In 2006, Methodist Le Bonheur Healthcare (MLH) created
the Congregational Health Network (CHN, TM pending) which works closely with
clergy in the most under-served zip codes of the city to improve access to care
and overall health status of the population. To best coordinate CHN resources
around high-utilization and address the largest health needs in the community,
MLH applied hot spotting and geographic information system (GIS) spatial
analysis techniques. These techniques were coupled with the community health
needs assessment process at MLH and qualitative, participatory research
findings captured in collaboration with church and other community partners. The
methodology, which we call “participatory hot spotting,” is based upon the
Camden Model, which leverages hot spotting to assess and prioritize community
need in the provision of charity care, but adds a participatory, qualitative
layer. In this study, spatial analysis was employed to evaluate hospital-based
inpatient and outpatient utilization and define costs of charity care for the
health system by area of residence. Ten zip codes accounted for 56% of total
system charity care costs. Among these, the largest zip code, as defined by a
percentage of total charity costs, contributed 18% of the inpatient utilization
and 17% of the cost. Further, this zip code (38109) contributed 69% of the
inpatient and 76% of the outpatient charity care volume and accounted for 75%
of inpatient and 76% of outpatient charity care costs for the system. These
findings were combined with grassroots intelligence that enabled a partnership
with clergy and community members and Cigna Healthcare to better coordinate
care in a place-based population health management strategy. Presentations of
the analytics have subsequently been made to HHS and the CDC, referred to by some
as the “Memphis Model”.
Cite this paper
T. Cutts, E. Rafalski, C. Grant and R. Marinescu, "Utilization of Hot Spotting to Identify Community Needs and Coordinate Care for High-Cost Patients in Memphis, TN," Journal of Geographic Information System
, Vol. 6 No. 1, 2014, pp. 23-29. doi: 10.4236/jgis.2014.61003
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