Super-Utilizers: Strategies To Address The Cost Conundrum
By Suma Gaddam, Former CIO, Care New England
The Cost Conundrum
There is an ancient proverb-Health is Wealth. While it may have many connotations, it seems a lot like a formula in the economics of population health. Try and apply this formula to an individual, a family, a community or even to a country. It may not be a coincidence that economically developed nations tend to have better health investments and as a result, superior outcomes such as better life expectancy, lower maternal and child mortality rates and less morbidity and disease burden. However, recent trends in population health outcomes in the United States seem to be going against this logic, indicating increasing healthcare expenditure has not necessarily resulted in proportionately better health outcomes. To understand this paradox, several questions are continuing to be asked such as-why does healthcare cost so much, what is the most expensive care setting, who is consuming the most amount of healthcare dollars and what are the most effective strategies to curb the spend. United States attributes 18% of its GDP (Gross Domestic Product) to healthcare. While five percent of the total population of the country, commonly known as super-utilizers account for fifty percent of that spend (an annual mean of $ 50,077 per person), if we can leverage strategies to tackle the twenty percent of the population that contribute to eighty percent of the spend, we may be able to rein in the cost-conundrum. The remaining fifty percent of the population accounts to less than three percent of the total healthcare expenditures (an annual mean of $276 per person).
To understand this paradox, several questions are continuing to be asked such as-why does healthcare cost so much, what is the most expensive care setting, who is consuming the most amount of healthcare dollars and what are the most effective strategies to curb the spend.
The Profile of a Super-Utilizer
There is adequate data from AHRQ’s, HCUP to build a profile of this super-utilizer population. Chronic disease is a common attribute among this group. One in four adults (approximately 25 percent of the population) in the country have physical and psychological conditions that coexist over a long duration of time, often leading to poor socio-economic conditions. Congestive Heart Failure, COPD, pneumonia, septicemia, mood and mental health disorders, substance-abuse, cancer and end-state renal disease were among the top diagnoses among the super-utilizer group. 77 percent of super-utilizers are over 45 years of age and 18 percent of them are between 18-44 years of age. Only five percent are from 0-17 years of age. 73 percent of super-utilizers is White while the Black and Hispanics constitute 21 percent and 6 percent constitute people of Asian descent. 70 percent of the healthcare spend among super-utilizers is attributable to inpatient and ambulatory (includes ED) care, 20 percent to prescription meds, 8 percent to home health and 2 percent to dental and other categories. 73 percent of super-utilizer spend is paid by Medicare and Private insurance while Medicaid pays for 12 percent and federal government pays for another 10 percent of the super utilizer spend.
The Minimal Data Set to Manage Super-User Population
The opportunity to save several trillion in healthcare expense has beckoned a variety of innovative financial models dubbed as value-based contracts by private insurance and the federal government. Whether these models would achieve the desired outcomes-health-wise and financially is to be seen. Integrated care, coupled with intense-complex care management strategies have been demonstrated to be effective in managing this population. However resource and investment intensive care management programs are incredibly difficult to set up and run, primarily due to the fact that these patients can be difficult to identify and track as they transition between a multitude of healthcare settings, can have a host of socio-economic issues and access to real-time, shared data to make decisions is next to impossible with lack of adequate interoperability in the technology. A new community-wide minimum data model proposed here in a two-part series may be difficult to set up, but not impossible where state or community-wide HIE (Health Information Exchange) infrastructure exists. The proposed longitudinal, patient-level minimum data model provides real-time (except for claims) access to care team members (physician/s, care manager/s, social workers, pharmacists and nurses) both clinical and socio-economic data to facilitate care management, care coordination and care transitions across the continuum of care.
There following constitute the essence of the shared longitudinal data model:
- A community-wide Super-Utilizer database
- All-Payer Claims Database
- Continuity Of Care Document (CCD) with discrete elements
- Diagnostic Results (Lab and Diagnostic Imaging)
- Closed-loop Referrals for Physical, Behavioral Health and Social Needs
- Prescription Medication Fill / Dispense History
- Patient-centered, Shared, Longitudinal Care Plans
- Advance Care Directives
A key prerequisite for advancing the care of the super-utilizer population is to identify them across the community such that all the essential community-wide care coordination can occur. There is some research related to the persistence of the super-utilizer status and is to be expected that this population status can be dynamic.
Community-wide Super-utilizer database with PCP (Primary Care Provider), ACO (Accountable Care Organization) & Care-team information
In 2014 AHRQ defined Super-utilizers as patients aged 1-64 years covered by Medicare or Medicaid with six or more ED visits and privately insured patients aged 1-64 years or Medicare patients aged 65 years and older with four or more ED visits. This specific utilization metric can be used to create and maintain a dynamic state / community-wide super-utilizer database. There are a significant number of additional supporting attributes such as total dollar spend, ALOS (Average Length of Stay), number of readmissions in the prior twelve months that can further help refine this population. The values of these attributes tend to be in orders of magnitude higher among this target population in comparison to the normal population. Other relevant data such as the patient-identified primary care provider, ACO and care-team attributions provided by the ACOs can be set up within the database at the patient-level.
The growing concern among the healthcare community to address the issue of disproportionate spend on the high-utilizers often dubbed as the super-utilizers has spurred a multitude of grants and innovation programs. While the success of the experiments has shown that these resource intense care management programs can be effective within an integrated network, the scalability of such complex care management models to the state and national level remains elusive. The proposed longitudinal, minimal data model is a framework built to follow the patient through the care continuum (care team) and can be a scalable and repeatable solution especially in communities that already have shared data exchange infrastructure or looking to set it up. The second part of this article will discuss the remainder of the data model proposed.
