scholarly journals Best Practices in Clinical Decision Support

2010 ◽  
Vol 01 (03) ◽  
pp. 331-345 ◽  
Author(s):  
Shobha Phansalkar ◽  
Meryl Bloomrosen ◽  
Robert Jenders ◽  
Anne Bobb ◽  
John Halamka ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision ◽  
Quality Data ◽  
Successful Implementation ◽  
Organizational Challenges ◽  
Grounded Theory Method ◽  
Effective Decision Support

Summary Background: Evidence demonstrates that clinical decision support (CDS) is a powerful tool for improving healthcare quality and ensuring patient safety. However, implementing and maintaining effective decision support interventions presents multiple technical and organizational challenges. Purpose: To identify best practices for CDS, using the domain of preventive care reminders as an example. Methods: We assembled a panel of experts in CDS and held a series of facilitated online and in-person discussions. We analyzed the results of these discussions using a grounded theory method to elicit themes and best practices. Results: Eight best practice themes were identified as important: deliver CDS in the most appropriate ways, develop effective governance structures, consider use of incentives, be aware of workflow, keep content current, monitor and evaluate impact, maintain high quality data, and consider sharing content. Keys themes within each of these areas were also described. Conclusion: Successful implementation of CDS requires consideration of both technical and socio-technical factors. The themes identified in this study provide guidance on crucial factors that need consideration when CDS is implemented across healthcare settings. These best practice themes may be useful for developers, implementers, and users of decision support.

scholarly journals 2346. Cost Savings Associated with Implementation of Clinical Decision Support for Clostridiodes difficile Testing

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S806-S807
Author(s):  
Cindy L Hoegg ◽  
Katie L Williams ◽  
Eric Shelov ◽  
Talene A Metjian ◽  
Ana Maria Cardenas ◽  
...  
Keyword(s):  
Decision Support ◽  
Literature Review ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Cost Savings ◽  
Clinical Decision ◽  
Antibiotic Use ◽  
False Positives ◽  
Exclusion Criteria ◽  
Laboratory Staff

Abstract Background Clinical decision support for Clostridioides difficile infection (CDI) diagnostics reduces inappropriate testing, leading to decreased need for isolation and antibiotic use. Our institution utilized manual discontinuation by laboratory staff of CDI testing for inappropriate specimens, including formed stool and age < 1 year. We aimed to assess the financial impact of instituting a CDI best practice alert at a quaternary care children’s hospital. Methods A multidisciplinary team mapped inappropriate testing criteria identified from literature review with discrete fields in our electronic health record (EHR, EpicCare) to design an alert. The exclusion criteria identified included: (1) age < 1 year; (2) positive C. difficile test within past 14 days; (3) less than or equal to 3 unformed stools in past 24 hours; (4) current receipt of CDI-directed therapy; or (5) laxative use or barium exposure in prior 48 hours. 6 months of data prior to implementation were reviewed to estimate impact of the alert. At implementation, any exclusion criteria detected in the EHR at the time of order entry triggered an alert to deter CDI testing. Cost estimates for averted tests (Quick Check Complete Assay/Illumigene) included cost of test ($50), cost of isolation/personal protective equipment ($159/day), and cost of treatment with oral vancomycin in false-positives ($2250/treatment course). Results In a 6-month pre-implementation period, 586 tests for CDI were ordered; of which, 23% were identified by our criteria as inappropriate. During the first 3 months of alert implementation, 256 tests were ordered, of which 105 (41%) caused the alert to fire. Of those, 56 tests were not ordered, for a 22% reduction in testing. Laboratory staff continued to manually stop tests not meeting criteria, such as patient age <1 year when possible. Based on avoidance of testing, use of PPE, and 10 day antibiotic treatment for false-positives (assumed 25% by literature review), this translated to cost savings of $69,916, and an annual cost savings of $279,664. Conclusion Implementation of an alert for select patients using a bioinformatics algorithm reduced inappropriate CDI testing. Clinical decision support for CDI can lead to substantial cost savings for both antibiotic use and isolation precautions. Disclosures All authors: No reported disclosures.

scholarly journals CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support

2019 ◽  
Vol 10 (01) ◽  
pp. 001-009 ◽  
Author(s):  
Barbara Jones ◽  
Dave Collingridge ◽  
Caroline Vines ◽  
Herman Post ◽  
John Holmen ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Health Care System ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision ◽  
Additional Patient ◽  
Free Text ◽  
Learning Health Care System ◽  
Care System

Background Local implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process. Objectives This article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations. Methods We implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis. Results Among 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection. Conclusion While adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system.

scholarly journals Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice–Aided Diagnosis: Interrupted Time Series Study (Preprint)

2019 ◽  
Author(s):  
Liyuan Tao ◽  
Chen Zhang ◽  
Lin Zeng ◽  
Shengrong Zhu ◽  
Nan Li ◽  
...  
Keyword(s):  
Time Series ◽  
Logistic Regression ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision Support Systems ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Interrupted Time Series Analysis ◽  
Aided Diagnosis

BACKGROUND Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. OBJECTIVE The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice–aided diagnosis in real-world research. METHODS This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. RESULTS The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all <italic>P</italic>&lt;.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, <italic>P</italic>=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, <italic>P</italic>=.01). Similar results were obtained in the subgroup analysis. CONCLUSIONS The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians’ diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.

scholarly journals Integrating the Practical Robust Implementation and Sustainability Model With Best Practices in Clinical Decision Support Design: Implementation Science Approach

10.2196/19676 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e19676
Author(s):  
Katy E Trinkley ◽  
Michael G Kahn ◽  
Tellen D Bennett ◽  
Russell E Glasgow ◽  
Heather Haugen ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Implementation Science ◽  
Clinical Decision Support ◽  
Integrated Approach ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Support Design ◽  
Robust Implementation ◽  
Sustainability Model

Background Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. Objective This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. Methods We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. Results Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. Conclusions Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.

Restrictive Transfusion Strategy and Clinical Decision Support Practices for Reducing RBC Transfusion Overuse

2019 ◽  
Vol 152 (5) ◽  
pp. 544-557
Author(s):  
James H Derzon ◽  
Nicole Clarke ◽  
Aaron Alford ◽  
Irwin Gross ◽  
Aryeh Shander ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Clinical Decision ◽  
Transfusion Practice ◽  
The Body ◽  
Transfusion Strategy ◽  
Restrictive Transfusion ◽  
Restrictive Transfusion Strategy ◽  
Rbc Transfusion

Abstract Objectives Assess support for the effectiveness of two separate practices, restrictive transfusion strategy and computerized physician order entry/clinical decision support (CPOE/CDS) tools, in decreasing RBC transfusions in adult surgical and nonsurgical patients. Methods Following the Centers for Disease Control and Prevention Laboratory Medicine Best Practice (LMBP) Systematic Review (A-6) method, studies were assessed for quality and evidence of effectiveness in reducing the percentage of patients transfused and/or units of blood transfused. Results Twenty-five studies on restrictive transfusion practice and seven studies on CPOE/CDS practice met LMBP inclusion criteria. The overall strength of the body of evidence of effectiveness for restrictive transfusion strategy and CPOE/CDS was rated as high. Conclusions Based on these procedures, adherence to an institutional restrictive transfusion strategy and use of CPOE/CDS tools for hemoglobin alerts or reminders of the institution’s restrictive transfusion policies are effective in reducing RBC transfusion overuse.

scholarly journals The Effect of Laboratory Test–Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review

2019 ◽  
Vol 3 (6) ◽  
pp. 1035-1048
Author(s):  
Nedra S Whitehead ◽  
Laurina Williams ◽  
Sreelatha Meleth ◽  
Sara Kennedy ◽  
Nneka Ubaka-Blackmoore ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Best Practice ◽  
Adverse Drug Events ◽  
Clinical Decision ◽  
Laboratory Medicine ◽  
Bibliographic Databases ◽  
Laboratory Monitoring ◽  
Meta Analyses

Abstract Background Laboratory and medication data in electronic health records create opportunities for clinical decision support (CDS) tools to improve medication dosing, laboratory monitoring, and detection of side effects. This systematic review evaluates the effectiveness of such tools in preventing medication-related harm. Methods We followed the Laboratory Medicine Best Practice (LMBP) initiative's A-6 methodology. Searches of 6 bibliographic databases retrieved 8508 abstracts. Fifteen articles examined the effect of CDS tools on (a) appropriate dose or medication (n = 5), (b) laboratory monitoring (n = 4), (c) compliance with guidelines (n = 2), and (d) adverse drug events (n = 5). We conducted meta-analyses by using random-effects modeling. Results We found moderate and consistent evidence that CDS tools applied at medication ordering or dispensing can increase prescriptions of appropriate medications or dosages [6 results, pooled risk ratio (RR), 1.48; 95% CI, 1.27–1.74]. CDS tools also improve receipt of recommended laboratory monitoring and appropriate treatment in response to abnormal test results (6 results, pooled RR, 1.40; 95% CI, 1.05–1.87). The evidence that CDS tools reduced adverse drug events was inconsistent (5 results, pooled RR, 0.69; 95% CI, 0.46–1.03). Conclusions The findings support the practice of healthcare systems with the technological capability incorporating test-based CDS tools into their computerized physician ordering systems to (a) identify and flag prescription orders of inappropriate dose or medications at the time of ordering or dispensing and (b) alert providers to missing laboratory tests for medication monitoring or results that warrant a change in treatment. More research is needed to determine the ability of these tools to prevent adverse drug events.

scholarly journals Evidence-Based Clinical Decision Support

2013 ◽  
Vol 22 (01) ◽  
pp. 120-127
Author(s):  
A. Wright ◽  
R. N. Shiffman
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Quality Of Healthcare ◽  
Evidence Based ◽  
Full Potential

Summary Background: Clinical decision support (CDS) is a key tool for enabling evidence-based medicine and improving the quality of healthcare. However, effective CDS faces a variety of challenges, including those relating to knowledge synthesis, capture, transformation, localization and maintenance. If not properly addressed, these challenges can limit the effectiveness of CDS, and potentially risk inaccurate or inappropriate interventions to clinicians. Objectives: (1) To describe an approach to CDS development using evidence as a basis for clinical decision support systems that promote effective care; (2) To review recent evidence regarding the effectiveness of selected clinical decision support systems. Method: Review and analysis of recent literature with identification of trends and best practices. Results: The state-of-the-art in CDS has advanced significantly, and many recent trials have shown CDS to be effective, although the results are mixed overall. Issues related to knowledge capture and synthesis, problems in knowledge transformation at the interface between knowledge authors and CDS developers, and problems specific to local CDS design and implementation can interfere with CDS development. Best practices, tools and techniques to manage them are described. Conclusions: CDS, when used well, can be effective, but further research is needed for it to reach its full potential.

scholarly journals The implementation, use and sustainability of a clinical decision support system for medication optimisation in primary care: A qualitative evaluation

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250946
Author(s):  
Mark Jeffries ◽  
Nde-Eshimuni Salema ◽  
Libby Laing ◽  
Azwa Shamsuddin ◽  
Aziz Sheikh ◽  
...  
Keyword(s):  
Primary Care ◽  
General Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Medication Safety ◽  
Qualitative Evaluation ◽  
Clinical Decision ◽  
Population Characteristics ◽  
Successful Implementation ◽  
General Practices

Background The quality and safety of prescribing in general practice is important, Clinical decision support (CDS) systems can be used which present alerts to health professionals when prescribing in order to identify patients at risk of potentially hazardous prescribing. It is known that such computerised alerts may improve the safety of prescribing in hospitals but their implementation and sustainable use in general practice is less well understood. We aimed to understand the factors that influenced the successful implementation and sustained use in primary care of a CDS system. Methods Participants were purposively recruited from Clinical Commissioning Groups (CCGs) and general practices in the North West and East Midlands regions of England and from the CDS developers. We conducted face-to-face and telephone-based semi-structured qualitative interviews with staff stakeholders. A selection of participants was interviewed longitudinally to explore the further sustainability 1–2 years after implementation of the CDS system. The analysis, informed by Normalisation Process Theory (NPT), was thematic, iterative and conducted alongside data collection. Results Thirty-nine interviews were conducted either individually or in groups, with 33 stakeholders, including 11 follow-up interviews. Eight themes were interpreted in alignment with the four NPT constructs: Coherence (The purpose of the CDS: Enhancing medication safety and improving cost effectiveness; Relationship of users to the technology; Engagement and communication between different stakeholders); Cognitive Participation (Management of the profile of alerts); Collective Action (Prescribing in general practice, patient and population characteristics and engagement with patients; Knowledge);and Reflexive Monitoring (Sustaining the use of the CDS through maintenance and customisation; Learning and behaviour change. Participants saw that the CDS could have a role in enhancing medication safety and in the quality of care. Engagement through communication and support for local primary care providers and management leaders was considered important for successful implementation. Management of prescribing alert profiles for general practices was a dynamic process evolving over time. At regional management levels, work was required to adapt, and modify the system to optimise its use in practice and fulfil local priorities. Contextual factors, including patient and population characteristics, could impact upon the decision-making processes of prescribers influencing the response to alerts. The CDS could operate as a knowledge base allowing prescribers access to evidence-based information that they otherwise would not have. Conclusions This qualitative evaluation utilised NPT to understand the implementation, use and sustainability of a widely deployed CDS system offering prescribing alerts in general practice. The system was understood as having a role in medication safety in providing relevant patient specific information to prescribers in a timely manner. Engagement between stakeholders was considered important for the intervention in ensuring prescribers continued to utilise its functionality. Sustained implementation might be enhanced by careful profile management of the suite of alerts in the system. Our findings suggest that the use and sustainability of the CDS was related to prescribers’ perceptions of the relevance of alerts. Shared understanding of the purpose of the CDS between CCGS and general practices particularly in balancing cost saving and safety messages could be beneficial.

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