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Do clinical decision-support reminders for medical providers improve isoniazid preventative therapy prescription rates among HIV-positive adults? Study protocol for a randomized controlled trial.

ABSTRACT: BACKGROUND:This document describes a research protocol for a study designed to estimate the impact of implementing a reminder system for medical providers on the use of isoniazid preventative therapy (IPT) for adults living with HIV in western Kenya. People living with HIV have a 5% to 10% annual risk of developing active tuberculosis (TB) once infected with TB bacilli, compared to a 5% lifetime risk in HIV-negative people with latent TB infection. Moreover, people living with HIV have a 20-fold higher risk of dying from TB. A growing body of literature suggests that IPT reduces overall TB incidence and is therefore of considerable benefit to patients and the larger community. However, in 2009, of the estimated 33 million people living with HIV, only 1.7 million (5%) were screened for TB, and about 85,000 (0.2%) were offered IPT. METHODS/DESIGN:This study will examine the use of clinical decision-support reminders to improve rates of initiation of preventative treatment in a TB/HIV co-morbid population living in a TB endemic area. This will be a pragmatic, parallel-group, cluster-randomized superiority trial with a 1:1 allocation to treatment ratio. For the trial, 20 public medical facilities that use clinical summary sheets generated from an electronic medical records system will participate as clusters. All HIV-positive adult patients who complete an initial encounter at a study cluster and at least one return encounter during the study period will be included in the study cohort. The primary endpoint will be IPT prescription at 3 months post the initial encounter. We will conduct both individual-level and cluster-level analyses. Due to the nature of the intervention, the trial will not be blinded. This study will contribute to the growing evidence base for the use of electronic health interventions in low-resource settings to promote high-quality clinical care, health system optimization and positive patient outcomes. Trial registration ClinicalTrials.gov NCT01934309, registered 29 August 2013.

SUBMITTER: Green EP

PROVIDER: S-EPMC4404590 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Project description:BackgroundEvidence-based therapy for heart failure remains underutilized at hospital discharge, particularly for patients with heart failure who are hospitalized for another cause. We developed clinical decision support (CDS) to recommend an angiotensin converting enzyme (ACE) inhibitor during hospitalization to promote its continuation at discharge. The CDS was designed to be implemented in both interruptive and non-interruptive versions.ObjectivesTo compare the effectiveness and implementation of interruptive and non-interruptive versions of a CDS to improve care for heart failure.MethodsHospitalizations of patients with reduced ejection fraction were pseudo-randomized to deliver interruptive or non-interruptive CDS alerts to providers based on even or odd medical record number. We compared discharge utilization of an ACE inhibitor or angiotensin receptor blocker (ARB) for these two implementation approaches. We also assessed adoption and implementation fidelity of the CDS.ResultsOf 958 hospitalizations, interruptive alert hospitalizations had higher rates of discharge utilization of ACE inhibitors or ARBs than non-interruptive alert hospitalizations (79.6% vs. 74.2%, p = 0.05). Utilization was higher for interruptive alert versus non-interruptive alert hospitalizations which were principally for causes other than heart failure (79.8% vs. 73.4%; p = 0.05) but no difference was observed among hospitalizations with a principal heart failure diagnosis (85.9% vs.81.7%; p = 0.49). As compared to non-interruptive hospitalizations, interruptive alert hospitalizations were more likely to have had: an alert with any response (40.6% vs. 13.1%, p < 0.001), contraindications reported (33.1% vs 11.3%, p < 0.001), and an ACE inhibitor ordered within twelve hours of the alert (17.6% vs 10.3%, p < 0.01). The response rate for the interruptive alert was 1.7%, and a median (25th, 75th percentile) of 14 (5,32) alerts were triggered per hospitalization.ConclusionsA CDS implemented as an interruptive alert was associated with improved quality of care for heart failure. Whether the potential benefits of CDS in improving cardiovascular care were worth the high burden of interruptive alerts deserves further consideration. CLINICALTRIALS.Gov identifierNCT02858674.

Project description:BackgroundDiabetes mellitus is a complex disease with serious complications. Electronic decision support, providing information that is shared and discussed by both patient and physician, encourages timely interventions and may improve the management of this chronic disease. However, it has rarely been tested in community-based primary care.MethodsIn this pragmatic randomized trial, we randomly assigned adult primary care patients with type 2 diabetes to receive the intervention or usual care. The intervention involved shared access by the primary care provider and the patient to a Web-based, colour-coded diabetes tracker, which provided sequential monitoring values for 13 diabetes risk factors, their respective targets and brief, prioritized messages of advice. The primary outcome measure was a process composite score. Secondary outcomes included clinical composite scores, quality of life, continuity of care and usability. The outcome assessors were blinded to each patient's intervention status.ResultsWe recruited sequentially 46 primary care providers and then 511 of their patients (mean age 60.7 [standard deviation 12.5] years). Mean follow-up was 5.9 months. The process composite score was significantly better for patients in the intervention group than for control patients (difference 1.27, 95% confidence interval [CI] 0.79-1.75, p < 0.001); 61.7% (156/253) of patients in the intervention group, compared with 42.6% (110/258) of control patients, showed improvement (difference 19.1%, p < 0.001). The clinical composite score also had significantly more variables with improvement for the intervention group (0.59, 95% CI 0.09-1.10, p = 0.02), including significantly greater declines in blood pressure (-3.95 mm Hg systolic and -2.38 mm Hg diastolic) and glycated hemoglobin (-0.2%). Patients in the intervention group reported greater satisfaction with their diabetes care.InterpretationA shared electronic decision-support system to support the primary care of diabetes improved the process of care and some clinical markers of the quality of diabetes care. (ClinicalTrials.gov trial register no. NCT00813085.).

Project description:Background Only 50% of eligible atrial fibrillation ( AF ) patients receive anticoagulation ( AC ). Feasibility and effectiveness of electronic medical record (EMR)-based interventions to profile and raise provider AC percentage is poorly understood. The SUPPORT-AF (Supporting Use of AC Through Provider Profiling of Oral AC Therapy for AF) study aims to improve rates of adherence to AC guidelines by developing and delivering supportive tools based on the EMR to providers treating patients with AF. Methods and Results We emailed cardiologists and community-based primary care providers affiliated with our institution reports of their AC percentage relative to peers. We also sent an electronic medical record-based message to these providers the day before an appointment with an atrial fibrillation patient who was eligible but not receiving AC . The electronic medical record message asked the provider to discuss AC with the patient if he or she deemed it appropriate. To assess feasibility, we tracked provider review of our correspondence. We also tracked the change in AC for intervention providers relative to alternate primary care providers not receiving our intervention. We identified 3786, 1054, and 566 patients cared for by 49 cardiology providers, 90 community-based primary care providers, and 88 control providers, respectively. At baseline, the percentage of AC was 71.3%, 63.5%, and 58.3% for these 3 respective groups. Intervention providers reviewed our e-mails and electronic medical record messages 45% and 96% of the time, respectively. For providers responding, patient refusal was the most common reason for patients not being on AC (21%) followed by high bleeding risk (19%). At follow-up 10 weeks later, change in AC was no different for either cardiology or community-based primary care providers relative to controls (0.2% lower and 0.01% higher, respectively). Conclusions Our intervention profiling AC was feasible, but not sufficient to increase AC in our population.

Project description:BackgroundColorectal Cancer (CRC) is the third leading cause of cancer death among men and women in the United States. Research has shown that the risk of CRC associates with genetic and lifestyle factors. It is possible to prevent or minimize certain CRC risks by adopting a healthy lifestyle. Existing Clinical Decision Support Systems (CDSS) mainly targeted physicians as the CDSS users. As a result, the availability of patient-oriented CDSS is limited. Our project is to develop patient-oriented CDSS for active CRC management.MethodsWe implemented an online patient-oriented CRC CDSS for the public to learn about CRC, assess CRC risk levels, understand personalized CRC risk factors, and seek professional advices for people with CRC concerns. The system is implemented based on the Django Model-View-Controller (MVC) framework with an extensible background MySQL database. A CRC absolute risk prediction model is applied to calculate the personalized CRC risk score with a user-friendly web survey. An interactive dashboard using advanced data visualization technics will display and interpret the risk scores and factors. Based on the risk assessment, a structured decision tree algorithm will provide the recommendations on customized CRC screening methods. The CDSS also provides a search function for preferred providers and hospitals based on geographical information and patient preferences.ResultsA prototype of the patient-oriented CRC CDSS has been developed. It provides an open assessment of potential CRC risks via an online survey. The CRC risk predictive model has been implemented. The prediction outcomes of risk levels and factors are presented to the users through a personalized interactive visualization interface, to guide the public on how to reduce the CRC risks by changing their living styles (such as smoking and drinking) and diet characteristics (such as consumptions of red meat and milk). The CDSS will also provide customized recommendations on screening methods based on the corresponding risk factors. For users seeking professional clinicians, the CDSS also provides a convenient tool for searching nearby hospitals and available doctors based on the location preferences and providers characteristics (such as gender, language, and specialty).ConclusionsThis CRC CDSS prototype provides a patient-friendly interface for CRC risk assessment and gives a personalized interpretation on important CRC risk factors. It is a useful tool to educate the public on CRC, to provide guidance on minimizing CRC risks, and to promote early CRC screening that reduces the CRC occurrences.

Project description:BackgroundComputerized clinical decision support systems (CDSSs) are a promising knowledge translation tool, but often fail to meaningfully influence the outcomes they target. Low CDSS provider uptake is a potential contributor to this problem but has not been systematically studied. The objective of this systematic review and meta-regression was to determine reported CDSS uptake and identify which CDSS features may influence uptake.MethodsMedline, Embase, CINAHL, and the Cochrane Database of Controlled Trials were searched from January 2000 to August 2020. Randomized, non-randomized, and quasi-experimental trials reporting CDSS uptake in any patient population or setting were included. The main outcome extracted was CDSS uptake, reported as a raw proportion, and representing the number of times the CDSS was used or accessed over the total number of times it could have been interacted with. We also extracted context, content, system, and implementation features that might influence uptake, for each CDSS. Overall weighted uptake was calculated using random-effects meta-analysis and determinants of uptake were investigated using multivariable meta-regression.ResultsAmong 7995 citations screened, 55 studies involving 373,608 patients and 3607 providers met full inclusion criteria. Meta-analysis revealed that overall CDSS uptake was 34.2% (95% CI 23.2 to 47.1%). Uptake was only reported in 12.4% of studies that otherwise met inclusion criteria. Multivariable meta-regression revealed the following factors significantly associated with uptake: (1) formally evaluating the availability and quality of the patient data needed to inform CDSS advice; and (2) identifying and addressing other barriers to the behaviour change targeted by the CDSS.Conclusions and relevanceSystem uptake was seldom reported in CDSS trials. When reported, uptake was low. This represents a major and potentially modifiable barrier to overall CDSS effectiveness. We found that features relating to CDSS context and implementation strategy best predicted uptake. Future studies should measure the impact of addressing these features as part of the CDSS implementation strategy. Uptake reporting must also become standard in future studies reporting CDSS intervention effects.RegistrationPre-registered on PROSPERO, CRD42018092337.

Project description:BackgroundThe prevalence of substance use disorders is higher among medical inpatients than in the general population, placing inpatient providers in a prime position to detect these patients and intervene.ObjectiveTo assess provider detection rates of substance use disorders among medical inpatients and to identify patient characteristics associated with detection.DesignData drawn from a cluster randomized controlled trial that tested the effectiveness of three distinct implementation strategies for providers to screen patients for substance use disorders and deliver a brief intervention (Clinical Trials.gov : NCT01825057).ParticipantsA total of 1076 patients receiving care from 13 general medical inpatient units in a large teaching hospital participated in this study.Main measuresData sources included patient self-reported questionnaires, a diagnostic interview for substance use disorders, and patient medical records. Provider detection was determined by diagnoses documented in medical records.Key resultsProvider detection rates were highest for nicotine use disorder (72.2%) and lowest for cannabis use disorder (26.4%). Detection of alcohol use disorder was more likely among male compared to female patients (OR (95% CI) = 4.0 (1.9, 4.8)). When compared to White patients, alcohol (OR (95% CI) = 0.4 (0.2, 0.6)) and opioid (OR (95% CI) = 0.2 (0.1, 0.7)) use disorders were less likely to be detected among Black patients, while alcohol (OR (95% CI) = 0.3 (0.0, 2.0)) and cocaine (OR (95% CI) = 0.3 (0.1, 0.9)) use disorders were less likely to be detected among Hispanic patients. Providers were more likely to detect nicotine, alcohol, opioid, and other drug use disorders among patients with higher addiction severity (OR (95% CI) = 1.20 (1.08-1.34), 1.62 (1.48, 1.78), 1.46 (1.07, 1.98), 1.38 (1.00, 1.90), respectively).ConclusionsFindings indicate patient characteristics, including gender, race, and addiction severity impact rates of provider detection. Instituting formal screening for all substances may increase provider detection and inform treatment decisions.

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Do clinical decision-support reminders for medical providers improve isoniazid preventative therapy prescription rates among HIV-positive adults? Study protocol for a randomized controlled trial.

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