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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT02355262
Other study ID # IRB00009862
Secondary ID NCI-2014-02326CR
Status Completed
Phase
First received
Last updated
Start date July 1, 2014
Est. completion date June 30, 2020

Study information

Verified date August 2020
Source OHSU Knight Cancer Institute
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

This randomized research trial studies the Community-based Health Information Technology (HIT) Tools for Cancer Screening and Health Insurance Promotion (CATCH-UP) intervention in increasing cancer screening and prevention care in uninsured patients at community health centers. The CATCH-UP intervention may contribute to increased rates of insurance coverage, leading to improved cancer screening and prevention rates in community health care settings, and general recommended preventive care.


Description:

PRIMARY OBJECTIVES:

I. Evaluate the effect of the CATCH-UP intervention on up-to-date status of cancer screening and preventive care received by patients.

II. Evaluate the effect of the CATCH-UP intervention on patients? insurance coverage rates.

III. Evaluate the intervention implementation process, patient and community health center (CHC) staff acceptance and use of the CATCH-UP tools, and the patient-, provider-, and system-level factors associated with successful implementation and sustainability of the tools, using mixed methods.

OUTLINE: CHC clinics are randomized to 1 of 2 groups. We compare between groups and with a matched-comparison group.

GROUP I (INTERVENTION ARM 1): Participants receive CATCH-UP tools which include a panel management/data aggregator system that identifies patients who may be eligible for public coverage but are not yet insured, or who are nearing coverage expiration, coupled with automated patient outreach and communication at baseline.

GROUP II (INTERVENTION ARM 2): Participants receive CATCH-UP tools which include a panel management/data aggregator system that identifies patients who may be eligible for public coverage but are not yet insured, or who are nearing coverage expiration, coupled with automated patient outreach and communication. Participants also receive additional implementation support such as trainings, assistance with workflows, and practice facilitation.

Matched-comparison group: A clinic-level matched comparison group will be derived from the OCHIN membership by using propensity score matching techniques. Comparison group clinics will not participate actively in any study activities but, as part of their member business associate agreement with OCHIN, have already agreed to provide data through OCHIN for pre- and post-implementation analysis in the study.


Recruitment information / eligibility

Status Completed
Enrollment 45
Est. completion date June 30, 2020
Est. primary completion date June 30, 2020
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria:

- Established patients at CHC sites

- Low-income

- Ethnically diverse populations with lower rates of cancer screening compared to national rates

- Uninsured patients

- Patients 18 to 64 years of age

- Clinics must be primary care sites, with greater than 1,000 adult patients in the past year, be located in a state that expanded Medicaid, and have implemented the OCHIN EHR prior to January 1, 2012

Study Design


Intervention

Other:
Informational Intervention
Receive CATCH-UP intervention

Locations

Country Name City State
United States OHSU Knight Cancer Institute Portland Oregon

Sponsors (5)

Lead Sponsor Collaborator
OHSU Knight Cancer Institute Kaiser Permanente, National Cancer Institute (NCI), Oregon Community Health Information Network, Oregon Health and Science University

Country where clinical trial is conducted

United States, 

References & Publications (9)

Angier H, Hoopes M, Gold R, Bailey SR, Cottrell EK, Heintzman J, Marino M, DeVoe JE. An early look at rates of uninsured safety net clinic visits after the Affordable Care Act. Ann Fam Med. 2015 Jan-Feb;13(1):10-6. doi: 10.1370/afm.1741. — View Citation

DeVoe JE, Angier H, Burdick T, Gold R. Health information technology: an untapped resource to help keep patients insured. Ann Fam Med. 2014 Nov-Dec;12(6):568-72. doi: 10.1370/afm.1721. — View Citation

DeVoe JE, Huguet N, Likumahuwa-Ackman S, Angier H, Nelson C, Marino M, Cohen D, Sumic A, Hoopes M, Harding RL, Dearing M, Gold R. Testing health information technology tools to facilitate health insurance support: a protocol for an effectiveness-implementation hybrid randomized trial. Implement Sci. 2015 Aug 25;10:123. doi: 10.1186/s13012-015-0311-4. — View Citation

DeVoe JE, Tillotson CJ, Marino M, O'Malley J, Angier H, Wallace LS, Gold R. Trends in Type of Health Insurance Coverage for US Children and Their Parents, 1998-2011. Acad Pediatr. 2016 Mar;16(2):192-9. doi: 10.1016/j.acap.2015.06.009. Epub 2015 Aug 18. — View Citation

Gold R, Burdick T, Angier H, Wallace L, Nelson C, Likumahuwa-Ackman S, Sumic A, DeVoe JE. Improve Synergy Between Health Information Exchange and Electronic Health Records to Increase Rates of Continuously Insured Patients. EGEMS (Wash DC). 2015 Aug 6;3(1):1158. doi: 10.13063/2327-9214.1158. eCollection 2015. — View Citation

Hatch B, Tillotson C, Angier H, Marino M, Hoopes M, Huguet N, DeVoe J. Using the electronic health record for assessment of health insurance in community health centers. J Am Med Inform Assoc. 2016 Sep;23(5):984-90. doi: 10.1093/jamia/ocv179. Epub 2016 Jan 23. — View Citation

Heintzman J, Marino M, Hoopes M, Bailey SR, Gold R, O'Malley J, Angier H, Nelson C, Cottrell E, Devoe J. Supporting health insurance expansion: do electronic health records have valid insurance verification and enrollment data? J Am Med Inform Assoc. 2015 Jul;22(4):909-13. doi: 10.1093/jamia/ocv033. Epub 2015 Apr 17. — View Citation

Hoopes MJ, Angier H, Gold R, Bailey SR, Huguet N, Marino M, DeVoe JE. Utilization of Community Health Centers in Medicaid Expansion and Nonexpansion States, 2013-2014. J Ambul Care Manage. 2016 Oct-Dec;39(4):290-8. doi: 10.1097/JAC.0000000000000123. — View Citation

Huguet N, Hoopes MJ, Angier H, Marino M, Holderness H, DeVoe JE. Medicaid Expansion Produces Long-Term Impact on Insurance Coverage Rates in Community Health Centers. J Prim Care Community Health. 2017 Oct;8(4):206-212. doi: 10.1177/2150131917709403. Epub 2017 May 17. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary Changes in the proportion of clinic patients who receive age- and gender-appropriate recommended cancer screening and preventive care (clinic-level) Pre- and post-implementation differences in proportion of patients with insurance continuity will be calculated between implementation and control community health centers (?difference-in-differences? analysis). Generalized linear/non-linear mixed models will be used, which offer flexible regression modeling to accommodate different sources of correlations (serial and intra-clinic), categorical and continuous covariates, and fixed and time-dependent covariates. Baseline to up to 6 years
Primary Changes in the proportion of clinic patients with insurance continuity Pre- and post-implementation differences in proportion of patients with insurance continuity will be calculated between implementation and control community health centers (?difference-in-differences? analysis). Generalized linear/non-linear mixed models will be used, which offer flexible regression modeling to accommodate different sources of correlations (serial and intra-clinic), categorical and continuous covariates, and fixed and time-dependent covariates. Serial and intra-clinic correlations will be modeled as random effects. Baseline to up to 6 years
Primary Total number of months uninsured (total gap months) The Community-based Health Information Technology (HIT) Tools for Cancer Screening and Health Insurance Promotion tool?s impact on total number of months uninsured (total gap months) will be evaluated. The distribution of total gap months will be examined before selecting a specific model to use for the analysis. Analytic models will be refined through an iterative process, guided by the hypotheses and preliminary analyses. Up to 6 years
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