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Abstract 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). This study aims to enhance patient-centered strategies by aligning support with each patient's specific needs, recognizing the diversity in their unique circumstances, beliefs, emotional states, knowledge levels, and access to resources. This research is based on observations from a single study focused on eight patients, which may have been a part of a larger project. Results The analysis of transcripts from eight patients and the Global Hypertension Practice Guidelines revealed 69 personalized barriers. These barriers were distributed as follows: emotional barriers (49%), knowledge barriers (24%), logical barriers (17%), and resource barriers (10%). The findings highlight the significant impact of emotional and knowledge-related challenges on hypertension self-management, including difficulties in home blood pressure monitoring and the use of monitoring tools. This study emphasizes the need for tailored interventions to address these prevalent barriers and improve hypertension management outcomes.

Abstract 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. A purposive and snowball technique was employed to recruit a national panel of system-level operational and content experts in home care. We collected data through progressive surveys and engagement sessions. In the first round of surveying, the panel scored each indicator on Importance, Actionable, and Interpretable criteria. The panel set the second round of ranking the remaining indicators’ consensus criteria. The panel ranked by importance their top five indicators from operational and system perspectives. Indicators selected by over 50% of the panel were accepted as consensus. Results We identified 13 panellists. 12 completed the first round which identified that 30 met the predetermined inclusion criteria. Eight completed the ranking exercise, with one of the eight completing one of two components. The second round resulted in three PQIs meeting the consensus criteria: one operational and two systems-policy-focused. The PQIs: “Average cost per day per home care client,” “Home care service cost (mean) per home care client 1y, 3y and 7y per health authority and provincially and nationally”, and “Home care funding as a percent of overall health care expenditures.” Conclusions The findings from this study offer a crucial foundation for assessing operational and health system outcomes. Notably, this research pioneers identifying key cost-based PQIs through a national expert panel and modified Delphi methodology. This study contributes to the literature on PQIs for home care and provides a basis for future research and practice. These selected PQIs should be applied to future research to test their applicability and validity within home care programming and outcomes. Researchers should apply these selected PQIs in future studies to evaluate their applicability and validity within home care programming and outcomes.

Abstract BackgroundElectronic 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. ObjectiveThis review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones. MethodsA scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. ResultsA total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms. ConclusionsBetter use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general.

Introduction Coding has become burdensome to healthcare systems due to patient complexity and resource requirements. In Alberta, Intelligent Medical Objects (IMO), an interface terminology mapping product, is integrated within the new province-wide Clinical Information System, named Connect Care (CC), to support documentation by clinicians and map clinical terminologies to ICD-10. This study evaluates comparability of terminologies mapped ICD-10 codes to the ICD-10 codes in DAD. Approach We conducted a retrospective analysis by linking acute care hospital DAD with CC between April 2021 and December 2023. The primary outcome was the level of agreement between ‘hospital problem list’ of CC and DAD for ICD-10-CA codes at a 3-digit level. The number of diagnoses and rate of unspecified codes were also compared. The outcome measures were stratified by physician specialty, hospital type and location, and length-of-stay (LOS). Results A total of 498,834 unique hospital records were linked. The average level of agreement between CC and DAD at 3-digit level of ICD-10-CA code was 43.4%. The average number of diagnoses captured in CC (3.91) was slightly lower than DAD (4.06), and the average rate of unspecified codes was higher in CC (26.4%) compared to DAD (23.0%). The level of agreement varied by specialty and length-of-stay with specialties with more complex patients and longer lengths-of-stay having the lowest agreement (43% for generalists and internal medicine and 33% for LOS >3 months). Conclusion Level of agreement between CC and DAD for ICD-10 data was identified as low, indicating significant disparities between terminology mappings and the coding process.

Introduction Globally, differences in coding practices and guidelines lead to variations in hospital-administrative data. Studies on patient safety found that better hospital-data quality is associated with higher rates of patient safety events. This study outlines our process of creating a standardized tool for countries to assess their hospital-administrative data quality. Approach We developed 24 indicators across five different dimensions through a Delphi-consensus method. To test applicability of these indicators, we approached representatives from 50 countries with an online survey comprised of qualitative and quantitative questions. Characteristics of each country and indicator scores were noted. A data quality overall score, out of 20, was calculated for each country based on survey responses. The score was classified into 3 data quality categories: high (≥18), moderate (13-17.9), and low (

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. A third reviewer resolved conflicts arising at the screening or study selection stages. The primary outcome was validated studies of ICD-10 based algorithms with both sensitivity and PPV of ≥70%. Studies with either sensitivity or PPV

Objective Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we assess how best to extract SDOH variables from inpatient electronic medical record (EMR) data. Approach Four social determinants were targeted: patient language barriers, employment status, education, and whether the patient lives alone. Inpatients aged 18 and older with records in the Calgary-wide EMR system were studied. Algorithms were developed on January 2019 hospital admissions (n=8,999), and validated on January 2018 hospital admissions (n=8,839). SDOH documented as structured data, which can be easily queried, were compared against those extracted from unstructured free-text notes. Results More than twice as many patients had an unstructured note documenting a language barrier than in the structured data; 12% of patients indicated by notes to be living alone had a partner in their structured marital status. The Positive Predictive Value (PPV) of the elements extracted from notes was high, at 99% (95% CI 94.0%-100.0%) for language barriers, 98% (95% CI 92.6%-99.9%) for living alone, 96% (95% CI 89.8%-98.8%) for unemployment, and 88% (95% CI 80.0%-93.1%) for retirement. Conclusions It is possible to extract SDOH elements from free text notes with high PPV. SDOH documentation was largely missing in structured data, and sometimes misleading. Implications Free text notes can be a fruitful source of information for projects using SDOH variables, such as machine learning/AI or health services research, and can offer insights not available from the structured data elements.

Objective Cardiovascular diseases (CVD) are the leading cause of mortality and morbidity worldwide. Traditionally, disease surveillance relies on data from surveys, registries, and administrative databases. As medical records undergo global digitization, electronic medical records (EMRs) are emerging as a crucial reservoir of real-world data. However, the extent EMRs are used in CVD surveillance is unknown. We are conducting a scoping review to assess the current state and effectiveness of EMR-based CVD surveillance worldwide. Approach Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews, we searched MEDLINE and EMBASE bibliographic databases to capture studies on prevalence, incidence, and trend measurements of CVDs using EMRs. Assessed factors include CVD types, modelling methodologies, data linkages, advantages, disadvantages, challenges, and solutions. Due to the qualitative nature of the review, collected data will be narratively synthesized to present overall perspectives. Results Our search algorithm yielded 11,979 citations, of which 5,886 abstracts were selected for screening, in progress at the time of this submission. The interim results indicate that most eligible reports came from the USA (49%), followed by the UK (13%), China (6%), Spain (4%) and Canada (3%). The most common diseases were coronary artery diseases (29%), followed by hypertension (26%), stroke (21%), and heart failure (15%). Conclusions Surveillance of CVD is crucial for prevention and health policy development. While EMRs can be a data source for surveillance, such potential has yet to be fully realized. Implications This study will inform existing research challenges and future opportunities of EMR-based CVD surveillance.

Objective To evaluate validity of hospital-discharged abstract administrative health data (DAD) over years through chart reviews. Methods We analyzed three chart review cohorts (4,008 patients in 2003, 3,045 in 2015, and 9,024 in 2022) in Calgary, Canada. Nurse reviewers determined the presence or absence of 17 clinical conditions employing similar protocols. The reviews were linked with DAD using a unique lifetime identifier, chart number, and admission date. We evaluated the validity of DAD, coded in ICD-10-Canada version, in recording conditions by comparing against chart reviews. The C-statistics was calculated in predicting in-hospital mortality. Results The mean difference in prevalence between chart reviews and DAD for these 17 conditions was 2.1% in 2003, 7.6% in 2015, and 6.3% in 2022. However, some conditions were relatively stable, such as diabetes (1.9%, 2.1%, and 1.1%) and metastatic cancer (0.3%, 1.1%, and 0.4%). For 17 conditions, the sensitivity ranged 39.6-85.1% in 2003, 1.3-85.2% in 2015, and 3.0-89.7% in 2022. The C-statistics for in-hospital mortality based on DAD was 0.84 in 2003, 0.81 in 2015, and 0.78 in 2022. Conclusion DAD increasingly under-coded conditions over 19 years. The validity of DAD decreased but remained relatively stable for certain conditions mandated for coding. The under-coding exerted minimal impact on in-hospital mortality prediction. Implications The under-coding could be primarily due to the increase of hospital patient volumes and the limited time allocated to coders. Consequently, there is a need to develop artificial intelligence methods based on electronic medical records to support coding practices and improve coding quality.

Objective Electronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs. Methods We developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts’ designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard. Results The cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns. Conclusion The proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs. Implication Variations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.

Objective Administrative data often underrepresents hospital adverse events (AEs) due to limitations in International Classification of Diseases (ICD) coding. By leveraging electronic medical records (EMRs), we aim to mitigate these discrepancies and enhance the precision of healthcare surveillance and performance evaluations. To this end, we have developed a machine learning (ML)-based approach that utilizes EMR text data to detect common AEs. Methods We sampled adult admissions from four Calgary hospitals (2017 - 2022). Registered nurses assessed charts for 17 AEs, and the results were used as reference standard. We compared two AE detection methods: the standard ICD-based method following Canadian guidelines, and our ML algorithm applied to EMR narratives. Sensitivity, positive predictive value (PPV), negative predictive value (NPV), and specificity for both methods were calculated and compared against the reference standard. Results We analyzed 9,566 patients, of whom 1,506 were identified with AEs. Of the 17 AEs, the sensitivity in ICD-coded data ranged from 0-37%, and in EMRs, it was between 75-100%. Both showed low PPV (0-50% ICD vs.1-34% EMR). ICD data had high specificity ranging from 99-100% and NPV (99%-100%), while EMRs had specificity between 68-94% and an NPV of 100%. Conclusion and Implications ML significantly enhances sensitivity for AE detection compared to ICD-10-CA coding, despite both methods experiencing low PPV due to imbalances in EMR data. This marked improvement in sensitivity highlights ML's potential to transform AE surveillance and reporting, promising significant advancements in patient safety and healthcare quality by enabling more accurate and comprehensive identification of AEs.

Routinely collected health data (RCD) including electronic health records, disease registries, health administrative data and wearables data are not specifically collected for research purposes. Analysis of these data poses unique methodological challenges that must be addressed when conducting research, particularly as availability and use increase. This scoping review aimed to identify methodological challenges in research using RCD from existing literature (registered protocol: https://doi.org/10.17605/OSF.IO/EBM4D). We searched 6 electronic databases, including medical, health economics, nursing and psychology research databases, between Jan 2015 and Jan 2023, combining multiple “RCD” and “research” search terms (e.g., epidemiologic, informatics, pharmaceutical research). After screening abstracts and full-texts, we doubly extracted methodological themes, categorizing them into different study stages. We screened more than 23,000 records and included 430 papers. Bias and confounding were the most common methodological issues identified, discussed in relation to both study design and data analysis. Data quality, including data accuracy, validation, completeness, timeliness and cleaning, also posed substantial challenges, particularly during data processing stage. Record linkage and conducting analyses using distributed health networks also pose unique methodological challenges. Heterogeneity, incorporating social determinants of health and statistical models that address methodological challenges are also described in the literature. External validity and reporting are important considerations for RCD research. Our review identified several methodological challenges facing researchers using RCD. These issues should be addressed to ensure methodologically sound research. These findings will inform the development of a standardized protocol template and accompanying educational platform aimed at enhancing methodological quality and transparency when conducting research using RCD.

Through a multi-phased mixed-method research program including an international scoping review and a modified-Delphi panel with expert policy/academic/operational representatives from across Canada, three health-system, financially focused quality and performance home care indicators were identified. This presentation will provide preliminary results of the application of the three financial indicators on overall health-system measures, system utilization and population outcomes within the province of Alberta. The time-period covered is the last five years data leading up to the start of the deceleration of the covid-19 global pandemic. Based on provincial annual financial reporting, baseline 2016/17 fiscal year home care expenditures were $585K, this rose to significantly to $716M for fiscal year 2019/20, with the most significant increase (13%) occurred between the 2017/18 and 2018/19 years. Prior to 2016/17 home care expenditures increased at a rate less than 3%. These significant home care investments increased the percent of the overall expenditures in Alberta from 3.94 to 4.6%. Furthermore, the average annual expenditures per home care client rose from $5,417 to $6,112. During this same time-period, numerous health-system indicators shifted to more favorable positions, with notable decreases in acute care days prior to death, number of those waiting in hospital for LTC, and preventing/delaying the need for LTC overall. Based on comparisons of home care investments on the overall health-system and key health-system performance indicators focused on measuring system efficiencies, preliminary examinations suggest that significant home care focused investments in Alberta significantly improved health-system performance, efficiencies, and overall outcomes for the population.

Abstract 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. To that end, we sought to assess the accuracy of diabetes International Classification of Diseases (ICD) coding in administrative databases among a cohort of confirmed NAFLD patients in Calgary, Alberta, Canada. Methods The Calgary NAFLD Pathway Database was linked to the following databases: Physician Claims, Discharge Abstract Database, National Ambulatory Care Reporting System, Pharmaceutical Information Network database, Laboratory, and Electronic Medical Records. Hemoglobin A1c and diabetes medication details were used to classify diabetes groups into absent, prediabetes, meeting glycemic targets, and not meeting glycemic targets. The performance of ICD codes among these groups was compared to this standard. Within each group, the total numbers of true positives, false positives, false negatives, and true negatives were calculated. Descriptive statistics and bivariate analysis were conducted on identified covariates, including demographics and types of interacted physicians. Results A total of 12,012 NAFLD patients were registered through the Calgary NAFLD Pathway Database and 100% were successfully linked to the administrative databases. Overall, diabetes coding showed a sensitivity of 0.81 and a positive predictive value of 0.87. False negative rates in the absent and not meeting glycemic control groups were 4.5% and 6.4%, respectively, whereas the meeting glycemic control group had a 42.2% coding error. Visits to primary and outpatient services were associated with most encounters. Conclusion Diabetes ICD coding in administrative databases can accurately detect true diabetic cases. However, patients with diabetes who meets glycemic control targets are less likely to be coded in administrative databases. A detailed understanding of the clinical context will require additional data linkage from primary care settings.

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. All reviewers underwent four iterative rounds of training aimed to enhance accuracy and foster consensus. Periodic monitoring was conducted at the beginning, middle, and end of the testing phase to ensure data quality. Weighted Kappa coefficients were calculated with their associated 95% confidence intervals (CIs).Results Seventy patient charts were reviewed. The overall agreement, measured by Conger's Kappa, was 0.80 (95% CI: 0.78-0.82). IRR scores remained consistently high (ranging from 0.70 to 0.87) throughout each phase.Conclusion Our study suggests the detailed manual for chart review and structured training regimen resulted in a consistently high level of agreement among our reviewers during the chart review process. This establishes a robust foundation for generating high-quality labeled data, thereby enhancing the potential for developing accurate machine learning algorithms.

Abstract 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. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision–recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. Results There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835–0.978), PR AUC of 0.637 (95% CI 0.528–0.746), and F1 score of 0.79 (0.67–0.90). Conclusions Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.

Abstract 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. Methods CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients’ chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Result Of the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical documents from the EMR, four document types were identified as the most influential text sources for identifying CeVD disease (“nursing transfer report,” “discharge summary,” “nursing notes,” and “inpatient consultation.”). The best performing NLP model was XGBoost, combining the Unified Medical Language System concepts extracted by cTAKES (e.g., top-ranked concepts, “Cerebrovascular accident” and “Transient ischemic attack”), and the term frequency-inverse document frequency vectorizer. Compared with ICD codes, the model achieved higher validity overall, such as sensitivity (25.0% vs 70.0%), specificity (99.3% vs 99.1%), PPV (82.6 vs. 87.8%), and NPV (90.8% vs 97.1%). Conclusion The NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied for future studies such as surveillance, and longitudinal studies.

Abstract 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. Methods Pediatric clinicians and academic researchers with experience using PROMs and PREMs were invited to complete a quantitative survey. Additionally, key stakeholders were qualitatively interviewed to understand current challenges in implementing pediatric PROMs and PREMs within AHS. Quantitative data gathered from 22 participants were descriptively analyzed, and qualitative data from 14 participants were thematically analyzed. Results Participants identified 33 PROMs and 6 PREMs showing diversity in the types of pediatric PROMs and PREMs currently being used in Alberta and their mode of administration. The qualitatively identified challenges were associated with patients, family caregivers, and clinicians. The absence of system-level support, such as integration within electronic medical records, is considered a significant system-level challenge. Conclusions The significant variation in the types of PROMs and PREMs used, the rationale for their use, and their mode of administration demonstrate the diverse and sporadic use of these measures in Alberta. These findings highlight the need for province-wide uniform implementation of pediatric PROMs and PREMs in Alberta. Our results could benefit healthcare organizations in developing evidence-based PROM and PREM implementation strategies in pediatrics.

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. To establish CHI as a health informatics leader, CHI has struck partnerships with stakeholders, including Alberta Health Services (AHS), Alberta Health (AH), and the Alberta Strategy for Patient-Oriented Research Unit (AbSPORU) among others. Through these close relationships, the CHI intake team facilitates access to Alberta's rich health data sources and educates researchers on the available health data in Alberta. The concept of a "One Stop Shop" for CSM and UC researchers encourages multidisciplinary collaboration, helps investigators access a wide range of datasets, and receive analytical support. Population-based data sets enable the development of methods to turn raw data into health information, improve health data collection, linkage, analysis, and quality, and applied studies creating clinical decision-support tools, prognostic tools, improved health surveillance methods, and health system performance indicators. CHI's ecosystem of diverse research expertise, cutting-edge technology, and embedded AHS analysts to support data access via a wide-ranging network of partnerships allows our provincial researchers, national and international collaborators tremendous opportunities for empirical research. It paves the way for implementing Precision Medicine in the real world.

Abstract 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 rule‐based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed. Results The study cohort consisted of 1904 patients with 50.8% females and 43.3% > 64 years of age. The final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and F1 score of 94.8%. Conclusions This study developed a highly valid rule‐based EMR algorithm that detects height and weight. This could allow large‐scale analyses using obesity that were previously not possible.

BackgroundElectronic 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. ObjectiveThe 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. MethodsWe 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. ResultsWe 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. ConclusionsThe 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.

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.Design Feasibility assessment study.Setting Outpatient paediatric asthma clinics in the city of Calgary, Canada.Participants Five paediatric patients, four family caregivers and three healthcare providers were recruited to pilot the implementation of PedsQL PROM using KidsPRO. Then, a survey was used to assess its feasibility among these study participants.Main outcome measures Participants’ understanding of using PROMs, the adequacy of support provided to them, the utility of using PROMs as part of their appointment, and their satisfaction with using PROMs.Analyses The quantitative data generated through closed-ended questions was analysed and represented in the form of bar charts for each category of study participants (ie, patients, their family caregivers and healthcare providers). The qualitative data generated through the open-ended questions were content analysed and categorised into themes.Results The experience of using PROMs was overwhelmingly positive among patients and their family caregivers, results were mixed among healthcare providers. Qualitative data collected through open-ended questions also complemented the quantitative findings.Conclusion The evidence from this study reveals that the implementation of PROMs in routine paediatric clinical care asthma clinics in Alberta is seems to be feasible.

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. AHS has created a centralised data repository from multiple sources, including practitioner claims data, hospital discharge data, and medications dispensed, available for research use through the provincial Data and Research Services (DRS) team. The DRS team is integrated within AHS to support researchers across the province with their data extraction and linkage requests. Furthermore, streamlined processes have been established, including: 1) ethics approval from a research ethics board, 2) any necessary operational approvals from AHS, and 3) a tripartite legal agreement dictating terms and conditions for data use, disclosure, and retention. This allows researchers to gain timely access to data. To meet the evolving and ever-expanding big-data needs, the University of Calgary, in partnership with AHS, has built high-performance computing (HPC) infrastructure to facilitate storage and processing of large datasets. When releasing data to researchers, the analytics team ensures that Alberta's Health Information Act's guiding principles are followed. The principal investigator also ensures data retention and disposition are according to the plan specified in ethics and per the terms set out by funding agencies. Even though there are disparities and variations in the data protection laws across the different provinces in Canada, the streamlined processes for research data access in Alberta are highly efficient.

Abstract Risk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta’s Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy.

Abstract Background Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients. Methods Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score. Results The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction. Conclusions This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients.

Abstract 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. We compare ICD-11 against ICD-10-CA (Canadian modification) and a reference standard dataset of diagnoses. Real-world usability encompasses code selection and time to code a complete inpatient chart using ICD-11 compared with ICD-10-CA. Methods and results A random sample of inpatient records previously coded using ICD-10-CA was selected from hospitals in Calgary, Alberta (N = 2896). Nurses examined these charts for conditions and healthcare-related harms. Clinical coders re-coded the same charts using ICD-11 codes. Inter-rater reliability (IRR) and coding time improved with ICD-11 coding experience (23.6 to 9.9 min average per chart). Code structure comparisons and challenges encountered are described. Overall, 86.3% of main condition codes matched. Coder comments regarding duplicate codes, missing codes, code finding issues enabled improvements to the ICD-11 Browser, Coding Tool, and Reference Guide. Training is essential for solid IRR with 17,000 diagnostic categories in the new ICD-11. As countries transition to ICD-11, our coding experiences and methods can inform users for implementation or field testing.

Abstract Background Patient-reported outcome measures (PROMs) play an important role in promoting and supporting patient and family-centered care. Implementing interventions like PROMs in routine clinical care require key stakeholders to change their behavior. The aim of this study was to utilize the Theoretical Domains Framework (TDF) to identify barriers and enablers to the implementation of PROMs in pediatric outpatient asthma clinics from healthcare providers’ perspective. Methods This TDF-guided qualitative descriptive study is part of a larger multi-phase project to develop the KidsPRO program, an electronic platform to administer, collect, and use PROMs in pediatrics. Semi-structured qualitative interviews were conducted with 17 participants, which included pediatricians, nurses, allied health professionals and administrative staff from outpatient asthma clinics. All the interviews were transcribed, deductively coded, inductively grouped in themes, and categorized into barriers and enablers. Results We identified 33 themes within 14 TDF domains, which were further categorized and tabulated into 16 barriers and 17 enablers to implementing PROMs in asthma clinics. Barriers to behavioral change were attributed to personal, clinical, non-clinical, and other system-level factors; they ranged from limited awareness of PROMs to language barriers and patient’s complex family background. Enablers ranged from a personal commitment to providing patient and family-centered care to administering PROMs electronically. Conclusion This implementation of science-based systematic inquiry captured the complexity of PROMs implementation in pediatric outpatient clinical care for asthma. Considering the consistency in barriers and enablers to implementing PROMs across patient populations and care settings, many findings of this study will be directly applicable to other pediatric healthcare settings.

Abstract Identifying high-risk individuals for targeted intervention may prevent or delay hypertension onset. We developed a hypertension risk prediction model and subsequent risk sore among the Canadian population using measures readily available in a primary care setting. A Canadian cohort of 18,322 participants aged 35–69 years without hypertension at baseline was followed for hypertension incidence, and 625 new hypertension cases were reported. At a 2:1 ratio, the sample was randomly divided into derivation and validation sets. In the derivation sample, a Cox proportional hazard model was used to develop the model, and the model's performance was evaluated in the validation sample. Finally, a risk score table was created incorporating regression coefficients from the model. The multivariable Cox model identified age, body mass index, systolic blood pressure, diabetes, total physical activity time, and cardiovascular disease as significant risk factors (p

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.Materials and methods A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV).Results The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99.Discussion Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery.

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. A total of 22 in-depth interviews were conducted between March and July 2020. Results The results of this study show that health researchers should aim to develop a mutually beneficial information-sharing partnership with communities, with an emphasis on the ethnocultural and socio-ecological aspects of health within populations. Conclusion The findings support the need for culturally sensitive and respectful engagement with the community, ethically sound research practices that make participants feel comfortable in sharing their information, and an easy sharing process to share health information feasibly.

BackgroundIn recent years, mHealth has increasingly been used to deliver behavioral interventions for disease prevention and self-management. Computing power in mHealth tools can provide unique functions beyond conventional interventions in provisioning personalized behavior change recommendations and delivering them in real time, supported by dialogue systems. However, design principles to incorporate these features in mHealth interventions have not been systematically evaluated. ObjectiveThe goal of this review is to identify best practices for the design of mHealth interventions targeting diet, physical activity, and sedentary behavior. We aim to identify and summarize the design characteristics of current mHealth tools with a focus on the following features: (1) personalization, (2) real-time functions, and (3) deliverable resources. MethodsWe will conduct a systematic search of electronic databases, including MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science for studies published since 2010. First, we will use keywords that combine mHealth, interventions, chronic disease prevention, and self-management. Second, we will use keywords that cover diet, physical activity, and sedentary behavior. Literature found in the first and second steps will be combined. Finally, we will use keywords for personalization and real-time functions to limit the results to interventions that have reported these design features. We expect to perform narrative syntheses for each of the 3 target design features. Study quality will be evaluated using the Risk of Bias 2 assessment tool. ResultsWe have conducted a preliminary search of existing systematic reviews and review protocols on mHealth-supported behavior change interventions. We have identified several reviews that aimed to evaluate the efficacy of mHealth behavior change interventions in a range of populations, evaluate methodologies for assessing mHealth behavior change randomized trials, and assess the diversity of behavior change techniques and theories in mHealth interventions. However, syntheses on the unique features of mHealth intervention design are absent in the literature. ConclusionsOur findings will provide a basis for developing best practices for designing mHealth tools for sustainable behavior change. Trial RegistrationPROSPERO CRD42021261078; https://tinyurl.com/m454r65t International Registered Report Identifier (IRRID)PRR1-10.2196/39093

Introduction Estimating COVID-19 cumulative incidence in Africa remains problematic due to challenges in contact tracing, routine surveillance systems and laboratory testing capacities and strategies. We undertook a meta-analysis of population-based seroprevalence studies to estimate SARS-CoV-2 seroprevalence in Africa to inform evidence-based decision making on public health and social measures (PHSM) and vaccine strategy.Methods We searched for seroprevalence studies conducted in Africa published 1 January 2020–30 December 2021 in Medline, Embase, Web of Science and Europe PMC (preprints), grey literature, media releases and early results from WHO Unity studies. All studies were screened, extracted, assessed for risk of bias and evaluated for alignment with the WHO Unity seroprevalence protocol. We conducted descriptive analyses of seroprevalence and meta-analysed seroprevalence differences by demographic groups, place and time. We estimated the extent of undetected infections by comparing seroprevalence and cumulative incidence of confirmed cases reported to WHO.PROSPERO: CRD42020183634.Results We identified 56 full texts or early results, reporting 153 distinct seroprevalence studies in Africa. Of these, 97 (63%) were low/moderate risk of bias studies. SARS-CoV-2 seroprevalence rose from 3.0% (95% CI 1.0% to 9.2%) in April–June 2020 to 65.1% (95% CI 56.3% to 73.0%) in July–September 2021. The ratios of seroprevalence from infection to cumulative incidence of confirmed cases was large (overall: 100:1, ranging from 18:1 to 954:1) and steady over time. Seroprevalence was highly heterogeneous both within countries—urban versus rural (lower seroprevalence for rural geographic areas), children versus adults (children aged 0–9 years had the lowest seroprevalence)—and between countries and African subregions.Conclusion We report high seroprevalence in Africa suggesting greater population exposure to SARS-CoV-2 and potential protection against COVID-19 severe disease than indicated by surveillance data. As seroprevalence was heterogeneous, targeted PHSM and vaccination strategies need to be tailored to local epidemiological situations.

Background: The initiatives of precision medicine and learning health systems require databases with rich and accurately captured data on patient characteristics. We introduce the Clinical Registry, AdminisTrative Data and Electronic Medical Records (CREATE) database, which includes linked data from 4 population databases: Alberta Provincial Project for Outcome Assessment in Coronary Heart 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. Methods: We used deterministic data linkage to link SCM electronic medical record data to APPROACH clinical registry data using patient identifier variables. The APPROACH-SCM data set was subsequently linked to DAD and NACRS to obtain inpatient and outpatient cohort data. We further validated the quality of the linkage, where applicable, in these databases by comparing against the Alberta Health Insurance Care Plan registry database. Results: We achieved 99.96% linkage across these 4 databases. Currently, there are 30,984 patients with 35,753 catheterizations in the CREATE database. The inpatient cohort contained 65.75% (20,373/30,984) of the patient sample, whereas the outpatient cohort contained 29.78% (9226/30,984). The infrastructure and the process to update and expand the database has been established. Conclusions: CREATE is intended to serve as a database for supporting big data analytics activities surrounding cardiac precision health. The CREATE database will be managed by the Centre for Health Informatics at the University of Calgary, and housed in a secure high-performance computing environment. Résumé: Contexte: Les initiatives en matière de médecine de précision et les systèmes de santé apprenants ont besoin de bases de données riches et exactes sur les caractéristiques des patients. Nous présentons ici la base de données CREATE (Clinical Registry, Administrative Data and Electronic Medical Records), qui regroupe les données couplées de quatre bases de données populationnelles : le registre clinique national APPROACH (Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease), le système de gestion des dossiers médicaux électroniques SCM (Sunrise Clinical Manager, utilisé à l’échelle municipale), la Base de données sur les congés des patients (BDCP), et le Système national d’information sur les soins ambulatoires (SNISA). Notre objectif est d’offrir une base de données portant précisément sur les maladies cardiovasculaires, afin de soutenir les activités en santé de précision nécessitant l’analyse de mégadonnées. Méthodologie: Nous avons utilisé une méthode de couplage déterministe pour apparier les données du système SCM à celles du registre APPROACH à l’aide de variables d’identification des patients. L’ensemble de données SCM-APPROACH a ensuite été couplé aux données de la BDCP et du SNISA, afin d’obtenir les données des cohortes des patients hospitalisés et des patients ambulatoires. Lorsque c’était possible, nous avons en outre validé la qualité du couplage en comparant les données à celles de la base de données du Régime d’assurance maladie de l’Alberta. Résultats: Nous avons obtenu un taux de couplage de 99,96 % pour les quatre bases de données. À l’heure actuelle, la base de données CREATE compte 30 984 patients ayant subi 35 753 cathétérismes. La cohorte des patients hospitalisés représente 65,75 % (20 373/30 984) de l’échantillon, tandis que la cohorte des patients ambulatoires représente 29,78 % (9226/30 984). L’infrastructure et le processus de mise à jour et d’expansion de la base de données ont été définis. Conclusions: La base de données CREATE est destinée à soutenir les activités d’analyse de mégadonnées nécessaires à la santé cardiaque de précision. Elle sera gérée par le Centre for Health Informatics de l’Université de Calgary et hébergée dans un environnement informatique à haut rendement sécurisé.

Background: Experience surveys provide an opportunity for patients to give their feedback about health care processes and services. Unfortunately, the most current surveys have been designed as “one-size fits-all” tools, and thus, do not take into account items pertaining to specific clinical groups. The objective of this study was to gain a deeper understanding of the specific aspects of care deemed important to cardiac surgery patients. Methods: Individual semistructured telephone interviews were conducted with a cohort of patients who had previously underwent cardiac surgery. Interviews were recorded and transcribed. Using a phenomenological approach, a thematic analysis was used to generate a list of themes and subthemes deemed important by participants. Results: Eight interviews were conducted in July and August 2019. Participants included 7 men and 1 woman, ranging from 55 to 84 years of age. Five key themes emerged from the data: (1) overall experience; (2) communication; (3) the physical hospital environment; (4) care needs and ongoing management; and (5) person-centred care. Our interviews revealed that participants had many overwhelmingly positive experiences with care. Through reports of their own experiences, participants highlighted important areas that might be improved. Conclusions: Our results confirm and expand upon those highlighted in quantitative research by our group. Findings and knowledge derived from this study might be used to inform quality improvement activities. These might also play a key role in the development of a patient experience survey, specifically for those who undergo cardiac surgery; thus addressing a potential limitation of surveys currently in use. Résumé: Contexte: Les sondages sur l’expérience offrent l’occasion aux patients de transmettre leur point de vue au sujet des services et des processus de soins de santé. Malheureusement, la plupart des sondages actuels sont conçus comme des outils universels, et ne prenent donc pas en considération certains aspects propres à des groupes cliniques précis. L’objectif de cette étude est de mieux cerner les aspects précis des soins qui sont jugés importants par les patients qui ont subi une intervention chirurgicale cardiaque. Méthodologie: Des entretiens téléphoniques individuels semi-structurés ont été menés avec une cohorte de patients ayant subi une intervention chirurgicale cardiaque. Les entretiens ont été enregistrés et retranscrits. Une analyse thématique utilisant une approche phénoménologique a été effectuée pour générer une liste de thèmes et de sous-thèmes jugés importants par les participants. Résultats: Huit entretiens ont été réalisés en juillet et en août 2019. Les participants regroupaient sept hommes et une femme, âgés de 55 à 84 ans. Les données recueillies ont permis de cerner cinq thèmes clés : 1) expérience globale; 2) communication; 3) environnement physique à l’hôpital; 4) besoins en matière de soins et prise en charge continue; et 5) soins axés sur le patient. Nos entretiens ont révélé que les participants avaient eu plusieurs expériences de soins extrêmement positives. En relatant leurs propres expériences, les participants ont souligné d’importants aspects qui pourraient être améliorés. Conclusions: Nos résultats confirment ceux des recherches quantitatives effectuées par notre groupe, et viennent les étayer. Les données et les résultats générés par cette étude pourraient être utilisés pour mettre au point des mesures visant l’amélioration de la qualité. Ils pourraient également jouer un rôle important dans la création d’un sondage sur l’expérience des patients, et plus précisément l’expérience des patients ayant subi une intervention chirurgicale cardiaque, et ainsi remédier aux limites possibles des sondages utilisés à l’heure actuelle.

Abstract 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.

Abstract Background Hypertension is a common chronic condition affecting nearly a quarter of Canadians. Hypertension surveillance in Canada typically relies on administrative data and/or national surveys. Routinely-captured data from primary care electronic medical records (EMRs) are a complementary source for chronic disease surveillance, with longitudinal patient-level details such as sociodemographics, blood pressure, weight, prescribed medications, and behavioural risk factors. As EMR data are generated from patient care and administrative tasks, assessing data quality is essential before using for secondary purposes. This study evaluated the quality of primary care EMR data from one province in Canada within the context of hypertension surveillance. Methods We conducted a cross-sectional, descriptive study using primary care EMR data collected by two practice-based research networks in Alberta, Canada. There were 48,377 adults identified with hypertension from 53 clinics as of June 2018. Summary statistics were used to examine the quality of data elements considered relevant for hypertension surveillance. Results Patient year of birth and sex were complete, but other sociodemographic information (ethnicity, occupation, education) was largely incomplete and highly variable. Height, weight, body mass index and blood pressure were complete for most patients (over 90%), but a small proportion of outlying values indicate data inaccuracies were present. Most patients had a relevant laboratory test present (e.g. blood glucose/glycated hemoglobin, lipid profile), though a very small proportion of values were outside a biologically plausible range. Details of prescribed antihypertensive medication, such as start date, strength, dose, frequency, were mostly complete. Nearly 80% of patients had a smoking status recorded, though only 66% had useful information (i.e. categorized as current, past, or never), and less than half had their alcohol use described; information related to amount, frequency or duration was not available. Conclusions Blood pressure and prescribed medications in primary care EMR data demonstrated good completeness and plausibility, and contribute valuable information for hypertension epidemiology and surveillance. The use of other clinical, laboratory, and sociodemographic variables should be used carefully due to variable completeness and suspected data errors. Additional strategies to improve these data at the point of entry and after data extraction (e.g. statistical methods) are required.

BackgroundHeart 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. We sought to develop and validate a novel risk model for the patient-specific prediction of time to HF hospitalization using routinely reported CMR variables, patient-reported health status, and electronic health information.MethodsStandardized data capture was performed for 1,775 consecutive patients with chronic systolic HF referred for CMR imaging. Patient demographics, symptoms, Health-related Quality of Life, pharmacy, and routinely reported CMR features were provided to both machine learning (ML) and competing risk Fine-Gray-based models (FGM) for the prediction of time to HF hospitalization.ResultsThe mean age was 59 years with a mean LVEF of 36 ± 11%. The population was evenly distributed between ischemic (52%) and idiopathic non-ischemic cardiomyopathy (48%). Over a median follow-up of 2.79 years (IQR: 1.59–4.04) 333 patients (19%) experienced HF related hospitalization. Both ML and competing risk FGM based models achieved robust performance for the prediction of time to HF hospitalization. Respective 90-day, 1 and 2-year AUC values were 0.87, 0.83, and 0.80 for the ML model, and 0.89, 0.84, and 0.80 for the competing risk FGM-based model in a holdout validation cohort. Patients classified as high-risk by the ML model experienced a 34-fold higher occurrence of HF hospitalization at 90 days vs. the low-risk group.ConclusionIn this study we demonstrated capacity for routinely reported CMR phenotypic markers and patient health information to be combined for the delivery of patient-specific predictions of time to HF hospitalization. This work supports an evolving migration toward multi-domain data collection for the delivery of personalized risk prediction at time of diagnostic imaging.

Abstract Background The Seattle Angina Questionnaire (SAQ) is a widely-used patient-reported outcomes measure in patients with heart disease. This study assesses the validity and reliability of the SAQ in a Canadian cohort of individuals with stable angina. Methods and results Data are from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) registry, a population-based registry of patients who received cardiac catheterization in Alberta, Canada. The cohort consists of 4052 patients undergoing cardiac catheterization for stable angina and completed the SAQ within 2 weeks. Exploratory factor analysis and confirmatory factor analysis (CFA) were used to assess the factorial structure of the SAQ. Internal and test–retest reliabilities of a new measure (i.e., SAQ-CAN) was measured using Cronbach α and intraclass correlation coefficient, respectively. CFA model fit was assessed using the root mean square error of approximation (RMSEA) and comparative fit index (CFI). Construct validity of the SAQ-CAN was assessed in relation to Hospital Anxiety and Depression Scales (HADS), Euro Quality of life 5 dimension (EQ5D), and original SAQ. Of the 4052 patients included in this analysis, 3281 (80.97%) were younger than 75 years old, while 3239 (79.94%) were male. Both exploratory and confirmatory factor analyses revealed a four-factorial structure consisting of 16 items that provided a better fit to the data (RMSEA = 0.049 [90% CI = (0.047, 0.052)]; CFI = 0.975). The 16-item SAQ demonstrated good to excellent internal reliability (Cronbach’s α range from 0.77 to 0.90), moderate to strong correlation with the Original SAQ and EQ5D but negligible correlations with HADS. Conclusion The SAQ-CAN has acceptable psychometric properties that are comparable to the original SAQ. We recommend its use for assessing coronary health outcomes in Canadian patients with Coronary Artery Disease.

Background: Despite their proven efficacy to reduce cardiovascular disease, statin medication use remains low in individuals at high risk of cardiovascular disease considering their widespread availability and safety. Our objective was to explore the perspectives of patients and family physicians with regard to the barriers and facilitators of statin use in primary care. Methods: In this qualitative descriptive study, we conducted 2 focus groups with patients (number, n = 8/6) and individual semistructured interviews with family physicians (n = 17) from community settings. Interviewers asked participants about barriers to and facilitators of statin use. Focus groups and interviews were digitally recorded, transcribed, and analyzed in duplicate using conventional content analysis. Results: Patients were averse to taking statins for a variety of reasons: medication avoidance and burden; inadequate buy-in for statin therapy; and difficulty remembering to take statins regularly. Family physicians perceived similar barriers and reported other barriers: lack of resources such as inadequate tracking systems; specialist-primary care provider guideline discordance; and lack of continuity and relationship. Patients expressed that key facilitators were patient education and support; splitting tablets to increase cost-effectiveness; and changing to a different statin or lower dose in those with side effects. Family physicians described several similar strategies to facilitate therapy as well as shared decision making and clinical decision support tools as enablers for improvement. Conclusions: We identified several important barriers to and facilitators of statin use at the patient and prescriber level. This information offers insight into strategies to improve statin use and the development of innovative programs and interventions. Résumé: Introduction: En dépit de leur efficacité prouvée pour réduire les maladies cardiovasculaires, l’utilisation des statines reste faible chez les individus exposés à un risque élevé de maladies cardiovasculaires si l’on considère leur grande disponibilité et leur innocuité. Notre objectif était d’examiner les perspectives des patients et des médecins de famille en ce qui concerne les obstacles et les facilitateurs de l’utilisation des statines en soins primaires. Méthodes: Dans la présente étude qualitative descriptive, nous avons mené 2 groupes de discussion composés de patients (nombre, n = 8/6) et des entrevues semi-structurées individuelles avec des médecins de famille (n = 17) en milieu communautaire. Les intervieweurs ont demandé aux participants quels étaient les obstacles et les facilitateurs de l’utilisation des statines. Les groupes de discussion et les entrevues étaient enregistrés numériquement, transcrits et analysés en duplicata à l’aide de l’analyse de contenu traditionnelle. Résultats: Les patients se sont opposés à la prise de statines pour plusieurs raisons : l’évitement et le fardeau des médicaments, l’adhésion insuffisante au traitement par statines et la difficulté à se souvenir de prendre régulièrement les statines. Les médecins de famille ont perçu des obstacles similaires et ont rapporté d’autres obstacles dont le manque de ressources telles que les systèmes de suivi inadéquats, la divergence entre les orientations des spécialistes et des prestataires de soins primaires, et le manque de continuité et de relation. Les patients ont exprimé que les principaux facilitateurs étaient l’éducation et le soutien offerts aux patients; le fractionnement des comprimés pour améliorer l’efficience; le changement vers une statine différente ou une dose plus faible chez ceux qui présentent des effets secondaires. Les médecins de famille ont décrit plusieurs stratégies semblables pour faciliter le traitement ainsi que la prise de décision partagée et les outils d’aide à la décision clinique qui facilitent l’amélioration. Conclusions: Nous avons déterminé plusieurs obstacles et facilitateurs importants de l’utilisation des statines au point de vue du patient et du prescripteur. Ces informations offrent un aperçu des stratégies pour améliorer l’utilisation des statines et l’élaboration d’interventions et de programmes innovateurs.

Abstract 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. Association rule mining was conducted on the re-abstracted data in four age groups (0–4, 20–44, 45–64; ≥ 65) to extract ICD-10 coding association rules at the three-digit (category of diagnosis) and four-digit levels (category of diagnosis with etiology, anatomy, or severity). The rules were reviewed by a panel of 5 physicians and 2 classification specialists using a modified Delphi rating process. We proposed and defined the variance and bias to assess data quality using the rules. Results After the rule mining process and the panel review, 388 rules at the three-digit level and 275 rules at the four-digit level were developed. Half of the rules were from the age group of ≥65. Rules captured meaningful age-specific clinical associations, with rules at the age group of ≥65 being more complex and comprehensive than other age groups. The variance and bias can identify rules with high bias and variance in Alberta data and provides directions for quality improvement. Conclusions A set of ICD-10 data quality rules were developed and validated by a clinical and classification expert panel. The rules can be used as a tool to assess ICD-coded data, enabling the monitoring and comparison of data quality across institutions, provinces, and countries.

Abstract Background Primary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients’ health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database. Methods EMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016–2018) and having hypertension according to a validated definition were included (n = 48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey. Results Among those with hypertension, 40.8% (n = 19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category. Conclusion Multiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).

BackgroundTo expand research and strategies to prevent disease, comprehensive and real-time data are essential. Health data are increasingly available from platforms such as pharmaceuticals, genomics, health care imaging, medical procedures, wearable devices, and internet activity. Further, health data are integrated with an individual’s sociodemographic information, medical conditions, genetics, treatments, and health care. Ultimately, health information generation and flow are controlled by the patient or participant; however, there is a lack of understanding about the factors that influence willingness to share health information. A synthesis of the current literature on the multifactorial nature of health information sharing preferences is required to understand health information exchange. ObjectiveThe objectives of this review are to identify peer-reviewed literature that reported factors associated with health information sharing and to organize factors into cohesive themes and present a narrative synthesis of factors related to willingness to share health information. MethodsThis review uses a rapid review methodology to gather literature regarding willingness to share health information within the context of eHealth, which includes electronic health records, personal health records, mobile health information, general health information, or information on social determinants of health. MEDLINE and Google Scholar were searched using keywords such as electronic health records AND data sharing OR sharing preference OR willingness to share. The search was limited to any population that excluded health care workers or practitioners, and the participants aged ≥18 years within the US or Canadian context. The data abstraction process using thematic analysis where any factors associated with sharing health information were highlighted and coded inductively within each article. On the basis of shared meaning, the coded factors were collated into major themes. ResultsA total of 26 research articles met our inclusion criteria and were included in the qualitative analysis. The inductive thematic coding process revealed multiple major themes related to sharing health information. ConclusionsThis review emphasized the importance of data generators’ viewpoints and the complex systems of factors that shape their decision to share health information. The themes explored in this study emphasize the importance of trust at multiple levels to develop effective information exchange partnerships. In the case of improving precision health care, addressing the factors presented here that influence willingness to share information can improve sharing capacity for individuals and allow researchers to reorient their methods to address hesitation in sharing health information.

Background Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm’s validity in Canadian EMR data. Methods Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard. Discussion The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs.

ObjectiveWe aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.MethodsWe systematically searched MEDLINE, EMBASE, Web of Science, Scopus, and the grey literature for studies predicting the risk of hypertension among the general adult population. Summary statistics from the individual studies were the C-statistic, and a random-effects meta-analysis was used to obtain pooled estimates. The predictive performance of pooled estimates was compared between traditional regression-based models and machine learning-based models. The potential sources of heterogeneity were assessed using meta-regression, and study quality was assessed using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) checklist.ResultsOf 14,778 articles, 52 articles were selected for systematic review and 32 for meta-analysis. The overall pooled C-statistics was 0.75 [0.73-0.77] for the traditional regression-based models and 0.76 [0.72-0.79] for the machine learning-based models. High heterogeneity in C-statistic was observed. The age (p = 0.011), and sex (p = 0.044) of the participants and the number of risk factors considered in the model (p = 0.001) were identified as a source of heterogeneity in traditional regression-based models.ConclusionWe attempted to provide a comprehensive evaluation of hypertension risk prediction models. Many models with acceptable-to-good predictive performance were identified. Only a few models were externally validated, and the risk of bias and applicability was a concern in many studies. Overall discrimination was similar between models derived from traditional regression analysis and machine learning methods. More external validation and impact studies to implement the hypertension risk prediction model in clinical practice are required.

Introduction Countries use varying coding standards, which impact international coded data comparability. The `main condition' (MC) field is coded within the Discharge Abstract Database as "reason for admission" or "largest resource use". Objective We offer a preliminary analysis on the frequency of and contributing factors to MC definition agreements within an inpatient Canadian dataset. Methods Six professional coders performed a chart review between August 2016 and June 2017 on 3,000 randomly selected inpatient charts from three acute care hospitals in Calgary, Alberta. Coders classified the MC as "reason for admission", "largest resource use" or "both". Patients were admitted between 1st January and 30th June 2015 and met the inclusion criteria if they were >18 years, had an Alberta personal health care number, and had an inpatient visit for any service outside of obstetrics. Agreement between the two MC definitions was stratified by length of stay (LOS), emergency department admission, hospital of origin, discharge location, age, sex, procedures, and comorbidities. Chi-square analysis and frequency of inconsistencies were reported. Results Only 34 (1.51%) of the 2,250 patient charts had disagreeing MC definitions. Age, emergency visit on admit, LOS, hospital, and discharge location were associated with MC agreement. Chronic conditions were seen more often in MC definition agreements, and acute conditions seen within those disagreeing. Conclusion There was a small proportion of cases in which the condition bringing the patient to hospital was not also the condition occupying the largest resources. Within disagreements, further research using a larger sample size is needed to explore the presence of MC in a secondary/tertiary condition, the association between patient complexity and disagreeing MC definitions, and the nature of the conditions seen in the inconsistent MC definitions.

Background: Simultaneous kidney-pancreas transplantation (SPK) has benefits for patients with kidney failure and type I diabetes mellitus, but is associated with greater perioperative risk compared with kidney-alone transplantation. Postoperative care settings for SPK recipients vary across Canada and may have implications for patient outcomes and hospital resource use. Objective: To compare outcomes following SPK transplantation between patients receiving postoperative care in the intensive care unit (ICU) compared with the ward. Design: Retrospective cohort study using administrative health data. Setting: In Alberta, the 2 transplant centers (Calgary and Edmonton) have different protocols for routine postoperative care of SPK recipients. In Edmonton, SPK recipients are routinely transferred to the ICU, whereas in Calgary, SPK recipients are transferred to the ward. Patients: 129 adult SPK recipients (2002-2019). Measurements: Data from the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD) were used to identify SPK recipients (procedure codes) and the outcomes of inpatient mortality, length of initial hospital stay (LOS), and the occurrence of 16 different patient safety indicators (PSIs). Methods: We followed SPK recipients from the admission date of their transplant hospitalization until the first of hospital discharge or death. Unadjusted quantile regression was used to determine differences in LOS, and age- and sex-adjusted marginal probabilities were used to determine differences in PSIs between centers. Results: There were no perioperative deaths and no major differences in the demographic characteristics between the centers. The majority of the SPK transplants were performed in Edmonton (n = 82, 64%). All SPK recipients in Edmonton were admitted to the ICU postoperatively, compared with only 11% in Calgary. There was no statistically significant difference in the LOS or probability of a PSI between the 2 centers (LOS for Edmonton vs Calgary:16 vs 13 days, P = .12; PSIs for Edmonton vs Calgary: 60%, 95% confidence interval [CI] = 0.50-0.71 vs 44%, 95% CI = 0.29-0.59, P = .08). Limitations: This study was conducted using administrative data and is limited by variable availability. The small sample size limited precision of estimated differences between type of postoperative care. Conclusions: Following SPK transplantation, we found no difference in inpatient outcomes for recipients who received routine postoperative ICU care compared with ward care. Further research using larger data sets and interventional study designs is needed to better understand the implications of postoperative care settings on patient outcomes and health care resource utilization.

Abstract Background The Charlson comorbidity index (CCI) is commonly used to adjust for patient casemix. We reevaluated the CCI in an ischemic stroke (IS) cohort to determine whether the original seventeen comorbidities and their weights are relevant. Methods We identified an IS cohort (N = 6988) from the Ontario Stroke Registry (OSR) who were discharged from acute hospitals (N = 100) between April 1, 2012 and March 31, 2013. We used hospital discharge ICD-10-CA data to identify Charlson comorbidities. We developed a multivariable Cox model to predict one-year mortality retaining statistically significant (P

Abstract Background The emergency department (ED) sensitive hospital standardized mortality ratio (ED-HSMR) measures risk-adjusted mortality for patients admitted to hospital with conditions for which ED care may improve health outcomes. This study aimed to describe in-hospital mortality across Canadian provinces using the ED-HSMR. Methods Hospital discharge data were analyzed from April 2009 to March 2012. The ED-HSMR was calculated as the ratio of observed deaths among patients with emergency-sensitive conditions in a hospital during a year (2010–11 or 2011–12) to the expected deaths for the same patients during the reference year (2009–10), multiplied by 100. The expected deaths were estimated using predictive models fitted from the reference year. Aggregated provincial ED-HSMR values were calculated. A HSMR value above or below 100 respectively means that more or fewer deaths than expected occurred within a province. Results During the study period, 1,335,379 patients were admitted to hospital in Canada with an emergency-sensitive condition as the most responsible diagnosis. More in-hospital deaths (95% confidence interval) than expected were respectively observed for the years 2010–11 and 2011–12 in Newfoundland [124.3 (116.3–132.6); & 117.6 (110.1–125.5)] and Nova Scotia [116.4 (110.7–122.5) & 108.7 (103.0–114.5)], while mortality was as expected in Prince Edward Island [99.9 (86.5–114.8) & 100.7 (87.5–115.3)] and Manitoba [99.2 (94.5–104.1) & 98.3 (93.5–103.3)], and less than expected in all other provinces and territories. Conclusions Our study revealed important variation in risk-adjusted mortality for patients admitted to hospital with emergency-sensitive conditions among Canadian provinces. The ED-HSMR may be a useful outcome indicator to complement existing process indicators in measuring ED performance. Trial registration N/A – Retrospective cohort study.

Following publication of the original manuscript [1], the authors noted several errors in Table 1. Details of the requested corrections are shown below: