The Economic Impact of Intensive Case Management on
Costly Uninsured Patients in Emergency Departments: An
Evaluation of New Mexico’s Care One Program
Brady P. Horn, Maurice Moffett, Cameron Crandall, Sam Howarth, Michael
Hensley, and David Sklar
Motivation
• The US healthcare system is the most costly in the world, yet does not
(necessarily) produce superior healthcare quality
• There is a high concentration of costs in a small portion of high-cost,
medically complex patients
• 70% of US health care costs are generated by 10% of patients (Mongan et al.,
2008)
• Complex patients (five or more chronic conditions) cost 17 times more, on
average per year, than individuals with no chronic conditions (Anderson,
2010)
• Perhaps worse, patients with complex conditions are at a higher risk
to receive inefficient, duplicative and poor quality care
Motivation
How do we contain costs and improve health outcomes for highcost, medically complex patients?
• Traditional approaches have involved
early identification, quality
improvement, and cost sharing
• However, improving care and
containing costs for high-cost,
complex patients has been shown to
be a considerable challenge
Motivation
• Case management/care coordination
• High-risk patients have difficulty managing the complex health care system and typically do not
seek care until after a catastrophic illness has occurred
• Many times the entry point to the hospital is the emergency department (ED), which can be
expensive and inefficient
• Mental health issues are often not well addressed during acute care hospitalization
• Case management programs: “target chronically ill persons ‘at risk’ for adverse outcomes and
expensive care and meet their needs by filling the gaps in current health care” (Chen, 2000)
• Positive clinical impact of care coordination/case management
• Improving follow-up rates and reducing repeat ED visits (Katz et al., 2004)
• Reduced hospital visits at 30 and 90 days (Coleman et al., 2006)
• Lack of evidence from an economic perspective
• Care Coordination does not reduce Medicare spending (Congressional Budget Office, 2012)
• Only a small proportion of ED costs appear to be preventable (Joynt et al., 2013)
• Only a few number of studies with (generally) weak designs (uncontrolled)
Motivation
The Care One Program
• Program at the University of New Mexico’s Health Sciences Center, designed to
target high-risk, complex patients and provide intensive care coordination
• Idea: patients receive the right kinds of care from the right types of health
professionals
• Team model: includes a physician, social worker/case manager, patient care coordinator,
and a mental health therapist
• Prioritization for specialty care consultation keeps patients from needing to use the ED for
acute problems
• Access to food stamps, handicapped parking access, and other programs are made
available according to patient need
• Question: what is the economic impact of the Care One program
• Specifically, how does this program impact healthcare expenditures?
Data
• Data was obtained from the provider billing group
• Outcome variable: individual level, pre-post billing charges
• Covariates: age, gender and race of the participant, the type of insurance
(Medicare, Medicaid) and comorbid conditions (ICD-9 codes)
• Quasi-Control group: patients with the highest costs but who were just below
the billing charges cutoff for the Care One program
• Limitation: collected on 01/01/2012
• Data was obtained from May 2007 to December 2012
• (Program started in 2003, but very few observations during the first couple years)
• 1,506 Care One patients and 1,588 control patients were used in this study
Odd Lag Structure
• Care One Cohort
• Patients are first flagged (top 1% of billing charges for the previous 12 months)
• Flagged individuals are reviewed and a subset (approximately 30%) are invited (mailed
invitation) to join the program
• If accepted, the patient was deemed active when they have their first encounter with Care
One
• Quasi-control group
• Identified by high costs in the last year (the point in time when patients would have been
“flagged”
• Thus, the differing time periods generated by the billing group could cause bias.
• To fix this problem, we identified the average time from identification to
enrollment for the Care One group (3 months) and lagged the control group by
this amount
• Initially, 12 months of pre- post- data were collected. After lagging, the resulting dataset
contains 9 months of both pre- and post-data
Empirical Specification
• Difference-in-difference strategy
𝑦𝑖𝑡𝑗 = 𝛼 + 𝜆1 ′𝑃𝑜𝑠𝑡𝑗 + 𝜆2 ′𝐶𝑎𝑟𝑒𝑂𝑛𝑒𝑖 + 𝜆3 ′𝐶𝑎𝑟𝑒𝑂𝑛𝑒𝑖 ∗ 𝑃𝑜𝑠𝑡𝑗 + 𝛽′𝑋𝑖𝑗𝑡 + 𝛾𝑡 + 𝜀𝑖𝑗𝑡
•
•
•
•
•
•
•
– indicates individual, - indicates year, and – indicates pre/post
- individual level billing charges
- indicator variable for after receiving treatment (being flagged for the control group)
- variable of interest (what is the impact of Care One on billing charges?)
- indicates other covariates and comorbidity indexes (Charlson and Elixhauser)
- time fixed effects
- random effect error term
Results
Discussion/Conclusion
• We estimate that there is an approximate reduction in billing charges
associated with the Care One program of $57,000
• The Care One program costs approximately $510,000 per year
($850/person)
• Studies have found that ED hospital costs range anywhere from 25% to 70% of
ED billing charges
• Highly non-linear trajectories of billing charges
• Future Work
• Obtain better control group (perhaps use propensity score matching)
• Better address the timing (lagged) aspect of the data
• Think about the non-linearities (timing) of high-cost, complex patients
Thanks!!
bhorn@unm.edu