Background: Hospital readmissions within 7 days after discharge are considered highly avoidable and undesirable for the patient and hospital. It is important to clarify gaps in the quality of inpatient care to identify strategies to address this problem. Aims: We aimed to describe the precipitating factors and determine the potential avoidability of 7-day readmissions after hospitalization for heart failure (HF). Methods: A health record audit was undertaken of patients discharged after hospitalization for HF from Calgary, Alberta hospitals. Content analysis was undertaken to identify factors precipitating readmission, and readmission's avoidability was qualitatively examined and scored based on descriptive categories. Results: Of 18,590 patients admitted to the hospital for HF during the study period, 191 HF patients were readmitted within 7 days (50% female; mean age 78 years). Potentially avoidable readmissions (57%) were due to unresolved symptoms, unaddressed social or self-care issues, adverse events from the index admission, high disability without added services, and discussions of palliative care without added services. Readmissions deemed less avoidable (43%) were due to new health issues, recurring symptoms post-stability at discharge, or refusal of care. Conclusion: Only half of hospital readmissions within 7 days after HF discharge were related to HF and more than half were scored as avoidable. We provide novel criteria for identifying the avoidability of 7-day readmissions that could be used for assessing HF patients' readiness for discharge and potentially reducing readmission rates. Keywords: Heart failure; Risk factors; Heart disease; Chronic disease; Readmission ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This doctoral study was supported by the Izaak Walton Killam Memorial Scholarship and Alberta Innovates - Health Solutions (AIHS) Clinician Researcher Fellowship. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Conjoint Health Research Ethics Board of Alberta of the University of Calgary gave ethical approval for this work (REB-E-25279) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data cannot be shared publicly because to respect the privacy of the participants. Data are available from the University of Calgary Institutional Data Access / Ethics Committee (contact via cfreb{at}ucalgary.ca) for researchers who meet the criteria for access to confidential data.

Background The most recent and 11th revision of the International Classification of Disease (ICD-11) is in use as of January 2022, and countries around the globe are now preparing for the implementation of ICD-11 and transition from the 10th revision (ICD-10). Translation of current coding is required for historical comparisons. Methods We applied the World Health Organization (WHO) mapping tables to current Centers for Disease Control and Prevention (CDC) Lists of ICD-10 coding of causes of death to assess what ICD-11 codes look like in an Alberta sample of causes of death (COD). We prepared frequency tables for a single year of causes of death in Alberta based on the CDC grouping of COD. Results The mapping success rate for the adult population was 96.6% for unique ICD-10 COD codes and 99.5% for deaths. The mapping success rate was 100% for children and infants for both ICD-10 COD codes and deaths. We mapped ICD-11 codes to identify the ten most frequently reported COD in Alberta for 24,645 deaths in adults, children, and infants in 2017. Conclusions Apart from two codes, all ICD-10 codes could be mapped to ICD-11 for COD. These findings suggest that the ability to translate from the two iterations of coding will be feasible for future applications of health services data.

Background: Recent years have seen a surge in the use of artificial intelligence (AI) in healthcare, including dermatology. This scoping review aimed to assess the emerging applications of AI use in the context of chronic, non-neoplastic dermatologic diseases. Methods: MEDLINE, Embase, PubMed and SCOPUS were searched on August 11, 2023 using variations of the search concepts “dermatology,” “artificial intelligence,” and 12 common chronic dermatologic conditions. Article screening and data extraction were completed, and each study was categorized into themes and conditions. Results: A total of 224 unique studies were included. The most prevalent conditions that were studied in the context of AI included psoriasis (n = 67), atopic dermatitis/eczema (n = 41) and acne (n = 36). The majority of AI applications involved clinical evaluation (n = 176), images (analysis, generation or segmentation) (n = 163) and data analysis (n = 46). Clinical evaluation was further divided into 2 subthemes: diagnosis (n = 104) and disease assessment (n = 67). Diagnostic and analytic applications of AI are limited by the training datasets available (quantity of training data, image quality) and insufficient diagnostic information provided (eg, the patient’s reported history of their lesion, disease/symptom onset and risk factors). Conclusions: Common applications of AI are predominantly as an automated diagnostic tool for evaluating disease severity/characteristics, while niche and novel applications were explored further. However, recognizing the limitations of technology is critical prior to the widespread application of AI in dermatological practice. The insights from the current study can inform clinical adoption of AI in dermatology, and highlight research gaps to guide future academic initiatives.

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.

Background: Case identification is important for health services research, measuring health system performance and risk adjustment, but existing methods based on manual chart review or diagnosis codes can be expensive, time consuming or of limited validity. We aimed to develop a hypertension case definition in electronic medical records (EMRs) for inpatient clinical notes using machine learning. Methods: A cohort of patients 18 years of age or older who were discharged from 1 of 3 Calgary acute care facilities (1 academic hospital and 2 community hospitals) between Jan. 1 and June 30, 2015, were randomly selected, and we compared the performance of EMR phenotype algorithms developed using machine learning with an algorithm based on the Canadian version of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD), in identifying patients with hypertension. Hypertension status was determined by chart review, the machine-learning algorithms used EMR notes and the ICD algorithm used the Discharge Abstract Database (Canadian Institute for Health Information). Results: Of our study sample (n = 3040), 1475 (48.5%) patients had hypertension. The group with hypertension was older (median age of 71.0 yr v. 52.5 yr for those patients without hypertension) and had fewer females (710 [48.2%] v. 764 [52.3%]). Our final EMR-based models had higher sensitivity than the ICD algorithm (> 90% v. 47%), while maintaining high positive predictive values (> 90% v. 97%). Interpretation: We found that hypertension tends to have clear documentation in EMRs and is well classified by concept search on free text. Machine learning can provide insights into how and where conditions are documented in EMRs and suggest nonmachine-learning phenotypes to implement.

Qualitative data analysis is produced frequently in healthcare settings, which is a time-consuming and skilled analytic task. The use of qualitative research findings in clinical settings takes years, which is sometimes obsolete knowledge as the health context is dynamic. Artificial Intelligence (AI)-based qualitative data analysis might present with rapid analysis of text-based data in real-time, thereby empowering qualitative researchers to expedite their analysis and facilitate timely use of the research findings. We tested an AI-based method to complement the manual analysis of text-based data from the verbatim transcripts of seven mall managers’ interviews. First, we prepared text data into a machine-calculable format and employed BERT model to extract sentence-level features in our case. Second, we implement TF-IDF-based keywords mining techniques to extract the main candidate themes from the interview transcripts to support text-based analysis, including: 1) primary cluster detection algorithm, and 2) keyword extraction algorithm. The extracted core themes provide qualitative researchers with a more comprehensive overview of the qualitative data. Most of the sentences clustered in meaningful short topics or sentences carrying independent and clear information. The extracted topics and clustered sentences reduced qualitative researchers’ workload by condensing and identifying meaningful concepts and naming them. This method combining contextualized word embeddings, unsupervised clustering, and keyword extraction techniques can significantly reduce the overall workload and time consumed in qualitative research using conventional methods.

Social Determinant of Health (SDOH) data are important targets for research and innovation in Health Information Systems (HIS). The ways we envision SDOH in "smart" information systems will play a considerable role in shaping future population health landscapes. Current methods for data collection can capture wide ranges of SDOH factors, in standardised and nonstandardised formats, from both primary and secondary sources. Advances in automating data linkage and text classification show particular promise for enhancing SDOH in HIS. One challenge is that social communication processes embedded in data collection are directly related to the inequalities that HIS attempt to measure and redress. To advance equity, it is imperative thatcare-providers, researchers, technicians, and administrators attend to power dynamics in HIS standards and practices. We recommend: 1. Investing in interdisciplinary and intersectoral knowledge generation and translation. 2. Developing novel methods for data discovery, linkage and analysis through participatory research. 3. Channelling information into upstream evidence-informed policy.

BACKGROUND & AIMS: Coronavirus disease 2019 (COVID-19) pandemic lockdown and restrictions had significant disruption to patient care. We aimed to evaluate the impact of COVID-19 restrictions on hospitalizations of patients with alcoholic and nonalcoholic cirrhosis as well as alcoholic hepatitis (AH) in Alberta, Canada. METHODS: We used validated International Classification of Diseases (ICD-9 and ICD-10) coding algorithms to identify liver-related hospitalizations for nonalcoholic cirrhosis, alcoholic cirrhosis, and AH in the province of Alberta between March 2018 and September 2020. We used the provincial inpatient discharge and laboratory databases to identify our cohorts. We used elevated alanine aminotransferase or aspartate aminotransferase, elevated international normalized ratio, or bilirubin to identify AH patients. We compared COVID-19 restrictions (April-September 2020) with prior study periods. Joinpoint regression was used to evaluate the temporal trends among the 3 cohorts. RESULTS: We identified 2916 hospitalizations for nonalcoholic cirrhosis, 2318 hospitalizations for alcoholic cirrhosis, and 1408 AH hospitalizations during our study time. The in-hospital mortality rate was stable in relation to the pandemic for alcoholic cirrhosis and AH. However, nonalcoholic cirrhosis patients had lower in-hospital mortality rate after March 2020 (8.5% vs 11.5%; P = .033). There was a significant increase in average monthly admissions in the AH cohort (22.1/10,000 admissions during the pandemic vs 11.6/10,000 admissions before March 2020; P < .001). CONCLUSIONS: Before and during COVID-19 monthly admission rates were stable for nonalcoholic and alcoholic cirrhosis; however, there was a significant increase in AH admissions. Because alcohol sales surged during the pandemic, future impact on alcoholic liver disease could be detrimental.

Background The International Classification of Diseases (ICD) is widely used by clinical coders worldwide for clinical coding morbidity data into administrative health databases. Accordingly, hospital data quality largely depends on the coders’ skills acquired during ICD training, which varies greatly across countries. Objective To characterise the current landscape of international ICD clinical coding training. Method An online questionnaire was created to survey the 194 World Health Organization (WHO) member countries. Questions focused on the training provided to clinical coding professionals. The survey was distributed to potential participants who met specific criteria, and to organisations specialised in the topic, such as WHO Collaborating Centres, to be forwarded to their representatives. Responses were analysed using descriptive statistics. Results Data from 47 respondents from 26 countries revealed disparities in all inquired topics. However, most participants reported clinical coders as the primary person assigning ICD codes. Although training was available in all countries, some did not mandate training qualifications, and those that did differed in type and duration of training, with college or university degree being most common. Clinical coding certificates most frequently entailed passing a certification exam. Most countries offered continuing training opportunities, and provided a range of support resources for clinical coders. Conclusion Variability in clinical coder training could affect data collection worldwide, thus potentially hindering international comparability of health data. Implications These findings could encourage countries to improve their resources and training programs available for clinical coders and will ultimately be valuable to the WHO for the standardisation of ICD training.

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.

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: 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.

Objective: Countries worldwide, including Canada, need tools for informed decision-making on the adoption of ICD-11. The purpose of the current study is to create a cost and outcome estimation framework for the transition from ICD-10-CA to ICD-11 in Canada and anticipate the benefits/outcomes and international considerations of ICD-11 adoption. Methods: This paper follows a cost and outcome evaluation framework. The costs of transitioning to the ICD-11 coding system are based on literature reviews and interviews adapted for the Canadian context, while the outcomes have been created using a novel methodology involving face and criterion validity. Results: While it has been difficult and infeasible to include all possible categories and variables identified by the interviewees into the framework, this paper provides a comprehensive guideline of the outcome and cost estimation methodology. Conclusion: The estimation technique adopted in this paper is unique and may be used as a benchmark methodology by other countries to evaluate the adoption of the ICD-11 coding system.

Background: The new International Classification of Diseases, Eleventh Revision for Mortality and Morbidity Statistics (ICD-11) was developed and released by the World Health Organization (WHO) in June 2018. Because ICD-11 incorporates new codes and features, training materials for coding with ICD-11 are urgently needed prior to its implementation. Objective: This study outlines the development of ICD-11 training materials, training processes and experiences of clinical coders while learning to code using ICD-11. Method: Six certified clinical coders were recruited to code inpatient charts using ICD-11. Training materials were developed with input from experts from the Canadian Institute for Health Information and the WHO, and the clinical coders were trained to use the new classification. Monthly team meetings were conducted to enable discussions on coding issues and to select the correct ICD-11 codes. The training experience was evaluated using qualitative interviews, a questionnaire and a coding quiz. Results: total of 3011 charts were coded using ICD-11. In general, clinical coders provided positive feedback regarding the training program. The average score for the coding quiz (multiple choice, True/False) was 84%, suggesting that the training program was effective. Feedback from the coders enabled the ICD-11 code content, electronic tooling and terminologies to be updated. Conclusion: This study provides a detailed account of the processes involved with training clinical coders to use ICD-11. Important findings from the interviews were reported at the annual WHO conferences, and these findings helped improve the ICD-11 browser and reference guide.

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.

ObjectiveNew codes developed in the International Classification of Diseases, 11th Revision for Mortality and Morbidity Statistics (ICD-11) needed testing. Field-testing involves real-world application of the new codes to examine data quality. This paper describes the field trial methods to create a dually coded database to field test ICD-11 against ICD-10-CA (a Canadian modification of the International Classification of Diseases, Tenth Revision), and a reference standard data set of diagnoses. ResultsA random sample of discharge records previously coded using ICD-10-CA was selected. Nurses re-examined these entire charts for specific conditions and patient safety events. Clinical coders re-coded the same charts using ICD-11 codes. Inpatients discharged from hospitals in Calgary, Alberta, were identified and a dually coded database was created (n=2897). Inter-rater reliability and coding time improved with ICD-11 coding experience. Clinical coder comments enabled content to be improved in the ICD-11 browser, Coding Tool, and ICD-11 Reference Guide. This paper describes the field trial, database creation methods, and contributions for ICD-11 improvement. Crucial future research will use this database to test ICD-11 before implementation in Canada.

Objectives The overall goal of this study is to identify priorities for cardiovascular (CV) health research that are important to patients and clinician-researchers. We brought together a group of CV patients and clinician-researchers new to patient-oriented research (POR), to build a multidisciplinary POR team and form an advisory committee for the Libin Cardiovascular Institute of Alberta. Design This qualitative POR used a participatory health research paradigm to work with participants in eliciting their priorities. Therefore, participants were involved in priority setting, and analysis of findings. Participants also developed a plan for continued engagement to support POR in CV health research. Setting Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Canada. Participants A total of 23 participants, including patients and family caregivers (n=12) and clinician-researchers (n=11). Results Participants identified barriers and facilitators to POR in CV health (lack of awareness of POR and poor understanding on the role of patients) and 10 research priorities for improving CV health. The CV health research priorities include: (1) CV disease prediction and prevention, (2) access to CV care, (3) communication with providers, (4) use of eHealth technology, (5) patient experiences in healthcare, (6) patient engagement, (7) transitions and continuity of CV care, (8) integrated CV care, (9) development of structures for patient-to-patient support and (10) research on rare heart diseases. Conclusions In this study, research priorities were identified by patients and clinician-researchers working together to improve CV health. Future research programme and projects will be developed to address these priorities. A key output of this study is the creation of the patient advisory council that will provide support and will work with clinician-researchers to improve CV health.

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.

Background The All Our Families (AOF) cohort study is a longitudinal population-based study which collected biological samples from 1948 pregnant women between May 2008 and December 2010. As the quality of samples can decline over time, the objective of the current study was to assess the association between storage time and RNA (ribonucleic acid) yield and purity, and confirm the quality of these samples after 7–10 years in long-term storage. Methods Maternal whole blood samples were previously collected by trained phlebotomists and stored in four separate PAXgene Blood RNA Tubes (PreAnalytiX) between 2008 and 2011. RNA was isolated in 2011 and 2018 using PAXgene Blood RNA Kits (PreAnalytiX) as per the manufacturer’s instruction. RNA purity (260/280), as well as RNA yield, were measured using a Nanodrop. The RNA integrity number (RIN) was also assessed from 5–25 and 111–130 months of storage using RNA 6000 Nano Kit and Agilent 2100 BioAnalyzer. Descriptive statistics, paired t-test, and response feature analysis using linear regression were used to assess the association between various predictor variables and quality of the RNA isolated. Results Overall, RNA purity and yield of the samples did not decline over time. RNA purity of samples isolated in 2011 (2.08, 95% CI: 2.08–2.09) were statistically lower (p<0.000) than samples isolated in 2018 (2.101, 95% CI: 2.097, 2.104), and there was no statistical difference between the 2011 (13.08 μg /tube, 95% CI: 12.27–13.89) and 2018 (12.64 μg /tube, 95% CI: 11.83–13.46) RNA yield (p = 0.2964). For every month of storage, the change in RNA purity is -0.01(260/280), and the change in RNA yield between 2011 and 2018 is -0.90 μ g / tube. The mean RIN was 8.49 (95% CI:8.44–8.54), and it ranged from 7.2 to 9.5. The rate of change in expected RIN per month of storage is 0.003 (95% CI 0.002–0.004), so while statistically significant, these results are not relevant. Conclusions RNA quality does not decrease over time, and the methods used to collect and store samples, within a population-based study are robust to inherent operational factors which may degrade sample quality over time.

Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components, both of which have various components, and points of data entry, management, and analysis. The "front end" refers to where the data are entered, primarily by healthcare workers (e.g. physicians and nurses). The second component of EHR systems is the electronic data warehouse, or "back-end," where the data are stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), analytical capacities, and requirements associated with using EHRs in a health research context.