Health System Improvement
The Value Institute works closely with our clinical partners to achieve excellence in patient outcomes, patient experience and high value care by providing essential program evaluation and research expertise.
Diabetes Fast Facts:
- Prevalence: 9.4%
- In 2015, 30.3 million Americans, or 9.4% of the population, had diabetes.
- Approximately 1.25 million American children and adults have type 1 diabetes.
- Undiagnosed: 24%
- Of the 30.3 million adults with diabetes, 7.2 million were undiagnosed.
- Prevalence in seniors: 25%
- The percentage of Americans age 65 and older remains high, at 25.2%, or 12.0 million seniors (diagnosed and undiagnosed).
- New cases: 1.5 million Americans diagnosed per year
- Prediabetes: 1 in 3 Americans
- In 2015, 84.1 million Americans age 18 and older had prediabetes.
- Deaths: 7th leading cause
- 79,535 death certificates listing it as the underlying cause of death, and a total of 252,806 death certificates listing diabetes as an underlying or contributing cause of death.
- Cost of diabetes:
- $327 billion: Total cost of diagnosed diabetes in the United States in 2017
- $237 billion was for direct medical costs
- $90 billion was in reduced productivity
- Average medical expenditures among people with diagnosed diabetes were 2.3 times higher than what expenditures would be in the absence of diabetes.
Diabetes Among Hispanic and Latino Americans:
Diabetes is the fifth leading cause of death among Hispanics/Latinos in the United States with Hispanics are more likely than the general population to develop diabetes. It is estimated that 2.5 million, or 10.4 percent of Hispanic and Latino Americans aged 20 and older have diabetes. Hispanics also are more likely to have undiagnosed diabetes than non-Hispanic whites and non-Hispanic blacks. Nearly half of Hispanic children born in the year 2000 are likely to develop diabetes during their lives.
Diabetes Prevention Programs: To understand how to build successful prevention programs targeting diabetes among Hispanic/Latino Americans, we have begun to engage the community in focus groups to obtain information that will improve the care we provide. We are focusing on gaining a rich understanding of dietary behaviors, physical activity patterns, access to care, and medications and medication adherence. We hope to build upon the evidence base for the Diabetes Prevention Program to culturally adapt and implement this program for this community.
Mapping ChristianaCare’s Medicaid Population
As ChristianaCare develops innovative care models to improve health and reduce medical costs, geographic information systems (GIS) can be leveraged to understand the spatial distribution of patient populations and their health outcomes.
Preliminary descriptive maps were created to identify geographic patterns among ChristianaCare patients insured by Medicaid. Medicaid members who reside in Delaware were geocoded by their home address and aggregated to census tracts to protect confidentiality. Members were also mapped separately according to diabetes status and risk contract (AmeriHealth or Highmark).
Across Delaware, 24% of ChristianaCare Medicaid members reside in 6% of the state’s census tracts (those highlighted in dark red). The three census tracts with the greatest numbers of Medicaid members were located in center-city Wilmington and Smyrna. Similar geographic trends were found among Medicaid members with diabetes and those on AmeriHealth risk contracts. These maps provide actionable data to support ChristianaCare in managing the care of its Medicaid members with an eye toward neighborhood-level determinants of health.
Global optimization of patient flow and hospital capacity
This study aims to optimize patient flow in ChristianaCare at the system level through a multi-method approach. A qualitative study using human factors principles was conducted with front line staff, middle management, and executives to capture their perspectives on flow challenges. Thematic extraction was performed to guide the subsequent numerical analysis and quantitative approaches. Anticipated outcomes from this study include insights into optimal allocation of beds to various patient types, decision-support on patient assignment to bed type based on predicted wait times, and predicted system-level performance on flow given various system-level interventions.