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Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial.

ABSTRACT: BACKGROUND:The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care. METHODS:A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate. RESULTS:Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was -?0.09 (95% CI -?0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI -?0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI -?0.91; 1.16, p-value = 0.81) and 0.12 (95% CI -?1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system. CONCLUSIONS:The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system. TRIAL REGISTRATION:ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

SUBMITTER: Heselmans A

PROVIDER: S-EPMC6947861 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial.

Heselmans Annemie A Delvaux Nicolas N Laenen Annouschka A Van de Velde Stijn S Ramaekers Dirk D Kunnamo Ilkka I Aertgeerts Bert B

Implementation science : IS 20200107 1

<h4>Background</h4>The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care.<h4>Methods</h4>A cluster-randomized trial with before-and-after measurements was performed in Belgian P ...[more]

PMID: 31910877

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Project description:BackgroundComputerized decision support systems (CDSSs) are increasingly used to improve quality of care. There is evidence for moderate to large effects from randomized controlled trials (RCTs), but evidence on their effectiveness when implemented at a national level is lacking. In the Netherlands, the Dutch College of General Practitioners (NHG) initiated their successful guideline program already 30 years ago. NHGDoc, a CDSS based on these NHG guidelines, covering multiple disease areas for general practice, was developed in 2006 with the aim to improve quality of primary care. In this paper, a protocol is presented to evaluate the uptake and effects of NHGDoc.MethodsA cluster RCT will be conducted among 120 general practices in the Netherlands. Eligible general practices will be randomized to receive either the regular NHGDoc decision support modules (control arm) or the regular modules plus an additional module on heart failure (intervention arm). The heart failure module consists of patient-specific alerts concerning the treatment of patients with heart failure. The effect evaluation will focus on performance indicators (e.g., prescription behavior) as well as on patient outcomes (e.g., hospital admissions) relevant in the domain of heart failure. Additionally, a process evaluation will be conducted to gain insight into the barriers and facilitators that affect the uptake and impact of NHGDoc.DiscussionResults of this study will provide insight in the uptake and impact of a multiple-domain covering CDSS for primary care implemented by a national guideline organization to improve the quality of primary care. Whereas the trial focuses on a specific domain of care-heart failure-conclusions of this study will shed light on the functioning of CDSSs covering multiple disease areas for primary care, particularly as this study also explores the factors contributing to the system's uptake and effectiveness.Trial registrationClinical trials NCT01773057.

Project description:Rationale & objectiveThe study aimed to develop, implement, and evaluate a clinical decision support (CDS) system for chronic kidney disease (CKD) in a primary care setting, with the goal of improving CKD care in adults.Study designThis was a cluster randomized trial.Setting & participantsA total of 32 Midwestern primary care clinics were randomly assigned to either receive usual care or CKD-CDS intervention. Between April 2019 and March 2020, we enrolled 6,420 patients aged 18-75 years with laboratory-defined glomerular filtration rate categories of CKD Stage G3 and G4, and 1 or more of 6 CKD care gaps: absence of a CKD diagnosis, suboptimal blood pressure or glycated hemoglobin levels, indication for angiotensin-converting enzyme inhibitor or angiotensin receptor blocker but not prescribed, a nonsteroidal anti-inflammatory agent on the active medication list, or indication for a nephrology referral.InterventionThe CKD-CDS provided personalized suggestions for CKD care improvement opportunities directed to both patients and clinicians at primary care encounters.OutcomesWe assessed the proportion of patients meeting each of 6 CKD-CDS quality metrics representing care gap resolution after 18 months.ResultsThe adjusted proportions of patients meeting quality metrics in CKD-CDS versus usual care were as follows: CKD diagnosis documented (26.6% vs 21.8%; risk ratio [RR], 1.17; 95% CI, 0.91-1.51); angiotensin-converting enzyme inhibitor or angiotensin receptor blocker prescribed (15.9% vs 16.1%; RR, 0.95; 95% CI, 0.76-1.18); blood pressure control (20.4% vs 20.2%; RR, 0.98; 95% CI, 0.84-1.15); glycated hemoglobin level control (21.4% vs 22.1%; RR, 1.00; 95% CI, 0.80-1.24); nonsteroidal anti-inflammatory agent not on the active medication list (51.5% vs 50.4%; RR, 1.03; 95% CI, 0.90-1.17); and referral or visit to a nephrologist (38.7% vs 36.1%; RR, 1.02; 95% CI, 0.79-1.32).LimitationsWe encountered an overall reduction in expected primary care encounters and obstacles to point-of-care CKD-CDS utilization because of the coronavirus disease 2019 pandemic.ConclusionsThe CKD-CDS intervention did not lead to a significant improvement in CKD quality metrics. The challenges to CDS use during the coronavirus disease 2019 pandemic likely influenced these results.FundingNational Institute of Diabetes and Digestive and Kidney Diseases (R18DK118463).Trial registrationclinicaltrials.gov Identifier: NCT03890588.

Project description:BackgroundThe CDC's Family History Public Health Initiative encourages adoption and increase awareness of family health history. To meet these goals and develop a personalized medicine implementation science research agenda, the Genomedical Connection is using an implementation research (T3 research) framework to develop and integrate a self-administered computerized family history system with built-in decision support into 2 primary care clinics in North Carolina.Methods/designThe family health history system collects a three generation family history on 48 conditions and provides decision support (pedigree and tabular family history, provider recommendation report and patient summary report) for 4 pilot conditions: breast cancer, ovarian cancer, colon cancer, and thrombosis. All adult English-speaking, non-adopted, patients scheduled for well-visits are invited to complete the family health system prior to their appointment. Decision support documents are entered into the medical record and available to provider's prior to the appointment. In order to optimize integration, components were piloted by stakeholders prior to and during implementation. Primary outcomes are change in appropriate testing for hereditary thrombophilia and screening for breast cancer, colon cancer, and ovarian cancer one year after study enrollment. Secondary outcomes include implementation measures related to the benefits and burdens of the family health system and its impact on clinic workflow, patients' risk perception, and intention to change health related behaviors. Outcomes are assessed through chart review, patient surveys at baseline and follow-up, and provider surveys. Clinical validity of the decision support is calculated by comparing its recommendations to those made by a genetic counselor reviewing the same pedigree; and clinical utility is demonstrated through reclassification rates and changes in appropriate screening (the primary outcome).DiscussionThis study integrates a computerized family health history system within the context of a routine well-visit appointment to overcome many of the existing barriers to collection and use of family history information by primary care providers. Results of the implementation process, its acceptability to patients and providers, modifications necessary to optimize the system, and impact on clinical care can serve to guide future implementation projects for both family history and other tools of personalized medicine, such as health risk assessments.

Project description:BackgroundClinical practice patterns greatly diverge from evidence-based recommendations to manage knee osteoarthritis conservatively before resorting to surgery.ObjectiveThis study aimed to tailor a guideline-based computerized decision support (CDS) intervention that facilitates the conservative management of knee osteoarthritis.MethodsExperts with backgrounds in clinical medicine, research, implementation, or health informatics suggested the most important recommendations for implementation, how to develop an implementation strategy, and how to form the CDS algorithms. In 6 focus group sessions, 8 general practitioners and 22 patients from Norway, Belgium, and Finland discussed the suggested CDS intervention and identified factors that would be most critical for the success of the intervention. The focus group moderators used the GUideline Implementation with DEcision Support checklist, which we developed to support consideration of CDS success factors.ResultsThe experts prioritized 9 out of 22 recommendations for implementation. We formed the concept for 6 CDS algorithms to support implementation of these recommendations. The focus group suggested 59 unique factors that could affect the success of the presented CDS intervention. Five factors (out of the 59) were prioritized by focus group participants in every country, including the perceived potential to address the information needs of both patients and general practitioners; the credibility of CDS information; the timing of CDS for patients; and the need for personal dialogue about CDS between the general practitioner and the patient.ConclusionsThe focus group participants supported the CDS intervention as a tool to improve the quality of care for patients with knee osteoarthritis through shared, evidence-based decision making. We aim to develop and implement the CDS based on these study results. Future research should address optimal ways to (1) provide patient-directed CDS, (2) enable more patient-specific CDS within the context of patient complexity, and (3) maintain user engagement with CDS over time.

Project description:BackgroundThe diagnosis of chronic kidney disease (CKD) is based on laboratory results easily extracted from electronic health records; therefore, CKD identification and management is an ideal area for targeted electronic decision support efforts. Early CKD management frequently occurs in primary care settings where primary care providers (PCPs) may not implement all the best practices to prevent CKD-related complications. Few previous studies have employed randomized trials to assess a CKD electronic clinical decision support system (eCDSS) that provided recommendations to PCPs tailored to each patient based on laboratory results.ObjectiveThe aim of this study was to report the trial design and implementation experience of a CKD eCDSS in primary care.MethodsThis was a 3-arm pragmatic cluster-randomized trial at an academic general internal medicine practice. Eligible patients had 2 previous estimated-glomerular-filtration-rates by serum creatinine (eGFRCr) <60 mL/min/1.73m2 at least 90 days apart. Randomization occurred at the PCP level. For patients of PCPs in either of the 2 intervention arms, the research team ordered triple-marker testing (serum creatinine, serum cystatin-c, and urine albumin-creatinine-ratio) at the beginning of the study period, to be completed when acquiring labs for regular clinical care. The eCDSS launched for PCPs and patients in the intervention arms during a regular PCP visit subsequent to completing the triple-marker testing. The eCDSS delivered individualized guidance on cardiovascular risk-reduction, potassium and proteinuria management, and patient education. Patients in the eCDSS+ arm also received a pharmacist phone call to reinforce CKD-related education. The primary clinical outcome is blood pressure change from baseline at 6 months after the end of the trial, and the main secondary outcome is provider awareness of CKD diagnosis. We also collected process, patient-centered, and implementation outcomes.ResultsA multidisciplinary team (primary care internist, nephrologists, pharmacist, and informaticist) designed the eCDSS to integrate into the current clinical workflow. All 81 PCPs contacted agreed to participate and were randomized. Of 995 patients initially eligible by eGFRCr, 413 were excluded per protocol and 58 opted out or withdrew, resulting in 524 patient participants (188 usual care; 165 eCDSS; and 171 eCDSS+). During the 12-month intervention period, 53.0% (178/336) of intervention patient participants completed triple-marker labs. Among these, 138/178 (77.5%) had a PCP appointment after the triple-marker labs resulted; the eCDSS was opened for 73.9% (102/138), with orders or education signed for 81.4% (83/102).ConclusionsSuccessful integration of an eCDSS into primary care workflows and high eCDSS utilization rates at eligible visits suggest this tailored electronic approach is feasible and has the potential to improve guideline-concordant CKD care.Trial registrationClinicalTrials.gov NCT02925962; https://clinicaltrials.gov/ct2/show/NCT02925962 (Archived by WebCite at http://www.webcitation.org/78qpx1mjR).International registered report identifier (irrid)DERR1-10.2196/14022.

Dataset Information

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial.

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Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial.

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