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Measuring Health Equity: Initial Implementation of Patient Demographic Data Collection

Measuring Health Equity: Initial Implementation of Patient Demographic Data Collection. Equity: A Core Component of Quality TC LHIN emphasis on “Health Equity Vision”. TC LHIN is committed to including equity dimensions and objectives in quality healthcare planning TC LHIN aims to:

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Measuring Health Equity: Initial Implementation of Patient Demographic Data Collection

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  1. Measuring Health Equity: Initial Implementation of Patient Demographic Data Collection

  2. Equity: A Core Component of QualityTC LHIN emphasis on “Health Equity Vision” • TC LHIN is committed to including equity dimensions and objectives in quality healthcare planning • TC LHIN aims to: “create and sustain a healthcare system in Toronto where all have equitable access to a full range of high-quality healthcare and support, and systemic and avoidable health inequities are steadily reduced”1

  3. TCLHIN Supports Patient Level Demographic Data Collection • TC LHIN recognizes that tracking and addressing health (in)equity necessitates adopting robust demographic data systems • TCLHIN has mandated patient level demographic data collection in hospitals • Target date for the initial implementation of demographic data collection: March 2013

  4. Equity: A Core Component of Quality • The American Institute of Medicine identifies the six pillars of quality healthcare as: efficiency, effectiveness, safety, timeliness, patient-centeredness, and equity2 • Equity is necessary condition for a “Culture of Quality” in hospitals3 • Unchecked inequity leads to increased pressure on hospital services, avoidable future costs, and reduced productivity4 • Disparities in health care have an impact on quality, safety, cost, and risk management5

  5. Equity: A Core Component of QualityImplications of inequity for Canadians • Equity-relevant variables such as income and race matter more than health behaviour in determining Canadians’ health outcomes6 • Children from low-income families require more hospital stays and show increased vulnerability to various illnesses, accidental injuries and mental health problems7 • Living conditions, age, income, immigrant status, and race are a few of the social factors shown to significantly affect diabetes, cardiovascular disease, mental health, and self-reported health (see figure on next slide)8

  6. Figure: Age-standardized prevalence of diabetes among urban-dwelling immigrants*, by sex and world region of birth, and among urban-dwelling long-term residents^ in Ontario (2005) * Granted residency status in Canada between 1985-2000 ^ Canadian-born residents and people granted permanent residence prior to 1985 Source: Bierman, A. S. et al. (2012). Social determinants of health and populations at risk. Ontario Women’s Health Equity Report: Improving Health & Promoting Health Equity in Ontario

  7. Why Collect Demographic Data? • Strengthen patient-centered care by using data to: • Provide information about the prevalence of specific conditions and outcomes in particular groups • Determine the composition of the community being served • Ensure health care serves individual needs • Ensure services are reaching all of the community effectively • Use data to evaluate and track programs and services • Demographic data is a necessary condition for monitoring equity and fulfilling health equity plans

  8. Perceived Challenges • Collection of information is illegal • Patients won’t want to provide information • Process will take too long, impede registration • Adapting IT systems to collect info costly • Uncertain how information will be used

  9. Vision for project • Foster an organization-wide culture that is committed to equity as a key element of quality care • Promote demographic data collection as a strategy for achieving equity in healthcare • Introduce support structures for sustainable and standardized demographic data collection: • Support the creation of a steering committee composed of inter-professional expertise and responsible for overseeing data collection • Support staff through the development and implementation of initial demographic data collection

  10. References and Further Reading • Gardner, B. (2008). Health Equity Discussion Paper . Toronto Central LHIN • Institute of Medicine, Committee on Quality of Health Care in America, IOM (2002). Crossing the Quality Chasm . Washington, DC: National Academy Press • ibid • Marmot, M., Shipley, M., Brunner, E., & Hemingway, H. (2001). Relative contribution of early life and adult socioeconomic factors to adult morbidity in the Whitehall II study. Journal of Epidemiology and Community Health, 55, 301-307 • Betancourt, J. R., Green, A. R., King, R. R., Tan-McGory, A., Cervantes, M., and Renfrew, M. (2009, February). Improving Quality and Achieving Equity: A Guide for Hospital Leaders. The Disparities Solutions Centre, Massachusetts General Hospital • Benzeval, M., Dilnot, A., Judge, K., & Taylor, J. (2001). Income and health over the lifecourse: Evidence and policy implications. In H. Graham (Ed.), Understanding Health Inequalities (pp. 96-112). Buckingham, UK: Open University Press. • Canadian Institute on Children's Health. (2000). The Health of Canada's Children: A CICH Profile 3rd Edition. Ottawa, Canada: Canadian Institute on Children's Health • Bierman, A. S. et al. (2012). Social determinants of health and populations at risk. Ontario Women’s Health Equity Report: Improving Health & Promoting Health Equity in Ontario.

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