CPOE with Evidence-Based Clinical Decision Support Improves Patient Outcomes: Part 2 – Proof from a Canadian Hospital

2014 ◽  
Vol 17 (4) ◽  
pp. 68-74
Author(s):  
Jeremy Theal ◽  
Denis Protti
Keyword(s):  
Decision Support ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Evidence Based ◽  
Canadian Hospital

scholarly journals The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sharare Taheri Moghadam ◽  
Farahnaz Sadoughi ◽  
Farnia Velayati ◽  
Seyed Jafar Ehsanzadeh ◽  
Shayan Poursharif
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Physician Practice ◽  
Positive Effects ◽  
Practice Performance

Abstract Background Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. Methods This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I2 were then used to calculate heterogeneity. Results On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. Conclusions Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

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.

Face-to-Face: Resident-led Radiology Medicine Rounds Facilitate Evidence-based Processes for Clinical Decision Support

2020 ◽  
Author(s):  
Adam E. Goldman-Yassen ◽  
Sara B. Strauss ◽  
Peter P. Vlismas ◽  
Anand D. Jagannath ◽  
Marshall Yuan ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Evidence Based ◽  
Face To Face

scholarly journals Mobilising computable biomedical knowledge: challenges for clinical decision support from a medical knowledge provider

2020 ◽  
Vol 27 (2) ◽  
pp. e100121 ◽  
Author(s):  
Kieran Walsh ◽  
Chris Wroe
Keyword(s):  
Decision Support ◽  
Problem Solving ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Healthcare Professionals ◽  
Medical Knowledge ◽  
Clinical Decision ◽  
Biomedical Knowledge ◽  
Procedural Skills ◽  
Safety Standards

IntroductionThis paper summarises a talk given at the first UK workshop on mobilising computable biomedical knowledge on 29 October 2019 in London. It examines challenges in mobilising computable biomedical knowledge for clinical decision support from the perspective of a medical knowledge provider.MethodsWe developed the themes outlined below after personally reflecting on the challenges that we have encountered in this field and after considering the barriers that knowledge providers face in ensuring that their content is accessed and used by healthcare professionals. We further developed the themes after discussing them with delegates at the workshop and listening to their feedback.DiscussionThere are many challenges in mobilising computable knowledge for clinical decision support from the perspective of a medical knowledge provider. These include the size of the task at hand, the challenge of creating machine interpretable content, the issue of standards, the need to do better in tracing how computable medical knowledge that is part of clinical decision support impacts patient outcomes, the challenge of comorbidities, the problem of adhering to safety standards and finally the challenge of integrating knowledge with problem solving and procedural skills, healthy attitudes and professional behaviours. Partnership is likely to be essential if we are to make progress in this field. The problems are too complex and interrelated to be solved by any one institution alone.

scholarly journals Republished: Clinical Decision Support for Early Recognition of Sepsis

2019 ◽  
Vol 34 (5) ◽  
pp. 494-501
Author(s):  
Robert C. Amland ◽  
Kristin E. Hahn-Cover
Keyword(s):  
Decision Support ◽  
Inflammatory Response ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Early Recognition ◽  
Clinical Decision ◽  
Is Implementation ◽  
System Activation ◽  
Infectious Illness ◽  
Inflammatory Syndrome

Sepsis is an inflammatory response triggered by infection, with a high in-hospital mortality rate. Early recognition and treatment can reverse the inflammatory response, with evidence of improved patient outcomes. One challenge clinicians face is identifying the inflammatory syndrome against the background of the patient’s infectious illness and comorbidities. An approach to this problem is implementation of computerized early warning tools for sepsis. This multicenter retrospective study sought to determine clinimetric performance of a cloud-based computerized sepsis clinical decision support system (CDS), understand the epidemiology of sepsis, and identify opportunities for quality improvement. Data encompassed 6200 adult hospitalizations from 2012 through 2013. Of 13% patients screened-in, 51% were already suspected to have an infection when the system activated. This study focused on a patient cohort screened-in before infection was suspected; median time from arrival to CDS activation was 3.5 hours, and system activation to diagnostic collect was another 8.6 hours.

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