Using data to improve individual health, practice performance and understand social determinants
This six-session course is all about applying a population health approach within traditional health service organizations. Students will learn about some of the common sources of data available to characterize the health of individuals, practices and local communities. Students will engage with data to identify “hotspots” and build population profiles, including the distribution of health outcomes and disease in a practice. Students will discuss innovative ways to understand the health care system at a population level through synthesizing clinical data and community data on social determinants.
- Identify common sources of data that can be used to characterize the health of individuals, practices and the local community in which the practice is located.
- Understand basic ways of manipulating data to identify “hotspots” and describe the distribution of health outcomes and disease in a practice population.
- Explore innovative ways to bring a population health perspective into primary care practice, including through synthesizing clinical data and community data on social determinants.
Two key processes are changing how primary health care operates in Canada. First, the increased used of electronic medical records (EMRs) now provides rich data over many years on individuals and entire practice populations. Such data is already being used in quality improvement efforts to target individuals for intervention. However, challenges remain in using data that is originally collected for only clinical purposes. Second, although primary care has always acknowledged it needs to address both individual and collective need, now it is 2 increasingly being seen as a resource to a geographically defined community (i.e. catchment area). Under policy changes such as “Patients First” in Ontario, providers are now being asked how they are serving local communities.
This reading course will begin with identifying common sources of data that are available to understand the health and determinants of health of individuals and a practice population. We will use such data to develop a practice report, applying a population health approach to this assessment. We will then discuss the complementary concepts of “shifting the curve” and “hot spotting”, and examine how they apply to primary care settings. We will work through a number of examples, including using data to understand the health of diabetics, patients with HIV infection and patients with multi-morbidity. It is important that leaders in primary care have some familiarity with conducting an assessment of the health of their practice population and this course aims to support such efforts.
This course will involve using Microsoft Excel only. No specific access to an electronic medical record (EMR) is required.
Textbook: none (readings below)
Case studies to explore during the course
- Case 1: Increasing cancer screening amongst "hard to reach" groups
- Case 2: Addressing social determinants of health
- Case 3: Linking with community social service agencies to fill the gap around "high cost users"
- Case 4: Using data to re-allocate human resources to reduce health inequities
- Case 5: Community asset mapping and community engagement
- Case 6: UTOPIAN and public health surveillance
1. Introduction & common sources of data
Wednesday July 20, 2016 2:00-5:00 pm 500 University Avenue, Room 365
Guest speaker: Dr. Frank Sullivan, Gordon F. Cheesbrough Chair in Family and Community Medicine, North York General Hospital; Director, University of Toronto Practice-Based Research Network (UTOPIAN); Physician, Family Medicine Teaching Unit, North York General Hospital; Professor, Dalla Lana School of Public Health, University of Toronto
Terry AL. Using your electronic medical record for research: a primer for avoiding pitfalls. Family Practice 2010; 27: 121-125 de Lusignan S. Routinely-collected general practice data are complex, but with systematic processing can be used for quality improvement and research. Informatics in Primary Care 2006; 14: 59-66 3 Choquet R. The Information Quality Triangle: a methodology to assess clinical information quality. Stud Health Technol Info 2010; 160: 699-703
2. Data extraction and cleaning
Wednesday July 27, 2016 2:00-5:00 pm DLSPH computer lab, HS-790
Guest speaker: Ms. Samantha Davie, Quality Improvement Specialist, St. Michael’s Hospital Academic Family Health Team Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20: 144- 151 Barkhuysen P et al. Is the quality of data in an electronic medical record sufficient for assessing the quality of primary care? J Am Med Inform Assoc 2014; 21: 692- 698
3. Analysis of clinical data: Basic concepts
Wednesday August 3, 2016 2:00-5:00 pm DLSPH computer lab, HS-790
Guest speaker: Dr. Michelle Greiver, Assistant Professor and Clinician Investigator, DFCM and North York General Hospital Bartlett G and Gagnon J. Physicians and knowledge translation of statistics: mind the gap. CMAJ 2016; 188 (1): 11-12. Price M. Adopting electronic medical records. CFP 2013; 59: e322-9
4. Developing a practice profile
Wednesday Aug 10, 2016 2-5 pm 500 University Avenue, Room 365
Guest speaker: Dr. Karen Tu, Family Physician, Toronto Western Hospital; Senior Scientist, Institute for Clinical Evaluative Sciences (ICES); Associate Professor, Department of Family and Community Medicine, Faculty of Medicine Jensen PB. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet. 2012 May 2;13(6):395-405. doi: 10.1038/nrg3208. Liaw WR et al. Teaching population health in the digital age: Community-oriented primary care 2.0. J Community Med Public Health Care 2015; 2:003 4 Ontario Public Health Standards. Population health assessment and surveillance protocol. MOHLTC 2008.
5. Using data to intervene
Wednesday August 17, 2016 2:00-5:00 pm 500 University Avenue, Room 365
Guest speaker: TBD Higgins TC, Crosson J, Peikes D, McNellis R, Genevro J, Meyers D. Using Health Information Technology to Support Quality Improvement in Primary Care. AHRQ Publication No. 15-0031- EF. Rockville, MD: Agency for Healthcare Research and Quality. March 2015. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. H ealth A ff (M illw o 2014 Jul;33(7):1123-31. doi: 10.1377/hlthaff.2014.0041.
Session 6. From the individual- to the population-level
Wednesday August 24, 2016 2:00-5:00 pm 500 University Avenue, Room 365
Guest speaker: Dr. Ross Upshur, Family Physician, Public Health Specialist and Scientist, Sinai Health System; Professor, Department of Family and Community Medicine, Faculty of Medicine and Division Head, Clinical Public Health, Dalla Lana School of Public Health Ivers N et al. “My approach to this job is… one person at a time.” CFP 2014; 60: 258-66 Casey JA et al. Using electronic health records for population health research: A review of methods and applications. Ann Rev Public Health 2016; 37: 61-81.
20% Attendance and active participation in class discussions
Students’ attendance will be verified at the beginning of each class, and students are expected to attend and participate. Completing the readings before class are essential and students will be graded based on the quality of the points raised, including reference to the readings. Absences policy: Absences will be addressed on a case-by-case basis. Students should contact the instructor in advance if they will be absent to arrange alternative ways to cover the material. 5
20% Mid-term assignment Description: Proposal for final project (see below). Format: Rationale, question being answered, proposed method.1 page maximum, Word document, single-spaced, 1 inch margins, 12 font Email to instructor by 2 pm Wed Aug 3, 2016
60% Final project Description: Each student will carry out a small project that aims to use clinical data and/or publicly available data to do one of the following:
- a) Conduct a "hot spotting" exercise to identify patients with a specific disease condition who are considered poorly controlled (e.g. patients with diabetes with A1c >9.5%) or patients who are on inappropriate medications (e.g. seniors on benzodiazepines)
- b) Develop a practice profile, i.e. an epidemiological report on patients served/rostered to a practice, with recommendations on planning primary care services
- c) Develop a catchment area profile, i.e. an epidemiological report on the population served, with recommendations on planning primary care services
Format: Relevant background, Methods used, Results, Discussion/Recommendations. 2,000 words + up to two tables or figures. Word document, single-spaced, 1-inch margins, 12 font
Email to instructor by 2 pm Wed Aug 24, 2016