Actively Recruiting

Phase Not Applicable
Age: 18Years +
All Genders
Healthy Volunteers
NCT05323409

Improving Comprehensive Care of Cancer Patients

Led by Baylor College of Medicine · Updated on 2025-09-19

340

Participants Needed

1

Research Sites

217 weeks

Total Duration

On this page

AI-Summary

What this Trial Is About

Cancer survivors have unique healthcare needs, including managing serious late effects, ongoing surveillance, lifestyle modifications to reduce second cancer risk, and psychosocial support. Nearly 70% of survivors have at least one comorbid chronic condition in addition to cancer, which complicates the delivery of quality cancer care. Medically underserved patients, who bear the highest burden of multiple chronic conditions, are at increased risk for poor outcomes during and after cancer treatment. Enhancing communication and collaboration between oncologists and primary care providers (PCPs) could improve health outcomes and care transitions for these patients, who often lack healthcare knowledge and access to supportive care. This study evaluates a novel shared care model for cancer survivors with chronic comorbidities, called OPTIMISE (Oncology-Primary Care Partnership to Improve Comprehensive Survivorship Care), in the largest safety-net healthcare system in Houston, Texas. Three hundred newly diagnosed breast, gastrointestinal, and hematological cancer patients being treated with curative intent and having comorbidities requiring ongoing management will be randomized to either OPTIMISE or Usual Medical Care (UMC). UMC patients will receive cancer treatment directed by their oncologist, a survivorship care plan (SCP) at the end of active treatment, and surveillance visits based on national guidelines. OPTIMISE patients will: 1) have an oncology nurse navigator assigned to their care team at diagnosis to facilitate oncologist-PCP communication; 2) receive coordinated care between their oncologist and PCP throughout cancer treatment and surveillance, facilitated by structured communication and referral processes; 3) receive an SCP that incorporates comorbidity management; and 4) follow a risk-stratified shared care model where some routine oncologist follow-up visits are replaced by PCP visits. Aim 1a evaluates OPTIMISE's impact on patient chronic disease self-management (primary outcome) and quality of life (secondary outcome). Aim 1b explores OPTIMISE's effects on healthcare use and patient unmet needs during and after treatment. Aim 2 examines OPTIMISE's impact on oncologist and PCP attitudes and care coordination. Aim 3 elucidates patient- and system-level factors influencing implementation outcomes. If effective, OPTIMISE could expand to other cancers and enhance care transitions in various medical settings.

CONDITIONS

Official Title

Improving Comprehensive Care of Cancer Patients

Who Can Participate

Age: 18Years +
All Genders
Healthy Volunteers

Eligibility Criteria

Eligible

You may qualify if you...

  • New diagnosis or within three months of treatment start for early-stage breast (I-IIIB), gastrointestinal (Stage I-III), or hematologic (Stage I-III) cancer
  • Receiving standard, definitive cancer therapies
  • Having one or more chronic health conditions or unhealthy lifestyle behaviors needing ongoing management during cancer treatment
  • Age over 18 years
  • Fluent in English or Spanish
  • Able to provide informed consent
  • Assigned to Harris Health oncologist and primary care provider who agree to participate
Not Eligible

You will not qualify if you...

  • Significant cognitive impairment or inability to provide consent for patients
  • No exclusion criteria for healthcare providers

AI-Screening

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Trial Site Locations

Total: 1 location

1

Baylor College of Medicine

Houston, Texas, United States, 77030

Actively Recruiting

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Research Team

H

Hoda J. Badr, PhD

CONTACT

A

Aylin Diaz-Valladares, MA

CONTACT

How is the study designed?

Study Type

INTERVENTIONAL

Masking

NONE

Allocation

RANDOMIZED

Model

PARALLEL

Primary Purpose

SUPPORTIVE_CARE

Number of Arms

2

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