scholarly journals A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists

2021 ◽  
Vol 12 (02) ◽  
pp. 199-207
Author(s):  
Liang Yan ◽  
Thomas Reese ◽  
Scott D. Nelson
Keyword(s):  
Patient Safety ◽  
Quality Of Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Controlled Trials ◽  
Clinical Pharmacists ◽  
Randomized Controlled ◽  
The Impact

Abstract Objective Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. Methods We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle–Ottawa Scale. Results Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. Conclusion These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

scholarly journals A/B testing using rapid randomized controlled trials to optimize clinical decision support (Preprint)

2019 ◽  
Author(s):  
Devin Mann ◽  
Adam Szerencsy ◽  
Leora Horwitz ◽  
Simon Jones ◽  
Masha Kuznetsova ◽  
...  
Keyword(s):  
Decision Support ◽  
Randomized Controlled Trials ◽  
Clinical Decision Support ◽  
Positive Impact ◽  
Clinical Decision ◽  
Controlled Trials ◽  
Alert Fatigue ◽  
Randomized Controlled ◽  
Registration Number ◽  
The Impact

BACKGROUND Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to understand and iteratively improve design choices embedded within CDS tools. OBJECTIVE This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS tools to improve their usability, reduce alert fatigue and promote quality of care. METHODS A multi-step process combining tools from user-centered design, A/B testing and implementation science is used to understand, ideate, prototype, test, analyze and improve each candidate CDS. CDS engagement metrics (alert views, ignores, orders) are used to evaluate which CDS version is superior. RESULTS Two experiments are highlighted to demonstrate the impact of the process. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care in a rapid RCT. The new alert text resulted in minimal impact but the failure triggered another round of testing that identified key issues and led to a 70% reduction in alert volume in the next round. In the second experiment, the process was used to test three versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as three supporting images. Three rounds of RCTs showed that the financial framing was 5-10% more effective than the other two but that adding images did not have a positive impact. CONCLUSIONS These data support the potential for this new process to rapidly develop, deploy and improve CDS within an EHR. This approach may be an important tool for improving the impact and experience of CDS. CLINICALTRIAL Our flu alert trial was registered in January 2018 with ClinicalTrials.gov, registration number NCT03415425. Our tobacco alert trial was registered in October 2018 with ClinicalTrials.gov, registration number NCT03714191.

scholarly journals Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials

10.2196/16651 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e16651
Author(s):  
Jonathan Austrian ◽  
Felicia Mendoza ◽  
Adam Szerencsy ◽  
Lucille Fenelon ◽  
Leora I Horwitz ◽  
...  
Keyword(s):  
Decision Support ◽  
Randomized Controlled Trials ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Controlled Trials ◽  
Alert Fatigue ◽  
Randomized Controlled ◽  
Significant Difference ◽  
Acceptance Rates ◽  
The Impact

Background Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. Objective This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. Methods A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. Results To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. Conclusions These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. Trial Registration Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191

scholarly journals Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care

2020 ◽  
Vol 44 (10) ◽  
Author(s):  
David L. Chin ◽  
Michelle H. Wilson ◽  
Ashley S. Trask ◽  
Victoria T. Johnson ◽  
Brittanie I. Neaves ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
System Data

The Role of Digital Clinical Decision Support Tool in Improving Quality of Intrapartum and Postpartum Care: Experiences From Two States of India.

2020 ◽  
Author(s):  
Gulnoza Usmanova ◽  
Kamlesh Lalchandani ◽  
Ashish Srivast ◽  
Chandra Joshi ◽  
Deepak Bhatt ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Multivariate Logistic Regression Analysis ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Clinical Practices ◽  
The Impact

Abstract Background: Computerized clinical decision support (CDSS) –digital information systems designed to improve clinical decision making by providers – are a promising tool for improving quality of care. This study aims to understand the uptake of ASMAN application (defined as completeness of electronic case sheets), the role of CDSS in improving adherence to key clinical practices and delivery outcomes. Methods: We have conducted secondary analysis of program data (government data) collected from 81 public facilities across four districts each in two sates of Madhya Pradesh and Rajasthan. The data collected between August –October 2017 (baseline) and the data collected between December 2019 – March 2020 was analysed (latest). Results: The completeness of electronic case sheets was low at postpartum period (40.5%), and in facilities with more than 300 deliveries a month (20.9%). In multivariate logistic regression analysis, the introduction of technology yielded to significant improvement in adherence to key clinical practices. We have observed reduction in fresh still births rates and asphyxia, but these results were not statistically significant in interrupted time series analysis. However, our analysis showed that identification of maternal complications has increased over the period of program implementation and at the same time referral outs decreased. Conclusions: Our study indicates CDSS has a potential to improve quality of intrapartum care and delivery outcome. Future studies with rigorous study design is required to understand the impact of technology in improving quality of maternity care.

Clinical decision support improves quality of care in patients with ulcerative colitis

2019 ◽  
Vol 49 (8) ◽  
pp. 1040-1051 ◽  
Author(s):  
Belinda Jackson ◽  
Jake Begun ◽  
Kathleen Gray ◽  
Leonid Churilov ◽  
Danny Liew ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Quality Of Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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