scholarly journals Developing a clinical decision support for opioid use disorders: a NIDA center for the clinical trials network working group report

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Gavin B. Bart ◽  
Andrew Saxon ◽  
David A. Fiellin ◽  
Jennifer McNeely ◽  
John P. Muench ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Working Group ◽  
Clinical Decision ◽  
Opioid Use ◽  
Group Report ◽  
Opioid Use Disorders

The role of experience and clinical decision support in clinical trial accrual within oncology.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1527-1527
Author(s):  
Waqas Haque ◽  
Ann M. Geiger ◽  
Celette Sugg Skinner ◽  
Rasmi Nair ◽  
Simon Craddock Lee ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Principal Investigator ◽  
Physician Practices ◽  
The Past ◽  
The Future ◽  
Clinical Trial Accrual

1527 Background: Patient accrual for cancer clinical trials is suboptimal. The complexity of applying eligibility criteria and enrolling patients may deter oncologists from recommending patients for a trial. As such, there is a need to understand how experience, training, and clinical decision support impact physician practices and intentions related to trial accrual. Methods: From May to September 2017, we conducted a survey on clinical trial accrual in a national sample of medical, surgical, and radiation oncologists. The 20-minute survey assessed barriers and facilitators to clinical trial accrual, including experience (e.g., “In the past 5 years, have you been a study or site PI of a trial?”), training (e.g., “Did you receive training about trial design and recruitment as part of medical school, residency, or fellowship? After fellowship?”), and clinical decision support (e.g., “What kind of clinical decision support has your practice implemented?). We used logistic regression to identify factors associated with frequency of discussing trials (with ≥25% of patients) and likelihood of recommending a trial to a patient (likely or very likely) in the future. Results: Survey respondents (n = 1,030) were mostly medical oncologists (59%), age 35-54 years (67%), male (74%), and not in academic practice (58%). About 18% of respondents (n = 183) reported discussing trials with ≥25% of their patients, and 80% reported being likely or very likely to recommend a trial to a patient in the future. Prior experience as principal investigator of a trial was associated with both frequency of discussing trials (OR 3.27, 95% CI 2.25, 4.75) and likelihood of recommending a trial in the future (OR 5.22, 95% CI 3.71, 7.34), as was receiving additional training in clinical trials after fellowship (discussion with patients: OR 2.48, 95% CI 1.80, 3.42; recommend in future: OR 1.92, 95% CI 1.37, 2.69). Implementing clinical decision support was not associated with discussing trials with ≥25% of patients (OR 1.12, 95% CI 0.76, 1.67), but was associated with being likely to recommend a trial in the future (OR 1.73, 95% CI 1.11, 2.71). Conclusions: In a national survey of oncologists, we observed differences in physician practices and intention related to clinical trial accrual. Whereas the vast majority (80%) reported being likely or very likely to recommend trials in the future, far fewer (20%) reported discussing trials with their patients within the past 5 years. Implementation of clinical decision support – electronic tools intended to optimize patient care and identification of patient eligibility – was not associated with frequency of past discussion of clinical trials but was associated with recommending a trial in the future. Given the stronger association between experience as a site Principal Investigator and recommending a trial, future research should explore how improving opportunities to lead a clinical trial impact trial accrual.

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 Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications

2019 ◽  
Vol 28 (01) ◽  
pp. 128-134 ◽  
Author(s):  
Farah Magrabi ◽  
Elske Ammenwerth ◽  
Jytte Brender McNair ◽  
Nicolet F. De Keizer ◽  
Hannele Hyppönen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Medical Informatics ◽  
Clinical Decision Support ◽  
Working Group ◽  
Care Delivery ◽  
Clinical Decision ◽  
Design Development ◽  
Ongoing Surveillance ◽  
Practical Implications

Objectives: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. Method: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: There is a rich history and tradition of evaluating AI in healthcare. While evaluators can learn from past efforts, and build on best practice evaluation frameworks and methodologies, questions remain about how to evaluate the safety and effectiveness of AI that dynamically harness vast amounts of genomic, biomarker, phenotype, electronic record, and care delivery data from across health systems. This paper first provides a historical perspective about the evaluation of AI in healthcare. It then examines key challenges of evaluating AI-enabled clinical decision support during design, development, selection, use, and ongoing surveillance. Practical aspects of evaluating AI in healthcare, including approaches to evaluation and indicators to monitor AI are also discussed. Conclusion: Commitment to rigorous initial and ongoing evaluation will be critical to ensuring the safe and effective integration of AI in complex sociotechnical settings. Specific enhancements that are required for the new generation of AI-enabled clinical decision support will emerge through practical application.

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 Efficacy of clinical decision support systems: methods and estimates

2020 ◽  
Vol 15 (4) ◽  
pp. 148-155
Author(s):  
Olga Yu. Rebrova
Keyword(s):  
Clinical Trials ◽  
Decision Support ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Patient ◽  
Health Technologies ◽  
Surrogate Outcomes ◽  
Small Influence

Clinical decision support (CDS) systems are the medical technologies that go through their life cycle. Evaluation ofeffectiveness and safety should be carried out at its various stages at the development, in clinical trials, licensing, clinical and economic analysis, health technologies assessment. To date, the effectiveness and safety of CDS systems vary and are ambiguous there are both successes and failures. Hundreds of clinical trials are carried out, and more than a hundred of systematic reviews are published. When evaluating the efficacy and safety of CDS systems, two types of outcomes are usually estimated: indicators of medical care (volume, time, costs, etc.), and patient outcomes (clinical and surrogate). A slight increase in physicians adherence to clinical guidelines has been observed, but ithad very small influence on surrogate outcomes, and there is no effect on clinical patient outcomes. A slight increase in risk with respect to patient outcomes was found in only a few studies. However, the methodological quality of the evidence is very low. In this regard, a few products based on artificial intelligence have so far approached the licensing phase. The field of CDS systems is developing, but not yet sufficiently studied, and there is a long way to real successes ahead. Meanwhile, there is a wide gap between the postulated and empirically demonstrated benefits of CDS systems.

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.

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