Implementation Of Clinical Decision Support System Research Articles

Using Clinical Decision Support to Improve Referral Rates in Severe Symptomatic Aortic Stenosis: A Quality Improvement Initiative.

Clinical decision support systems are used to ensure compliance with guidelines and can assist providers in improving quality of care. This quality improvement initiative was designed to evaluate the use of a clinical decision support system to improve specialist referral rate for patients with severe aortic stenosis. A clinical decision support system for cardiology and primary care providers was implemented to improve diagnosis of severe aortic stenosis. The ordering provider received an electronic medical record in-basket alert providing feedback and recommendations for referral to specialist for evaluation. The echocardiogram data were evaluated for change in specialist referral rate. Before clinical decision support system implementation, the referral rate was 72% for a 3-month period. All providers ordering echocardiograms received clinical decision support system notification if patient results met criteria based on valve severity (aortic valve area < 1.0 cm, mean gradient ≥ 40 mm Hg, peak velocity ≥ 4.0 m/s). After implementation, clinical decision support system referral rate was 97.5%, a 24.6% increase in referral rates (P < .001). Low referral rates for patients with severe aortic stenosis are a recognized challenge. Utilizing the clinical decision support system to improve awareness of quality guidelines and recommendations was associated with increased adherence to referral guidelines by providers. This innovation is pertinent to practice and enhances the functionality of the electronic medical record by providing real-time feedback to providers to improve practice. Referral rates for patients with severe aortic stenosis can be improved with use of provider clinical decision support system.

Impact of a Clinical Decision Support System on Guideline Adherence of Surveillance Recommendations for Colonoscopy After Polypectomy.

Background: Surveillance colonoscopy is required in patients with polyps due to an elevated colorectal cancer (CRC) risk; however, studies suggest substantial overuse and underuse of surveillance colonoscopy. The goal of this study was to characterize guideline adherence of surveillance recommendations after implementation of an electronic medical record (EMR)-based Colonoscopy Pathology Reporting and Clinical Decision Support System (CoRS). Methods: We performed a retrospective cohort study of patients who underwent colonoscopy with polypectomy at a safety-net healthcare system before (n=1,822) and after (n=1,320) implementation of CoRS in December 2013. Recommendations were classified as guideline-adherent or nonadherent according to the US Multi-Society Task Force on CRC. We defined surveillance recommendations shorter and longer than guideline recommendations as potential overuse and underuse, respectively. We used multivariable generalized linear mixed models to identify correlates of guideline-adherent recommendations. Results: The proportion of guideline-adherent surveillance recommendations was significantly higher post-CoRS than pre-CoRS (84.6% vs 77.4%; P<.001), with fewer recommendations for potential overuse and underuse. In the post-CoRS period, CoRS was used for 89.8% of cases and, compared with cases for which it was not used, was associated with a higher proportion of guideline-adherent recommendations (87.0% vs 63.4%; RR, 1.34; 95% CI, 1.23-1.42). In multivariable analysis, surveillance recommendations were also more likely to be guideline-adherent in patients with adenomas but less likely among those with fair bowel preparation and those with family history of CRC. Of 203 nonadherent recommendations, 70.4% were considered potential overuse, 20.2% potential underuse, and 9.4% were not provided surveillance recommendations. Conclusions: An EMR-based CoRS was widely used and significantly improved guideline adherence of surveillance recommendations.

Characterizing Workflow to Inform Clinical Decision Support Systems in Nursing Homes.

Clinical decision support systems (CDSS) hold promise to influence clinician behavior at the point of care in nursing homes (NHs) and improving care delivery. However, the success of these interventions depends on their fit with workflow. The purpose of this study was to characterize workflow in NHs and identify implications of workflow for the design and implementation of CDSS in NHs. We conducted a descriptive study at 2 NHs in a metropolitan area of the Mountain West Region of the United States. We characterized clinical workflow in NHs, conducting 18 observation sessions and interviewing 15 staff members. A multilevel work model guided our data collection and framework method guided data analysis. The qualitative analysis revealed specific aspects of multilevel workflow in NHs: (a) individual, (b) work group/unit, (c) organization, and (d) industry levels. Data analysis also revealed several additional themes regarding workflow in NHs: centrality of ongoing relationships of staff members with the residents to care delivery in NHs, resident-centeredness of care, absence of memory aids, and impact of staff members' preferences on work activities. We also identified workflow-related differences between the two settings. Results of this study provide a rich understanding of the characteristics of workflow in NHs at multiple levels. The design of CDSS in NHs should be informed by factors at multiple levels as well as the emergent processes and contextual factors. This understanding can allow for incorporating workflow considerations into CDSS design and implementation.

THE PROBLEM OF KNOWLEDGE TRANSLATION IN HEALTHCARE: TOOLS FOR ITS SOLUTION IN THE AREA OF PATIENT SAFETY

Adherence to evidence-based clinical guidelines enhances patient safety, but the level of knowledge implementation into routine practice remains unsatisfactory. The concept of knowledge translation (KT) was proposed as a «process that includes the synthesis, dissemination, exchange, and ethically sound application of knowledge» to define the framework of knowledge transfer from research to practical activities aimed at improving the quality and safety of healthcare. Although Russian authors pay much attention to translational medicine, the problem of implementation research is poorly discussed. Modern information technologies including clinical decision support systems (CDSS) play a crucial role in KT, but the evidence of their effectiveness is poor. The authors describe a systematic approach to the practical implementation of knowledge imbedded in clinical guidelines based on the use of CDSS. CDSS implementation was accompanied by organizational measures that ensured the overall success: creation of standard operating protocols and quality register, clinical audit with feedback, and staff training. The effectiveness of this approach in reducing the risk of inhospital complications in Moscow general hospital is illustrated by the example of hospital-acquired venous thromboembolism (HA-VTE) prevention. Implementation of a comprehensive program for HA-VTE prevention in 2014−2016 helped to increase the coverage with VTE and bleeding risk assessment from 15% to 80% (p0.0001), to reduce the frequency of pharmacological prophylaxis defects from 50.6% to 23% (p=0.01), to increase the compliance with anticoagulant use from 50% to 76% (p=0.0005), without increasing the rate of hemorrhagic complications. The HA-VTE rates decreased from 10 to 4.25 cases per 1000 overall hospital admissions (p=0.001), from 8 to 1.27 cases per 1000 operations in surgical patients (p=0.001) and from 12 to 6.38 cases per 1000 hospitalizations in therapeutic patients (p=0.06; with a statistically significant downward linear trend for HA-VTE rate, p=0.038).

Examining Workflow in a Pediatric Emergency Department to Develop a Clinical Decision Support for an Antimicrobial Stewardship Program.

Clinical decision support (CDS) embedded into the electronic health record (EHR), is a potentially powerful tool for institution of antimicrobial stewardship programs (ASPs) in emergency departments (EDs). However, design and implementation of CDS systems should be informed by the existing workflow to ensure its congruence with ED practice, which is characterized by erratic workflow, intermittent computer interactions, and variable timing of antibiotic prescription. This article aims to characterize ED workflow for four provider types, to guide future design and implementation of an ED-based ASP using the EHR. Workflow was systematically examined in a single, tertiary-care academic children's hospital ED. Clinicians with four roles (attending, nurse practitioner, physician assistant, resident) were observed over a 3-month period using a tablet computer-based data collection tool. Structural observations were recorded by investigators, and classified using a predetermined set of activities. Clinicians were queried regarding timing of diagnosis and disposition decision points. A total of 23 providers were observed for 90 hours. Sixty-four different activities were captured for a total of 6,060 times. Among these activities, nine were conducted at different frequency or time allocation across four roles. Moreover, we identified differences in sequential patterns across roles. Decision points, whereby clinicians then proceeded with treatment, were identified 127 times. The most common decision points identified were: (1) after/during examining or talking to patient or relative; (2) after talking to a specialist; and (3) after diagnostic test/image was resulted and discussed with patient/family. The design and implementation of CDS for ASP should support clinicians in various provider roles, despite having different workflow patterns. The clinicians make their decisions about treatment at different points of overall care delivery practice; likewise, the CDS should also support decisions at different points of care.

Clinical Decision Support for In-Hospital AKI.

AKI carries a significant mortality and morbidity risk. Use of a clinical decision support system (CDSS) might improve outcomes. We conducted a multicenter, sequential period analysis of 528,108 patients without ESRD before admission, from October of 2012 to September of 2015, to determine whether use of a CDSS reduces hospital length of stay and in-hospital mortality for patients with AKI. We compared patients treated 12 months before (181,696) and 24 months after (346,412) implementation of the CDSS. Coprimary outcomes were hospital mortality and length of stay adjusted by demographics and comorbidities. AKI was diagnosed in 64,512 patients (12.2%). Crude mortality rate fell from 10.2% before to 9.4% after CDSS implementation (odds ratio, 0.91; 95% confidence interval [95% CI], 0.86 to 0.96; P=0.001) for patients with AKI but did not change in patients without AKI (from 1.5% to 1.4%). Mean hospital duration decreased from 9.3 to 9.0 days (P<0.001) for patients with AKI, with no change for patients without AKI. In multivariate mixed-effects models, the adjusted odds ratio (95% CI) was 0.76 (0.70 to 0.83) for mortality and 0.66 (0.61 to 0.72) for dialysis (P<0.001). Change in adjusted hospital length of stay was also significant (incidence rate ratio, 0.91; 95% CI, 0.89 to 0.92), decreasing from 7.2 to 6.0 days for patients with AKI. Results were robust to sensitivity analyses and were sustained for the duration of follow-up. Hence, implementation of a CDSS for AKI resulted in a small but sustained decrease in hospital mortality, dialysis use, and length of stay.

Economic and organizational impact of a clinical decision support system on laboratory test ordering

BackgroundWe studied the impact of a clinical decision support system (CDSS) implemented in a few wards of two Italian health care organizations on the ordering of redundant laboratory tests under different perspectives: (1) analysis of the volume of tests, (2) cost analysis, (3) end-user satisfaction before and after the installation of the CDSS.Methods(1) and (2) were performed by comparing the ordering of laboratory tests between an intervention group of wards where a CDSS was in use and a second (control) group where a CDSS was not in use; data were compared during a 3-month period before (2014) and a 3-month period after (2015) CDSS installation. To measure end-user satisfaction (3), a questionnaire based on POESUS was administered to the medical staff.ResultsAfter the introduction of the CDSS, the number of laboratory tests requested decreased by 16.44% and costs decreased by 16.53% in the intervention group, versus an increase in the number of tests (+3.75%) and of costs (+1.78%) in the control group. Feedback from practice showed that the medical staff was generally satisfied with the CDSS and perceived its benefits, but they were less satisfied with its technical performance in terms of slow response time.ConclusionsThe implementation of CDSSs can have a positive impact on both the efficiency of care provision and health care costs. The experience of using a CDSS can also result in good practice to be implemented by other health care organizations, considering the positive result from the first attempt to gather the point of view of end-users in Italy.

Comprehensive process model of clinical information interaction in primary care: results of a "best-fit" framework synthesis.

To describe a new, comprehensive process model of clinical information interaction in primary care (Clinical Information Interaction Model, or CIIM) based on a systematic synthesis of published research. We used the "best fit" framework synthesis approach. Searches were performed in PubMed, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Library and Information Science Abstracts, Library, Information Science and Technology Abstracts, and Engineering Village. Two authors reviewed articles according to inclusion and exclusion criteria. Data abstraction and content analysis of 443 published papers were used to create a model in which every element was supported by empirical research. The CIIM documents how primary care clinicians interact with information as they make point-of-care clinical decisions. The model highlights 3 major process components: (1) context, (2) activity (usual and contingent), and (3) influence. Usual activities include information processing, source-user interaction, information evaluation, selection of information, information use, clinical reasoning, and clinical decisions. Clinician characteristics, patient behaviors, and other professionals influence the process. The CIIM depicts the complete process of information interaction, enabling a grasp of relationships previously difficult to discern. The CIIM suggests potentially helpful functionality for clinical decision support systems (CDSSs) to support primary care, including a greater focus on information processing and use. The CIIM also documents the role of influence in clinical information interaction; influencers may affect the success of CDSS implementations. The CIIM offers a new framework for achieving CDSS workflow integration and new directions for CDSS design that can support the work of diverse primary care clinicians.

Barriers and strategies in Implementing Clinical Decision Support System in Hospitals: A Case Study in Iran

Introduction: Most modern medical issues are inherently complicated and accurate decisions are not always likely to be made based on logical reasons. Furthermore, the huge volume of information relevant to a simple diagnostic area makes this decision making even more troublesome. Hence, with the advent of technology, there is an ever increasing need for the Clinical Decision Support System (CDSS) in hospitals. This study has been conducted in an Iranian hospital with the aim of identifying the most significant barriers for implementing CDSS and suggesting appropriate strategies to remove them. Methods: This qualitative cross-sectional study was conducted in 2015. The sample population of the study included 180 physicians and nurses in Shahid Mohammadi hospital in Bandar Abbas whose performance was changed using CDSS. The participants were selected using stratified sampling from 23 different wards in the hospital. First, the barriers and strategies for implementation of CDSS in other countries were extracted from a review study which was used to make a preliminary model. Then, the results of a questionnaire and Delphi tests in three rounds were included in the final model. Results: The most significant barriers in implementing CDSS were categorized into 6 groups according to the participants. These include barriers from human resources and infrastructures as well as financial, technical, environmental and legal ones. The barriers for them were divided into 5 categories including structural, technical, financial, human and environmental ones. Conclusion: Since the most significant obstacle in implementing CDSS in this hospital was from humans, the hospital can use the barriers provided in 5 categories and better benefit from the system. These barriers are training the human resources before they start working, engaging them in implementation process of the CDSS and using evidence-based scientific databases in CDSS while removing fundamental barriers to the system.

Investigating Clinical Decision Support Systems Success Factors with Usability Testing

Clinical Decision Support Systems (CDSS) have been used widely since 2000s to improve the healthcare quality. CDSS can be utilized to support healthcare services as a tool to diagnose, predict, as well as to provide clinical interpretation, alert, and reminder. There are many researches of CDSS implementation on literatures but not many of them present the evidence of CDSS successful implementation. In spite of the potential use of CDSS, there are some researches that reveal the failures of CDSS implementation. This paper contributes to CDSS development by investigating and exploring CDSS success factors with usability testing. The testing involves participants from different types of backgrounds (physicians, IT developers, and students). The participants are being asked to experience three different CDSS to predict cardiovascular risk factors. The result of the research shows that involving different type of users give more insight to design process. It can be concluded that user center design is very critical to produce successful CDSS.

Computer decision support for acute kidney injury: current and future.

Growing awareness regarding the impact of acute kidney injury (AKI) as a grave consequence of critical illnesses resulted in the expansion of the need for early detection and appropriate management strategies. Clinical decision support systems (CDSS) can generate information to improve the care of AKI patients by providing point-of-care accurate patient-specific information and recommendations. Our objective is to describe the characteristics of CDSS and review the current knowledge regarding the impact of CDSS on patients in the acute care settings, and specifically for AKI. Several recent systematic analyses showed the positive impact of CDSS on critically ill patients care processes. These studies also highlighted the scarcity of data regarding the effect of CDSS on the patient outcomes. In the field of AKI, there have been several reports to describe development and validation of homegrown CDSS and electronic alert systems. A large number of investigations showed the implementation of CDSS could improve the quality of AKI care; although, only in a very small subgroup of these studies patient outcomes improved. The heterogeneity of these studies in their size, design, and conduct has produced controversial findings; hence, this has left the field completely open for further investigations.

Effects of a computerized provider order entry and a clinical decision support system to improve cefazolin use in surgical prophylaxis: a cost saving analysis.

Background:Computerized Provider Order Entry (CPOE) and Clinical Decision Support System (CDSS) help practitioners to choose evidence-based decisions, regarding patients’ needs. Despite its use in developed countries, in Brazil, the impact of a CPOE/CDSS to improve cefazolin use in surgical prophylaxis was not assessed yet.Objective:We aimed to evaluate the impact of a CDSS to improve the use of prophylactic cefazolin and to assess the cost savings associated to inappropriate prescribing.Methods:This is a cross-sectional study that compared two different scenarios: one prior CPOE/CDSS versus after software implementation. We conducted twelve years of data analysis (3 years prior and 9 years after CDSS implementation), where main outcomes from this study included: cefazolin Defined Daily Doses/100 bed-days (DDD), crude costs and product of costs-DDD (cost-DDD/100 bed-days). We applied a Spearman rho non-parametric test to assess the reduction of cefazolin consumption through the years.Results:In twelve years, 84,383 vials of cefazolin were dispensed and represented 38.89 DDD/100 bed-days or USD 44,722.99. Surgical wards were the largest drug prescribers and comprised >95% of our studied sample. While in 2002, there were 6.31 DDD/100 bed-days, 9 years later there was a reduction to 2.15 (p<0.05). In a scenario without CDSS, the hospital would have consumed 75.72 DDD/100 bed-days, which is equivalent to USD 116 998.07. It is estimated that CDSS provided USD 50,433.39 of cost savings.Conclusion:The implementation of a CPOE/CDSS helped to improve prophylactic cefazolin use by reducing its consumption and estimated direct costs.

Stakeholder’s Perspective of Clinical Decision Support System

Clinical Decision Support System (CDSS) has potential opportunities to improve overall safety, quality and cost-effectiveness of healthcare. The CDSS has existed for more than four decades, but its adoption rate by medical communities is not encouraging even in the countries that have been a pioneer in developing them. At many sites, it was problematic, stalled in the planning stages or never even attempted. To date, CDSS is considered as a partially successful system. Several current challenges have not been adequately addressed during the development of CDSS. As per latest research, the lists of challenges are: improve the human-computer interface, disseminate best practices in CDSS design, development, and implementation, summarize patient-level information, prioritize and filter recommendations to the user, create an architecture for sharing executable CDSS modules and services, combine recommendations for patients with co-morbidities, prioritize CDSS content development and implementation, create Internet-accessible clinical decision support repositories, use free text information to drive clinical decision support, and mine huge clinical databases to create new CDSS. The preceding list has been considered as challenges due to unmet expectations of CDSS’s stakeholders, such as CDSS product development and maintenance team (product owner, project managers, system architect, system designers, system developers, system administrators, and system maintenance team), sales and marketing personnel, end-users. We found that most of the CDSS literature talked about the challenges and their details, but they do not throw enough light from CDSS stakeholders’ perspective while building and upon using CDSS. This paper describes CDSS from various stakeholders’ perspective, highlighting the challenges faced by them in owning, building, and using them.

Decision support systems in clinical practice: The case of venous thromboembolism prevention.

Decision support systems in clinical practice: The case of venous thromboembolism prevention.

Clinical Decision Support Systems and Prevention: A Community Guide Cardiovascular Disease Systematic Review.

Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors. An existing systematic review (search period, January 1975-January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011-October 2012). Data analysis was conducted in 2013. A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels. CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality.

Can clinical pharmacist interventions be substituted by a clinical decision support system in the electronic patient record?

Background and objectives Dematerialized drug-related problems (DRPs) interventions might be provided by clinical decision support systems (CDSS) to allow clinical pharmacists (CP) to intervene on the most complex situations. Moreover, this would help prioritize pertinent information to display to the physician to avoid alert fatigue. To determine which DRPs could be dematerialized and integrated into CDSS and to assess the feasibility of dematerialization. Methods Data were collected over a six months prospective study, conducted in internal medicine wards. Identified DRPs were confronted to a beforehand developed classification of CDSS in order to allocate them in different CDSS classes. A score of implementation complexity was developed to determine the feasibility of dematerialization in the CDSS. This score was divided in 3 parts each scored from 1 to 4: technical, axes of CDSS; accessibility, data type; organizational, impact on population affected in institutions. It was stratified in 3 groups of complexity of implementation: low level: [3–6 points[; medium level: [6–9[; high level: [9–12]. Results Three hundred and eighty-three DRPs were highlighted by the CP whose 32 were excluded from this study (main reason: data loss). Dematerialization of 20/351 (5.7%) DRPs were classified as impossible (e.g. drug-drug interaction (DDI) for more than 2 drugs). The remaining 331 could technically be integrated into CDSS and among them, 89/331 (26.9%) were related to the co-medications (e.g. 78 related to DDI), and 196/331 (59.2%) to the patient (e.g. 44 targeting untreated indication). According to the score of implementation complexity, DRPs were classified from the lowest to the highest level of implementation complexity as follows: 40% low (e.g. co-medication), 24% medium (e.g. drug-lab interaction) and 35% high complexity of dematerialization (e.g. drug-lifestyle interaction). The CP realized 146 interventions on the 331 DRPs, with a rate of 50% for low, 62% for medium and 44% for high levels of complexities. The rate of physician's acceptance was comparable for the medium (84%) and low (85%) levels and the best rate was for the highest complexity level (94%). Discussion and conclusions Most of CP's interventions could theoretically be replaced by implementation of CDSS. However, even when a dematerialization is possible, the real pertinence of the alerts is not guarantee. Indeed, in many situations, information related to the decision-making is still insufficient to generate alerts considering weighing of interests. This point merits to be evaluated in further research.

Clinical Decision Support using a Terminology Server to improve Patient Safety.

Clinical Decision Support Systems (CDSS) are software applications that support clinicians in making healthcare decisions providing relevant information for individual patients about their specific conditions. The lack of integration between CDSS and Electronic Health Record (EHR) has been identified as a significant barrier to CDSS development and adoption. Andalusia Healthcare Public System (AHPS) provides an interoperable health information infrastructure based on a Service Oriented Architecture (SOA) that eases CDSS implementation. This paper details the deployment of a CDSS jointly with the deployment of a Terminology Server (TS) within the AHPS infrastructure. It also explains a case study about the application of decision support to thromboembolism patients and its potential impact on improving patient safety. We will apply the inSPECt tool proposal to evaluate the appropriateness of alerts in this scenario.

Implementation of a clinical decision support system improves compliance with restrictive transfusion policies in hematology patients.

There is increasing evidence for restrictive red blood cell (RBC) transfusion but compliance with recommended transfusion triggers is variable. A clinical decision support system (CDSS) has been found to reduce unnecessary transfusion in some clinical settings when physicians are advised they are noncompliant with the current guidelines. The objective was to assess the impact of a CDSS for blood product ordering in patients with hematologic disease. All platelet (PLT) and RBC transfusions were identified in hematology patients in three periods: before (baseline), immediately after (CDSS1), and 7 months after implementation of CDSS for blood ordering (CDSS2). Compliance with the recommended transfusion triggers was monitored for all orders made by CDSS or non-CDSS methods during each period. Ninety-seven patients with a variety of hematologic diagnoses received 502 RBC and 572 PLT transfusions during the three periods with no significant difference in 1) the mean number of transfusions per patient, 2) the proportion of patients transfused, 3) posttransfusion hemoglobin (Hb), and 4) pre- and posttransfusion PLT count, although mean pretransfusion Hb decreased. The proportion of noncompliant RBC and PLT transfusion requests improved from baseline to CDSS2 (69.0% to 43.4% p ≤ 0.005 for RBCs; and 41.9% to 31.2%, p = 0.16 for PLT) when all orders were compared, although this improvement was not significant at the 5% level for PLTs. The introduction of CDSS for blood product ordering supported by education and physician feedback in the hematology setting had an immediate impact on improving compliance with guidelines for restrictive transfusion practice.

Adoption of clinical decision support systems in a developing country: Antecedents and outcomes of physician's threat to perceived professional autonomy

ObjectiveThe basic objective of this research is to study the antecedents and outcomes of professional autonomy which is a central construct that affects physicians’ intention to adopt clinical decision support systems (CDSS). The antecedents are physicians’ attitude toward knowledge sharing and interactivity perception (about CDSS) and the outcomes are performance expectancy and intention to adopt CDSS. Besides, we include (1) the antecedents of attitude toward knowledge sharing—subjective norms, social factors and OCB (helping behavior) and (2) roles of physicians’ involvement in decision making, computer self-efficacy and effort expectancy in our framework. MethodsData from a stratified sample of 335 Malaysian physicians working in 12 public and private hospitals in Malaysia were collected to test the hypotheses using Structural Equation Modeling (SEM). ResultsThe important findings of our research are: (1) factors such as perceived threat to professional autonomy, performance expectancy, and physicians’ involvement in making decision about CDSS have significant impact on physicians’ intention to adopt CDSS; (2) physicians’ attitude toward knowledge sharing, interactivity perception and computer self-efficacy of physicians play a crucial role in influencing their perceived threat to professional autonomy; and (3) social network, shared goals and OCB (helping behavior) impact physicians’ attitude toward knowledge sharing. ConclusionsThe findings provide a comprehensive understanding of the factors that influence physicians’ intention to adopt CDSS in a developing country. The results can help hospital managers manage CDSS implementation in an effective manner.

Differences in pneumonia treatment between high-minority and low-minority neighborhoods with clinical decision support system implementation

Background: The relationship between clinical decision support systems (CDSS) and quality is a relatively new, and in light of the new health information technology (HIT) legislation, policy-relevant area. Moreover, very few studies exist examining the link between HIT and healthcare disparities. The purpose of this article is to examine the association between CDSS and the treatment of pneumonia care within high-minority (≥29.1% non-White, non-Hispanic) and low-minority (<29.1%) Zip Code Tabulation Areas (ZCTAs). Research design: This study employed a cross-sectional design and used 2009 data from the American Hospital Association, the Centers for Medicare and Medicaid Services and the Research Triangle Institute. Adjusted analysis controlled for a hospital’s propensity to use CDSS. Results: In the unadjusted analysis, hospitals in high-minority ZCTAs had lower pneumonia quality composite scores than their low-minority counterparts. When adjusting for other hospital and ZCTA-level variables, we found that CDSS use had stronger positive associations with quality in high-minority hospitals. Conclusions: Results support policy directives may support higher quality improvements by focusing CDSS adoption in high-minority hospitals.