Cancer survivorship algorithms as clinical tools for primary care providers.

Abstract

100 Background: Clinical decision tools (CDTs) such as survivorship algorithms may be valuable resources for primary care providers who provide post-treatment care for cancer survivors. Our objective was to assess providers’ perceptions, adoption, and satisfaction with clinical practice algorithms tailored to site-specific cancer survivorship clinics. Methods: Eligible providers were those assigned to one of 9 site-specific survivorship clinics, (breast, colorectal, genitourinary, gynecology, head and neck, lymphoma, melanoma, stem cell transplant, and thyroid). Potential respondents were invited to participate by emails. Voluntary return of the survey indicated a provider’s informed consent. Providers had the choice to participate by clicking on a link embedded in an email. Once the link was activated, the user was taken to a 10-item survey with questions asking about the usability, awareness, and satisfaction with the algorithms specific to their clinic. Descriptive statistics (i.e. frequencies and percentages) were used to summarize the responses. Results: Of 35 providers assigned in the survivorship clinics, 18 responded resulting in a 51% response rate. The majority of respondents (94.4%) were aware of the survivorship algorithms specific to their clinic. Over 75% reported using the algorithms occasionally (16.7%), frequently (33%) and always (33.3%). The major barrier to using the algorithms was a lack of awareness on to access the algorithms. Over half of the providers (55.6%) preferred using the digital versions of the algorithms. 68% strongly agreed the algorithms were practical to use and implement in their clinical setting. The majority of providers’ reported being satisfied (62.5%) or very satisfied (25.0%) with the algorithms tailored to their site specific clinic. Conclusions: Survivorship practice algorithms were perceived as useful clinical resources to deliver coordinated care to cancer survivors with diverse cancer diagnoses. Future work is needed to determine the impact of the algorithms on providers’ practice with cancer survivors.