Background: Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective: This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods: A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results: A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions: Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Background: Patient-reported experience surveys allow administrators, clinicians, and researchers to quantify and improve health care by receiving feedback directly from patients. Existing research has focused primarily on quantitative analysis of survey items, but these measures may collect optional free-text comments. These comments can provide insights for health systems but may not be analyzed due to limited resources and the complexity of traditional textual analysis. However, advances in machine learning–based natural language processing provide opportunities to learn from this traditionally underused data source. Objective: This study aimed to apply natural language processing to model topics found in free-text comments of patient-reported experience surveys. Methods: Consumer Assessment of Healthcare Providers and Systems–derived patient experience surveys were collected and linked to administrative inpatient records by the provincial health services organization responsible for inpatient care. Unsupervised topic modeling with automated labeling was performed with BERTopic. Sentiment analysis was performed to further assist in topic description. Results: Between April 2016 and February 2020, 43.4% (43,522/100,272) adult patients and 46.9% (3501/7464) pediatric caregivers included free-text responses on completed patient experience surveys. Topic models identified 86 topics among adult survey responses and 35 topics among pediatric responses that included elements of care not currently surveyed by existing questionnaires. Frequent topics were generally positive. Conclusions: We found that with limited tuning, BERTopic identified care experience topics with interpretable automated labeling. Results are discussed in the context of person-centered care, patient safety, and health care quality improvement. Furthermore, we note the opportunity for the identification of temporal and site-specific trends as a method to identify patient care and safety concerns. As the use of patient experience measurement increases in health care, we discuss how machine learning can be leveraged to provide additional insight on patient experiences.

The COVID-19 pandemic affected access to care, and the associated public health measures influenced the transmission of other infectious diseases. The pandemic has dramatically changed antibiotic prescribing in the community. We aimed to determine the impact of the COVID-19 pandemic and the resulting control measures on oral antibiotic prescribing in long-term care facilities (LTCFs) in Alberta and Ontario, Canada using linked administrative data. Antibiotic prescription data were collected for LTCF residents 65 years and older in Alberta and Ontario from 1 January 2017 until 31 December 2020. Weekly prescription rates per 1000 residents, stratified by age, sex, antibiotic class, and selected individual agents, were calculated. Interrupted time series analyses using SARIMA models were performed to test for changes in antibiotic prescription rates after the start of the pandemic (1 March 2020). The average annual cohort size was 18,489 for Alberta and 96,614 for Ontario. A significant decrease in overall weekly prescription rates after the start of the pandemic compared to pre-pandemic was found in Alberta, but not in Ontario. Furthermore, a significant decrease in prescription rates was observed for antibiotics mainly used to treat respiratory tract infections: amoxicillin in both provinces (Alberta: −0.6 per 1000 LTCF residents decrease in weekly prescription rate, p = 0.006; Ontario: −0.8, p < 0.001); and doxycycline (−0.2, p = 0.005) and penicillin (−0.04, p = 0.014) in Ontario. In Ontario, azithromycin was prescribed at a significantly higher rate after the start of the pandemic (0.7 per 1000 LTCF residents increase in weekly prescription rate, p = 0.011). A decrease in prescription rates for antibiotics that are largely used to treat respiratory tract infections is in keeping with the lower observed rates for respiratory infections resulting from pandemic control measures. The results should be considered in the contexts of different LTCF systems and provincial public health responses to the pandemic.

Objectives To ensure sufficient resources to care for patients with COVID-19, healthcare systems delayed non-urgent surgeries to free capacity. This study explores the consequences of delaying non-urgent surgery on surgical care and healthcare resource use. Design This is a population-based retrospective cohort study. Setting This study took place in Alberta, Canada, from December 2018 to December 2021. Participants Adult patients scheduled for surgery in Alberta during the study period were included. Primary and secondary outcomes measures The proportion of surgeries completed and surgery wait time were the primary outcomes. The secondary outcomes were healthcare resource use (hospital length of stay, emergency room visits and physician visits). The association between the primary outcomes and patient and surgery-related variables was explored using regression. Results There were 202 470 unique patients with 259 677 scheduled surgeries included. Fewer surgeries were completed throughout the pandemic compared with before; in the fourth wave, there was a decrease from 79% pre-COVID-19 to 67%. There was a decrease in wait time for those who had surgery completed during COVID-19 (from 105 to 69 days). Having surgery completed and the wait for surgery were associated with the geographical zone, COVID-19 wave, and the surgery type and priority. There was a decrease in all measures of healthcare resource use and an increase in hospital and all-cause mortality during COVID-19 compared with before COVID-19. Conclusions The change in the proportion of scheduled surgeries completed and the wait time for completed surgery was modest and associated with COVID-19 wave and surgery-related variables, which was aligned with policies enacted during COVID-19 for surgery. The decrease in healthcare resource use suggests the effects of the COVID-19 pandemic may be delayed and may result in many patients presenting with advanced disease requiring surgical care.

BACKGROUND Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. OBJECTIVE This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. METHODS A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. RESULTS A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. CONCLUSIONS Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed. CLINICALTRIAL