Automated extraction of weight, height, and obesity in electronic medical records are highly valid.

Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under-reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule-based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs). Methods: In this cross-sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. 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.

Health data governance for research use in Alberta.

Alberta has rich clinical and health services data held under the custodianship of Alberta Health and Alberta Health Services (AHS), which is not only used for clinical and administrative purposes but also disease surveillance and epidemiological research. Alberta is the largest province in Canada with a single payer centralised health system, AHS, and a consolidated data and analytics team supporting researchers across the province. This paper describes Alberta's data custodians, data governance mechanisms, and streamlined processes followed for research data access. 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.

Rheum4U: Development and testing of a web-based tool for improving the quality of care for patients with rheumatoid arthritis.

OBJECTIVES: To develop a web-based tool (Rheum4U) to capture clinically meaningful data to direct treatment. Rheum4U integrates longitudinal clinical data capture of rheumatoid arthritis (RA) disease activity measures and patient-reported outcomes measures (PROMs). This study tests the feasibility, acceptability and efficiency of Rheum4U among patients and healthcare providers. METHODS: Rheum4U was developed in two phases: P1 design and development; and P2 pilot testing. P1: A working group of rheumatologists and researchers (n=13) performed a prioritisation exercise to determine data elements to be included in the platform. The specifications were finalised and supplied to the platform developer. Alpha testing was performed to correct initial software bugs. 18 testers (physicians, nurses and recruited non-patient lay-testers) beta tested Rheum4U for usability. P2: Rheum4U was piloted in 2 rheumatology clinics and evaluated for feasibility, efficiency and acceptability using interviews, observation and questionnaires with patients and healthcare providers. RESULTS: 110 RA patients, 9 rheumatologists and 9 allied health providers participated in the pilot. Mean patient age was 53 years and 74% were female. The majority (86%) were satisfied or very satisfied with online data entry and 79% preferred it to paper entry. Healthcare providers found Rheum4U easy and clear to use (90%), and they perceived that it improved their job performance (91%). Completeness and easy availability of the patient information improved clinic efficiency. CONCLUSIONS: Rheum4U highlights the benefits of a web-based tool for clinical care, quality improvement and research in the clinic and this study provides valuable information to inform full platform implementation.