Electronic medical record-assisted design of a cluster-randomized trial to improve diabetes care and outcomes.
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ABSTRACT:
SUBMITTER: Love TE
PROVIDER: S-EPMC2359510 | biostudies-other | 2008 Apr
REPOSITORIES: biostudies-other
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Project description:BackgroundTo improve the documentation and treatment of tobacco use in primary care, we developed and implemented a 3-part electronic health record enhancement: (1)smoking status icons, (2) tobacco treatment reminders, and (3) a Tobacco Smart Form that facilitated the ordering of medication and fax and e-mail counseling referrals.MethodsWe performed a cluster-randomized controlled trial of the enhancement in 26 primary care practices between December 19, 2006, and September 30, 2007. The primary outcome was the proportion of documented smokers who made contact with a smoking cessation counselor. Secondary outcomes included coded smoking status documentation and medication prescribing.ResultsDuring the 9-month study period, 132 630 patients made 315 962 visits to study practices. Coded documentation of smoking status increased from 37% of patients to 54% (+17%) in intervention practices and from 35% of patients to 46% (+11%) in control practices (P < .001 for the difference in differences). Among the 9589 patients who were documented smokers at the start of the study, more patients in the intervention practices were recorded as nonsmokers by the end of the study (5.3% vs 1.9% in control practices; P < .001). Among 12 207 documented smokers, more patients in the intervention practices made contact with a cessation counselor (3.9% vs 0.3% in control practices; P < .001). Smokers in the intervention practices were no more likely to be prescribed smoking cessation medication (2% vs 2% in control practices; P = .40).ConclusionThis electronic health record-based intervention improved smoking status documentation and increased counseling assistance to smokers but not the prescription of cessation medication.
| S-EPMC3005286 | biostudies-literature
Project description:Tobacco dependence treatment for hospitalized smokers results in long-term cessation if treatment continues at least 30 days post-discharge. Health information technology may facilitate ongoing tobacco dependence treatment after hospital discharge. To describe the use and impact of a new decision support tool and order set for inpatient physicians, addressing tobacco dependence treatment for hospitalized smokers, embedded in an electronic health record (EHR). In a cluster-randomized trial, 254 physicians were randomized (1:1) to either receive or not receive the decision support tool and order set, which were embedded in the Epic (Madison, WI) EHR used at 2 hospitals in a single city. When an adult patient was admitted to a medical service, an electronic alert appeared if the patient was coded in the EHR as a smoker. For physicians randomized to the intervention, the alert linked to an order set to prescribe tobacco treatment medications and refer the patient to the state tobacco quitline. Additionally, "tobacco use disorder" was added to the patient's problem list, and an e-mail was sent to the patient's primary care provider (PCP). In the control arm, an alert fired with no screen visibility. Generalized estimating equations were used to model the data. Since August 2013, the alert has appeared for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication (35 vs. 29%, P < 0.0001), populate the problem list with tobacco use disorder (41 vs. 2%, P < 0.0001), and make a referral to the state smokers' quitline (30 vs. 0%, P < 0.0001). In addition, intervention physicians sent an e-mail to the patient's PCP 4152 (99%) times. Designing and implementing an order set and alert for tobacco treatment in an EHR is feasible and helps physicians place more orders for tobacco treatment medication, referrals to the state smokers' quitline, and e-mails to patients' PCPs. Data on cessation outcomes are pending.www.ClinicalTrials.gov (NCT01691105).
| S-EPMC5526813 | biostudies-literature
Project description:Composite models that combine medical imaging with electronic medical records (EMR) improve predictive power when compared to traditional models that use imaging alone. The digitization of EMR provides potential access to a wealth of medical information, but presents new challenges in algorithm design and inference. Previous studies, such as Phenome Wide Association Study (PheWAS), have shown that EMR data can be used to investigate the relationship between genotypes and clinical conditions. Here, we introduce Phenome-Disease Association Study to extend the statistical capabilities of the PheWAS software through a custom Python package, which creates diagnostic EMR signatures to capture system-wide co-morbidities for a disease population within a given time interval. We investigate the effect of integrating these EMR signatures with radiological data to improve diagnostic classification in disease domains known to have confounding factors because of variable and complex clinical presentation. Specifically, we focus on two studies: First, a study of four major optic nerve related conditions; and second, a study of diabetes. Addition of EMR signature vectors to radiologically derived structural metrics improves the area under the curve (AUC) for diagnostic classification using elastic net regression, for diseases of the optic nerve. For glaucoma, the AUC improves from 0.71 to 0.83, for intrinsic optic nerve disease it increases from 0.72 to 0.91, for optic nerve edema it increases from 0.95 to 0.96, and for thyroid eye disease from 0.79 to 0.89. The EMR signatures recapitulate known comorbidities with diabetes, such as abnormal glucose, but do not significantly modulate image-derived features. In summary, EMR signatures present a scalable and readily applicable.
| S-EPMC6844192 | biostudies-literature
Project description:Primary care providers often fail to identify patients who are overweight or obese or discuss weight management with them. Electronic health record-based tools may help providers with the assessment and management of overweight and obesity.We describe the design of a trial to examine the effectiveness of electronic health record-based tools for the assessment and management of overweight and obesity among adult primary care patients, as well as the challenges we encountered.We developed several new features within the electronic health record used by primary care practices affiliated with Brigham and Women's Hospital in Boston, MA. These features included (1) reminders to measure height and weight, (2) an alert asking providers to add overweight or obesity to the problem list, (3) reminders with tailored management recommendations, and (4) a Weight Management screen. We then conducted a pragmatic, cluster-randomized controlled trial in 12 primary care practices.We randomized 23 clinical teams ("clinics") within the practices to the intervention group (n?=?11) or the control group (n?=?12). The new features were activated only for clinics in the intervention group. The intervention was implemented in two phases: the height and weight reminders went live on 15 December 2011 (Phase 1), and all of the other features went live on 11 June 2012 (Phase 2). Study enrollment went from December 2011 through December 2012, and follow-up ended in December 2013. The primary outcomes were 6-month and 12-month weight change among adult patients with body mass index ?25 who had a visit at one of the primary care clinics during Phase 2. Secondary outcome measures included the proportion of patients with a recorded body mass index in the electronic health record, the proportion of patients with body mass index ?25 who had a diagnosis of overweight or obesity on the electronic health record problem list, and the proportion of patients with body mass index ?25 who had a follow-up appointment about their weight or were prescribed weight loss medication.We encountered challenges in our development of an intervention within the existing structure of an electronic health record. For example, although we decided to randomize clinics within primary care practices, this decision may have introduced contamination and led to some imbalance of patient characteristics between the intervention and control practices. Using the electronic health record as the primary data source reduced the cost of the study, but not all desired data were recorded for every participant.Despite the challenges, this study should provide valuable information about the effectiveness of electronic health record-based tools for addressing overweight and obesity in primary care.
| S-EPMC4863225 | biostudies-literature
Project description:INTRODUCTION:Staphylococci are the most commonly identified pathogens in bloodstream infections. Identification of Staphylococcus aureus in blood culture (SAB) requires a prompt and adequate clinical management. The detection of coagulase-negative staphylococci (CoNS), however, corresponds to contamination in about 75% of the cases. Nevertheless, antibiotic therapy is often initiated, which contributes to the risk of drug-related side effects. We developed a computerised clinical decision support system (HELP-CDSS) that assists physicians with an appropriate management of patients with Staphylococcus bacteraemia. The CDSS is evaluated using data of the Data Integration Cent ers (DIC) established at each clinic. DICs transform heterogeneous primary clinical data into an interoperable format, and the HELP-CDSS displays information according to current best evidence in bacteraemia treatment. The overall aim of the HELP-CDSS is a safe but more efficient allocation of infectious diseases specialists and an improved adherence to established guidelines in the treatment of SAB. METHODS AND ANALYSIS:The study is conducted at five German university hospitals and is designed as a stepped-wedge cluster randomised trial. Over the duration of 18 months, 135 wards will change from a control period to the intervention period in a randomised stepwise sequence. The coprimary outcomes are hospital mortality for all patients to establish safety, the 90-day disease reoccurrence-free survival for patients with SAB and the cumulative vancomycin use for patients with CoNS bacteraemia. We will use a closed, hierarchical testing procedure and generalised linear mixed modelling to test for non-inferiority of the CDSS regarding hospital mortality and 90-day disease reoccurrence-free survival and for superiority of the HELP-CDSS regarding cumulative vancomycin use. ETHICS AND DISSEMINATION:The study is approved by the ethics committee of Jena University Hospital and will start at each centre after local approval. Results will be published in a peer-reviewed journal and presented at scientific conferences. TRIAL REGISTRATION NUMBER:DRKS00014320.
| S-EPMC7044885 | biostudies-literature
Project description:BACKGROUND:Many patients with or at risk for chronic kidney disease (CKD) in the primary care setting are not receiving recommended care. OBJECTIVE:The objective of this study is to determine whether a multifaceted, low-cost intervention compared with usual care improves the care of patients with or at risk for CKD in the primary care setting. DESIGN:A pragmatic cluster-randomized trial, with an embedded qualitative process evaluation, will be conducted. SETTING:The study population comes from the Electronic Medical Record Administrative data Linked Database®, which includes clinical data for more than 140 000 rostered adults cared for by 194 family physicians in 34 clinics across Ontario, Canada. The 34 primary care clinics will be randomized to the intervention or control group. INTERVENTION:The intervention group will receive resources from the "CKD toolkit" to help improve care including practice audit and feedback, printed educational materials for physicians and patients, electronic decision support and reminders, and implementation support. MEASUREMENTS:Patients with or at risk for CKD within participating clinics will be identified using laboratory data in the electronic medical records. Outcomes will be assessed after dissemination of the CKD tools and after 2 rounds of feedback on performance on quality indicators have been sent to the physicians using information from the electronic medical records. The primary outcome is the proportion of patients aged 50 to 80 years with nondialysis-dependent CKD who are on a statin. Secondary outcomes include process of care measures such as screening tests, CKD recognition, monitoring tests, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker prescriptions, blood pressure targets met, and nephrologist referral. Hierarchical analytic modeling will be performed to account for clustering. Semistructured interviews will be conducted with a random purposeful sample of physicians in the intervention group to understand why the intervention achieved the observed effects. CONCLUSIONS:If our intervention improves care, then the CKD toolkit can be adapted and scaled for use in other primary care clinics which use electronic medical records. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02274298.
| S-EPMC5453629 | biostudies-literature
Project description:Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences.We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC.Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required.The methods are developed for equal-sized clusters, whereas cluster size may vary in practice.The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.
| S-EPMC3237741 | biostudies-other
Project description:ImportancePhysicians frequently use cardiac monitoring, or telemetry, beyond the duration recommended by published practice standards, resulting in "alarm fatigue" and excess cost. Prior studies have demonstrated an association between multicomponent quality improvement interventions and safe reduction of telemetry duration.ObjectiveTo determine if a single-component intervention, a targeted electronic health record (EHR) alert, could achieve similar gains to multicomponent interventions and safely reduce unnecessary monitoring.Design, setting, and participantsThis cluster-randomized clinical trial was conducted between November 2016 and May 2017 on the general medicine service of the Division of Hospital Medicine at the University of California, San Francisco Medical Center and included physicians of 12 inpatient medical teams (6 intervention, 6 control).InterventionsThe EHR alert was randomized to half of the teams on the general medicine service. The alert displayed during daytime hours when physicians attempted to place an order for patients not in the intensive care unit whose telemetry order duration exceeded the recommended duration for a given indication.Main outcomes and measuresThe primary outcome was telemetry monitoring hours per hospitalization, which was measured using time-stamped orders data from the EHR database. Physician responses to the alert were collected using EHR reporting tools. The potential adverse outcomes of rapid-response calls and medical emergency events were measured by counting the notes documenting these events in the EHR.ResultsOf the 1021 patients included in this study, in the intervention arm, there was a mean (SD) age of 64.5 (18.9) and 215 (45%) were women; in the control arm, there was a mean (SD) age of 63.8 (19.1) and 249 (46%) were women. The 12 teams were stratified to 8 house-staff teams and 4 hospitalist teams, with 499 hospitalizations analyzed in the intervention arm and 567 hospitalizations analyzed in the control arm. The alert prompted a significant reduction in telemetry monitoring duration (-8.7 hours per hospitalization; 95% CI, -14.1 to -3.5 hours; P = .001) with no significant change in rapid-response calls or medical emergency events. The most common physician response to the alert was to discontinue telemetry monitoring (62% of 200 alerts).Conclusions and relevanceA targeted EHR alert can safely and successfully reduce cardiac monitoring by prompting discontinuation when appropriate. This single-component electronic intervention is less resource intensive than typical multicomponent interventions that include human resources.Trial registrationClinicalTrials.gov identifier: NCT02529176.
| S-EPMC6583411 | biostudies-literature
Project description:BACKGROUND:Smokers usually abstain from tobacco while hospitalized but relapse after discharge. Inpatient interventions may encourage sustained quitting. We previously demonstrated that a decision support tool embedded in an electronic health record (EHR) improved physicians' treatment of hospitalized smokers. This report describes the effect on quit rates of this decision support tool and order set for hospitalized smokers. METHODS:In a single hospital system, 254 physicians were randomized 1:1 to receive a decision support tool and order set, embedded in the EHR. When an adult patient was admitted to a medical service, an electronic alert appeared if current smoking was recorded in the EHR. For physicians receiving the intervention, the alert linked to an order set for tobacco treatment medications and electronic referral to the state tobacco quitline. Additionally, "Tobacco Use Disorder" was added to the patient's problem list, and a secure message was sent to the patient's primary care provider (PCP). In the control arm, no alert appeared. Patients were contacted by phone at 1, 6, and 12?months; those reporting tobacco abstinence at 12?months were asked to return to measure exhaled carbon monoxide. Generalized estimating equations were used to model the data. RESULTS:From 2013 to 2016, the alert fired for 10,939 patients (5391 intervention, 5548 control). Compared to control physicians, intervention physicians were more likely to order tobacco treatment medication, populate the problem list with tobacco use disorder, refer to the quitline, and notify the patient's PCP. In a subset of 1044 patients recruited for intensive follow-up, one-year quit rates for intervention and control patients were, respectively, 11.5% and 11.6%, (p?=?0.94), after controlling for age, sex, race, ethnicity, and insurance. Similarly, there were no differences in 1- and 6-month quit rates. CONCLUSIONS:Although we were able to improve processes of care, long-term tobacco quit rates were unchanged. This likely reflects, in part, the need for sustained quitting interventions, and higher-than-expected quit rates in controls. Future enhancements should improve prescription of medications for smoking cessation at discharge, engagement of primary care providers, and perhaps direct engagement of patients in a more longitudinal approach. TRIAL REGISTRATION:ClinicalTrials.gov, NCT01691105 . Registered on September 12, 2012.
| S-EPMC6343239 | biostudies-literature
Project description:BackgroundWe sought to systematically standardize the documentation of clinical and laboratory features in Kawasaki disease (KD) on the day of initial treatment and correlate the presentation with clinical outcomes.MethodsKawasaki disease features and classification were documented by the attending physician using a standardized documentation tool on the day of treatment for KD, including confidence in the KD diagnosis on a 4-point scale. Incomplete KD was further classified using American Heart Association (AHA) criteria (sufficient or insufficient) and baseline echocardiogram data. We prospectively recorded intravenous immunoglobulin (IVIG) resistance, coronary artery abnormalities (CAAs), periungual peeling, and retrospectively identified subsequent diagnoses of autoimmune/inflammatory disease.ResultsFrom November 2012 to October, 2015, 162 patients were treated for KD: 105 with complete KD (Group 1), 7 with incomplete KD based on CAAs on day of KD diagnosis (Group 2), 23 with incomplete KD meeting AHA criteria (Group 3), and 27 with incomplete KD and insufficient AHA criteria (Group 4). Group 4 patients had lower baseline median C-reactive protein levels (Group 4 median 4.65 mg/dL [interquartile range {IQR}, 2.3-13.6] vs Group 1 median 8.0 mg/dL [IQR, 4.5-17], Group 2 median 13.9 mg/dL [IQR, 1.4-18.2], Group 3 median 13.3 mg/dL [IQR, 4.9-20.2]), and no coronary abnormalities developed, although 11% had IVIG resistance. Group 4 had higher rates of subsequent autoimmune/inflammatory conditions diagnosed (11.1% in Group 4 vs <5% for all others, P = .02).ConclusionsStandardized documentation and classification of KD features may be useful to correlate with clinical outcomes, including subsequent diagnosis of autoimmune/autoinflammatory disease. Among patients with incomplete KD who did not meet AHA criteria and had a normal baseline echocardiogram, the IVIG resistance rate may have been related to a lower likelihood of an accurate diagnosis of KD.
| S-EPMC7107491 | biostudies-literature