |pmid=10029504 |pmc=PMC1070343}}</ref> Differentiation and centralization are particularly important in distinguishing unique clusters of organizations. High differentiation typically occurs with low centralization, which suggests that a broader scope of activity is more difficult to centrally coordinate. Integration is also important, but the authors find that health networks and systems typically engage in both ownership-based and contractual-based integration or they are not integrated at all.
Ash and Bates<ref name="AshFact">{{cite journal |title=Factors and forces affecting EHR system adoption: Report of a 2004 ACMI discussion |journal=Journal of the American Medical Informatics Association |author=Ash, J.S.; Bates, D.W. |volume=12 |issue=1 |pages=8–12 |year=2005 |doi=10.1197/jamia.M1684 |pmid=15492027 |pmc=PMC543830}}</ref> studied the EHR adoption rates and the factors and forces affecting system adoption through surveys (85/650, 13.1%). Only 106 of the 650 (16.3%) of hospitals surveyed had adopted some form of EHR, 63/106 (59.4%) had implemented a full Computerized Provider Order Entry (CPOE) solution, and the other 43/106 (40.6%) implemented a partial CPOE solution. A full one-third of adopters were either Veterans Affairs or military hospitals. Additionally, 481/650 (73.8%) of those who planned to implement a full solution intended to do so within 5 years. Ash and Bates<ref name="AshFact" /> also found that the size of hospital is positively-associated with component adoption; specifically CPOE adoption. The authors inferred from their results that the primary reasons to adopt the EHR is to gain the quality-of-care advantages of CPOE. This inference reinforced our inclusion of CPOE as a dependent variable.
Factors that influence health information system (HIS) adoption in US hospitals have been studied by others as well (n=1441).<ref name="WangFact">{{cite journal |title=Factors influencing health information system adoption in American hospitals |journal=Health Care Management Review |author=Wang, B.B.; Wan, T.T.; Burke, D.E.; Bazzoli, G.J.; Lin, B.Y. |volume=30 |issue=1 |pages=44-51 |year=2005 |pmid=15773253}}</ref> Results showed that HIS adoption is influenced by the hospital market, organizational, and financial factors. Larger, system-affiliated, and for-profit hospitals with more preferred provider organization contracts are more likely to adopt managerial information systems than other hospitals. Operating revenue is positively associated with HIS adoption. The study also identified hostility as an aspect of environmental uncertainty, and that organizations often turn to technological adoption to regain competitive advantage.
A knowledge-based taxonomy of critical factors for adopting an EHR was developed from a systematic literature review.<ref name="CastilloAKnow">{{cite journal |title=A knowledge-based taxonomy of critical factors for adopting electronic health record systems by physicians: a systematic literature review |journal=BMC Medical Informatics & Decision Making |author=Castillo, V.H.; Martínez-García, A.I.; Pulido, J.R. |volume=10 |pages=60 |year=2010 |doi=10.1186/1472-6947-10-60 |pmid=20950458 |pmc=PMC2970582}}</ref> The researchers selected 68 of 3400 (2.00%) articles to identify six factors of adoption, listed in order of importance: user attitude toward information systems, workflow impact, interoperability, technical support, communication among users, and expert support.
Alternative measures of EHR adoption among hospitals have been studied.<ref name="BlavinAlt">{{cite journal |title=Alternative measures of electronic health record adoption among hospitals |journal=American Journal of Managed Care |author=Blavin, F.E.; Buntin, M.J.; Friedman, C.P. |volume=16 |issue=12 Suppl HIT |pages=e293-e301 |year=2010 |pmid=21322296}}</ref> Authors analyzed a 2009 information technology supplement survey distributed by the American Hospital Association (AHA). The survey focused on 24 EHR functionalities in various areas: electronic clinical documentation, results viewing, CPOE, and clinical decision support. They found that 142 of 3937 (3.60%) acute-care hospitals in the United States of responding hospitals have implemented all 24 functions, 386/3937 (9.80%) of hospitals have implemented at least 20 functions, and 1437/3937 (36.50%) have implemented at least one-half of the functions. The researchers added that EHR adoption is a complex process.
Others have studied the relationship between hospital financial position and the adoption of the EHR.<ref name="GinnHos">{{cite journal |title=Hospital financial position and the adoption of electronic health records |journal=Journal of Healthcare Management |author=Ginn, G.O.; Shen, J.J.; Moseley, C.B. |volume=56 |issue=5 |pages=337–50; discussion 351–2 |year=2011 |pmid=21991681}}</ref> Through a cross-sectional study of secondary data from several sources, including the AHA (2442/5752, 42.51% acute-care hospitals in the United States), researchers identified five independent and one dependent variable. Of the five independent variables (IVs), only liquidity was positively-associated with EHR. Asset turnover was negatively-associated with EHR adoption. Bed size, a control variable, was positively-associated with EHR adoption. The authors concluded that hospitals adopt EHRs as a strategic move to better align themselves with their environment.
Because commonly used elements of organizational strategy are difficult to change, several of the variables were categorized as internal organizational factors. Research has assessed variables of hospital influence in five categories: (1) capacity as measured by number of beds in groupings by intervals of 100, (2) management, or ownership, (3) organizational focus, or teaching status, (4) competitive location and alternatives, and (5) state regulatory pressures.<ref name="FarleyCase">{{cite journal |title=Case-mix specialization in the market for hospital services |journal=Health Services Research Journal |author=Farley, D.E.; Hogan, C. |volume=25 |issue=5 |pages=757–783 |year=1990 |pmid=2123838 |pmc=PMC1065663}}</ref>
Although resources have been consumed to study factors associated with adoption of HIT, there is a gap in the literature that provides a conceptual model to guide the design of empirical models. It may seem backward to design a conceptual model after so many studies have already been conducted, but the gap remains. The aim of this study was to develop a conceptual model from a systematic literature review that associates both internal and external factors associated with adoption of the EHR. The intent of the conceptual model is to enable future empirical models.
==Methods==
===Literature review process===
Search terms were selected based on the experience of the authors in the field of health care administration. The time frame of 1993-2013 was selected as convenience. It was assumed that 2 decades would be sufficient to capture trends.
Figure 1 illustrates the literature review process that identified 83 sources consisting of empirical studies, articles, editorials, commentaries, opinion papers, organizational theories, and text books. The intent of no limits to the type of papers was to mitigate the risk of missing something significant from a study that was not catalogued properly within a key word catalogue like the Dublin Core.
The 83 records were reviewed for content and evidence. After discarding 58 articles for lack of evidence, three additional references were added because they were key concepts upon which other studies were based. Of the remaining 25 articles, a list of factors was identified as IVs. Some factors were grouped under a similar category for the purposes of simplification of the conceptual model. The dependent variable (DV) started as adoption of the EHR, but the studies from those chosen were not as specific. From personal experience, many studies seem to discuss the EHR, but call it something else: most commonly the EMR. That is why EMR was included in the search. Because so few ERHs exist without some form of CPOE, the latter term was included in the search criteria.
Our study combines the influences highlighted by previous work and examines determinants of EHR adoption. Examining EHR adoption at the health care organization level will demonstrate validity between this study and others that have used the hospital as the unit of analysis.
[[File:Fig1 Kruse JMIRMedInfo2014 2-1.jpg|800px]]
{{clear}}
<blockquote>'''Figure 1.''' Literature review process</blockquote>
===EHR adoption and internal organizational influence===
Several influences in the environment exert pressure on the health care organization to adopt EHR. Influences range from incentives from the federal government to the nature of local competitive community. US federal incentives provide a heavy influence for EHR implementation, under specific conditions, and imposes penalties for a lack of EHR implementation.
The internal politics of one organization serve as one source of influence. A hospital is part of a community, which serves as an external influence. Further, if a hospital is also part of a larger multihospital system (MHS), then the politics of the broad MHS will also exert influence on local decisions.
===EHR adoption and external environmental influence===
The patient is external to the organization, and for our study, the patient primarily serves as an external influence. Although some employees of the health care organization might also be patients, and this relationship could create a small internal influence, this study considers those few stake holders in the internal organizational factor of users. The providers are internal to the organization, and for our study, providers serve as an internal organizational influence. The payer is a significant influence<ref name="PfefferTheEx2" />, and the Center for Medicare and Medicaid Services (CMS) serves as a good example of this significant influence.<ref name="NHEFact">{{cite web |url=https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet.html |title=NHE Fact Sheet |publisher=Centers for Medicare and Medicaid Services |work=CMS.gov |date=2014 |accessdate=18 March 2014}}</ref> The HITECH act provides monetary incentives for EHR adoption. Those who do not implement all aspects specified in the stages of adoption are not eligible for the incentives. In this way, the CMS disincentivizes those organizations that do not adopt the EHR. If payments from the CMS were of little consequence to the health care organization’s revenue, then the health care organization might decide differently about EHR adoption. A competing health care organization is an external market force in the environment. Third-party payers might compare health care organizations based on maturity of automation because mature clinical components like CPOE will result in more accurate billing. Such forces incentivize a health care organization to adopt the EHR.
===Overview of the conceptual model===
The premise for an EHR adoption conceptual model is that that environmental influences affect organizational strategy of the health care organizations that adopt the EHR.<ref name="RogersDiff5" /><ref name="PfefferTheEx2" /><ref name="AshFact" /><ref name="CastilloAKnow" /><ref name="GinnHos" /><ref name="FarleyCase" /> Diffusion of Innovation theory provides three categories of a predictive model for EHR adoption: innovation determinants, organizational determinants, and environmental determinants.<ref name="RogersDiff5" /> Resource Dependence Theory provides a category of a predictive model for EHR adoption, the competitive environment. In construction of the EHR adoption conceptual model, several constructs emerged.<ref name="PfefferTheEx2" />
Elements of organizational strategy are not variables that can be easily changed<ref name="BazzoliATax" />; therefore, elements typically ascribed to strategy, such as size, ownership, and fiscal stability, will be absorbed into the IVs of influence. This research proposes a model, whereby environmental factors are associated with an organization’s decision to adopt the EHR.
Resource Dependence Theory explains environmental influences and the external interdependence of organizations.<ref name="PfefferTheEx2" /> The authors’ premise is that the external environment creates a social context and plays an important role in how organizational decisions are made. The interdependence of organizations widens the field of stakeholders, and this relationship effect should be defined.
Disparate stakeholders have different interests with reference to different components of the EHR. These interests may be different in the SR interests versus the LR interests. SR interests are those that are immediate, such as current year expenditures. LR interests are further out when all inputs are variable. The SR interests of cost can often compete with the LR potential of cost savings and greater safety. Both the SR and LR interests are affected by the external environment.<ref name="HendersonHealth" />
In a highly competitive environment, SR cost implications could often win over any long-term savings. The number of patients in a market is fixed in the SR, and a highly competitive market will affect each competitor’s share of that market. The SR costs of EHR implementation might be insurmountable by an organization in this market because it could not afford to lose ground without significant capital reserves or the ability to borrow cheaply.<ref name="HendersonHealth" /> However, in a less competitive market, the LR interests of potential cost savings have a better chance of influencing the decision to implement an EHR because the costs incurred in the SR are justified by the long-term benefits.<ref name="HendersonHealth" />
External stakeholders that control resources important to the health care organization can exert significant influence. For instance, a health care organization that receives a significant amount of revenue from the CMS will be influenced more by incentives provided by the CMS than an organization that receives a significant cash flow from private third parties. The relative influence of various external stakeholders may be captured by an analysis of the structure of the market in which a health care organization operates.
Stakeholders have varying interests with regard to the capabilities and effects of EHR components depending upon their relationship with the health care organization. Private payers have both SR and LR interests in the EHR. In the SR, their focus is on minimizing expenditures. Because the health care organization would pass on the implementation costs through higher contract costs, payers would not be equal in the SR. In addition, the disruption of EHR implementation could potentially affect care processes and therefore increase claims. Payers would be interested in the LR benefits of the EHR: potential cost savings, better disease management, and increased safety. However, the SR interests of the private payers might overshadow the LR benefits of the EHR. Public payers enable care of the indigent and elderly. As part of the [[United States Department of Health and Human Services]] (HHS), the CMS is highly interested in disease management, public health, safety, and research, and it may value these LR capabilities of the EHR more than the SR costs. The CMS, as part of HHS, would also favor the EHR because it supports the Presidential directive to promote the establishment of the Nationwide Health Information Network that links electronic patient records through health information exchanges.
Providers and patients value face time with each other. During EHR implementation, providers might spend less time in communication with patients. Providers must adapt their processes and clinic-to-administrative schedules. Any disruption or action that is perceived as deleterious to this relationship could result in a negative reaction to EHR implementation. As a result, physicians might oppose EHR adoption, or they might simply support the EHR solution with the shortest implementation time or least administrative burden. Patients might not like the reduced face time with the provider, but they might be attracted to EHR components such as e-prescribing, e-results, personal health records, and email access to the provider. These desirable features are available to the patient when the health care organization chooses to adopt various portions of the CPOE component to the EHR.


==Conflicts of interest==
==Conflicts of interest==

Revision as of 21:07, 9 November 2015

Full article title Factors associated with adoption of health information technology: A conceptual model based on a systematic review
Journal JMIR Medical Informatics
Author(s) Kruse, Clemens Scott; DeShazo, Jonathan; Forest, Kim; Fulton, Lawrence
Author affiliation(s) Texas State University, Virginia Commonwealth University, Baylor University
Primary contact Email: scottkruse@txstate.edu; Phone: 1.210.355.4742
Editors Eysenbach, G.
Year published 2014
Volume and issue 2 (1)
Page(s) e9
DOI 10.2196/medinform.3106
ISSN 2291-9694
Distribution license Creative Commons Attribution 2.0
Website http://medinform.jmir.org/2014/1/e9/
Download http://medinform.jmir.org/article/download/medinform_v2i1e9/2 (PDF)

Abstract

Background: The Health Information Technology for Economic and Clinical Health Act (HITECH) allocated $19.2 billion to incentivize adoption of the electronic health record (EHR). Since 2009, Meaningful Use Criteria have dominated information technology (IT) strategy. Health care organizations have struggled to meet expectations and avoid penalties to reimbursements from the Center for Medicare and Medicaid Services (CMS). Organizational theories attempt to explain factors that influence organizational change, and many theories address changes in organizational strategy. However, due to the complexities of the health care industry, existing organizational theories fall short of demonstrating association with significant health care IT implementations. There is no organizational theory for health care that identifies, groups, and analyzes both internal and external factors of influence for large health care IT implementations like adoption of the EHR.

Objective: The purpose of this systematic review is to identify a full-spectrum of both internal organizational and external environmental factors associated with the adoption of health information technology (HIT), specifically the EHR. The result is a conceptual model that is commensurate with the complexity of with the health care sector.

Methods: We performed a systematic literature search in PubMed (restricted to English), EBSCO Host, and Google Scholar for both empirical studies and theory-based writing from 1993-2013 that demonstrated association between influential factors and three modes of HIT: EHR, electronic medical record (EMR), and computerized provider order entry (CPOE). We also looked at published books on organizational theories. We made notes and noted trends on adoption factors. These factors were grouped as adoption factors associated with various versions of EHR adoption.

Results: The resulting conceptual model summarizes the diversity of independent variables (IVs) and dependent variables (DVs) used in articles, editorials, books, as well as quantitative and qualitative studies (n=83). As of 2009, only 16.30% (815/4999) of nonfederal, acute-care hospitals had adopted a fully interoperable EHR. From the 83 articles reviewed in this study, 16/83 (19%) identified internal organizational factors and 9/83 (11%) identified external environmental factors associated with adoption of the EHR, EMR, or CPOE. The conceptual model for EHR adoption associates each variable with the work that identified it.

Conclusions: Commonalities exist in the literature for internal organizational and external environmental factors associated with the adoption of the EHR and/or CPOE. The conceptual model for EHR adoption associates internal and external factors, specific to the health care industry, associated with adoption of the EHR. It becomes apparent that these factors have some level of association, but the association is not consistently calculated individually or in combination. To better understand effective adoption strategies, empirical studies should be performed from this conceptual model to quantify the positive or negative effect of each factor.

Keywords: electronic health record (EHR); electronic medical record (EMR); health information technology (HIT); medical information systems; computerized provider order entry (CPOE); adoption

Introduction

Background

The US Government passed the Health Information Technology for Economic and Clinical Health (HITECH) act[1] to incentivize adoption of the electronic health record (EHR) and to assuage the short run (SR) effects of cost to the health care organization in the adoption process. The three phases of Meaningful Use consume information technology (IT) strategies in the SR because of the HITECH act’s timeline for health care organizations to qualify for monetary incentives.[2][3]

Adoption of the EHR is a significant goal. International vernacular for the EHR varies; for example, electronic patient record, computerized patient records, electronic medical records (EMRs), and digital medical record. The defining difference, as defined by the Institute of Medicine, the health arm of the US National Academy of Sciences, focuses on the longitudinal and interoperable nature of the electronic patient record.[4] Without these capabilities, the patient record is greatly limited in scope. The longitudinal and interoperable nuances of the EHR are not the only significant advantages; there are eventual cost savings as well.

Studies estimate that adoption of the EHR could eventually save more than $813 billion annually, prevent 200,000 adverse drug events, and enhance the doctor-patient relationship through increased communication.[5] Unfortunately, these benefits are realized in the long run (LR), while the investment to adopt the EHR is expended in the SR. A large deficit in the SR could inhibit a health care organization’s ability to compete or survive in heavily competitive environment.

The environment of health care is unique in a competitive environment. The health care organization develops an organizational strategy based on the local environment. To increase an organization’s ability to compete, its strategy might also include cost reduction, and EHR adoption runs counter to this goal in the SR. The health care environment faces many sources of influence, including a reluctance to accept technology.

There has been a tremendous amount of research dedicated to the study of acceptance of technology, specifically the Technology Acceptance Model (TAM).[6] More recent work has suggested modifications to the TAM that explain a perception of usefulness and intentions from the aspect of social influence and the cognitive instrumental process.[7][8] Several organizational theories have been developed. These focus on the sources of influence and the reason for their existence.

Organizational theories

Organizational theories address influence, but none adequately addresses the complexity of the health care organization. Payers, providers, and patients all control resources that exert influence. The nature of the competitive environment will also exert influence on decisions. External influence from those who control resources can be explained through resource dependence theory.[9][10] Internal and external influences can be explained by the Diffusion of Innovation Theory through its introduction of compatibility, complexity, trialability, observability, and relative advantage.[11][12][13][14]

According to resource dependence theory, health care organizations with the greatest level of dependence on other organizations that control the resources will feel the greatest level of environmental influence on its decisions.[14] The Resource Dependence Theory describes an external interdependence of organizations. External Control of Organizations,[14] which is an adaptation of Resource Dependence Theory, provides good insight for this study. The authors’ premise is that the external environment creates a social context and plays an important role in how organizational decisions are made. The lack of absolute independence requires some degree of interorganizational exchange of goods or services.[14] As organizations build and negotiate relationships with each other in the exchange of resources, positions of power are established. No one organization can provide all of its own resources, so each organization becomes dependent on the other organizations that control the resources.

Similar to Resource Dependence, the Diffusion of Innovation Theory describes a social system that influences through communication channels.[11][12][13] Diffusion of Innovation attempts to explain how “an innovation, is communicated through channels over time among members of a social system”.[13] This theory accounts for 49-97% of variance in the rate of adoption of innovation through five factors: compatibility, complexity, trialability, observability, and relative advantage. These factors are sorted into three categories of a predictive model for EHR adoption: innovation determinants, organizational determinants, and environmental determinants.[8] The next several paragraphs exercise the five factors to this study.

The concept of compatibility[13] goes beyond answering the question, “is a product/service right for a market?” It also asks, “is the market ready for the product/service?” For instance, the Chevy Nova failed in Spanish-speaking markets because in Spanish the word “Nova” means “does not go.” Promotion of conservation techniques to farmers in the United States initially failed because farmers associated conservation with lower crop yield. Boiling water to sanitize it makes perfect sense to a market that is familiar with germ theory, but primitive tribes in Peru only heated water for sicker, weaker members; as a result, the concept failed when initially introduced and dysentery continued to flourish. In relation to this study, the concept of compatibility might ask, “is the market ready for the EHR?”

The concept of complexity[13] is appropriate to this study because innovation can be a double-edged sword. On one hand, it is new and may offer some improvement to a product or service. However, it might also be perceived as too complex; and perception can be a powerful force. If the Baby Boomer generation perceives computers to be too complex, and this perception causes computer anxiety, its users may reject its adoption and use.[15] The older physicians in a hospital have greater seniority, and are therefore, more influential in the hospital’s decision to adopt the EHR. Would this same generation of providers influence the health care organization considering EHR adoption?

The concept of trialability[13] applies more to the early-adopter group than the other groups: innovators, early-majority, late-majority, and laggards. In the early phase of promotion for a new product or service, the vendor might lower the risk of adoption by offering free trials or samples to potential users. Once the user is confident of the new item’s efficacy, then he/she is more likely to pay full price for its use. When a new producer of an EHR enters the marketplace, it must incentivize the use of its product because it is not known in the industry. The user accepts a risk by trying the new EHR, but the risk is overcome by the incentive. Once the new EHR gains momentum in the industry, adoption enters the early-majority phase. The new EHR has already gained momentum in the industry, and the producer does not need to incentivize its use.

The concept of observability[13] is also highly applicable to this study. Decision makers in a hospital that has not yet adopted an EHR will observe the experiences of other hospitals that have adopted it. Vendors will promote or advertise specifically to the nonadopters and help them observe how the EHR can benefit its organization. External players in the health care organization’s competitive environment will provide some level of observability.

Relative advantage is a multifaceted concept for this study. In health care, the most important factor is provision of health, as well as the treatment and prevention of disease. If adoption of the EHR speaks directly to the health care organization’s primary purpose, then it might provide relative advantage over competitors that have not adopted it. Another concept is that of social prestige.[13] Unless a health care organization can serve as an example to other health care organizations (observability), there may not be a sufficient level of relative advantage to be considered.

Strategy and decision making

Strategy can be a multifaceted concept, and organizations around the world hire strategy experts to help identify and focus on a market forces. An operational definition of strategy is borrowed from education[16] and is adapted to health care: strategy is defined as instruments by which health care organizations manage their organizational processes and deal with their environments in order to select a portfolio of activities and find appropriate position in the health care industry (italics indicate a change in wording from the authors’ definition). It follows that adoption of an EHR would alter how a health care organization manages its organizational processes, so this definition of strategy is a good fit for the health care industry. However, two significant considerations in the health care environment are the level of local competiveness, and how health care organizations compete.[17]

Studies have shown that decision making in the health care industry is often based on how the organization competes, whether in a single-market or multimarket environment.[18] In either environment, decision-making varies on competition, and the health care industry competes in clusters.[18] The way health care organizations compete will also affect its organizational structure. A four-cluster solution was identified as a reliable, internally valid, and stable model for health networks and a five-cluster solution for health systems.[19] Differentiation and centralization are particularly important in distinguishing unique clusters of organizations. High differentiation typically occurs with low centralization, which suggests that a broader scope of activity is more difficult to centrally coordinate. Integration is also important, but the authors find that health networks and systems typically engage in both ownership-based and contractual-based integration or they are not integrated at all.

Ash and Bates[20] studied the EHR adoption rates and the factors and forces affecting system adoption through surveys (85/650, 13.1%). Only 106 of the 650 (16.3%) of hospitals surveyed had adopted some form of EHR, 63/106 (59.4%) had implemented a full Computerized Provider Order Entry (CPOE) solution, and the other 43/106 (40.6%) implemented a partial CPOE solution. A full one-third of adopters were either Veterans Affairs or military hospitals. Additionally, 481/650 (73.8%) of those who planned to implement a full solution intended to do so within 5 years. Ash and Bates[20] also found that the size of hospital is positively-associated with component adoption; specifically CPOE adoption. The authors inferred from their results that the primary reasons to adopt the EHR is to gain the quality-of-care advantages of CPOE. This inference reinforced our inclusion of CPOE as a dependent variable.

Factors that influence health information system (HIS) adoption in US hospitals have been studied by others as well (n=1441).[21] Results showed that HIS adoption is influenced by the hospital market, organizational, and financial factors. Larger, system-affiliated, and for-profit hospitals with more preferred provider organization contracts are more likely to adopt managerial information systems than other hospitals. Operating revenue is positively associated with HIS adoption. The study also identified hostility as an aspect of environmental uncertainty, and that organizations often turn to technological adoption to regain competitive advantage.

A knowledge-based taxonomy of critical factors for adopting an EHR was developed from a systematic literature review.[22] The researchers selected 68 of 3400 (2.00%) articles to identify six factors of adoption, listed in order of importance: user attitude toward information systems, workflow impact, interoperability, technical support, communication among users, and expert support.

Alternative measures of EHR adoption among hospitals have been studied.[23] Authors analyzed a 2009 information technology supplement survey distributed by the American Hospital Association (AHA). The survey focused on 24 EHR functionalities in various areas: electronic clinical documentation, results viewing, CPOE, and clinical decision support. They found that 142 of 3937 (3.60%) acute-care hospitals in the United States of responding hospitals have implemented all 24 functions, 386/3937 (9.80%) of hospitals have implemented at least 20 functions, and 1437/3937 (36.50%) have implemented at least one-half of the functions. The researchers added that EHR adoption is a complex process.

Others have studied the relationship between hospital financial position and the adoption of the EHR.[24] Through a cross-sectional study of secondary data from several sources, including the AHA (2442/5752, 42.51% acute-care hospitals in the United States), researchers identified five independent and one dependent variable. Of the five independent variables (IVs), only liquidity was positively-associated with EHR. Asset turnover was negatively-associated with EHR adoption. Bed size, a control variable, was positively-associated with EHR adoption. The authors concluded that hospitals adopt EHRs as a strategic move to better align themselves with their environment.

Because commonly used elements of organizational strategy are difficult to change, several of the variables were categorized as internal organizational factors. Research has assessed variables of hospital influence in five categories: (1) capacity as measured by number of beds in groupings by intervals of 100, (2) management, or ownership, (3) organizational focus, or teaching status, (4) competitive location and alternatives, and (5) state regulatory pressures.[25]

Although resources have been consumed to study factors associated with adoption of HIT, there is a gap in the literature that provides a conceptual model to guide the design of empirical models. It may seem backward to design a conceptual model after so many studies have already been conducted, but the gap remains. The aim of this study was to develop a conceptual model from a systematic literature review that associates both internal and external factors associated with adoption of the EHR. The intent of the conceptual model is to enable future empirical models.

Methods

Literature review process

Search terms were selected based on the experience of the authors in the field of health care administration. The time frame of 1993-2013 was selected as convenience. It was assumed that 2 decades would be sufficient to capture trends.

Figure 1 illustrates the literature review process that identified 83 sources consisting of empirical studies, articles, editorials, commentaries, opinion papers, organizational theories, and text books. The intent of no limits to the type of papers was to mitigate the risk of missing something significant from a study that was not catalogued properly within a key word catalogue like the Dublin Core.

The 83 records were reviewed for content and evidence. After discarding 58 articles for lack of evidence, three additional references were added because they were key concepts upon which other studies were based. Of the remaining 25 articles, a list of factors was identified as IVs. Some factors were grouped under a similar category for the purposes of simplification of the conceptual model. The dependent variable (DV) started as adoption of the EHR, but the studies from those chosen were not as specific. From personal experience, many studies seem to discuss the EHR, but call it something else: most commonly the EMR. That is why EMR was included in the search. Because so few ERHs exist without some form of CPOE, the latter term was included in the search criteria.

Our study combines the influences highlighted by previous work and examines determinants of EHR adoption. Examining EHR adoption at the health care organization level will demonstrate validity between this study and others that have used the hospital as the unit of analysis.

Fig1 Kruse JMIRMedInfo2014 2-1.jpg

Figure 1. Literature review process

EHR adoption and internal organizational influence

Several influences in the environment exert pressure on the health care organization to adopt EHR. Influences range from incentives from the federal government to the nature of local competitive community. US federal incentives provide a heavy influence for EHR implementation, under specific conditions, and imposes penalties for a lack of EHR implementation.

The internal politics of one organization serve as one source of influence. A hospital is part of a community, which serves as an external influence. Further, if a hospital is also part of a larger multihospital system (MHS), then the politics of the broad MHS will also exert influence on local decisions.

EHR adoption and external environmental influence

The patient is external to the organization, and for our study, the patient primarily serves as an external influence. Although some employees of the health care organization might also be patients, and this relationship could create a small internal influence, this study considers those few stake holders in the internal organizational factor of users. The providers are internal to the organization, and for our study, providers serve as an internal organizational influence. The payer is a significant influence[14], and the Center for Medicare and Medicaid Services (CMS) serves as a good example of this significant influence.[26] The HITECH act provides monetary incentives for EHR adoption. Those who do not implement all aspects specified in the stages of adoption are not eligible for the incentives. In this way, the CMS disincentivizes those organizations that do not adopt the EHR. If payments from the CMS were of little consequence to the health care organization’s revenue, then the health care organization might decide differently about EHR adoption. A competing health care organization is an external market force in the environment. Third-party payers might compare health care organizations based on maturity of automation because mature clinical components like CPOE will result in more accurate billing. Such forces incentivize a health care organization to adopt the EHR.

Overview of the conceptual model

The premise for an EHR adoption conceptual model is that that environmental influences affect organizational strategy of the health care organizations that adopt the EHR.[13][14][20][22][24][25] Diffusion of Innovation theory provides three categories of a predictive model for EHR adoption: innovation determinants, organizational determinants, and environmental determinants.[13] Resource Dependence Theory provides a category of a predictive model for EHR adoption, the competitive environment. In construction of the EHR adoption conceptual model, several constructs emerged.[14]

Elements of organizational strategy are not variables that can be easily changed[19]; therefore, elements typically ascribed to strategy, such as size, ownership, and fiscal stability, will be absorbed into the IVs of influence. This research proposes a model, whereby environmental factors are associated with an organization’s decision to adopt the EHR.

Resource Dependence Theory explains environmental influences and the external interdependence of organizations.[14] The authors’ premise is that the external environment creates a social context and plays an important role in how organizational decisions are made. The interdependence of organizations widens the field of stakeholders, and this relationship effect should be defined.

Disparate stakeholders have different interests with reference to different components of the EHR. These interests may be different in the SR interests versus the LR interests. SR interests are those that are immediate, such as current year expenditures. LR interests are further out when all inputs are variable. The SR interests of cost can often compete with the LR potential of cost savings and greater safety. Both the SR and LR interests are affected by the external environment.[17]

In a highly competitive environment, SR cost implications could often win over any long-term savings. The number of patients in a market is fixed in the SR, and a highly competitive market will affect each competitor’s share of that market. The SR costs of EHR implementation might be insurmountable by an organization in this market because it could not afford to lose ground without significant capital reserves or the ability to borrow cheaply.[17] However, in a less competitive market, the LR interests of potential cost savings have a better chance of influencing the decision to implement an EHR because the costs incurred in the SR are justified by the long-term benefits.[17]

External stakeholders that control resources important to the health care organization can exert significant influence. For instance, a health care organization that receives a significant amount of revenue from the CMS will be influenced more by incentives provided by the CMS than an organization that receives a significant cash flow from private third parties. The relative influence of various external stakeholders may be captured by an analysis of the structure of the market in which a health care organization operates.

Stakeholders have varying interests with regard to the capabilities and effects of EHR components depending upon their relationship with the health care organization. Private payers have both SR and LR interests in the EHR. In the SR, their focus is on minimizing expenditures. Because the health care organization would pass on the implementation costs through higher contract costs, payers would not be equal in the SR. In addition, the disruption of EHR implementation could potentially affect care processes and therefore increase claims. Payers would be interested in the LR benefits of the EHR: potential cost savings, better disease management, and increased safety. However, the SR interests of the private payers might overshadow the LR benefits of the EHR. Public payers enable care of the indigent and elderly. As part of the United States Department of Health and Human Services (HHS), the CMS is highly interested in disease management, public health, safety, and research, and it may value these LR capabilities of the EHR more than the SR costs. The CMS, as part of HHS, would also favor the EHR because it supports the Presidential directive to promote the establishment of the Nationwide Health Information Network that links electronic patient records through health information exchanges.

Providers and patients value face time with each other. During EHR implementation, providers might spend less time in communication with patients. Providers must adapt their processes and clinic-to-administrative schedules. Any disruption or action that is perceived as deleterious to this relationship could result in a negative reaction to EHR implementation. As a result, physicians might oppose EHR adoption, or they might simply support the EHR solution with the shortest implementation time or least administrative burden. Patients might not like the reduced face time with the provider, but they might be attracted to EHR components such as e-prescribing, e-results, personal health records, and email access to the provider. These desirable features are available to the patient when the health care organization chooses to adopt various portions of the CPOE component to the EHR.

Conflicts of interest

None declared.

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Abbreviations

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In several cases the PubMed ID was missing and was added to make the reference more useful.

Per the distribution agreement, the following copyright information is also being added:

©Clemens Scott Kruse, Jonathan DeShazo, Forest Kim, Lawrence Fulton. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.05.2014.