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Pre-Med Student: Examining the Relationship Between the Quality of Family Physicians and Life Expectancy in the State of Georgia
EP30809
Poster Title: Pre-Med Student: Examining the Relationship Between the Quality of Family Physicians and Life Expectancy in the State of Georgia
Submitted on 15 Oct 2019
Author(s): Raine Foulkes, Aziza Rodriguez, and Daniel Cannon
Affiliations: Foothills Area Health Education Center (AHEC)
Poster Views: 92
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Poster Information
Abstract: As healthcare systems develop a broader understanding of social determinants of health and begin to focus on value rather than volume, family physicians are being judged based on markers of their care through quality metrics espoused by Medicare and other payers. The average life expectancy data from 2014 shows that Georgia’s life expectancy (77.38 years) was lower than the national average (79.08 years). The cluster of 26 counties in North Georgia had an average life expectancy which was significantly better than the rest of the state. This research sought out to find the relationship between quality physician care data and life expectancy in the state of Georgia. Medicare provides publicly available physician compare data which reports quality by percentage of charts evaluated using a variety of metrics. This study isolated four metrics that family physicians report on: colorectal cancer screening, documentation of current medications, pneumonia vaccination status for older adults, and influenza immunization. This study is aimed at uncovering why certain counties have higher life expectancies than other counties in Georgia. The potential correlation between life expectancy and quality of family physicians and other factors that may affect life expectancy are investigated in this analysis. Additionally, the limitations of publicly available Medicare data are outlined. The data set was small, consisting of 128 individual family medicine physicians (FMP) and 7 groups (containing ~108 FMP) out of approximately 2,712 active FMP in Georgia. Not all metrics believed to be significant to life expectancy could be analyzed due to the small sample size and/or they were not reported by our target groups. Although the metrics analyzed showed no correlation to life expectancy, this study provides an example of how publicly available Medicare data can be used to gain insight into what factors do and do not influence life expectancy. Future studies may benefit from expanding their analysis of the quality metrics that are reported by Medicare.Summary: We hypothesize that there is a direct correlation between the quality of family physicians and life expectancy in Georgia.
If there is a correlation between increased life expectancy and the high quality of family physicians, it will lay the groundwork for further investigation into how some family physicians are providing higher quality of care. A model can be built to replicate these higher quality practices and eventually increase the average life expectancy in Georgia.
References: 1. Garcia MC, FaulM, MassettiG, et al. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR SurveillSumm. 2017;66(2):1-7. doi:10.15585/mmwr.ss6602a1
2. Physician Compare Datasets. Data.Medicare.gov. https://data.medicare.gov/data/physician-compare. Published 2016. Accessed June 1, 2019.
3. US Health Map. Institute for Health Metrics and Evaluation. https://vizhub.healthdata.
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