Evaluation of In-Hospital and Community-Based Healthcare Utilization and Costs During the Coronavirus 2019 (COVID-19) Pandemic in Alberta, Canada: A Population-Based Descriptive Study.

Background: Assessing the financial burden of COVID-19 is important for planning health services and resource allocation to inform future pandemic response.

Objectives: This study examines the changing dynamics in healthcare utilization patterns and costs from a public healthcare perspective during the COVID-19 pandemic in Alberta, Canada.

Design: Population-based descriptive study.

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.

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.

Machine learning risk estimation and prediction of death in continuing care facilities using administrative data.

In this study, we aimed to identify the factors that were associated with mortality among continuing care residents in Alberta, during the coronavirus disease 2019 (COVID-19) pandemic. We achieved this by leveraging and linking various administrative datasets together. Then, we examined pre-processing methods in terms of prediction performance.

Healthcare utilization and adverse outcomes stratified by sex, age and long-term care residency using the Alberta COVID-19 Analytics and Research Database (ACARD): a population-based descriptive study.

Background: Understanding the epidemiology of Coronavirus Disease of 2019 (COVID-19) in a local context is valuable for both future pandemic preparedness and potential increases in COVID-19 case volume, particularly due to variant strains.

Methods: Our work allowed us to complete a population-based study on patients who tested positive for COVID-19 in Alberta from March 1, 2020 to December 15, 2021. We completed a multi-centre, retrospective population-based descriptive study using secondary data sources in Alberta, Canada.

Hypertension identification using inpatient clinical notes from electronic medical records: an explainable, data-driven algorithm study.

Background: Case identification is important for health services research, measuring health system performance and risk adjustment, but existing methods based on manual chart review or diagnosis codes can be expensive, time consuming or of limited validity. We aimed to develop a hypertension case definition in electronic medical records (EMRs) for inpatient clinical notes using machine learning.

Methods: A cohort of patients 18 years of age or older who were discharged from 1 of 3 Calgary acute care facilities (1 academic hospital and 2 community hospitals) between Jan.

A Time Series Analysis Evaluating Antibiotic Prescription Rates in Long-Term Care during the COVID-19 Pandemic in Alberta and Ontario, Canada.

The COVID-19 pandemic affected access to care, and the associated public health measures influenced the transmission of other infectious diseases. The pandemic has dramatically changed antibiotic prescribing in the community. We aimed to determine the impact of the COVID-19 pandemic and the resulting control measures on oral antibiotic prescribing in long-term care facilities (LTCFs) in Alberta and Ontario, Canada using linked administrative data.

Validity of ICD-10 codes for COVID-19 patients with hospital admissions or ED visits in Canada: a retrospective cohort study.

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.

Categorising patient concerns using natural language processing techniques.

Objectives: Patient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.

CREATE: A New Data Resource to Support Cardiac Precision Health.

Background: The initiatives of precision medicine and learning health systems require databases with rich and accurately captured data on patient characteristics. We introduce the linical gistry, dminisrative Data and lectronic Medical Records (CREATE) database, which includes linked data from 4 population databases: lberta rovincial oject for utcome ssessment in oronary eart Disease (APPROACH; a national clinical registry), Sunrise Clinical Manager (SCM) electronic medical record (city-wide), the Discharge Abstract Database (DAD), and the National Ambulatory Care Reporting System (NACRS). The intent of this work is to introduce a cardiovascular-specific database for pursuing precision health activities using big data analytics.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.

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.

Development and validation of data quality rules in administrative health data using association rule mining.

Background: Data quality assessment presents a challenge for research using coded administrative health data. The objective of this study is to develop and validate a set of coding association rules for coded diagnostic data.

Methods: We used the Canadian re-abstracted hospital discharge abstract data coded in International Classification of Disease, 10th revision (ICD-10) codes.

Enhancing ICD-Code-Based Case Definition for Heart Failure Using Electronic Medical Record Data.

Background: Surveillance and outcome studies for heart failure (HF) require accurate identification of patients with HF. Algorithms based on International Classification of Diseases (ICD) codes to identify HF from administrative data are inadequate owing to their relatively low sensitivity. Detailed clinical information from electronic medical records (EMRs) is potentially useful for improving ICD algorithms.