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.
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.
Study Type
OBSERVATIONAL
Enrollment
45
Receive CATCH-UP intervention
OHSU Knight Cancer Institute
Portland, Oregon, United States
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.
Time frame: Baseline to up to 6 years
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.
Time frame: Baseline to up to 6 years
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.
Time frame: Up to 6 years
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