Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components: the front end, where the data is entered by healthcare workers including physicians and nurses, and the back-end electronic data warehouse where the data is stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), and analytical capacities and requirements associated with using EHRs.
Background Electronic 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. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A 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. Results A 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. Conclusions Recognizing 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.
ObjectiveTo evaluate the validity of COVID-19 International Classification of Diseases, 10th Revision (ICD-10) codes and their combinations.DesignRetrospective cohort study.SettingAcute care hospitals and emergency departments (EDs) in Alberta, Canada.ParticipantsPatients who were admitted to hospital or presented to an ED in Alberta, as captured by local administrative databases between 1 March 2020 and 28 February 2021, who had a positive COVID-19 test and/or a COVID-19-related ICD-10 code.Main outcome measuresThe sensitivity, positive predictive value (PPV) and 95% CIs for ICD-10 codes were computed. Stratified analysis on age group, sex, symptomatic status, mechanical ventilation, hospital type, patient intensive care unit (ICU) admission, discharge status and season of pandemic were conducted.ResultsTwo overlapping subsets of the study population were considered: those who had a positive COVID-19 test (cohort A, for estimating sensitivity) and those who had a COVID-19-related ICD-10 code (cohort B, for estimating PPV). Cohort A included 17 979 ED patients and 6477 inpatients while cohort B included 33 675 ED patients and 18 746 inpatients. Of inpatients, 9.5% in cohort A and 8.1% in cohort B received mechanical ventilation. Over 13% of inpatients were admitted to ICU. The length of hospital stay was 6 days (IQR: 3–14) for cohort A and 8 days (IQR: 3–19) for cohort B. In-hospital mortality was 15.9% and 38.8% for cohort A and B, respectively. The sensitivity for ICD-10 code U07.1 (COVID-19, virus identified) was 82.5% (81.8%–83.2%) with a PPV of 93.1% (92.6%–93.6%). The combination of U07.1 and U07.3 (multisystem inflammatory syndrome associated with COVID-19) had a sensitivity of 82.5% (81.9%–83.2%) and PPV of 92.9% (92.4%–93.4%).ConclusionsIn Alberta, ICD-10 COVID-19 codes (U07.1 and U07.3) were coded well with high validity. This indicates administrative data can be used for COVID-19 research and pandemic management purposes.
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