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

• 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

How do we contain costs and improve health outcomes for high-cost, medically complex patients?

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)

MotivationThe 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

• – 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

𝑦 𝑖𝑡𝑗=𝛼+𝜆1 ′ 𝑃𝑜𝑠 𝑡 𝑗+𝜆2 ′𝐶𝑎𝑟𝑒𝑂𝑛𝑒𝑖+𝜆3 ′𝐶𝑎𝑟𝑒𝑂𝑛𝑒𝑖∗𝑃𝑜𝑠𝑡 𝑗+𝛽 ′ 𝑋 𝑖𝑗𝑡+𝛾 𝑡+𝜀𝑖𝑗𝑡

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