A co-location model for cancer diagnostic services designed by researchers at Dana-Farber Cancer Institute has reported a reduction in the time it takes to diagnose cancer in a community health center in Boston, MA, that cares for a historically underserved population. The study, published in the Journal of Clinical Oncology (JCO) Oncology Practice, found the co-location model, which has been in operation for 12 years, reduced time to cancer diagnosis from a median of 32 days to 12 days.
“The primary goal of this intervention was to decrease the time it took to complete cancer diagnoses,” said senior author Christopher Lathan, MD, MS, MPH, chief clinical access and equity ofﬁcer and associate medical director at Dana-Farber. “We got it down to 12 days, which was surprising and amazing.”
Cancer disparities are well documented in the US among Black, Indigenous, and People of Color. According to the American Cancer Society, Black people have lower ﬁve-year cancer survival rates than white people, and are more likely to be diagnosed with an advanced stage of cancer, when treatment is more difﬁcult and less successful.
Dr Lathan and colleagues initiated the program in 2012 in partnership with a federally qualiﬁed health center (FQHC) that serves a predominantly Black community with primary care and other health services. Dana-Farber, a National Cancer Institute-designated Cancer Center, designed the program to bring oncology diagnostics and cancer-speciﬁc patient navigation services from the NCI-designated cancer center into the community health center to provide cancer diagnostic services.
At the start of the intervention, ﬁve Dana-Farber oncologists and the oncology nurse navigator would see patients referred by providers at the health center to evaluate any cancer or hematology related concerns, including abnormal laboratory values, scans, sudden weight loss, family history of cancer, or follow-up care related to a previous cancer diagnosis. The Dana-Farber team practiced in the community hospital under the FQHC license.
The oncology diagnostics team did not provide direct cancer treatment; they worked with the nurse navigator to help direct patients to the right next step, be it prevention services, diagnostic tests, or care at a treatment center. For instance, a patient in need of a scan might be directed to a local hospital with scanning capabilities or to Dana-Farber, depending on the patient’s history, preference, and insurance.
The entire program was built with clinical nurse navigation at its foundation.
“This was a clinic set up to help primary care providers with any cancer related question,” said Dr Lathan. “Our goal was to respond with a reasonable and sensible plan as quickly as possible.”
Such co-location models have been successful before in other areas of medicine, such as cardiology, but this is the ﬁrst to attempt to provide cancer diagnostics.
The program was designed to be iterative and responsive. For instance, over time, the clinic developed a lung cancer screening program and an oral health program.
“By being present in the clinic day in and day out, year in and year out, you build trust with the providers and the patients,” said Dr Lathan. “We were able to listen to the practitioners and develop programs over time that support their needs.”
An additional unexpected result was that 10 percent of the patients in the cohort enrolled in clinical trials. This doubles the historical rate of ﬁve percent for marginalized populations. The subset of patients seen at the clinic who were diagnosed with cancer enrolled in clinical trials at an even higher rate.
“The clinical trial numbers are intriguing, but more research is required to understand this change,” said Dr Lathan. Further research is also required to understand the cost-effectiveness of the intervention, its scalability, and to get a qualitative sense of how patients responded to the model.
“We don’t want to over-generalize and say we have a solution for the complex challenge of disparities in cancer care delivery,” said Dr Lathan, who noted caveats in the paper, including the relatively small number of patients involved in the cohort and limited information about cancer care and outcomes prior to the program’s implementation. “But I do think this is a model that could be further evaluated and hopefully utilized and adjusted in many different ways to speciﬁcally improve the diagnosis of all cancers.”
The model is now in operation in two other FQHCs in Boston.Back To Top
Co-Location Model for Cancer Diagnostic Services Decreases Wait Times for Underserved Populations. Appl Rad Oncol.