Complex Care Management for Chronic Disease

Overseen By William Turner, BA

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of California, Los Angeles

Disqualifiers: Current care management, Recent care management, Active referral

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

5000 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What data supports the effectiveness of the AI Cost Prediction Model treatment for chronic disease management?

The AI-driven clinical care pathway for COPD patients showed a significant 48% reduction in 30-day readmission rates, demonstrating the potential effectiveness of AI models in managing chronic conditions by predicting risks and optimizing care. ^{1 2 3 4 5}

Is the Complex Care Management for Chronic Disease treatment safe for humans?

The research articles do not provide specific safety data for the Complex Care Management for Chronic Disease treatment or its related models, such as the AI Cost Prediction Model, in humans. ^{1 2 6 7 8}

How does the Complex Care Management treatment for chronic disease differ from other treatments?

Complex Care Management for chronic disease is unique because it uses a comprehensive approach that includes risk stratification to identify patients who would benefit most from intensive care, unlike traditional treatments that may not tailor interventions to individual patient needs. This method aims to improve health outcomes and reduce costs by focusing resources on high-risk patients. ^{1 2 5 8 9}

What is the purpose of this trial?

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.

Research Team

Richard K Leuchter, MD

Principal Investigator

University of California, Los Angeles

Eligibility Criteria

This trial is for UCLA's Medicare Advantage patients who are waiting to enroll in the Proactive Care program, which aims to reduce healthcare usage by providing personalized care. Patients will be chosen based on an AI Cost Prediction model that identifies those with potentially high future medical costs.

Inclusion Criteria

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

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1 month

Randomization and Enrollment

Patients are randomized into either the Proactive Care program or a waitlist control group

1 month

1 visit (in-person or virtual)

Intervention

Participants in the Proactive Care group receive personalized care navigation and case management

10 months

Follow-up

Participants are monitored for outcomes such as days alive and out of hospital, emergency department visits, and healthcare expenditures

12 months

Treatment Details

Interventions

• AI Cost Prediction Model

Trial Overview The study is testing a new method of enrolling patients into the Proactive Care program using an AI Cost Prediction model. It compares outcomes between patients enrolled through this model and those on a waitlist control group over a period of 10 months.

Participant Groups

Group I: Complex Care Management Active Control 1 Intervention

Patients randomized to be contacted for enrollment into the complex care management program called ProActive Care.

Group II: Care as usual Active Control 1 Intervention

Patient randomized to be put on a waitlist for being contacted for enrollment into ProActive Care (ie, not enrolled in ProActive Care).

Who Is Running the Clinical Trial?

University of California, Los Angeles

Lead Sponsor

Trials

1,594

Recruited

10,430,000+

References

1. United States pubmed.ncbi.nlm.nih.gov

Comparative effectiveness of total population versus disease-specific neural network models in predicting medical costs. [2019]

2. Canada pubmed.ncbi.nlm.nih.gov

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods. [2020]

3. England pubmed.ncbi.nlm.nih.gov

An AI-driven clinical care pathway to reduce 30-day readmission for chronic obstructive pulmonary disease (COPD) patients. [2023]

4. United States pubmed.ncbi.nlm.nih.gov

Hospital inpatient costs for adults with multiple chronic conditions. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

Active Redesign of a Medicaid Care Management Strategy for Greater Return on Investment: Predicting Impactability. [2018]

6. United States pubmed.ncbi.nlm.nih.gov

Improving risk stratification using AI and social determinants of health. [2023]

7. Canada pubmed.ncbi.nlm.nih.gov

A Roadmap for Optimizing Asthma Care Management via Computational Approaches. [2020]

8. Spain pubmed.ncbi.nlm.nih.gov

[Predicting individual risk of high healthcare cost to identify complex chronic patients]. [2018]

9. United States pubmed.ncbi.nlm.nih.gov

Improving primary care for patients with chronic illness: the chronic care model, Part 2. [2022]