scholarly journals Computerized Clinical Decision Support System for Emergency Department–Initiated Buprenorphine for Opioid Use Disorder: User-Centered Design (Preprint)

2018 ◽  
Author(s):  
Jessica M Ray ◽  
Osama M Ahmed ◽  
Yauheni Solad ◽  
Matthew Maleska ◽  
Shara Martel ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Emergency Care ◽  
Clinical Decision ◽  
Opioid Use Disorder ◽  
Care Pathways ◽  
User Centered Design ◽  
Opioid Use ◽  
Prototype Development ◽  
User Centered

BACKGROUND Emergency departments (EDs) frequently care for individuals with opioid use disorder (OUD). Buprenorphine (BUP) is an effective treatment option for patients with OUD that can safely be initiated in the ED. At present, BUP is rarely initiated as a part of routine ED care. Clinical decision support (CDS) could accelerate adoption of ED-initiated BUP into routine emergency care. OBJECTIVE This study aimed to design and formatively evaluate a user-centered decision support tool for ED initiation of BUP for patients with OUD. METHODS User-centered design with iterative prototype development was used. Initial observations and interviews identified workflows and information needs. The design team and key stakeholders reviewed prototype designs to ensure accuracy. A total of 5 prototypes were evaluated and iteratively refined based on input from 26 attending and resident physicians. RESULTS Early feedback identified concerns with the initial CDS design: an alert with several screens. The timing of the alert led to quick dismissal without using the tool. User feedback on subsequent iterations informed the development of a flexible tool to support clinicians with varied levels of experience with the intervention by providing both one-click options for direct activation of care pathways and user-activated support for critical decision points. The final design resolved challenging navigation issues through targeted placement, color, and design of the decision support modules and care pathways. In final testing, users expressed that the tool could be easily learned without training and was reasonable for use during routine emergency care. CONCLUSIONS A user-centered design process helped designers to better understand users’ needs for a Web-based clinical decision tool to support ED initiation of BUP for OUD. The process identified varying needs across user experience and familiarity with the protocol, leading to a flexible design supporting both direct care pathways and user-initiated decision support.

Developing Clinical Decision Support System for Sleep Staging Tasks by Formulating Explanations from Artificial Intelligence: User-Centered Design and Evaluation. (Preprint)

2021 ◽  
Author(s):  
Jeonghwan Hwang ◽  
Taeheon Lee ◽  
Honggu Lee ◽  
Seonjeong Byun
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Design Process ◽  
Clinical Decision Support ◽  
User Study ◽  
Clinical Decision ◽  
Sleep Staging ◽  
User Centered Design ◽  
Iterative Design ◽  
User Centered

BACKGROUND Despite the unprecedented performances of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces block the adoption of these AI systems in practice. OBJECTIVE The aim of this study was to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered fashion. METHODS User needs for the system were identified during interviews with polysomnographic technicians. User observation sessions were conducted to understand the workflow of the practitioners during sleep scoring. Iterative design process was performed to ensure easy integration of the tool into clinical workflows. Then, we evaluated the system with polysomnographic technicians. We measured the improvements in sleep staging accuracies after adopting our tool and assessed qualitatively how the participants perceived and used the tool. RESULTS The user study revealed that technicians desire explanations relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of the AI predictions. Here, technicians could evaluate whether AI models properly locate and use those patterns during prediction. Based on this, information in AI models that is closely related to sleep EEG patterns was formulated and visualized during the iterative design process. Furthermore, we developed a different visualization strategy for each pattern based on the way the technicians interpreted the EEG recordings with these patterns during their workflows. Generally, the tool evaluation results from the nine polysomnographic technicians were positive. Quantitatively, technicians achieved better classification performances after reviewing the AI-generated predictions with the proposed system; classification accuracies measured with Macro-F1 scores improved from 60.20 to 62.71. Qualitatively, participants reported that the provided information from the tool effectively supported them, and they were able to develop notable adoption strategies for the tool. CONCLUSIONS Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

scholarly journals Utilizing a user-centered approach to develop and assess pharmacogenomic clinical decision support for thiopurine methyltransferase

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Khoa A. Nguyen ◽  
Himalaya Patel ◽  
David A. Haggstrom ◽  
Alan J. Zillich ◽  
Thomas F. Imperiale ◽  
...  
Keyword(s):  
Decision Support ◽  
User Interface ◽  
Phase I ◽  
Clinical Decision Support ◽  
Phase Ii ◽  
Usability Testing ◽  
Clinical Decision ◽  
Thiopurine Methyltransferase ◽  
User Centered Design ◽  
User Centered

Abstract Background A pharmacogenomic clinical decision support tool (PGx-CDS) for thiopurine medications can help physicians incorporate pharmacogenomic results into prescribing decisions by providing up-to-date, real-time decision support. However, the PGx-CDS user interface may introduce errors and promote alert fatigue. The objective of this study was to develop and evaluate a prototype of a PGx-CDS user interface for thiopurine medications with user-centered design methods. Methods This study had two phases: In phase I, we conducted qualitative interviews to assess providers’ information needs. Interview transcripts were analyzed through a combination of inductive and deductive qualitative analysis to develop design requirements for a PGx-CDS user interface. Using these requirements, we developed a user interface prototype and evaluated its usability (phase II). Results In total, 14 providers participated: 10 were interviewed in phase I, and seven providers completed usability testing in phase II (3 providers participated in both phases). Most (90%) participants were interested in PGx-CDS systems to help improve medication efficacy and patient safety. Interviews yielded 11 themes sorted into two main categories: 1) health care providers’ views on PGx-CDS and 2) important design features for PGx-CDS. We organized these findings into guidance for PGx-CDS content and display. Usability testing of the PGx-CDS prototype showed high provider satisfaction. Conclusion This is one of the first studies to utilize a user-centered design approach to develop and assess a PGx-CDS interface prototype for Thiopurine Methyltransferase (TPMT). This study provides guidance for the development of a PGx-CDS, and particularly for biomarkers such as TPMT.

scholarly journals Pharmacogenomics and Opioid Use Disorder: Clinical Decision Support in an African American Cohort

2019 ◽  
Vol 111 (6) ◽  
pp. 674-681 ◽  
Author(s):  
Earl B. Ettienne ◽  
Adaku Ofoegbu ◽  
Mary K. Maneno ◽  
Jayla Briggs ◽  
Ginikannwa Ezeude ◽  
...  
Keyword(s):  
African American ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Opioid Use Disorder ◽  
Opioid Use

scholarly journals A pilot study of the functionality and clinician acceptance of a clinical decision support tool to improve primary care of opioid use disorder

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Rebecca C. Rossom ◽  
JoAnn M. Sperl-Hillen ◽  
Patrick J. O’Connor ◽  
A. Lauren Crain ◽  
Laurel Nightingale ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Primary Care Providers ◽  
Opioid Use Disorder ◽  
Care Providers ◽  
Opioid Use ◽  
Convenience Sample ◽  
Increased Risk

Abstract Objective Most Americans with opioid use disorder (OUD) do not receive indicated medical care. A clinical decision support (CDS) tool for primary care providers (PCPs) could address this treatment gap. Our primary objective was to build OUD-CDS tool and demonstrate its functionality and accuracy. Secondary objectives were to achieve high use and approval rates and improve PCP confidence in diagnosing and treating OUD. Methods A convenience sample of 55 PCPs participated. Buprenorphine-waivered PCPs (n = 8) were assigned to the intervention. Non-waivered PCPs (n = 47) were randomized to intervention (n = 24) or control (n = 23). Intervention PCPs received access to the OUD-CDS, which alerted them to patients at potentially increased risk for OUD or overdose and guided diagnosis and treatment. Control PCPs provided care as usual. Results The OUD-CDS was functional and accurate following extensive multi-phased testing. PCPs used the OUD-CDS in 5% of encounters with at-risk patients, far less than the goal of 60%. OUD screening confidence increased for all intervention PCPs and OUD diagnosis increased for non-waivered intervention PCPs. Most PCPs (65%) would recommend the OUD-CDS and found it helpful with screening for OUD and discussing and prescribing OUD medications. Discussion PCPs generally liked the OUD-CDS, but use rates were low, suggesting the need to modify CDS design, implementation strategies and integration with existing primary care workflows. Conclusion The OUD-CDS tool was functional and accurate, but PCP use rates were low. Despite low use, the OUD-CDS improved confidence in OUD screening, diagnosis and use of buprenorphine. NIH Trial registration NCT03559179. Date of registration: 06/18/2018. URL: https://clinicaltrials.gov/ct2/show/NCT03559179

scholarly journals User-centred clinical decision support to implement emergency department-initiated buprenorphine for opioid use disorder: protocol for the pragmatic group randomised EMBED trial

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e028488 ◽  
Author(s):  
Edward R Melnick ◽  
Molly Moore Jeffery ◽  
James D Dziura ◽  
Jodi A Mao ◽  
Erik P Hess ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Fixed Effects ◽  
Randomised Trial ◽  
Clinical Decision ◽  
Opioid Use Disorder ◽  
Independent Study ◽  
Minimal Risk ◽  
Opioid Use ◽  
Secondary Outcomes

IntroductionThe goal of this trial is to determine whether implementation of a user-centred clinical decision support (CDS) system can increase adoption of initiation of buprenorphine (BUP) into the routine emergency care of individuals with opioid use disorder (OUD).MethodsA pragmatic cluster randomised trial is planned to be carried out in 20 emergency departments (EDs) across five healthcare systems over 18 months. The intervention consists of a user-centred CDS integrated into ED clinician electronic workflow and available for guidance to: (1) determine whether patients presenting to the ED meet criteria for OUD, (2) assess withdrawal symptoms and (3) ascertain and motivate patient willingness to initiate treatment. The CDS guides the ED clinician to initiate BUP and facilitate follow-up. The primary outcome is the rate of BUP initiated in the ED. Secondary outcomes are: (1) rates of receiving a referral, (2) fidelity with the CDS and (3) rates of clinicians providing any ED-initiated BUP, referral for ongoing treatment and receiving Drug Addiction Act of 2000 training. Primary and secondary outcomes will be analysed using generalised linear mixed models, with fixed effects for intervention status (CDS vs usual care), prespecified site and patient characteristics, and random effects for study site.Ethics and disseminationThe protocol has been approved by the Western Institutional Review Board. No identifiable private information will be collected from patients. A waiver of informed consent was obtained for the collection of data for clinician prescribing and other activities. As a minimal risk implementation study of established best practices, an Independent Study Monitor will be utilised in place of a Data Safety Monitoring Board. Results will be reported in ClinicalTrials.gov and published in open-access, peer-reviewed journals, presented at national meetings and shared with the clinicians at participating sites via a broadcast email notification of publications.Trial registration numberNCT03658642; Pre-results.

scholarly journals Creation of an algorithm for clinical decision support for treatment of opioid use disorder with buprenorphine in primary care

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Adriane M. dela Cruz ◽  
Robrina Walker ◽  
Ronny Pipes ◽  
Sidarth Wakhlu ◽  
Madhukar H. Trivedi
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Medication Adherence ◽  
Clinical Decision Support ◽  
Universal Screening ◽  
Clinical Decision ◽  
Opioid Use Disorder ◽  
Expert Consensus ◽  
Opioid Use ◽  
Number Of Patients

Abstract Background The treatment capacity for opioid use disorder (OUD) lags far behind the number of patients in need of treatment. Capacity is limited, in part, by the limited number of physicians who offer office based OUD treatment with buprenorphine. Measurement based care (MBC) has been proposed as a means to support primary care physicians in treating OUD. Here, we propose a set of measures and a clinical decision support algorithm to provide MBC for the treatment of OUD. Methods We utilized literature search and expert consensus to identify measures for universal screening and symptom tracking. We used expert consensus to create the clinical decision support algorithm. Results The Tobacco, Alcohol, Prescription medication, and other Substance use (TAPS) tool was selected as the best published measure for universal screening in primary care. No published measure was identified as appropriate for symptom tracking or medication adherence; therefore, we created the OUD Symptom Checklist from the DSM-5 criteria for OUD and the Patient Adherence Questionnaire for Opioid Use Disorder Treatment (PAQ-OUD) to assess medication adherence. We developed and present a clinical decision support algorithm to provide direct guidance regarding treatment interventions during the first 12 weeks of buprenorphine treatment. Conclusion Creation of these tools is the necessary first step for implementation of MBC for the treatment of OUD with buprenorphine in primary care. Further work is needed to test the feasibility and acceptability of these tools. Trial Registration ClinicalTrials.gov; NCT04059016; 16 August 2019; retrospectively registered; https://clinicaltrials.gov/ct2/show/NCT04059016

scholarly journals User-centered design to improve clinical decision support in primary care

2017 ◽  
Vol 104 ◽  
pp. 56-64 ◽  
Author(s):  
Julian Brunner ◽  
Emmeline Chuang ◽  
Caroline Goldzweig ◽  
Cindy L. Cain ◽  
Catherine Sugar ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
User Centered Design ◽  
User Centered
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