5000 Participants Needed

Complex Care Management for Chronic Disease

RK
WT
Overseen ByWilliam Turner, BA
Age: 18+
Sex: Any
Trial Phase: Academic
Sponsor: University of California, Los Angeles
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new method for enrolling patients into a personalized care program at UCLA. The focus is on using an AI tool, the AI Cost Prediction Model, to identify Medicare Advantage patients who might need extra support due to high healthcare costs. Participants are divided into two groups: one group receives an invitation to join the care program, while the other is placed on a waitlist. Patients with a UCLA Managed Care Plan flagged by the AI for high predicted healthcare costs may be suitable for this trial. As an unphased study, this trial offers a unique opportunity to participate in innovative research that could enhance personalized healthcare.

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 prior data suggests that this method is safe for enrolling patients into Proactive Care?

Research has shown that AI tools can help manage long-term illnesses. These tools analyze large amounts of health data to predict disease progression and identify patients who may need extra care. Studies have found that AI in healthcare can make treatment more effective and tailored to patients with complex health needs.

Regarding safety, the AI Cost Prediction Model acts as a tool, not a drug or medical device, so it doesn't pose direct health risks. Instead, it aids doctors in making better decisions about patient care. No evidence suggests any negative effects directly caused by the AI model, as it primarily supports medical staff in decision-making.

AI use in healthcare is increasing, and its role in managing chronic diseases is generally well-accepted, aiming to make care more efficient and personalized.12345

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores a novel approach to managing chronic diseases using an AI Cost Prediction Model. Unlike traditional methods that focus primarily on direct patient care and medication, this trial seeks to enhance care management with predictive analytics. The AI model aims to anticipate healthcare costs and optimize resource allocation, potentially improving patient outcomes and reducing unnecessary expenses. By integrating advanced technology into chronic disease management, this trial could pave the way for more efficient and personalized care solutions.

What evidence suggests that this AI Cost Prediction Model is effective for enrolling patients into Proactive Care?

Research has shown that programs designed to manage complex care can assist with chronic diseases. In this trial, participants may enroll in the complex care management program called ProActive Care. One study found that personalized care reduced anxiety and depression, although it didn't completely relieve symptoms. Another study found that care models with strong teamwork significantly lowered patients' blood pressure. Reviews of collaborative care models have often found them effective in managing chronic conditions. These findings suggest that personalized and team-based care approaches can improve certain health outcomes for patients with complex needs.678910

Who Is on the Research Team?

RK

Richard K Leuchter, MD

Principal Investigator

University of California, Los Angeles

Are You a Good Fit for This Trial?

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 for a Trial Participant

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

What Are the Treatments Tested in This Trial?

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.
How Is the Trial Designed?
2Treatment groups
Active Control
Group I: Complex Care ManagementActive Control1 Intervention
Group II: Care as usualActive Control1 Intervention

Find a Clinic Near You

Who Is Running the Clinical Trial?

University of California, Los Angeles

Lead Sponsor

Trials
1,594
Recruited
10,430,000+

Citations

Association Between Care Management and Outcomes ...Accountable care organization–reported care management and coordination activities were not associated with improved outcomes among patients with complex needs.
Health outcomes of patients in the Complex Chronic ...Individual-based care was more effective in reducing anxiety and depression, but overall symptom relief was limited. Our findings highlight the ...
Advancing Chronic Disease Practice Through the ...Chronic disease data are a foundation that can inform interventions to promote healthy communities, support healthy behaviors and lifestyles, ...
Teamwork and its impact on chronic disease clinical ...Studies with 4–5 team components were more effective in reducing systolic blood pressure and diastolic blood pressure. Heterogeneity should be considered, and ...
Effectiveness of care models for chronic disease ...First, collaborative care, the CCM, and other chronic disease management models constituted 55%, 25%, and 20% of the reviews, respectively.
Precision management in chronic disease: An AI ...This paper highlights how technological innovation can improve chronic disease management, particularly by enhancing care efficiency and personalizing health ...
Predictive analytics support for complex chronic medical ...This study sought to explore the potential role of predictive analytics in the decision processes of physician managers treating complex multiple chronic ...
(PDF) AI-Driven Predictive Models for Chronic Disease ...AI-driven predictive models offer a promising solution by leveraging large-scale healthcare data to forecast disease progression, identify high- ...
AI Applications for Chronic Condition Self-ManagementBackground: Artificial intelligence (AI) has potential in promoting and supporting self-management in patients with chronic conditions.
What is AI predictive analytics in healthcareAI predictive analytics in healthcare refers to using AI, machine learning algorithms, and big data to predict medical outcomes, patient risks, and operational ...
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