Barriers to using clinical decision support in ambulatory care: Do clinics in health systems fare better?

2021 ◽  
Author(s):  
Yunfeng Shi ◽  
Alejandro Amill-Rosario ◽  
Robert S Rudin ◽  
Shira H Fischer ◽  
Paul Shekelle ◽  
...  
Keyword(s):  
Decision Support ◽  
Ambulatory Care ◽  
Health Systems ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
User Acceptance ◽  
Probability Models ◽  
Percentage Points ◽  
Rural Clinics ◽  
Linear Probability

Abstract Objective We quantify the use of clinical decision support (CDS) and the specific barriers reported by ambulatory clinics and examine whether CDS utilization and barriers differed based on clinics’ affiliation with health systems, providing a benchmark for future empirical research and policies related to this topic. Materials and Methods Despite much discussion at the theoretic level, the existing literature provides little empirical understanding of barriers to using CDS in ambulatory care. We analyze data from 821 clinics in 117 medical groups, based on in Minnesota Community Measurement’s annual Health Information Technology Survey (2014-2016). We examine clinics’ use of 7 CDS tools, along with 7 barriers in 3 areas (resource, user acceptance, and technology). Employing linear probability models, we examine factors associated with CDS barriers. Results Clinics in health systems used more CDS tools than did clinics not in systems (24 percentage points higher in automated reminders), but they also reported more barriers related to resources and user acceptance (26 percentage points higher in barriers to implementation and 33 points higher in disruptive alarms). Barriers related to workflow redesign increased in clinics affiliated with health systems (33 points higher). Rural clinics were more likely to report barriers to training. Conclusions CDS barriers related to resources and user acceptance remained substantial. Health systems, while being effective in promoting CDS tools, may need to provide further assistance to their affiliated ambulatory clinics to overcome barriers, especially the requirement to redesign workflow. Rural clinics may need more resources for training.

Clinical Decision Support System Adoption & Implementation in African Health Systems: A SWOT Analysis. (Preprint)

2021 ◽  
Author(s):  
Victoria Oluwafunmilola Kolawole
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Health Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support System ◽  
Swot Analysis ◽  
Clinical Decision ◽  
Developed Countries ◽  
Evidence Based ◽  
African Health

BACKGROUND The clinical decision support system (CDSS) has been an important achievement of health technology in the 21st century. In developed countries, it has transformed the way health services are being delivered and has shown to be a tool that reduces medical errors and misdiagnoses in Healthcare. However, CDSS remains underutilized in developing countries in Africa. OBJECTIVE This study aims to review the literature to improve our understanding of the “strengths, weaknesses, opportunities and threats (SWOT)” associated with CDSS implementation in African health systems. METHODS This study included a literature review conducted in PubMed with a total of 19 articles between the year 2010 to date (past 10years) reviewed for key themes and categorized into one of 4 possible areas within the SWOT analysis. RESULTS Articles reviewed showed common strengths of efficiency at the workplace, Improved healthcare quality, benefits in developed countries, good examples of evidence-based decision making. unreliable electric power supply, inconsistent Internet connectivity, clinician's limited computer skills, and lack of enough published evidence of benefits in developing countries are listed as a weakness. The opportunities are high demand for evidence-based practice in healthcare, a strong demand for quality healthcare, growing interest to use modern technologies. The common threats identified are government policy, political instability, low funding and resistance of use by providers. CONCLUSIONS There’s the need to work on the technical, organizational and financial barriers to ensure high adoption and implementation of the CDSS in African Health systems. Also, the lag on the knowledge available on its impact in developing countries must be worked on by supporting more studies to add to the body of knowledge.

scholarly journals Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis (Preprint)

2017 ◽  
Author(s):  
Saif Khairat ◽  
David Marc ◽  
William Crosby ◽  
Ali Al Sanousi
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Critical Review ◽  
Task Analysis ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Care Providers

BACKGROUND Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall. OBJECTIVE The purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance. METHODS A critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician). RESULTS Favorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs. CONCLUSIONS This research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients.

scholarly journals Clinical Decision Support and Palivizumab

2015 ◽  
Vol 06 (04) ◽  
pp. 769-784 ◽  
Author(s):  
A. Hogan ◽  
J. Michel ◽  
A.R. Localio ◽  
D. Karavite ◽  
L. Song ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Intervention Group ◽  
Clinical Decision ◽  
Partial Recovery ◽  
Patient Identification ◽  
Percentage Points ◽  
Comprehensive Intervention ◽  
Syncytial Virus ◽  
To Receive

SummaryBackground and Objectives: Palivizumab can reduce hospitalizations due to respiratory syncytial virus (RSV), but many eligible infants fail to receive the full 5-dose series. The efficacy of clinical decision support (CDS) in fostering palivizumab receipt has not been studied. We sought a comprehensive solution for identifying eligible patients and addressing barriers to palivizumab administration.Methods: We developed workflow and CDS tools targeting patient identification and palivizumab administration. We randomized 10 practices to receive palivizumab-focused CDS and 10 to receive comprehensive CDS for premature infants in a 3-year longitudinal cluster-randomized trial with 2 baseline and 1 intervention RSV seasons.Results: There were 356 children eligible to receive palivizumab, with 194 in the palivizumab-focused group and 162 in the comprehensive CDS group. The proportion of doses administered to children in the palivizumab-focused intervention group increased from 68.4% and 65.5% in the two baseline seasons to 84.7% in the intervention season. In the comprehensive intervention group, proportions of doses administered declined during the baseline seasons (from 71.9% to 62.4%) with partial recovery to 67.9% during the intervention season. The palivizumab-focused group improved by 19.2 percentage points in the intervention season compared to the prior baseline season (p < 0.001), while the comprehensive intervention group only improved 5.5 percentage points (p = 0.288). The difference in change between study groups was significant (p = 0.05).Conclusions: Workflow and CDS tools integrated in an EHR may increase the administration of palivizumab. The support focused on palivizumab, rather than comprehensive intervention, was more effective at improving palivizumab administration.

scholarly journals Understanding User Acceptance of Clinical Decision Support Systems to Promote Increased Cancer Screening Rates in a Primary Care Practice

2020 ◽  
Vol 11 ◽  
pp. 215013272095883
Author(s):  
Elizabeth A. Kelsey ◽  
Jane W. Njeru ◽  
Rajeev Chaudhry ◽  
Karen M. Fischer ◽  
Darrell R. Schroeder ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Cancer Screening ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Screening Tests ◽  
Cancer Screening Tests

Objective: Clinical decision support systems (CDDSs) in the electronic medical record (EMR) have been implemented in primary care settings to identify patients due for cancer screening tests, while functioning as a real time reminder system. There is little known about primary care providers (PCPs) perspective or user acceptance of CDSS. The purpose of this study was to investigate primary care provider perceptions of utilizing CDSS alerts in the EMR to promote increased screening rates for breast cancer, cervical cancer, and colorectal cancer. Methods: An electronic survey was administered to PCPs in a Midwest Health Institution community internal medicine practice from September 25, 2019 through November 27, 2019. Results: Among 37 participants (9 NP/Pas and 28 MD/DOs), the NP/PA group was more likely to agree that alerts were helpful (50%; P-value = .0335) and the number of alerts (89%; P = .0227) in the EMR was appropriate. The NP/PA group also was more likely to find alerts straightforward to use (78%, P = .0239). Both groups agreed about feeling comfortable using the health maintenance alerts ( MD/DO = 79%; NP/PA = 100%). Conclusion: CDSSs can promote and facilitate ordering of cancer screening tests. The use of technology can promptly identify patients due for a test and act as a reminder to the PCP. PCPs identify these alerts to be a beneficial tool in the EMR when they do not interrupt workflow and provide value to patient care. More work is needed to identify factors that could optimize alerts to be even more helpful, particularly to MD/DO groups.

scholarly journals Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study (Preprint)

2020 ◽  
Author(s):  
Mengting Ji ◽  
Georgi Z Genchev ◽  
Hengye Huang ◽  
Ting Xu ◽  
Hui Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Measurement Instrument ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Evaluation Framework

BACKGROUND Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. OBJECTIVE The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. METHODS An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. RESULTS The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (<i>χ<sub>26</sub><sup>2</sup></i>=36.984; <i>P</i>=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. CONCLUSIONS User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

scholarly journals Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

10.2196/25929 ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. e25929
Author(s):  
Mengting Ji ◽  
Georgi Z Genchev ◽  
Hengye Huang ◽  
Ting Xu ◽  
Hui Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Measurement Instrument ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Evaluation Framework

Background Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence–enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. Objective The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence–enabled clinical decision support system evaluation framework. Methods An artificial intelligence–enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. Results The Cronbach α of the measurement instrument was 0.963, and its content validity was 0.943. Values of average variance extracted ranged from 0.582 to 0.756, and values of the heterotrait-monotrait ratio ranged from 0.376 to 0.896. The final model had a good fit (χ262=36.984; P=.08; comparative fit index 0.991; goodness-of-fit index 0.957; root mean square error of approximation 0.052; standardized root mean square residual 0.028). Variables in the final model accounted for 89% of the variance in the user acceptance dimension. Conclusions User acceptance is the central dimension of artificial intelligence–enabled clinical decision support system success. Acceptance was directly influenced by perceived ease of use, information quality, service quality, and perceived benefit. Acceptance was also indirectly influenced by system quality and information quality through perceived ease of use. User acceptance and perceived benefit were interrelated.

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