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Evaluation of AI Cost Prediction Model to Enroll Patients in Complex Care Management Program

Evaluation of MA Proactive Care Program Using Cost Prediction Model With Randomized Waitlist

Status
Completed
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06916247
Enrollment
4962
Registered
2025-04-08
Start date
2024-07-31
Completion date
2026-04-28
Last updated
2026-07-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Chronic Disease

Keywords

High utilization, Complex care management, Cost prediction model

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.

Interventions

BEHAVIORALComplex care management program

Intensive outpatient care management program that includes contact from nurses and case managers to help coordinate care, detect clinical red flags, and reduce overall unplanned acute care utilization.

Sponsors

University of California, Los Angeles
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
DOUBLE (Investigator, Outcomes Assessor)

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
Days alive and out of hospital (DAOH) at 120 days from randomization120 days after randomizationThe sum of the number of days that a patient is not hospitalized under inpatient or observation status, and alive, out of a maximum of 120 days post-randomization.

Secondary

MeasureTime frameDescription
Days alive and out of hospital (DAOH) at 30 days from randomization30 days after randomizationThe sum of the number of days that a patient is not hospitalized under inpatient or observation status, and alive, out of a maximum of 30 days post-randomization.
Days alive and out of hospital (DAOH) at 90 days from randomization90 days after randomizationThe sum of the number of days that a patient is not hospitalized under inpatient or observation status, and alive, out of a maximum of 90 days post-randomization.
Days alive and out of hospital (DAOH) at 10 months from randomization10 months post-randomizationThe sum of the number of days that a patient is not hospitalized under inpatient or observation status, and alive, out of a maximum of 300 days post-randomization.
Total healthcare expenditures at 10 months from randomization10 months post-randomizationThe sum of all healthcare expenditures (inpatient, outpatient, prescription, etc.) as determined by claims data.
All-cause hospitalizations at 10 months from randomization10 months post-randomizationThe total number of hospitalizations under inpatient or observation status for any cause
All-cause emergency department visits at 10 months from randomization10 months post-randomizationThe total number of emergency department visits that did not result in hospitalization, for any cause.
All-cause mortality at 10 months from randomization10 months post-randomizationThe total number of deaths from any cause
Ambulator contact days10 months post-randomizationThe number of days a patient spends outside the home receiving health care, defined as the total number of days with a primary care or specialty care office visit, test, imaging, procedure, or treatment

Countries

United States

Contacts

PRINCIPAL_INVESTIGATORRichard K Leuchter, MD

University of California, Los Angeles

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Jul 17, 2026