OBJECTIVE: Electronic health records are increasingly utilized for observational and clinical research. Identification of cohorts using electronic health records is an important step in this process. Previous studies largely focused on the methods of cohort selection, but there is little evidence on the impact of underlying vocabularies and mappings between vocabularies used for cohort selection. We aim to compare the cohort selection performance using Australian Medicines Terminology to Anatomical Therapeutic Chemical (ATC) mappings from 2 different sources. These mappings were taken from the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) and the Pharmaceutical Benefits Scheme (PBS) schedule. MATERIALS AND METHODS: We retrieved patients from the electronic Practice Based Research Network data repository using 3 ATC classification groups (A10, N02A, N06A). The retrieved patients were further verified manually and pooled to form a reference standard which was used to assess the accuracy of mappings using precision, recall, and F measure metrics. RESULTS: The OMOP-CDM mappings identified 2.6%, 15.2%, and 24.4% more drugs than the PBS mappings in the A10, N02A and N06A groups respectively. Despite this, the PBS mappings generally performed the same in cohort selection as OMOP-CDM mappings except for the N02A Opioids group, where a significantly greater number of patients were retrieved. Both mappings exhibited variable recall, but perfect precision, with all drugs found to be correctly identified. CONCLUSION: We found that 1 of the 3 ATC groups had a significant difference and this affected cohort selection performance. Our findings highlighted that underlying terminology mappings can greatly impact cohort selection accuracy. Clinical researchers should carefully evaluate vocabulary mapping sources including methodologies used to develop those mappings.

Recently, an important public debate emerged about the digital afterlife of any personal data stored in the cloud. Such debate brings also to attention the importance of transparent management of electronic health record (EHR) data of deceased patients. In this perspective paper, we look at legal and regulatory policies for EHR data post mortem. We analyze observational research situations using EHR data that do not require institutional review board approval. We propose creation of a deceased subject integrated data repository (dsIDR) as an effective tool for piloting certain types of research projects. We highlight several dsIDR challenges in proving death status, informed consent, obtaining data from payers and healthcare providers and the involvement of next of kin.

We describe the application of a suite of tools called RetroGuide to facilitate the retrospective analysis of lifetime electronic health record data. Several clinical and informatics questions are explored in a cohort of intensive care patients. The analysis is targeted at monitoring the use and evaluating the outcomes of a computerized glucose management protocol developed at Intermountain Healthcare. We present a graphical RetroGuide scenario which models several clinical events happening prior, during and after the execution of the current protocol. We briefly review previous protocol evaluations and compare them to RetroGuide's capabilities and results.

Workflow editors and engines can be used to model medical processes and execute them on real coded medical data from large data repositories. Such framework re-uses existing workflow technology and applies it to the healthcare domain. It satisfies many requirements for collaboration of champion clinicians with data analysts and developers of future potential decision support modules. Results enable to have insights into differences in workflows, data collection patterns and patient structure among different hospitals or providers. The methodology can provide support to descriptive clinical research studies, quality improvement teams and decision support development teams.