scholarly journals Interrupted Time Series of User‐centered Clinical Decision Support Implementation for Emergency Department–initiated Buprenorphine for Opioid Use Disorder

2020 ◽  
Vol 27 (8) ◽  
pp. 753-763
Author(s):  
Wesley C. Holland ◽  
Bidisha Nath ◽  
Fangyong Li ◽  
Kaitlin Maciejewski ◽  
Hyung Paek ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Interrupted Time Series ◽  
Clinical Decision ◽  
Opioid Use Disorder ◽  
Opioid Use ◽  
User Centered

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.

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

10.2196/16912 ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. e16912 ◽  
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 P<.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%, P=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, P=.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.

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