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Purpose: The optimal delivery of survivorship care, particularly within primary care, remains poorly understood. We established the Johns Hopkins Primary Care for Cancer Survivors (PCCS) clinic in 2015 to address care challenges unique to cancer survivors. To better understand the care from the PCCS clinic, we interviewed patients about their perception of care delivery, survivorship care, and care coordination. Methods: We conducted semi-structured interviews with adult survivors of any cancer type seen in the PCCS clinic. A priori and in vivo coding of verbatim transcripts was part of the thematic analysis. Results: Seventeen cancer survivors were interviewed (ages 37–78). Themes that emerged were (1) optimal care and (2) the PCCS experience. Subthemes respectively included the ideal role of the primary care provider (1), telehealth/COVID-19 challenges and opportunities (1), patient-derived value from the PCCS clinic (2), and improving the PCCS model (2). Overall, PCCS patients expected and experienced high-quality, comprehensive primary care by providers with cancer survivorship expertise. Patients reported telehealth benefits and challenges for survivorship care during the COVID-19 pandemic. Conclusions: PCCS patients perceived receiving high-quality primary care and valued being seen in a primary care–based survivorship clinic. The PCCS clinic can serve as a model of primary care–based cancer survivorship. Implications for Cancer Survivors: Ideal primary care provider roles and care coordination are important factors for high-quality survivorship care and can be provided by a specialized cancer survivorship clinic in primary care. [ABSTRACT FROM AUTHOR]

Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O'Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data. [ABSTRACT FROM AUTHOR]