Because the collection of mental health information through interviews is expensive and time consuming, interest in using population-based administrative health data to conduct research on depression has increased. However, there is concern that misclassification of disease diagnosis in the underlying data might bias the results. Our objective was to determine the validity of International Classification of Disease (ICD)-9 and ICD-10 administrative health data case definitions for depression using review of family physician (FP) charts as the reference standard. Trained chart reviewers reviewed 3362 randomly selected charts from years 2001 and 2004 at 64 FP clinics in Alberta (AB) and British Columbia (BC), Canada. Depression was defined as presence of either: 1) documentation of major depressive episode, or 2) documentation of specific antidepressant medication prescription plus recorded depressed mood. The charts were linked to administrative data (hospital discharge abstracts and physician claims data) using personal health numbers. Validity indices were estimated for six administrative data definitions of depression using three years of administrative data. Depression prevalence by chart review was 15.9–19.2% depending on year, region, and province. An ICD administrative data definition of ‘2 depression claims with depression ICD codes within a one-year window OR 1 discharge abstract data (DAD) depression diagnosis’ had the highest overall validity, with estimates being 61.4% for sensitivity, 94.3% for specificity, 69.7% for positive predictive value, and 92.0% for negative predictive value. Stratification of the validity parameters for this case definition showed that sensitivity was fairly consistent across groups, however the positive predictive value was significantly higher in 2004 data compared to 2001 data (78.8 and 59.6%, respectively), and in AB data compared to BC data (79.8 and 61.7%, respectively). Sensitivity of the case definition is often moderate, and specificity is often high, possibly due to undercoding of depression. Limitations to this study include the use of FP charts data as the reference standard, given the potential for missed or incorrect depression diagnoses. These results suggest that that administrative data can be used as a source of information for both research and surveillance purposes, while remaining aware of these limitations.

Background: Electronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research. Objective: The objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences. Methods: We used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results. Results: We present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized. Conclusions: The Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Background and aims: We report on a cost analysis study, using population level data to determine the emergency service costs avoided from emergency overdose management at supervised consumption services (SCS). Design: We completed a cost analysis from a payer’s perspective. In this setting, there is a single-payer model of service delivery. Setting: In Calgary, Canada, ‘Safeworks Harm Reduction Program’ was established in late 2017 and offers 24/7 access to SCS. The facility is a nurse-led service, available for client drop-in. We conducted a cost analysis for the entire duration of the program from November 2017 to January 2020, a period of 2 years and 3 months. Methods: We assessed costs using the following factors from government health databases: monthly operational costs of providing services for drug consumption, cost of providing ambulance pre-hospital care for clients with overdoses who could not be revived at the facility, cost of initial treatment in an emergency department, and benefit of costs averted from overdoses that were successfully managed at the SCS. Results: The proportion of clients who have overdosed at the SCS has decreased steadily for the duration of the program. The number of overdoses that can be managed on site at the SCS has trended upward, currently 98%. Each overdose that is managed at the SCS produces approximately $1600 CAD in cost savings, with a savings of over $2.3 million for the lifetime of the program. Conclusion: Overdose management at an SCS creates cost savings by offsetting costs required for managing overdoses using emergency department and pre-hospital ambulance services.

Background: The International Classification of Diseases (ICD) is the reference standard for reporting diseases and health conditions globally. Variations in ICD use and data collection across countries can hinder meaningful comparisons of morbidity data. Thus, we aimed to characterize ICD and hospital morbidity data collection features worldwide. Methods: An online questionnaire was created to poll the World Health Organization (WHO) member countries that were using ICD. The survey included questions focused on ICD meta-features and hospital data collection systems, and was distributed via SurveyMonkey using purposive and snowball sampling. Accordingly, senior representatives from organizations specialized in the topic, such as WHO Collaborating Centers, and other experts in ICD coding were invited to fill out the survey and forward the questionnaire to their peers. Answers were collated by country, analyzed, and presented in a narrative form with descriptive analysis. Results: Responses from 47 participants were collected, representing 26 different countries using ICD. Results indicated worldwide disparities in the ICD meta-features regarding the maximum allowable coding fields for diagnosis, the definition of main condition, and the mandatory type of data fields in the hospital morbidity database. Accordingly, the most frequently reported answers were “reason for admission” as main condition definition (n = 14), having 31 or more diagnostic fields available (n = 12), and “Diagnoses” (n = 26) and “Patient demographics” (n = 25) for mandatory data fields. Discrepancies in data collection systems occurred between but also within countries, thereby revealing a lack of standardization both at the international and national level. Additionally, some countries reported specific data collection features, including the use or misuse of ICD coding, the national standards for coding or lack thereof, and the electronic abstracting systems utilized in hospitals. Conclusions: Harmonizing ICD coding standards/guidelines should be a common goal to enhance international comparisons of health data. The current international status of ICD data collection highlights the need for the promotion of ICD and the adoption of the newest version, ICD-11. Furthermore, it will encourage further research on how to improve and standardize ICD coding.

Objective To evaluate the validity of COVID-19 International Classification of Diseases, 10th Revision (ICD-10) codes and their combinations. Design Retrospective cohort study. Setting Acute care hospitals and emergency departments (EDs) in Alberta, Canada. Participants Patients 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 measures The 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. Results Two 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%). Conclusions In 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.

Background: The International Classification of Diseases (ICD) is widely used by clinical coders worldwide for clinical coding morbidity data into administrative health databases. Accordingly, hospital data quality largely depends on the coders’ skills acquired during ICD training, which varies greatly across countries. Objective: To characterise the current landscape of international ICD clinical coding training. Method: An online questionnaire was created to survey the 194 World Health Organization (WHO) member countries. Questions focused on the training provided to clinical coding professionals. The survey was distributed to potential participants who met specific criteria, and to organisations specialised in the topic, such as WHO Collaborating Centres, to be forwarded to their representatives. Responses were analysed using descriptive statistics. Results: Data from 47 respondents from 26 countries revealed disparities in all inquired topics. However, most participants reported clinical coders as the primary person assigning ICD codes. Although training was available in all countries, some did not mandate training qualifications, and those that did differed in type and duration of training, with college or university degree being most common. Clinical coding certificates most frequently entailed passing a certification exam. Most countries offered continuing training opportunities, and provided a range of support resources for clinical coders. Conclusion: Variability in clinical coder training could affect data collection worldwide, thus potentially hindering international comparability of health data. Implications: These findings could encourage countries to improve their resources and training programs available for clinical coders and will ultimately be valuable to the WHO for the standardisation of ICD training.