Clinical trial NCT06916247: Evaluation of AI Cost Prediction Model to Enroll Patients in Complex Care Management Program
Official title: Evaluation of MA Proactive Care Program Using Cost Prediction Model With Randomized Waitlist
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Chronic Disease
Keywords: High utilization
Complex care management
Cost prediction model
Lead sponsor: University of California, Los Angeles
Sponsor class: OTHER
First posted: 2025-04-08
Enrollment: 5000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: PREVENTION

Brief summary: Currently, UCLA Health (specifically the Office of Population Health and Accountable Care, or OPHAC) runs a complex care management program called Proactive Care (goal is to reduce care utilization by providing personalized care navigation/case management). Every month, an AI Population Risk tool runs to identify around 250 of the 480,000 or so UCLA primary care patients, and RNs contact these 250 patients to enroll in Proactive Care. Starting in December 2024, OPHAC launched a new method of enrolling UCLA's Medicare Advantage (MA) patients into Proactive Care: an AI Cost Prediction model. The idea is the same-- the top 250 highest predicted cost patients will be enrolled in Proactive Care. The investigators will evaluate this model and subsequent enrollment into the program by randomizing the waitlist of MA patients waiting to enroll in Proactive Care, thereby creating a control group. The top 500 highest predicted cost patients will be identified each month, and following a 1:1 randomization, 250 will be contacted for enrollment and the rest will be put on a wait-list control group for 10 months unless otherwise requested by their provider to be enrolled in the Proactive Care program earlier.


Eligibility criteria: Inclusion Criteria:

* At least 18 years of age
* Enrolled in a UCLA Managed Care Plan
* Cost prediction model identifies patient as having high predicted costs over the next 12 months

Exclusion Criteria:

* Currently enrolled in any UCLA care management program
* Enrolled in any UCLA care management program in the last 12 months
* Already has an active referral to a care management program

Primary outcomes:
measure: Days alive and out of hospital (DAOH) at 120 days from randomization
timeFrame: 120 days after randomization
Secondary outcomes:
measure: Days alive and out of hospital (DAOH) at 30 days from randomization
timeFrame: 30 days after randomization
measure: Days alive and out of hospital (DAOH) at 90 days from randomization
timeFrame: 90 days after randomization
measure: Days alive and out of hospital (DAOH) at 10 months from randomization
timeFrame: 10 months post-randomization
measure: Total healthcare expenditures at 10 months from randomization
timeFrame: 10 months post-randomization
measure: All-cause hospitalizations at 10 months from randomization
timeFrame: 10 months post-randomization
measure: All-cause emergency department visits at 10 months from randomization
timeFrame: 10 months post-randomization
measure: All-cause mortality at 10 months from randomization
timeFrame: 10 months post-randomization
measure: Ambulator contact days
timeFrame: 10 months post-randomization
Other outcomes:
measure: Cost prediction model performance over 22 months, as measured by area under the receiver operating characteristic curve
timeFrame: 12 months after the final cohort enrolls
measure: Protocol fidelity over the 10 months of enrollment
timeFrame: 14 months post-randomization
measure: Avoidable hospitalizations
timeFrame: 10 months post-randomization
measure: Avoidable emergency department visits
timeFrame: 10 months post-randomization
Locations:
city: Los Angeles
country: United States
facility: UCLA Health
state: California
Study officials:
affiliation: University of California, Los Angeles
name: Richard K Leuchter, MD
role: PRINCIPAL_INVESTIGATOR
References: