Systematic Patient Navigation Strategies to Scale Breast Cancer Disparity Reduction by Improved Cancer Prevention and Care Delivery Processes

PURPOSE: Patient navigation uses trained personnel to eliminate barriers to timely care across all phases of the health care continuum, thereby reducing health disparities. However, patient navigation has yet to be systematized in implementation models to improve processes of care at scale rather than remain a band-aid approach focused solely on improving care for the individual patient. The 4R systems engineering approach (right information and right treatment to the right patient at the right time) uses project management discipline principles to develop care sequence templates that serve as patient-centered project plans guiding patients and their care team. METHODS: A case-study approach focused on the underserved patient shows how facilitators to timely breast cancer screening and care pragmatically identified as emergent data by patient navigators can be actionized by iteratively revising 4R care sequence templates to incorporate new insights as they emerge. RESULTS: Using a case study of breast cancer screening of a low-income patient, we illustrate how 4R care sequence templates can be revised to incorporate emergent facilitators to care identified through patient navigation. CONCLUSION: Use of care sequence templates can inform the care team to optimize a particular patient’s care, while functioning as a learning health care system for process improvement of patient care and patient navigation scaling. A learning health care system approach that systematically integrates data patterns emerging from multiple patient navigation experiences through in-person navigators and 4R care sequence templates may improve processes of care and allow patient navigation scaling to reduce cancer disparities.

Governance of a Learning Health Care System for Oncology: Patient Recommendations

PURPOSE: The learning health care system (LHS) was designed to enable real-time learning and research by harnessing data generated during patients’ clinical encounters. This novel approach begets ethical questions regarding the oversight of users and uses of patient data. Understanding patients’ perspectives is vitally important. MATERIALS AND METHODS: We conducted democratic deliberation sessions focused on CancerLinQ, a real-world LHS. Experts presented educational content, and then small group discussions were held to elicit viewpoints. The deliberations centered around whether policies should permit or deny certain users and uses of secondary data. De-identified transcripts of the discussions were examined by using thematic analysis. RESULTS: Analysis identified two thematic clusters: expectations and concerns, which seemed to inform LHS governance recommendations. Participants expected to benefit from the LHS through the advancement of medical knowledge, which they hoped would improve treatments and the quality of their care. They were concerned that profit-driven users might manipulate the data in ways that could burden or exploit patients, hinder medical decisions, or compromise patient-provider communication. It was recommended that restricted access, user fees, and penalties should be imposed to prevent users, especially for-profit entities, from misusing data. Another suggestion was that patients should be notified of potential ethical issues and included on diverse, unbiased governing boards. CONCLUSION: If patients are to trust and support LHS endeavors, their concerns about for-profit users must be addressed. The ethical implementation of such systems should consist of patient representation on governing boards, transparency, and strict oversight of for-profit users.

Patient Preferences Regarding Informed Consent Models for Participation in a Learning Health Care System for Oncology

PURPOSE: The expansion of learning health care systems (LHSs) promises to bolster research and quality improvement endeavors. Stewards of patient data have a duty to respect the preferences of the patients from whom, and for whom, these data are being collected and consolidated. METHODS: We conducted democratic deliberations with a diverse sample of 217 patients treated at 4 sites to assess views about LHSs, using the example of CancerLinQ, a real-world LHS, to stimulate discussion. In small group discussions, participants deliberated about different policies for how to provide information and to seek consent regarding the inclusion of patient data. These discussions were recorded, transcribed, and de-identified for thematic analysis. RESULTS: Of participants, 67% were female, 61% were non-Hispanic Whites, and the mean age was 60 years. Patients' opinions about sharing their data illuminated 2 spectra: trust/distrust and individualism/collectivism. Positions on these spectra influenced the weight placed on 3 priorities: promoting societal altruism, ensuring respect for persons, and protecting themselves. In turn, consideration of these priorities seemed to inform preferences regarding patient choices and system transparency. Most advocated for a policy whereby patients would receive notification and have the opportunity to opt out of including their medical records in the LHS. Participants reasoned that such a policy would balance personal protections and societal welfare. CONCLUSION: System transparency and patient choice are vital if patients are to feel respected and to trust LHS endeavors. Those responsible for LHS implementation should ensure that all patients receive an explanation of their options, together with standardized, understandable, comprehensive materials.