By: Akshay Rajaram, Kieran Moore, and Muhammad Mamdani
Introduction
Health care data are growing exponentially. Reports suggest that the United States generated 153 exabytes of health care data in 2013 and that an estimated 2314 exabytes will be produced in 2020.1 These data, which come from varied sources (electronic medical records [EMRs], medical imaging, laboratories, pharmacies, billing, etc), are being harmonized both within and across institutions to drive meaningful change for patients. For example, in Ontario, different research groups are linking demographic, clinical, social, and utilization data from various disparate databases, including social service agencies, to identify patients who are or are at risk of becoming high-cost health care users and to support them with shared care plans.2,3
As patients age and acquire multiple comorbidities, their care becomes increasingly complex. A recent study showed that nearly 40% of patients admitted to general internal medicine services were older than 80 years of age and had a median of 6 comorbidities.4 Family physicians will increasingly need to leverage the recent advances in data acquisition, storage, and analytics to make sound clinical decisions and ensure safe transfers of care as these patients move across the system. Despite a call to action in 2010 and the development and integration of e-health competencies into the CanMEDS framework, progress in the areas of informatics and analytics training has been slow.5
Given the current focus on competency-based medical education, Canadian family medicine (FM) residency programs must establish a minimum level of competence in informatics and analytics for graduates to succeed in this new environment. In this commentary, we propose the essential knowledge and skills for all FM trainees (preamble), highlight established training programs that might be used as a springboard for designing our own programs (pearls and potential), describe new and local interdisciplinary analytics initiatives relevant to FM residents (promise), and identify barriers to scaling these initiatives across the country (pitfalls).
Read the full article published June 12, 2019: Rajaram, A., Moore, K., Mamdani, M. (2019). Preparing family medicine trainees for the information revolution. CFP: College of Family Physicians of Canada, 65(6) 390-392.
References:
- Standford University School of Medicine. Stanford Medicine 2017 health trends report. Harnessing the power of data in health. Stanford, CA: Stanford University; 2017. Available from: https://med.stanford.edu/content/dam/sm/sm-news/documents/StanfordMedicineHealthTrendsWhitePaper2017.pdf. Accessed 2019 Apr 16. Google Scholar
- Chechulin Y, Nazerian A, Rais S, Malikov K. Predicting patients with high risk of becoming high-cost healthcare users in Ontario (Canada). Healthc Policy 2014: 9(3): 68-79. Google Scholar
- Webster PC. Analytics-driven health care growing in Ontario. CMAJ 2014: 186(2): 99. Epub 2014 Jan 13. Google Scholar
- Verma AA., Guo Y., Kwan JL., Lapointe-Shaw L., Rawal S., Tang T., et al. Patient characteristics, resource use and outcomes associated with general internal medicine hospital care: the General Medicine Inpatient Initiative (GEMINI) retrospective cohort study. CMAJ Open 2017;5(4):E842–9. Epub 2017 Dec 13.
- Strauss, S., Canadian medical schools slow to integrate health informatics into curriculum. CMAJ 2010;182(12):E551–2. Epub 2010 Jul 19. Google Scholar
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