Developing a Computational Phenotype of the Fourth Universal Definition of Myocardial Infarction for Inpatients.

: The fourth universal definition of myocardial infarction (MI) introduced the differentiation of acute myocardial injury from MI. In this study, we developed a computational phenotype for distinct identification of acute myocardial injury and MI within electronic medical records (EMRs). : Two cohorts were used from a Calgary-wide EMR system: a chart review of 3042 randomly selected inpatients from Dec 2014 to Jun 2015; and 11,685 episodes of care that included cardiac catheterization from Jan 2013 to Apr 2017.

Applying an ICD-10 to ICD-11 mapping tool to identify causes of death codes in an Alberta dataset.

Background: The most recent and 11th revision of the International Classification of Disease (ICD-11) is in use as of January 2022, and countries around the globe are now preparing for the implementation of ICD-11 and transition from the 10th revision (ICD-10). Translation of current coding is required for historical comparisons.

Methods: We applied the World Health Organization (WHO) mapping tables to current Centers for Disease Control and Prevention (CDC) Lists of ICD-10 coding of underlying causes of death to assess what ICD-11 codes look like in an Alberta sample of causes of death (COD).

The centre for health informatics: a novel approach to accelerating the field of health data science.

Precision Medicine and Precision Public Health are approaches to improve population health. Achieving these goals requires innovation in health informatics. The Centre for Health Informatics (CHI) within the Cumming School of Medicine (CSM) at the University of Calgary (UC), Canada, was created to respond to this need by fostering multidisciplinary collaborations, building capacity by recruiting and training outstanding faculty and students, and harnessing Alberta's rich health data to advance health informatics.

Extracting social determinants of health from inpatient electronic medical records using natural language processing.

Background: Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract them from inpatient electronic medical record (EMR) data using techniques compatible with current EMR systems.

Methods: Four social determinants were targeted: patient language barriers, employment status, education, and whether the patient lives alone.

Case Identification of Depression in Inpatient Electronic Medical Records: Scoping Review.

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders.

Identifying personalized barriers for hypertension self-management from TASKS framework.

Objective: Effective management of hypertension requires not only medical intervention but also significant patient self-management. The challenge, however, lies in the diversity of patients' personal barriers to managing their condition. The objective of this research is to identify and categorize personalized barriers to hypertension self-management using the TASKS framework (Task, Affect, Skills, Knowledge, Stress).

Identifying cost-based quality and performance indicators for home care: a modified delphi method study.

Background: This study, part of a multi-study program, aimed to identify a core set of cost-based quality and performance indicators using a modified Delphi research approach. Conceptually, this core set of cost-based indicators is intended for use within a broader health system performance framework for evaluating home care programming in Canada.

Methods: This study used findings from a recently published scoping review identifying 34 cost-focused home care program PQIs.

Assessing agreement between population-level administrative pharmaceutical databases and patient-reported medication dispensation in cardiac rehabilitation patients.

Background: Pharmacoepidemiology has emerged as a crucial field in evaluating the use and effects of medications in large populations to ensure their safe and effective use. This study aimed to assess the agreement of cardiac medication use between a provincial medication database, the Pharmaceutical Information Network (PIN), and reconciled medication data from confirmation through patient interviews for patients referred to cardiac rehabilitation.

Methods: The study included data from patients referred to the TotalCardiology Rehabilitation CR program, and medication data was available in both TotalCardiology Rehabilitation charts and PIN.

Development of Data Quality Indicators for Improving Hospital International Classification of Diseases-Coded Health Data Quality Globally.

Background: Hospital inpatient data, coded using the International Classification of Diseases (ICD), is widely used to monitor diseases, allocate resources and funding, and evaluate patient outcomes. As such, hospital data quality should be measured before use; however, currently, there is no standard and international approach to assess ICD-coded data quality.

Objective: To develop a standardized method for assessing hospital ICD-coded data quality that could be applied across countries: Data quality indicators (DQIs).

Validated administrative data based ICD-10 algorithms for chronic conditions: A systematic review.

Objective: This systematic review aimed to identify ICD-10 based validated algorithms for chronic conditions using health administrative data.

Methods: A comprehensive systematic literature search using Ovid MEDLINE, Embase, PsycINFO, Web of Science and CINAHL was performed to identify studies, published between 1983 and May 2023, on validated algorithms for chronic conditions using administrative health data. Two reviewers independently screened titles and abstracts and reviewed full text of selected studies to complete data extraction.

Achieving high inter-rater reliability in establishing data labels: a retrospective chart review study.

Background: In medical research, the effectiveness of machine learning algorithms depends heavily on the accuracy of labeled data. This study aimed to assess inter-rater reliability (IRR) in a retrospective electronic medical chart review to create high quality labeled data on comorbidities and adverse events (AEs).

Methods: Six registered nurses with diverse clinical backgrounds reviewed patient charts, extracted data on 20 predefined comorbidities and 18 AEs.

Real-world Use and Outcomes of Sodium-Glucose Cotransporter-2 Inhibitors in Adults With Diabetes and Heart Failure: A Population-level Cohort Study in Alberta, Canada.

Objectives: Since 2016, clinical guidelines have recommended sodium-glucose cotransporter-2 inhibitors (SGLT2is) for people with type 2 diabetes with heart failure. We examined SGLT2i dispensation, factors associated with dispensation, and heart failure hospitalization and all-cause mortality in people with diabetes and heart failure.

Methods: This retrospective, population-based cohort study identified people with diabetes and heart failure between January 1, 2014, and December 31, 2017, in Alberta, Canada, and followed them for a minimum of 3 years for SGLT2i dispensation and outcomes.

Health data governance for research use in Alberta.

Alberta has rich clinical and health services data held under the custodianship of Alberta Health and Alberta Health Services (AHS), which is not only used for clinical and administrative purposes but also disease surveillance and epidemiological research. Alberta is the largest province in Canada with a single payer centralised health system, AHS, and a consolidated data and analytics team supporting researchers across the province. This paper describes Alberta's data custodians, data governance mechanisms, and streamlined processes followed for research data access.

Evaluating the coding accuracy of type 2 diabetes mellitus among patients with non-alcoholic fatty liver disease.

Background: Non-alcoholic fatty liver disease (NAFLD) describes a spectrum of chronic fattening of liver that can lead to fibrosis and cirrhosis. Diabetes has been identified as a major comorbidity that contributes to NAFLD progression. Health systems around the world make use of administrative data to conduct population-based prevalence studies.

BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study.

Background: Inpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls.

Objective: This study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model.

Automated extraction of weight, height, and obesity in electronic medical records are highly valid.

Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs).

Methods: In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs.

Exploring the reliability of inpatient EMR algorithms for diabetes identification.

Introduction: Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms.

Use of Epic Electronic Health Record System for Health Care Research: Scoping Review.

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.

Implementing paediatric patient-reported outcome measures in outpatient asthma clinics: a feasibility assessment study.

Objective: Implementation of patient-reported outcome measures (PROMs) is limited in paediatric routine clinical care. The KidsPRO programme has been codesigned to facilitate the implementation of PROMs in paediatric healthcare settings. Therefore, this study (1) describes the development of innovative KidsPRO programme and (2) reports on the feasibility of implementing PedsQL (Pediatric Quality of Life Inventory) PROM in asthma clinics using the KidsPRO programme.

Color coded health data: factors related to willingness to share health information in South Asian community members in Canada.

Introduction: Data unavailability poses multiple challenges in many health fields, especially within ethnic subgroups in Canada, who may be hesitant to share their health data with researchers. Since health information availability is controlled by the participant, it is important to understand the willingness to share health information by an ethnic population to increase data availability within ethnocultural communities.

Methods: We employed a qualitative descriptive approach to better understand willingness to share health information by South Asian participants and operated through a lens that considered the cultural and sociodemographic aspect of ethnocultural communities.

Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing.

Background: Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders' abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes.

Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

Background: Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection.

Methods: This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020.

Uptake of pediatric patient-reported outcome and experience measures and challenges associated with their implementation in Alberta: a mixed-methods study.

Background: Implementing Patient-reported Outcome Measures (PROMs) and Patient-reported Experience Measures (PREMs) is an effective way to deliver patient- and family-centered care (PFCC). Although Alberta Health Services (AHS) is Canada's largest and fully integrated health system, PROMs and PREMs are yet to be routinely integrated into the pediatric healthcare system. This study addresses this gap by investigating the current uptake, barriers, and enablers for integrating PROMs and PREMs in Alberta's pediatric healthcare system.

Urban design and cardio-metabolic risk factors.

Accumulating evidence suggests that the built environment may be associated with cardiovascular disease via its influence on health behaviours. The aim of this study was to estimate the associations between traditional and novel neighbourhood built environment metrics and clinically assessed cardio-metabolic risk factors among a sample of adults in Canada. A total of 7171 participants from Albertas Tomorrow Project living in Alberta, Canada, were included.