Dataset Information

Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data*. An Agile Framework.

ABSTRACT: BACKGROUND:Creation of a new electronic health record (EHR)-based registry often can be a "one-off" complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch. Such an approach will not scale to meet the emerging demand for specialty registries to support population health and value-based care. OBJECTIVE:To determine if the creation of EHR-based specialty registries could be markedly accelerated by employing (a) a finite core set of EHR data collection principles and methods, (b) concurrent engineering of data extraction and data warehouse design using a common dimensional data model for all registries, and (c) agile development methods commonly employed in new product development. METHODS:We adopted as guiding principles to (a) capture data as a byproduct of care of the patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust set of EHR data capture tool types, and (d) leverage our existing technology toolkit. Registries were defined by a shared condition (recorded on the Problem List) or a shared exposure to a procedure (recorded on the Surgical History) or to a medication (recorded on the Medication List). Any EHR fields needed - either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM) - were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined "grains" from the EHR into the EDW model. All calculated CQM values were stored in a single Fact table in the EDW crossing all registries. Registry-specific dashboards were created in the EHR to display both (a) real-time patient lists of registry patients and (b) EDW-generated CQM data. Agile project management methods were employed, including co-development, lightweight requirements documentation with User Stories and acceptance criteria, and time-boxed iterative development of EHR features in 2-week "sprints" for rapid-cycle feedback and refinement. RESULTS:Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic disease registries, with 111 new EHR data collection and clinical decision support tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting on both real-time patient care gaps and summarized and vetted CQM measure performance trends. CONCLUSIONS:This study suggests concurrent design of EHR data collection tools and reporting can quickly yield useful EHR structured data for chronic disease registries, and bodes well for efforts to migrate away from manual abstraction. This work also supports the view that in new EHR-based registry development, as in new product development, adopting agile principles and practices can help deliver valued, high-quality features early and often.

SUBMITTER: Kannan V

PROVIDER: S-EPMC5608102 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data*. An Agile Framework.

Kannan Vaishnavi V Fish Jason S JS Mutz Jacqueline M JM Carrington Angela R AR Lai Ki K Davis Lisa S LS Youngblood Josh E JE Rauschuber Mark R MR Flores Kathryn A KA Sara Evan J EJ Bhat Deepa G DG Willett DuWayne L DL

Methods of information in medicine 20170614 99

<h4>Background</h4>Creation of a new electronic health record (EHR)-based registry often can be a "one-off" complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The nex ...[more]

PMID: 28930362

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Project description:Importance:Cardiovascular disease is the leading cause of death in the United States. To improve cardiovascular outcomes, primary care must have valid methods of assessing performance on cardiovascular clinical quality measures, including aspirin use (aspirin measure), blood pressure control (BP measure), and smoking cessation counseling and intervention (smoking measure). Objective:To compare observed performance scores measured using 2 imperfect reference standard data sources (medical record abstraction [MRA] and electronic health record [EHR]-generated reports) with misclassification-adjusted performance scores obtained using bayesian latent class analysis. Design, Setting, and Participants:This cross-sectional study used a subset of the 2016 aspirin, BP, and smoking performance data from the Healthy Hearts for Oklahoma Project. Each clinical quality measure was calculated for a subset of a practice's patient population who can benefit from recommended care (ie, the eligible population). A random sample of 380 eligible patients were included for the aspirin measure; 126, for the BP measure; and 115, for the smoking measure. Data were collected from 21 primary care practices belonging to a single large health care system from January 1 to December 31, 2018, and analyzed from February 21 to April 17, 2019. Main Outcomes and Measures:The main outcomes include performance scores for the aspirin, BP, and smoking measures using imperfect MRA and EHRs and estimated through bayesian latent class models. Results:A total of 621 eligible patients were included in the analysis. Based on MRA and EHR data, observed aspirin performance scores were 76.0% (95% bayesian credible interval [BCI], 71.5%-80.1%) and 74.9% (95% BCI, 70.4%-79.1%), respectively; observed BP performance scores, 80.6% (95% BCI, 73.2%-86.9%) and 75.1% (95% BCI, 67.2%-82.1%), respectively; and observed smoking performance scores, 85.7% (95% BCI, 78.6%-91.2%) and 75.4% (95% BCI, 67.0%-82.6%), respectively. Misclassification-adjusted estimates were 74.9% (95% BCI, 70.5%-79.1%) for the aspirin performance score, 75.0% (95% BCI, 66.6%-82.5%) for the BP performance score, and 83.0% (95% BCI, 74.4%-89.8%) for the smoking performance score. Conclusions and Relevance:Ensuring valid performance measurement is critical for value-based payment models and quality improvement activities in primary care. This study found that extracting information for the same individuals using different data sources generated different performance score estimates. Further research is required to identify the sources of these differences.

Project description:ImportanceImplant registries provide valuable information on the performance of implants in a real-world setting, yet they have traditionally been expensive to establish and maintain. Electronic health records (EHRs) are widely used and may include the information needed to generate clinically meaningful reports similar to a formal implant registry.ObjectivesTo quantify the extractability and accuracy of registry-relevant data from the EHR and to assess the ability of these data to track trends in implant use and the durability of implants (hereafter referred to as implant survivorship), using data stored since 2000 in the EHR of the largest integrated health care system in the United States.Design, setting, and participantsRetrospective cohort study of a large EHR of veterans who had 45 351 total hip arthroplasty procedures in Veterans Health Administration hospitals from 2000 to 2017. Data analysis was performed from January 1, 2000, to December 31, 2017.ExposuresTotal hip arthroplasty.Main outcomes and measuresNumber of total hip arthroplasty procedures extracted from the EHR, trends in implant use, and relative survivorship of implants.ResultsA total of 45 351 total hip arthroplasty procedures were identified from 2000 to 2017 with 192 805 implant parts. Data completeness improved over the time. After 2014, 85% of prosthetic heads, 91% of shells, 81% of stems, and 85% of liners used in the Veterans Health Administration health care system were identified by part number. Revision burden and trends in metal vs ceramic prosthetic femoral head use were found to reflect data from the American Joint Replacement Registry. Recalled implants were obvious negative outliers in implant survivorship using Kaplan-Meier curves.Conclusions and relevanceAlthough loss to follow-up remains a challenge that requires additional attention to improve the quantitative nature of calculated implant survivorship, we conclude that data collected during routine clinical care and stored in the EHR of a large health system over 18 years were sufficient to provide clinically meaningful data on trends in implant use and to identify poor implants that were subsequently recalled. This automated approach was low cost and had no reporting burden. This low-cost, low-overhead method to assess implant use and performance within a large health care setting may be useful to internal quality assurance programs and, on a larger scale, to postmarket surveillance of implant performance.

Project description:ObjectivesTo identify patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) in clinical quality registries, for people with cutaneous melanoma, to inform a new Australian Melanoma Clinical Outcomes Registry; and describe opportunities and challenges of routine PROM/PREM collection, especially in primary care.DesignSystematic review.Primary and secondary outcome measuresWhich PROMs and PREMs are used in clinical quality registries for people with cutaneous melanoma, how they are collected, frequency of collection, participant recruitment methods and funding models for each registry.Results1134 studies were identified from MEDLINE, PreMEDLINE, Embase, PsychInfo, Cochrane Database of Abstracts of Reviews of Effects databases and TUFTS Cost-Effectiveness Analysis Registry, alongside grey literature, from database inception to 5th February 2020. Following screening, 14 studies were included, identifying four relevant registries: Dutch Melanoma Registry, Adelphi Real-World Disease-Specific Programme (Melanoma), Patient-Reported Outcomes Following Initial treatment and Long-term Evaluation of Survivorship Registry, and Cancer Experience Registry. These used seven PROMs: EuroQol-5 Dimensions, Functional Assessment of Cancer-General (FACT-G) and FACT-Melanoma (FACT-M), European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Cancer 30 (EORTC QLQ-C30), Fatigue Assessment Scale Hospital Anxiety and Depression Scale, Patient-Reported Outcome Measures Information System-29 and one PREM; EORTC QLQ-Information Module 26. PROMs/PREMs in registries were reported to improve transparency of care; facilitate clinical auditing for quality assessment; enable cost-effectiveness analyses and create large-scale research platforms. Challenges included resource burden for data entry and potential collection bias toward younger, more affluent respondents. Feedback from patients with melanoma highlighted the relevance of PROMs/PREMs in assessing patient outcomes and patient experiences.ConclusionsClinical registries indicate PROMs/PREMs for melanoma care can be incorporated and address important gaps, however cost and collection bias may limit generalisability.Prospero registration numberCRD42018086737.

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Dataset Information

Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data*. An Agile Framework.

Publications

Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data*. An Agile Framework.

Similar Datasets

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