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Estimating Generic Drug Use with Electronic Health Records Data from a Health Care Delivery System: Implications for Quality Improvement and Research.

ABSTRACT: BACKGROUND:Generic drug use in the outpatient setting is typically measured with adjudicated pharmacy claims; however, not all delivery systems have access to these data for their clinical populations. OBJECTIVE:To develop an algorithm to estimate generic drug use in an outpatient setting using electronic health records (EHR) data. METHODS:Twenty-five therapeutic classes were chosen with the potential for low generic use that were prescribed to managed care beneficiaries in a health care system in Northern California. An algorithm was developed to estimate generic drug use based on medication names and dispense-as-written requests from electronic prescriptions, as well as information on generic availability at the time the prescriptions were written. The algorithm was used to quantify a generic utilization rate (GUR) across therapeutic classes and was validated by comparing the estimated GUR to the true GUR, using pharmacy claims corresponding to prescriptions in the same patient cohort. RESULTS:Among managed care beneficiaries, 104,859 prescriptions were identified for drugs in the therapeutic classes of interest with corresponding pharmacy claims. The algorithm estimated a GUR of 73.7% across 25 unique classes. The actual GUR based on pharmacy claims was 73.1%. Sensitivity (97%) and specificity (89%) of the algorithm were high, and total percentage of agreement was 95%. CONCLUSIONS:An algorithm that estimates generic drug use performed well in a population of managed care beneficiaries. Health care delivery systems may apply methods described in this article to quantify generic drug use in their ambulatory populations for quality improvement and research initiatives, particularly when pharmacy claims are unavailable. DISCLOSURES:This study was funded by a grant from the U.S. Food and Drug Administration in cooperative agreement with the Johns Hopkins School of Medicine and the Palo Alto Medical Foundation Research Institute (1U01FD005267-01). Romanelli has received research grant support from Pfizer and Janssen Scientific Affairs. Authors have no other conflicts to disclose. Romanelli and Segal contributed the study concept and design. Nimbal took the lead in data collection, assisted by Romanelli. All authors were involved with data interpretation and revision of the manuscript. The manuscript was written by Romanelli and Nimbal.

SUBMITTER: Nimbal V

PROVIDER: S-EPMC7258177 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Estimating Generic Drug Use with Electronic Health Records Data from a Health Care Delivery System: Implications for Quality Improvement and Research.

Nimbal Vani V Segal Jodi B JB Romanelli Robert J RJ

Journal of managed care & specialty pharmacy 20161001 10

<h4>Background</h4>Generic drug use in the outpatient setting is typically measured with adjudicated pharmacy claims; however, not all delivery systems have access to these data for their clinical populations.<h4>Objective</h4>To develop an algorithm to estimate generic drug use in an outpatient setting using electronic health records (EHR) data.<h4>Methods</h4>Twenty-five therapeutic classes were chosen with the potential for low generic use that were prescribed to managed care beneficiaries in ...[more]

PMID: 27668562

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Project description:BackgroundElectronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.ObjectiveThe objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.Research designA retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.SubjectsA total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.MeasuresPercentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.ResultsAttrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.ConclusionThis study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

Project description:Use of the electronic health record (EHR) has become a routine part of perioperative care in the United States. Secondary use of EHR data includes research, quality, and educational initiatives. Fundamental to secondary use is a framework to ensure fidelity, transparency, and completeness of the source data. In developing this framework, competing priorities must be considered as to which data sources are used and how data are organized and incorporated into a useable format. In assembling perioperative data from diverse institutions across the United States and Europe, the Multicenter Perioperative Outcomes Group (MPOG) has developed methods to support such a framework. This special article outlines how MPOG has approached considerations of data structure, validation, and accessibility to support multicenter integration of perioperative EHRs. In this multicenter practice registry, MPOG has developed processes to extract data from the perioperative EHR; transform data into a standardized format; and validate, deidentify, and transfer data to a secure central Coordinating Center database. Participating institutions may obtain access to this central database, governed by quality and research committees, to inform clinical practice and contribute to the scientific and clinical communities. Through a rigorous and standardized approach to ensure data integrity, MPOG enables data to be usable for quality improvement and advancing scientific knowledge. As of March 2019, our collaboration of 46 hospitals has accrued 10.7 million anesthesia records with associated perioperative EHR data across heterogeneous vendors. Facilitated by MPOG, each site retains access to a local repository containing all site-specific perioperative data, distinct from source EHRs and readily available for local research, quality, and educational initiatives. Through committee approval processes, investigators at participating sites may additionally access multicenter data for similar initiatives. Emerging from this work are 4 considerations that our group has prioritized to improve data quality: (1) data should be available at the local level before Coordinating Center transfer; (2) data should be rigorously validated against standardized metrics before use; (3) data should be curated into computable phenotypes that are easily accessible; and (4) data should be collected for both research and quality improvement purposes because these complementary goals bolster the strength of each endeavor.

Project description:BackgroundParticipant recruitment, especially for frail, elderly, hospitalized patients, remains one of the greatest challenges for many research groups. Traditional recruitment methods such as chart reviews are often inefficient, low-yielding, time consuming, and expensive. Best Practice Alert (BPA) systems have previously been used to improve clinical care and inform provider decision making, but the system has not been widely used in the setting of clinical research.ObjectiveThe primary objective of this quality-improvement initiative was to develop, implement, and refine a silent Best Practice Alert (sBPA) system that could maximize recruitment efficiency.MethodsThe captured duration of the screening sessions for both methods combined with the allotted research coordinator hours in the Emerald-COPD (chronic obstructive pulmonary disease) study budget enabled research coordinators to estimate the cost-efficiency.ResultsPrior to implementation, the sBPA system underwent three primary stages of development. Ultimately, the final iteration produced a system that provided similar results as the manual Epic Reporting Workbench method of screening. A total of 559 potential participants who met the basic prescreen criteria were identified through the two screening methods. Of those, 418 potential participants were identified by both methods simultaneously, 99 were identified only by the Epic Reporting Workbench Method, and 42 were identified only by the sBPA method. Of those identified by the Epic Reporting Workbench, only 12 (of 99, 12.12%) were considered eligible. Of those identified by the sBPA method, 30 (of 42, 71.43%) were considered eligible. Using a side-by-side comparison of the sBPA and the traditional Epic Reporting Workbench method of screening, the sBPA screening method was shown to be approximately four times faster than our previous screening method and estimated a projected 442.5 hours saved over the duration of the study. Additionally, since implementation, the sBPA system identified the equivalent of three additional potential participants per week.ConclusionsAutomation of the recruitment process allowed us to identify potential participants in real time and find more potential participants who meet basic eligibility criteria. sBPA screening is a considerably faster method that allows for more efficient use of resources. This innovative and instrumental functionality can be modified to the needs of other research studies aiming to use the electronic medical records system for participant recruitment.

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Estimating Generic Drug Use with Electronic Health Records Data from a Health Care Delivery System: Implications for Quality Improvement and Research.

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Estimating Generic Drug Use with Electronic Health Records Data from a Health Care Delivery System: Implications for Quality Improvement and Research.

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