scholarly journals Clinicians' Values and Preferences for Medication Adherence and Cost Clinical Decision Support in Primary Care: A Qualitative Study

2020 ◽  
Vol 11 (03) ◽  
pp. 405-414
Author(s):  
Shubha Bhat ◽  
Catherine Grace Derington ◽  
Katy E. Trinkley
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Medication Adherence ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Patient Specific ◽  
Specific Information ◽  
Impact Patient Care ◽  
Primary Care Clinicians ◽  
The Cost

Abstract Background Medication nonadherence and unaffordability are prevalent, burdensome issues in primary care. In response, technology companies are capitalizing on clinical decision support (CDS) to deliver patient-specific information regarding medication adherence and costs to clinicians using electronic health records (EHRs). To maximize adoption and usability, these CDS tools should be designed with consideration of end users' values and preferences. Objective This article evaluates primary care clinicians' values and preferences for a medication adherence and cost CDS. Methods We conducted semistructured interviews with primary care clinicians with prescribing privileges and EHR access to identify clinicians' perceptions of and approaches to assessing medication adherence and costs, and to determine perceived values and preferences for medication adherence and cost CDS. Interviews were conducted until saturation of responses was reached. ATLAS.ti was used for thematic analysis. Results Among 26 clinicians interviewed, themes identified included a high value, but moderate need for a medication adherence CDS and high value and need for cost CDS. Clinicians expressed the cost CDS would provide actionable solutions and greatly impact patient care. Another theme identified was a desire for medication adherence and cost CDS to be separate tools yet integrated into workflow. The majority of clinicians preferred a medication adherence CDS that integrated claims data and actively displayed data using color-coded adherence categories within patients' medication lists in the EHR. For the cost CDS, clinicians preferred medication out-of-pocket costs and a list of cheaper or payor-preferred alternatives to display within the order queue of the EHR. Conclusion We identified valuable insights regarding clinician values and preferences for medication adherence and cost CDS. Overall, primary care clinicians feel CDS for medication adherence and cost are valuable and prefer them to be separate. These insights should be used to inform the design, implementation, and EHR integration of future medication and cost CDS tools.

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 Clinical Decision Support for Worker Health: A Five-Site Qualitative Needs Assessment in Primary Care Settings

2020 ◽  
Vol 11 (04) ◽  
pp. 635-643
Author(s):  
Joan S. Ash ◽  
Dian Chase ◽  
Sherry Baron ◽  
Margaret S. Filios ◽  
Richard N. Shiffman ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Primary Care Providers ◽  
Care Providers ◽  
Technical Feasibility ◽  
Primary Care Settings ◽  
Worker Health ◽  
Operational Issues

Abstract Background Although patients who work and have related health issues are usually first seen in primary care, providers in these settings do not routinely ask questions about work. Guidelines to help manage such patients are rarely used in primary care. Electronic health record (EHR) systems with worker health clinical decision support (CDS) tools have potential for assisting these practices. Objective This study aimed to identify the need for, and barriers and facilitators related to, implementation of CDS tools for the clinical management of working patients in a variety of primary care settings. Methods We used a qualitative design that included analysis of interview transcripts and observational field notes from 10 clinics in five organizations. Results We interviewed 83 providers, staff members, managers, informatics and information technology experts, and leaders and spent 35 hours observing. We identified eight themes in four categories related to CDS for worker health (operational issues, usefulness of proposed CDS, effort and time-related issues, and topic-specific issues). These categories were classified as facilitators or barriers to the use of the CDS tools. Facilitators related to operational issues include current technical feasibility and new work patterns associated with the coordinated care model. Facilitators concerning usefulness include users' need for awareness and evidence-based tools, appropriateness of the proposed CDS for their patients, and the benefits of population health data. Barriers that are effort-related include additional time this proposed CDS might take, and other pressing organizational priorities. Barriers that are topic-specific include sensitive issues related to health and work and the complexities of information about work. Conclusion We discovered several themes not previously described that can guide future CDS development: technical feasibility of the proposed CDS within commercial EHRs, the sensitive nature of some CDS content, and the need to assist the entire health care team in managing worker health.

Computerized Clinical Decision Support Systems for Prescribing in Primary Care: Main Characteristics and Implementation Impact. Protocol of an Evidence and Gap Map.

2021 ◽  
Author(s):  
Hector Acosta-Garcia ◽  
Ingrid Ferrer-López ◽  
Juan Ruano-Ruiz ◽  
Bernardo Santos-Ramos ◽  
Teresa Molina-López
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Grey Literature ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Cochrane Library

Abstract Background Computerized clinical decision support systems are used by clinicians at the point-of-care to improve quality of healthcare processes (prescribing error prevention, adherence to clinical guidelines...) and clinical outcomes (preventive, therapeutic, and diagnostics). Attempts to summarize results of computerized clinical decision support systems to support prescription in primary care have been challenging, and most systematic reviews and meta-analyses failed due to an extremely high degree of heterogeneity present among the included primary studies. The aim of our study will be to synthesize the evidence, considering all methodological factors that could explain these differences, and to build an evidence and gap map to identify important remaining research questions. Methods A literature search will be conducted from January 2010 onwards in Medline, Embase, The Cochrane Library and Web of Science databases. Two reviewers will independently screen all citations, full-text and abstract data. The study methodological quality and risk of bias will be appraised using appropriate tools if applicable. A flow diagram with the screened studies will be presented, and all included studies will be displayed using interactive evidence and gap maps. Results will be reported in accordance with recommendations from The Campbell Collaboration on the development of evidence and gap maps. Discussion Evidence behind computerized clinical decision support systems to support prescription use in primary care, has so far been difficult to be synthesized. Evidence and gap maps represent an innovative approach that has emerged and is increasingly being used to address a broader research question, where multiple types of intervention and outcomes reported may be evaluated. Broad inclusion criteria have been chosen with regards to study designs, in order to collect all available information. Regarding the limitations we will only include English and Spanish language studies from the last 10 years, we will not perform a grey literature search, and we will not carry out a meta-analysis due to the predictable heterogeneity of available studies. Systematic Review registration: This study is registered in Open Science Framework https://bit.ly/2RqKrWp

859-P: Impact of a Prediabetes Clinical Decision Support System on CV Risk Factor Control in Primary Care: Randomized Trial

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 859-P
Author(s):  
JAY R. DESAI ◽  
A. LAUREN CRAIN ◽  
DANIEL SAMAN ◽  
JOANN M. SPERL-HILLEN ◽  
CLAYTON ALLEN ◽  
...  
Keyword(s):  
Primary Care ◽  
Risk Factor ◽  
Decision Support ◽  
Decision Support System ◽  
Randomized Trial ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Risk Factor Control

scholarly journals Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial

2020 ◽  
Author(s):  
Nicolas Delvaux ◽  
Veerle Piessens ◽  
Tine De Burghgraeve ◽  
Pavlos Mamouris ◽  
Bert Vaes ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Laboratory Test ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Diagnostic Error ◽  
Order Sets ◽  
Cluster Randomized ◽  
Physician Order Entry ◽  
Test Ordering

Abstract Background Inappropriate laboratory test ordering poses an important burden for healthcare. Clinical decision support systems (CDSS) have been cited as promising tools to improve laboratory test ordering behavior. The objectives of this study were to evaluate the effects of an intervention that integrated a clinical decision support service into a computerized physician order entry (CPOE) on the appropriateness and volume of laboratory test ordering, and on diagnostic error in primary care.Methods This study was a pragmatic, cluster randomized, open label, controlled clinical trial. Setting 280 general practitioners (GPs) from 72 primary care practices in Belgium. Patients Patients aged ≥18 years with a laboratory test order for at least one of 17 indications; cardiovascular disease management, hypertension, check-up, chronic kidney disease (CKD), thyroid disease, type 2 diabetes mellitus, fatigue, anemia, liver disease, gout, suspicion of acute coronary syndrome (ACS), suspicion of lung embolism, rheumatoid arthritis, sexually transmitted infections (STI), acute diarrhea, chronic diarrhea, and follow-up of medication. Interventions The CDSS was integrated into a computerized physician order entry (CPOE) in the form of evidence-based order sets that suggested appropriate tests based on the indication provided by the general physician. Measurements The primary outcome of the ELMO study was the proportion of appropriate tests over the total number of ordered tests and inappropriately not-requested tests. Secondary outcomes of the ELMO study included diagnostic error, test volume and cascade activities.Results CDSS increased the proportion of appropriate tests by 0.21 (95% CI 0.16 - 0.26, p<.0001) for all tests included in the study. GPs in the CDSS arm ordered 7 (7.15 (95% CI 3.37 - 10.93, p=.0002)) tests fewer per panel. CDSS did not increase diagnostic error. The absolute difference in proportions was a decrease of 0.66% (95% CI 1.4% decrease - 0.05% increase) in possible diagnostic error.Conclusions A CDSS in the form of order sets, integrated within the CPOE improved appropriateness and decreased volume of laboratory test ordering without increasing diagnostic error. Trial Registration Clinicaltrials.gov Identifier: NCT02950142, registered on October 25, 2016Funding source This study was funded through the Belgian Health Care Knowledge Centre (KCE) Trials Programme agreement KCE16011.

scholarly journals Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Nicolas Delvaux ◽  
Veerle Piessens ◽  
Tine De Burghgraeve ◽  
Pavlos Mamouris ◽  
Bert Vaes ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Laboratory Test ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Diagnostic Error ◽  
Order Sets ◽  
Cluster Randomized ◽  
Physician Order Entry ◽  
Test Ordering

Abstract Background Inappropriate laboratory test ordering poses an important burden for healthcare. Clinical decision support systems (CDSS) have been cited as promising tools to improve laboratory test ordering behavior. The objectives of this study were to evaluate the effects of an intervention that integrated a clinical decision support service into a computerized physician order entry (CPOE) on the appropriateness and volume of laboratory test ordering, and on diagnostic error in primary care. Methods This study was a pragmatic, cluster randomized, open-label, controlled clinical trial. Setting Two hundred eighty general practitioners (GPs) from 72 primary care practices in Belgium. Patients Patients aged ≥ 18 years with a laboratory test order for at least one of 17 indications: cardiovascular disease management, hypertension, check-up, chronic kidney disease (CKD), thyroid disease, type 2 diabetes mellitus, fatigue, anemia, liver disease, gout, suspicion of acute coronary syndrome (ACS), suspicion of lung embolism, rheumatoid arthritis, sexually transmitted infections (STI), acute diarrhea, chronic diarrhea, and follow-up of medication. Interventions The CDSS was integrated into a computerized physician order entry (CPOE) in the form of evidence-based order sets that suggested appropriate tests based on the indication provided by the general physician. Measurements The primary outcome of the ELMO study was the proportion of appropriate tests over the total number of ordered tests and inappropriately not-requested tests. Secondary outcomes of the ELMO study included diagnostic error, test volume, and cascade activities. Results CDSS increased the proportion of appropriate tests by 0.21 (95% CI 0.16–0.26, p < 0.0001) for all tests included in the study. GPs in the CDSS arm ordered 7 (7.15 (95% CI 3.37–10.93, p = 0.0002)) tests fewer per panel. CDSS did not increase diagnostic error. The absolute difference in proportions was a decrease of 0.66% (95% CI 1.4% decrease–0.05% increase) in possible diagnostic error. Conclusions A CDSS in the form of order sets, integrated within the CPOE improved appropriateness and decreased volume of laboratory test ordering without increasing diagnostic error. Trial registration ClinicalTrials.gov Identifier: NCT02950142, registered on October 25, 2016

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