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.

Consequences of delaying non-urgent surgeries during COVID-19: a population-based retrospective cohort study in Alberta, Canada.

Objectives: To ensure sufficient resources to care for patients with COVID-19, healthcare systems delayed non-urgent surgeries to free capacity. This study explores the consequences of delaying non-urgent surgery on surgical care and healthcare resource use.

Design: This is a population-based retrospective cohort study.

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.

Hospital and Physician Variability in Revascularization Decisions and Outcomes for Patients With 3-Vessel and Left Main Coronary Artery Disease: A Population-Based Cohort Study.

Background: Hospital- and physician-level variation for selection of percutaneous coronary intervention versus coronary artery bypass grafting (CABG) for patients with coronary artery disease has been associated with outcome differences. However, most studies excluded patients treated medically.

Methods And Results: From 2010 to 2019, adults with 3-vessel or left main coronary artery disease at 3 hospitals (A, B, C) in Alberta, Canada, were categorized by treatment with medical therapy, percutaneous coronary intervention, or CABG.

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.

Myocardial Infarction Within 30 Days of Discharge From an Emergency Department: A Descriptive Study of Albertan Women.

Background: Cardiovascular diseases (CVDs) are the leading cause of premature death for Canadian women, which may be due partly to a lack of awareness of the presentation of acute coronary events in emergency departments (EDs). To address an identified gap in women's cardiovascular care, we sought to describe the clinical and comorbid factors of women who, following discharge from an ED, suffered a myocardial infarction (MI).

Methods: Descriptive analyses were completed on a cohort of women who presented to an ED in Alberta, Canada, between January 1, 2010 and December 31, 2020, were discharged, and within 30 days of their index ED visit, were admitted to the hospital with an MI.

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.

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.

A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings.

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.

Interpreting and coding causal relationships for quality and safety using ICD-11.

Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10.

Impact of myocardial perfusion and coronary calcium on medical management for coronary artery disease.

Aims: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) remains one of the most widely used imaging modalities for the diagnosis and prognostication of coronary artery disease (CAD). Despite the extensive prognostic information provided by MPI, little is known about how this influences the prescription of medical therapy for CAD. We evaluated the relationship between MPI with computed tomography (CT) attenuation correction and prescription of acetylsalicylic acid (ASA) and statins.

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.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus.

Optimal medical therapy improves outcomes in patients with diabetes mellitus and acute myocardial infarction.

Aims: We aimed to explored the association between the use of optimal medical therapy (OMT) in patients with myocardial infarction (AMI) and diabetes mellitus (DM) and clinical outcomes.

Methods: Bleeding complications in a Multicenter registry of patients discharged with diagnosis of Acute Coronary Syndrome (BleeMACS) is an international registry that enrolled participants with acute coronary syndrome followed up for at least 1 year across 15 centers from 2003 to 2014. Baseline characteristics and endpoints were analyzed.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism.

Effect of a Continuous Bedside Pressure Mapping System for Reducing Interface Pressures: A Randomized Clinical Trial.

Importance: Continuous bedside pressure mapping (CBPM) technology can assist in detecting skin areas with excessive interface pressure and inform efficient patient repositioning to prevent the development of pressure injuries (PI).

Objective: To evaluate the efficacy of CBPM technology in reducing interface pressure and the incidence of PIs.

Design, Setting, And Participants: This parallel, 2-group randomized clinical trial was performed at a tertiary acute care center.

Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models.

Background: Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.

Objective: This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.