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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis.

Background: Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms.

Field testing a new ICD coding system: methods and early experiences with ICD-11 Beta Version 2018.

Objective: A beta version (2018) of International Classification of Diseases, 11th Revision for MMS (ICD-11), needed testing. Field-testing involves real-world application of the new codes to examine usability. We describe creating a dataset and characterizing the usability of ICD-11 code set by coders.

International Classification of Diseases clinical coding training: An international survey.

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.

Machine Learning Patient-Specific Prediction of Heart Failure Hospitalization Using Cardiac MRI-Based Phenotype and Electronic Health Information.

Background: Heart failure (HF) hospitalization is a dominant contributor of morbidity and healthcare expenditures in patients with systolic HF. Cardiovascular magnetic resonance (CMR) imaging is increasingly employed for the evaluation of HF given capacity to provide highly reproducible phenotypic markers of disease. The combined value of CMR phenotypic markers and patient health information to deliver predictions of future HF events has not been explored.

Decision algorithm for when to use the ICD-11 3-part model for healthcare harms.

Accurate data collection of healthcare-related adverse events provides a foundation for quality and health system improvement. The International Classification of Diseases for Mortality and Morbidity Statistics, 11th revision (ICD-11 MMS) includes new codes to identify harm or injury and the events or actions leading to the adverse events. However, it is difficult to choose the correct codes without in-depth understanding of which event may be classified as an injury or harm.

Supervised consumption site enables cost savings by avoiding emergency services: a cost analysis study.

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.

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.

THE PREVALENCE OF INSOMNIA AND SLEEP APNEA IN DISCHARGE ABSTRACT DATA: A CALL TO IMPROVE DATA QUALITY.

Insomnia and sleep apnea are associated with a variety of comorbid conditions and carry a symptom burden to patients. As the prevalence of insomnia and sleep apnea continue to rise, it is imperative that appropriate tools are implemented to accurately capture their prevalence in acute care settings. A retrospective chart review was conducted on 3,074 inpatient charts in Calgary, Alberta.

Training and experience of coding with the World Health Organization's International Classification of Diseases, Eleventh Revision.

Background: The new International Classification of Diseases, Eleventh Revision for Mortality and Morbidity Statistics (ICD-11) was developed and released by the World Health Organization (WHO) in June 2018. Because ICD-11 incorporates new codes and features, training materials for coding with ICD-11 are urgently needed prior to its implementation.

Objective: This study outlines the development of ICD-11 training materials, training processes and experiences of clinical coders while learning to code using ICD-11.