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Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.

ABSTRACT: Objectives:Data derived from primary care electronic medical records (EMRs) are being used for research and surveillance. Case definitions are required to identify patients with specific conditions in EMR data with a degree of accuracy. The purpose of this study is to identify and provide a summary of case definitions that have been validated in primary care EMR data. Materials and Methods:We searched MEDLINE and Embase (from inception to June 2016) to identify studies that describe case definitions for clinical conditions in EMR data and report on the performance metrics of these definitions. Results:We identified 40 studies reporting on case definitions for 47 unique clinical conditions. The studies used combinations of International Classification of Disease version 9 (ICD-9) codes, Read codes, laboratory values, and medications in their algorithms. The most common validation metric reported was positive predictive value, with inconsistent reporting of sensitivity and specificity. Discussion:This review describes validated case definitions derived in primary care EMR data, which can be used to understand disease patterns and prevalence among primary care populations. Limitations include incomplete reporting of performance metrics and uncertainty regarding performance of case definitions across different EMR databases and countries. Conclusion:Our review found a significant number of validated case definitions with good performance for use in primary care EMR data. These could be applied to other EMR databases in similar contexts and may enable better disease surveillance when using clinical EMR data. Consistent reporting across validation studies using EMR data would facilitate comparison across studies. Systematic review registration:PROSPERO CRD42016040020 (submitted June 8, 2016, and last revised June 14, 2016).

SUBMITTER: McBrien KA

PROVIDER: S-EPMC7646917 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.

McBrien Kerry A KA Souri Sepideh S Symonds Nicola E NE Rouhi Azin A Lethebe Brendan C BC Williamson Tyler S TS Garies Stephanie S Birtwhistle Richard R Quan Hude H Fabreau Gabriel E GE Ronksley Paul E PE

Journal of the American Medical Informatics Association : JAMIA 20181101 11

<h4>Objectives</h4>Data derived from primary care electronic medical records (EMRs) are being used for research and surveillance. Case definitions are required to identify patients with specific conditions in EMR data with a degree of accuracy. The purpose of this study is to identify and provide a summary of case definitions that have been validated in primary care EMR data.<h4>Materials and methods</h4>We searched MEDLINE and Embase (from inception to June 2016) to identify studies that descri ...[more]

PMID: 30137498

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Project description:BackgroundElectronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.ObjectiveThis review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.MethodsA scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.ResultsA total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule-based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.ConclusionsRecognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Project description:BACKGROUND:Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems. OBJECTIVE:This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses. METHODS:We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes. RESULTS:A total of 125 condition-defining "standard" SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th-75th percentiles: 1-5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th-75th percentiles: 8-85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th-75th percentiles: 63-976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs. CONCLUSION:SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, "smarter" EHR feature configuration, and clinical-translational research employing EHR-derived data.

Project description:BackgroundTo date, there has been no validated method to identify cases of pelvic floor disorders in primary care electronic medical record (EMR) data. We aimed to develop and validate symptom-based case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse in women, for use in primary care epidemiologic or clinical research.MethodsOur retrospective study used EMR data from the Southern Alberta Primary Care Research Network (SAPCReN) and the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in southern Alberta. Trained researchers remotely reviewed a random sample of EMR charts of women aged 18 years or older from 6 rural and urban clinics to validate case definitions for urinary incontinence, fecal incontinence and pelvic organ prolapse. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), and estimated SAPCReN prevalence as appropriate.ResultsCharts of 900 women were included. Sensitivity was 81.9% (95% confidence interval [CI] 75.1-87.2) for urinary incontinence, 61.2% (95% CI 46.2-74.5) for fecal incontinence, and 51.8% (95% CI 40.6-62.8) for pelvic organ prolapse. Corresponding specificity values were 71.9% (95% CI 68.4-75.1), 99.2% (95% CI 98.2-99.6) and 98.8% (95% CI 97.7-99.4), PPVs 40.6% (95% CI 35.4-46.0), 81.1% (95% CI 64.3-91.4) and 81.1% (95% CI 67.6-90.1), and NPVs 94.4% (95% CI 92.1-96.1), 97.8% (95% CI 96.5-98.6) and 95.3% (95% CI 93.6-96.6). The SAPCReN-observed prevalence for urinary incontinence was 29.7% (95% CI 29.3-30.0), but the adjusted prevalence was 2.97%.InterpretationThe case definition for urinary incontinence met our standard for validity (sensitivity and specificity > 70%), and the case definitions for fecal incontinence and pelvic organ prolapse had PPVs greater than 80%. The urinary incontinence definition may be used in epidemiologic research, and those for fecal incontinence and pelvic organ prolapse may be used in quality-improvement studies or creation of disease registries. Our symptom-based case definitions could also be adapted for research in other EMR settings.

Project description:BackgroundInterventional studies are the fundamental method for obtaining answers to clinical questions. However, these studies are sometimes difficult to conduct because of insufficient financial or human resources or the rarity of the disease in question. One means of addressing these issues is to conduct a non-interventional observational study using electronic health record (EHR) databases as the data source, although how best to evaluate the suitability of an EHR database when planning a study remains to be clarified. The aim of the present study is to identify and characterize the data sources that have been used for conducting non-interventional observational studies in Japan and propose a flow diagram to help researchers determine the most appropriate EHR database for their study goals.MethodsWe compiled a list of published articles reporting observational studies conducted in Japan by searching PubMed for relevant articles published in the last 3 years and by searching database providers' publication lists related to studies using their databases. For each article, we reviewed the abstract and/or full text to obtain information about data source, target disease or therapeutic area, number of patients, and study design (prospective or retrospective). We then characterized the identified EHR databases.ResultsIn Japan, non-interventional observational studies have been mostly conducted using data stored locally at individual medical institutions (663/1511) or collected from several collaborating medical institutions (315/1511). Whereas the studies conducted with large-scale integrated databases (330/1511) were mostly retrospective (73.6%), 27.5% of the single-center studies, 47.6% of the multi-center studies, and 73.7% of the post-marketing surveillance studies, identified in the present study, were conducted prospectively. We used our findings to develop an assessment flow diagram to assist researchers in evaluating and choosing the most suitable EHR database for their study goals.ConclusionsOur analysis revealed that the non-interventional observational studies were conducted using data stored local at individual medical institutions or collected from collaborating medical institutions in Japan. Disease registries, disease databases, and large-scale databases would enable researchers to conduct studies with large sample sizes to provide robust data from which strong inferences could be drawn.

Project description:BACKGROUND:The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. METHODS:Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. RESULTS:A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). CONCLUSIONS:This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

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Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.

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Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.

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