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.

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

2020 ◽  
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.

scholarly journals Applying Clinical Decision Support Design Best Practices With the Practical Robust Implementation and Sustainability Model Versus Reliance on Commercially Available Clinical Decision Support Tools: Randomized Controlled Trial

10.2196/24359 ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. e24359
Author(s):  
Katy E Trinkley ◽  
Miranda E Kroehl ◽  
Michael G Kahn ◽  
Larry A Allen ◽  
Tellen D Bennett ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Clinical Decision Support ◽  
Controlled Trial ◽  
Clinical Decision ◽  
User Centered Design ◽  
Support Design ◽  
Robust Implementation ◽  
Sustainability Model ◽  
User Centered

Background Limited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations. Objective This study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool. Methods We conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomized trial across 28 primary care clinics. Both alerts aimed to improve beta-blocker prescribing for heart failure. The enhanced alert was informed by CDS best practices and the Practical, Robust, Implementation, and Sustainability Model (PRISM) IS framework, whereas the commercial alert followed vendor-supplied specifications. Following PRISM, the enhanced alert was informed by iterative, multilevel stakeholder input and the dynamic interactions of the internal and external environment. Outcomes aligned with PRISM’s evaluation measures, including patient reach, clinician adoption, and changes in prescribing behavior. Clinicians exposed to each alert were interviewed to identify design features that might influence adoption. The interviews were analyzed using a thematic approach. Results Between March 15 and August 23, 2019, the enhanced alert fired for 61 patients (106 alerts, 87 clinicians) and the commercial alert fired for 26 patients (59 alerts, 31 clinicians). The adoption and effectiveness of the enhanced alert were significantly higher than those of the commercial alert (62% vs 29% alerts adopted, P<.001; 14% vs 0% changed prescribing, P=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert. Conclusions The results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool. Trial Registration ClinicalTrials.gov NCT04028557; http://clinicaltrials.gov/ct2/show/NCT04028557

scholarly journals Applying Clinical Decision Support Design Best Practices With the Practical Robust Implementation and Sustainability Model Versus Reliance on Commercially Available Clinical Decision Support Tools: Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Katy E Trinkley ◽  
Miranda E Kroehl ◽  
Michael G Kahn ◽  
Larry A Allen ◽  
Tellen D Bennett ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practices ◽  
Clinical Decision Support ◽  
Controlled Trial ◽  
Clinical Decision ◽  
User Centered Design ◽  
Support Design ◽  
Robust Implementation ◽  
Sustainability Model ◽  
User Centered

BACKGROUND Limited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations. OBJECTIVE This study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool. METHODS We conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomized trial across 28 primary care clinics. Both alerts aimed to improve beta-blocker prescribing for heart failure. The enhanced alert was informed by CDS best practices and the Practical, Robust, Implementation, and Sustainability Model (PRISM) IS framework, whereas the commercial alert followed vendor-supplied specifications. Following PRISM, the enhanced alert was informed by iterative, multilevel stakeholder input and the dynamic interactions of the internal and external environment. Outcomes aligned with PRISM’s evaluation measures, including patient reach, clinician adoption, and changes in prescribing behavior. Clinicians exposed to each alert were interviewed to identify design features that might influence adoption. The interviews were analyzed using a thematic approach. RESULTS Between March 15 and August 23, 2019, the enhanced alert fired for 61 patients (106 alerts, 87 clinicians) and the commercial alert fired for 26 patients (59 alerts, 31 clinicians). The adoption and effectiveness of the enhanced alert were significantly higher than those of the commercial alert (62% vs 29% alerts adopted, <i>P</i>&lt;.001; 14% vs 0% changed prescribing, <i>P</i>=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert. CONCLUSIONS The results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool. CLINICALTRIAL ClinicalTrials.gov NCT04028557; http://clinicaltrials.gov/ct2/show/NCT04028557

scholarly journals AMDIS Case Conference

2010 ◽  
Vol 01 (01) ◽  
pp. 68-78 ◽  
Author(s):  
D. Levick ◽  
R. Schreiber ◽  
J. Graham
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Best Practices ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Case Conference ◽  
Alerting System

SummaryClinical decision support that provides enhanced patient safety at the point of care frequently encounters significant pushback from clinicians who find the process intrusive or time-consuming. We present a hypothetical medical center’s dilemma about its allergy alerting system and discuss similar problems faced by real hospitals. We then share some lessons learned and best practices for institutions who wish to implement these tools themselves.

Malignant hyperthermia susceptibility: utilization of genetic results in an electronic medical record to increase safety

2020 ◽  
Vol 21 (17) ◽  
pp. 1207-1215
Author(s):  
Jordan F Baye ◽  
Natasha J Petry ◽  
Shauna L Jacobson ◽  
Michelle M Moore ◽  
Bethany Tucker ◽  
...  
Keyword(s):  
Decision Support ◽  
Malignant Hyperthermia ◽  
Clinical Decision Support ◽  
Perioperative Complications ◽  
Implementation Strategies ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Malignant Hyperthermia Susceptibility ◽  
Alert System ◽  
Genotyping Platform

Aim: This manuscript describes implementation of clinical decision support for providers concerned with perioperative complications of malignant hyperthermia susceptibility. Materials & methods: Clinical decision support for malignant hyperthermia susceptibility was implemented in 2018 based around our pre-emptive genotyping platform. We completed a brief descriptive review of patients who underwent pre-emptive testing, focused particularly on RYR1 and CACNA1S genes. Results: To date, we have completed pre-emptive genetic testing on more than 10,000 patients; 13 patients having been identified as a carrier of a pathogenic or likely pathogenic variant of RYR1 or CACNA1S. Conclusion: An alert system for malignant hyperthermia susceptibility – as an extension of our pre-emptive genomics platform – was implemented successfully. Implementation strategies and lessons learned are discussed herein.

scholarly journals AI in Healthcare practice: opportunities and challenges for clinical decision support systems

2019 ◽  
Author(s):  
David R. Millen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support Systems ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Healthcare Applications

In the past few years there has been great optimism about the potential benefits of incorporating AI (cognitive) capabilities into healthcare products and services. Indeed, progress in Natural Language Processing (NLP) has made electronic health records both more accessible and comprehensible, advances in image processing algorithms has helped to early identify tumors, and large datasets with new discovery services can help with breakthrough insights in life sciences and drug discovery. Importantly, new AI-based solutions are embedded in the sociotechnical systems of clinical care and within complex regulatory environments and globally diverse cultural frameworks. In this talk, I will present several case studies of novel AI – based healthcare applications that have been introduced in recent years and share lessons learned along the way. Particular focus will be on design research challenges for healthcare products, including understanding complex workflows within clinical settings and highly specialized and diverse mental modals, and understanding multiple stakeholders and interdependent participants. Design considerations and emerging opportunities for AI-based clinical decision support systems will also be shared.

Towards an Intelligent Integrated Approach for Clinical Decision Support

Big Data ◽  
2016 ◽  
pp. 1987-2005
Author(s):  
Rajendra Akerkar
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Medical Information ◽  
Clinical Decision Support Systems ◽  
Integrated Approach ◽  
Clinical Decision ◽  
Research Direction ◽  
Integrated System ◽  
Intelligent Integrated System

Nowadays, making use of big data is becoming mainstream in different enterprises and industry sectors. The medical sector is no exception. Specifically, medical services, which generate and process enormous volumes of medical information and medical device data, have been quickening big data utilization. In this chapter, we present a concept of an intelligent integrated system for direct support of decision making of physicians. This is a work in progress and the focus is on decision support for pharmacogenomics, which is the study of the relationship between a specific person's genetic makeup and his or her response to drug treatment. Further, we discuss a research direction considering the current shortcomings of clinical decision support systems.

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.

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