مدلسازی طولی پذیرش کاربر فن آوری با یک سیستم پشتیبانی تصمیم گیری بالینی تطبیقی شواهد
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|5788||2012||10 صفحه PDF||سفارش دهید||8158 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Available online 5 November 2012
This paper presents multiple innovations associated with an electronic health record system developed to support evidence-based medicine practice, and highlights a new construct, based on the technology acceptance model, to explain end users' acceptance of this technology through a lens of continuous behavioral adaptation and change. We show that this new conceptualization of technology acceptance reveals a richer level of detail of the developmental course whereby individuals adjust their behavior gradually to assimilate technology use. We also show that traditional models such as technology acceptance model (TAM) are not capable of delineating this longitudinal behavioral development process. Our TAM-derived analysis provides a lens through which we summarize the significance of this project to research and practice. We show that our application is an excellent exemplar of the “end-to-end” IS design realization process; it has drawn upon multiple disciplines to formulate and solve challenges in medical knowledge engineering, just-in-time provisioning of computerized decision-support advice, diffusion of innovation and individual users' technology acceptance, usability of human-machine interfaces in healthcare, and sociotechnical issues associated with integrating IT applications into a patient care delivery environment.
Evidence-based medicine is the “conscientious, explicit, and judicious use of current best evidence in making medical decisions about the care of individual patients” . There has been a general consensus that continuous, comprehensive practice of evidence-based medicine has tremendous potential to improve quality of care and reduce practice variation. However, there is also a widely acknowledged gap between clinicians' awareness of these care standards and their consistent application of the standards in practice. Clinical decision support systems (CDSS)—in particular, evidence-adaptive decision support systems—provide decision aids with a knowledge base constructed from and continually adapting to new research and practice based evidence of medicine . Such decision aids address a current need in healthcare decision support for tools that use reliable patient data, decision models and problem solving methods to address challenges in performance requirements, data and knowledge forms and generalizability to other application areas . However, while there is evidence that CDSS can improve clinician guideline compliance, and thus patient health  and , widespread use of such systems has not become available due to numerous technological, behavioral, and organizational barriers. These facts motivate the present research. Clinical Reminder System (CRS) is a research-oriented clinical information system iteratively designed and developed through a 7-year joint effort by researchers from the H. John Heinz III College at Carnegie Mellon University (CMU) and medical practitioners at the Western Pennsylvania Hospital (WPH). CRS is an evidence-adaptive CDSS that aims to improve the quality of patient care by providing clinicians with just-in-time alerts and advisories based on best known evidence-based medicine guidelines and individual patients' health descriptors and treatment conditions. Of the four functions that a computerized CDSS may provide —administrative support, managing clinical complexity and details, cost control, and decision support—CRS is designed to supply all except cost control. CRS has been developed in the context of increased pressure to use electronic health records (EHR) to improve quality of care and patient safety, in the form of recommendations from professional organizations such as the Institute of Medicine and Federal mandates contained in the American Reinvestment and Recovery Act of 2009. However, adoption rates for EHRs in the U.S. are low compared to other industrialized countries . Additionally, while CDSS technologies demonstrate great potential to improve quality of care and patient safety in laboratory and clinical trial settings (e.g., ), once deployed for routine use in the field, they often fail to obtain adequate usage by medical practitioners and consequently fail to achieve those anticipated benefits on clinical performance and patient outcomes . For example, through a systematic review, Shojania et al.  found that computerized medication safety alerts are overridden by clinician users in 49% to 96% of cases including those for preventing severe drug–drug interaction events. In a more recent review, Shojania et al.  reported that point-of-care CDSS reminders have produced much smaller clinically significant improvements than those generally expected. Factors contributing to this missing link between the deployment of CDSS and the achievement of long-term end user adherence remain underexplored. To enlarge the research base of knowledge regarding adoption and clinically relevant use of CDSS and EHR generally, CRS has operationalized research-based methods and models via a carefully designed application that has been evaluated in clinicians' day-to-day patient care routines. This process has generated research insights into reengineering the system's technological designs to improve its usability as well as informing tailored behavioral interventions for addressing the user resistance encountered. As an exemplar of the “end-to-end” IS design realization process, the CRS project draws upon multiple disciplines including decision science, computer science, information systems, and behavioral and social sciences to formulate and solve challenges in (1) medical knowledge engineering; (2) just-in-time provisioning of computerized decision-support advice; (3) diffusion of innovation and individual users' technology acceptance; (4) usability of human-machine interfaces in healthcare; and (5) sociotechnical issues when integrating technological systems into the reality of a patient care delivery environment. The CRS project hence embodies a “methodological pluralism” approach called by researchers  which demands extreme additional attention be paid to medical practitioners' work contexts, their preferences and constraints, and the social and organizational environments in which technologies and users are situated. The purpose of this paper is twofold: to summarize a new understanding of the importance of rigorous and adaptive clinical IT design to bridge academic research and practice generated through our previously published work based on developing, evaluating, and iteratively improving CRS, and to use this understanding to frame novel insights provided by CRS regarding the behavioral underpinnings of technology acceptance that may inform more useful and usable technology designs as well as more effective diffusion strategies and use policies. We achieve the first goal by reviewing the research contributions of the CRS project: analysis of longitudinal usage rates and causes of dissatisfaction with an early version of the application, and, with a re-engineered version of CRS, user interface analysis to identify navigational patterns and opportunities for usability improvements, and social network analysis to reveal the nature of users' social interactions the relationship to individual clinicians' system utilization. We achieve the second goal by introducing a new model of technology adoption that addresses the limitations of the well-known technology acceptance model (TAM) through accommodation of the longitudinal course of acceptance behavior formation, development, and institutionalization relying on “actual system use” as computer-recorded objective usage instead of self-reported surrogates.
نتیجه گیری انگلیسی
This paper proposes a new conceptualization of technology acceptance—constituting institutionalized use and developmental pattern—to study the longitudinal behavioral adaptation and change. This new view of technology acceptance is presented in the context of a highly-engineered application that has been extensively revised to account for observed trends in usage and user feedback and which we feel embodies best research practices for IT development and evaluation. To operationalize the developmental pattern construct, we used a semi-parametric, group-based modeling approach that identifies distinct patterns of trajectories within a population. We validated this model in an empirical setting where a clinical decision-support system was introduced to a group of internal medicine residents. We show that the new model, an extension to the original TAM incorporating four objective measures of actual usage from an implemented EHR, is able to reveal richer details of end users' acceptance of technology, while the original TAM performs poorly in explaining observed developmental behavior when relying on traditional self-reported usage measures derived from the Cork et al.  survey instrument. The stream of research on electronic health records represented by our work on CRS, including the TAM extension, as discussed in this paper, embodies a number of features identified by  as essential for the health of the DSS discipline: it is directly relevant to medical practice; it is based on directly-measured usage of a professional-quality IT artifact, and it has benefitted from external funding. As such, our work makes a contribution to resolving the “tension between academic rigor and professional relevance” (p. 667). This new notion of technology acceptance supports our multidimensional analysis of application usage: sophisticated users of IT applications have high expectations of application quality, and traditional notions of comfort with IT are not associated with levels of usage. Thus, future analyses of health IT applications must rigorously address ‘simple usage’—instances of interaction with system to understand adoption; ‘complex usage’—details of interaction with user interface (including exception management), and ‘usage context’—how users interact with each other and reinforce system usage, or lack thereof.