Dataset Information

Effects of an Electronic Medical Record Intervention on Appropriateness of Transthoracic Echocardiograms: A Prospective Study.

ABSTRACT:

Background

Transthoracic echocardiograms (TTEs) account for approximately half of U.S. spending on cardiac imaging. We developed an electronic medical record (EMR)-based decision-support algorithm for TTE ordering and hypothesized that it would increase the appropriateness of TTE orders.

Methods

This prospective observational study was performed at the Veterans Affairs Ann Arbor Healthcare System. From October to December 2016 (preintervention), consecutive TTEs ordered in the inpatient, outpatient, and emergency department settings were included. In May 2017, a decision-support algorithm was incorporated into the EMR, giving immediate feedback to providers. Chart review was performed for TTEs ordered from June to August 2017 (early intervention) and from June to August 2018 (late intervention). Appropriateness was determined based on the 2011 appropriate use criteria for echocardiography.

Results

Appropriate TTE orders increased from 87.6% preintervention to 94.5% at early intervention (z = 0.00018) but decreased to 90.0% at late intervention (z = 0.51, compared with preintervention). Among patients with no previous TTEs in our system, 95.3% of TTEs were appropriate, compared with 87.7% of TTEs for patients with prior TTEs within 30 days prior (odds ratio = 2.85; 95% CI, 1.18-6.31; P = .005).

Conclusions

The EMR algorithm initially increased the percentage of appropriate TTEs, but this effect decayed over time. Further study is needed to develop EMR-based interventions that will have lasting impacts on provider ordering patterns.

SUBMITTER: Chen W

PROVIDER: S-EPMC7955652 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Effects of an Electronic Medical Record Intervention on Appropriateness of Transthoracic Echocardiograms: A Prospective Study.

Chen Weihan W Saxon David T DT Henry Michael P MP Herald John R JR Holleman Rob R Zawol Debbie D Sivils Stacy S Kenaan Mohamad A MA Kolias Theodore J TJ Gurm Hitinder S HS Bhave Nicole M NM

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 20201101 2

<h4>Background</h4>Transthoracic echocardiograms (TTEs) account for approximately half of U.S. spending on cardiac imaging. We developed an electronic medical record (EMR)-based decision-support algorithm for TTE ordering and hypothesized that it would increase the appropriateness of TTE orders.<h4>Methods</h4>This prospective observational study was performed at the Veterans Affairs Ann Arbor Healthcare System. From October to December 2016 (preintervention), consecutive TTEs ordered in the inp ...[more]

PMID: 33139140

Similar Datasets

Project description:BackgroundThe rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs.ObjectiveThe aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes.MethodsData were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality.ResultsThe EWS algorithm scored patients' daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS.ConclusionsIn this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients' better health outcomes in target medical facilities.

Project description:BackgroundElectronic medical record (EMR) chronic disease measurement can help direct primary care prevention and treatment strategies and plan health services resource management. Incomplete data and poor consistency of coded disease values within EMR problem lists are widespread issues that limit primary and secondary uses of these data. These issues were shared by the McMaster University Sentinel and Information Collaboration (MUSIC), a primary care practice-based research network (PBRN) located in Hamilton, Ontario, Canada.ObjectiveWe sought to develop and evaluate the effectiveness of new EMR interface tools aimed at improving the quantity and the consistency of disease codes recorded within the disease registry across the MUSIC PBRN.MethodsWe used a single-arm prospective trial design with preintervention and postintervention data analysis to assess the effect of the intervention on disease recording volume and quality. The MUSIC network holds data on over 75,080 patients, 37,212 currently rostered. There were 4 MUSIC network clinician champions involved in gap analysis of the disease coding process and in the iterative design of new interface tools. We leveraged terminology standards and factored EMR workflow and usability into a new interface solution that aimed to optimize code selection volume and quality while minimizing physician time burden. The intervention was integrated as part of usual clinical workflow during routine billing activities.ResultsAfter implementation of the new interface (June 25, 2017), we assessed the disease registry codes at 3 and 6 months (intervention period) to compare their volume and quality to preintervention levels (baseline period). A total of 17,496 International Classification of Diseases, 9th Revision (ICD9) code values were recorded in the disease registry during the 11.5-year (2006 to mid-2017) baseline period. A large gain in disease recording occurred in the intervention period (8516/17,496, 48.67% over baseline), resulting in a total of 26,774 codes. The coding rate increased by a factor of 11.2, averaging 1419 codes per month over the baseline average rate of 127 codes per month. The proportion of preferred ICD9 codes increased by 17.03% in the intervention period (11,007/17,496, 62.91% vs 7417/9278, 79.94%; χ21=819.4; P<.001). A total of 45.03% (4178/9278) of disease codes were entered by way of the new screen prompt tools, with significant increases between quarters (Jul-Sep: 2507/6140, 40.83% vs Oct-Dec: 1671/3148, 53.08%; χ21=126.2; P<.001).ConclusionsThe introduction of clinician co-designed, workflow-embedded disease coding tools is a very effective solution to the issues of poor disease coding and quality in EMRs. The substantial effectiveness in a routine care environment demonstrates usability, and the intervention detail described here should be generalizable to any setting. Significant improvements in problem list coding within primary care EMRs can be realized with minimal disruption to routine clinical workflow.

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.

Dataset Information

Effects of an Electronic Medical Record Intervention on Appropriateness of Transthoracic Echocardiograms: A Prospective Study.

Background

Methods

Results

Conclusions

Publications

Effects of an Electronic Medical Record Intervention on Appropriateness of Transthoracic Echocardiograms: A Prospective Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets