Dataset Information

Electronic clinical quality measure reporting challenges: findings from the Medicare EHR Incentive Program's Controlling High Blood Pressure Measure.

ABSTRACT: Objective:To identify physician and practice characteristics associated with high clinical and technical performance on the electronic clinical quality measure (eCQM) that calculates the proportion of patients with hypertension who have controlled blood pressure. Materials and Methods:The study included 268?602 physicians participating in the Medicare Electronic Health Record Incentive Program between 2011 and 2014. Independent variables included delivery reform participation and physician, practice level, and area characteristics. Successful technical performance was a reported eCQM with non-zero values in both the numerator and denominator. Successful clinical performance was a reported eCQM value of ?70% hypertension control. Results:Physicians with longer experience using certified health information technology, participants in delivery reform programs, and specialists that traditionally manage hypertension were 5%-15% more likely to achieve 70% control. Physicians in smaller and rural practices and a subset of physicians unlikely to primarily manage hypertension were more likely to submit measures with a zero value in either the numerator or denominator. Discussion:More physicians are using eCQMs to track and report their quality improvement efforts. This research presents the first examination of national eCQM data to identify physician and practice-level characteristics associated with performance. Conclusion:With careful selection of measures relevant to the clinician's specialty, complete data entry, and support for continuous quality improvement, health care professionals can excel technically and clinically. As care delivery transitions from fee-for-service to quality- and value-based models, high performers may realize financial gains and better patient outcomes. These analyses suggest patterns that may inform steps to improve performance.

SUBMITTER: Heisey-Grove DM

PROVIDER: S-EPMC7647130 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Electronic clinical quality measure reporting challenges: findings from the Medicare EHR Incentive Program's Controlling High Blood Pressure Measure.

Heisey-Grove Dawn M DM Wall Hilary K HK Wright Janet S JS

Journal of the American Medical Informatics Association : JAMIA 20180201 2

<h4>Objective</h4>To identify physician and practice characteristics associated with high clinical and technical performance on the electronic clinical quality measure (eCQM) that calculates the proportion of patients with hypertension who have controlled blood pressure.<h4>Materials and methods</h4>The study included 268 602 physicians participating in the Medicare Electronic Health Record Incentive Program between 2011 and 2014. Independent variables included delivery reform participation and ...[more]

PMID: 28525558

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Project description:IntroductionEffective electronic health record (EHR)-based training interventions facilitate improved EHR use for healthcare providers. One such training intervention is simulation-based training that emphasises learning actual tasks through experimentation in a risk-free environment without negative patient outcomes. EHR-specific simulation-based training can be employed to improve EHR use, thereby enhancing healthcare providers' skills and behaviours. Despite the potential advantages of this type of training, no study has identified and mapped the available evidence. To fill that gap, this scoping review will synthesise the current state of literature on EHR simulation-based training.Methods and analysisThe Arksey and O'Malley methodological framework will be employed. Three databases (PubMed, Embase and Cumulative Index to Nursing and Allied Health Literature) will be searched for published articles. ProQuest and Google Scholar will be searched to identify unpublished articles. Databases will be searched from inception to 29 January 2020. Only articles written in English, randomised control trials, cohort studies, cross-sectional studies and case-control studies will be considered for inclusion. Two reviewers will independently screen titles and abstracts against inclusion and exclusion criteria. Then, they will review full texts to determine articles for final inclusion. Citation chaining will be conducted to manually screen references of all included studies to identify additional studies not found by the search. A data abstraction form with relevant characteristics will be developed to help address the research question. Descriptive numerical analysis will be used to describe characteristics of included studies. Based on the extracted data, research evidence of EHR simulation-based training will be synthesised.Ethics and disseminationSince no primary data will be collected, there will be no formal ethical review. Research findings will be disseminated through publications, presentations and meetings with relevant stakeholders.

Project description:ObjectiveTo understand if providers who had additional electronic health record (EHR) training improved their satisfaction, decreased personal EHR-use time, and decreased turnaround time on tasks.Materials and methodsThis pre-post study with no controls evaluated the impact of a supplemental EHR training program on a group of academic and community practice clinicians that previously had go-live group EHR training and 20 months experience using this EHR on self-reported data, calculated EHR time, and vendor-reported metrics.ResultsProviders self-reported significant improvements in their knowledge of efficiency tools in the EHR after training and doubled (significant) their preference list entries (mean pre = 38.1 [65.88], post = 63.5 [90.47], P < .01). Of the 7 EHR satisfaction variables, only 1 self-reported variable significantly improved after training: Control over my workload in the EHR (mean pre = 2.7 [0.96], post = 3.0 [1.04], P < .01). There was no significant decrease in their calculated EHR usage outside of clinic (mean pre = 0.39 [0.77] to post = 0.37 [0.48], P = .73). No significant difference was seen in turnaround time for patient calls (mean pre = 2.3 [2.06] days, post = 1.9 [1.76] days, P = .08) and results (mean before = 4.0 [2.79] days, after = 3.2 [2.33] days, P = .03).DiscussionMultiple sources of data provide a holistic view of the provider experience in the EHR. This study suggests that individualized EHR training can improve the knowledge of EHR tools and satisfaction with their perceived control of EHR workload, however this did not translate into less Clinician Logged-In Outside Clinic (CLOC) time, a calculated metric, nor quicker turnaround on in box tasks. CLOC time emerged as a potential less-costly surrogate metric for provider satisfaction in EHR work than surveying clinicians. Further study is required to understand the cost-benefit of various interventions to decrease CLOC time.ConclusionsThis supplemental EHR training session, 20 months post go-live, where most participants elected to receive 2 or fewer sessions did significantly improve provider satisfaction with perceived control over their workload in the EHR, but it was not effective in decreasing EHR-use time outside of clinic. CLOC time, a calculated metric, could be a practical trackable surrogate for provider satisfaction (inverse correlation) with after-hours time spent in the EHR. Further study into interventions that decrease CLOC time and improve turnaround time to respond to inbox tasks are suggested next steps.

Project description:BackgroundThe predominant implementation paradigm of electronic health record (EHR) systems in low- and middle-income countries (LMICs) relies on standalone system installations at facilities. This implementation approach exacerbates the digital divide, with facilities in areas with inadequate electrical and network infrastructure often left behind. Mobile health (mHealth) technologies have been implemented to extend the reach of digital health, but these systems largely add to the problem of siloed patient data, with few seamlessly interoperating with the EHR systems that are now scaled nationally in many LMICs. Robust mHealth applications that effectively extend EHR systems are needed to improve access, improve quality of care, and ameliorate the digital divide.ObjectiveWe report on the development and scaled implementation of mUzima, an mHealth extension of the most broadly deployed EHR system in LMICs (OpenMRS).MethodsThe "Guidelines for reporting of health interventions using mobile phones: mobile (mHealth) evidence reporting assessment (mERA)" checklist was employed to report on the mUzima application. The World Health Organization (WHO) Principles for Digital Development framework was used as a secondary reference framework. Details of mUzima's architecture, core features, functionalities, and its implementation status are provided to highlight elements that can be adapted in other systems.ResultsmUzima is an open-source, highly configurable Android application with robust features including offline management, deduplication, relationship management, security, cohort management, and error resolution, among many others. mUzima allows providers with lower-end Android smartphones (version 4.4 and above) who work remotely to access historical patient data, collect new data, view media, leverage decision support, conduct store-and-forward teleconsultation, and geolocate clients. The application is supported by an active community of developers and users, with feature priorities vetted by the community. mUzima has been implemented nationally in Kenya, is widely used in Rwanda, and is gaining scale in Uganda and Mozambique. It is disease-agnostic, with current use cases in HIV, cancer, chronic disease, and COVID-19 management, among other conditions. mUzima meets all WHO's Principles of Digital Development, and its scaled implementation success has led to its recognition as a digital global public good and its listing in the WHO Digital Health Atlas.ConclusionsGreater emphasis should be placed on mHealth applications that robustly extend reach of EHR systems within resource-limited settings, as opposed to siloed mHealth applications. This is particularly important given that health information exchange infrastructure is yet to mature in many LMICs. The mUzima application demonstrates how this can be done at scale, as evidenced by its adoption across multiple countries and for numerous care domains.

Dataset Information

Electronic clinical quality measure reporting challenges: findings from the Medicare EHR Incentive Program's Controlling High Blood Pressure Measure.

Publications

Electronic clinical quality measure reporting challenges: findings from the Medicare EHR Incentive Program's Controlling High Blood Pressure Measure.

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