Large Language Models to Identify Advance Care Planning in Patients With Advanced Cancer.

Context: Efficiently tracking Advance Care Planning (ACP) documentation in electronic heath records (EHRs) is essential for quality improvement and research efforts. The use of large language models (LLMs) offers a novel approach to this task.

Objectives: To evaluate the ability of LLMs to identify ACP in EHRs for patients with advanced cancer and compare performance to gold-standard manual chart review and natural language processing (NLP).

Changing treatment patterns for hepatocellular carcinoma: A Surveillance, Epidemiology, and End Results-Medicare study.

Background: From 2007 to 2017, sorafenib was the sole systemic therapy for hepatocellular carcinoma (HCC), but nine new therapies were approved from 2017 to 2022. No studies have yet examined population-level treatment patterns for HCC since these approvals.

Methods: For this retrospective cohort, Surveillance, Epidemiology, and End Results (SEER)-Medicare data were used to identify patients who had HCC diagnosed between 2014 and 2019 with claims through 2020.

Barriers to Serious Illness Conversations Among Patients with Advanced Cancer: A Qualitative Study.

Purpose: Serious illness conversations (SICs) are discussions between clinicians and cancer patients about illness understanding, information preferences, and goals of care. Interventions to prompt SICs increase SIC rates and improve care delivery near the end of life. This embedded substudy examined SIC barriers and facilitators among "refractory" patients without an SIC despite enrollment in an SIC clinical trial.

Using Oncology Treatment Pathway Data to Evaluate Serious Illness Communication, Care Utilization, and End-of-Life Care for Patients With Cancer.

Purpose: Oncology treatment pathways provide decision support and encourage guideline adherence. Pathway data combined with electronic health record (EHR) data can identify patient populations with poor prognoses, low serious illness conversation (SIC) rates, and high acute care utilization that may benefit from targeted interventions.

Patients And Methods: We conducted a retrospective cohort analysis among adults with cancer treated at seven affiliated sites of the Dana-Farber Cancer Institute (DFCI) who had navigations within 21 treatment pathways between July 29, 2019, and March 8, 2023.

County-level jail and state-level prison incarceration and cancer mortality in the United States.

This study examined the association of county-level jail and state-level prison incarceration rates and cancer mortality rates in the United States. Incarceration rates (1995-2018) were sourced from national data and categorized into quartiles. County- and state-level mortality rates (2000-2019) with invasive cancer as the underlying cause of death were obtained from the National Vital Statistics System.

Spending Analysis of Machine Learning-Based Communication Nudges in Oncology.

Background: Serious illness conversations (SICs) in the outpatient setting may improve mood and quality of life among patients with cancer and decrease aggressive end-of-life care. Interventions informed by behavioral economics may increase rates of SICs between oncology clinicians and patients, but the impact of these interventions on end-of-life spending is unknown.

Methods: This study is a secondary analysis of a stepped-wedge cluster randomized, controlled trial that involved nine medical oncology practices and their high-risk patients at a large academic institution between June 2019 and April 2020.

Let Us Have the Conversation: Serious Illness Communication in Oncology: Definitions, Barriers, and Successful Approaches.

Serious illness communications are crucial elements of care delivery for patients with cancer. High-quality serious illness communications are composed of open, honest discussions between patients, caregivers, and clinicians regarding patient's communication preferences, expected illness trajectory, prognosis, and risks and benefits of any recommended care. High-quality communication ideally starts at the time of a patients' cancer diagnosis, allows space for and response to patient emotions, elicits patients' values and care preferences, and is iterative and longitudinal.

Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

Background: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death.

Objective: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death.

Creating a culture for change: Lessons from behavioral economics and complexity science to increase serious illness conversations for patients with cancer.

Patient-centered cancer care requires communication between patients and clinicians about patients' goals, values, and preferences. Serious illness communication improves patient and caregiver outcomes, the value and quality of cancer care, and the well-being of clinicians. Despite these benefits, there are competing factors including time, capacity, bandwidth, and resistance.

Remote Patient-Reported Outcomes and Activity Monitoring to Improve Patient-Clinician Communication Regarding Symptoms and Functional Status: A Randomized Controlled Trial.

Purpose: Routine collection of patient-generated health data (PGHD) may promote earlier recognition of symptomatic and functional decline. This trial assessed the impact of an intervention integrating remote PGHD collection with patient nudges on symptom and functional status understanding between patients with advanced cancer and their oncology team.

Methods: This three-arm randomized controlled trial was conducted from November 19, 2020, to December 17, 2021, at a large tertiary oncology practice.

Cancer equity for those impacted by mass incarceration.

The cancer disparities between people with incarceration histories compared with those who do not have those histories are vast. Opportunities for bolstering cancer equity among those impacted by mass incarceration exist in criminal legal system policy; carceral, community, and public health linkages; better cancer prevention, screening, and treatment services in carceral settings; expansion of health insurance; education of professionals; and use of carceral sites for health promotion and transition to community care. Clinicians, researchers, persons with a history of incarceration, carceral administrators, policy makers, and community advocates could play a cancer equity role in each of these areas.

Cancer Screening Rates and Outcomes for Justice-Involved Individuals: A Scoping Review.

Individuals who have been incarcerated or under community supervision have elevated cancer mortality. This review summarizes existing knowledge on implementation and outcomes of cancer screening for justice-involved individuals to identify opportunities for reducing cancer disparities. This scoping review identified 16 studies published between January 1990 and June 2021 that reported cancer screening rates and outcomes in U.

Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial.

Importance: Serious illness conversations (SICs) between oncology clinicians and patients are associated with improved quality of life and may reduce aggressive end-of-life care. However, most patients with cancer die without a documented SIC.

Objective: To test the impact of behavioral nudges to clinicians to prompt SICs on the SIC rate and end-of-life outcomes among patients at high risk of death within 180 days (high-risk patients) as identified by a machine learning algorithm.

Effect of a MUC5AC Antibody (NPC-1C) Administered With Second-Line Gemcitabine and Nab-Paclitaxel on the Survival of Patients With Advanced Pancreatic Ductal Adenocarcinoma: A Randomized Clinical Trial.

Importance: Treatment options are limited for patients with advanced pancreatic ductal adenocarcinoma (PDAC) beyond first-line 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX), with such individuals commonly being treated with gemcitabine and nab-paclitaxel.

Objective: To determine whether NPC-1C, an antibody directed against MUC5AC, might increase the efficacy of second-line gemcitabine and nab-paclitaxel in patients with advanced PDAC.

Design, Setting, And Participants: This multicenter, randomized phase II clinical trial enrolled patients with advanced PDAC between April 2014 and March 2017 whose disease had progressed on first-line FOLFIRINOX.

Association of Oncologist Participation in Medicare's Oncology Care Model With Patient Receipt of Novel Cancer Therapies.

Importance: Medicare's Oncology Care Model (OCM) was an alternative payment model that tied performance-based payments to cost and quality goals for participating oncology practices. A major concern about the OCM regarded inclusion of high-cost cancer therapies, which could potentially disincentivize oncologists from prescribing novel therapies.

Objective: To examine whether oncologist participation in the OCM changed the likelihood that patients received novel therapies vs alternative treatments.

Oncologist Perceptions of Algorithm-Based Nudges to Prompt Early Serious Illness Communication: A Qualitative Study.

Early serious illness conversations (SICs) about goals of care and prognosis improve mood, quality of life, and end-of-life care quality. Algorithm-based behavioral nudges to oncologists increase the frequency and timeliness of such conversations. However, clinicians' perspectives on such nudges are unknown.

Breast Medical Oncologists' Perspectives of Telemedicine for Breast Cancer Care: A Survey Study.

Purpose: The COVID-19 pandemic forced rapid adoption of telemedicine (TM) for breast oncology visits in the United States, but the appropriate role of postpandemic TM is uncertain. We sought to understand physician and advance practice practitioner perspectives on the use of TM for outpatient breast cancer care through an electronically administered survey.

Methods: Breast medical oncology clinicians at two academic cancer centers and five satellite locations affiliated with the Dana Farber Cancer Institute and the Massachusetts General Cancer Center were invited to respond to a 21-question survey administered in September 2021 about clinicians' perceptions and attitudes toward TM during the previous 12 months.

Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial.

Background: While health systems have implemented multifaceted interventions to improve physician and patient communication in serious illnesses such as cancer, clinicians vary in their response to these initiatives. In this secondary analysis of a randomized trial, we identified phenotypes of oncology clinicians based on practice pattern and demographic data, then evaluated associations between such phenotypes and response to a machine learning (ML)-based intervention to prompt earlier advance care planning (ACP) for patients with cancer.

Methods And Findings: Between June and November 2019, we conducted a pragmatic randomized controlled trial testing the impact of text message prompts to 78 oncology clinicians at 9 oncology practices to perform ACP conversations among patients with cancer at high risk of 180-day mortality, identified using a ML prognostic algorithm.

Patient and clinician nudges to improve symptom management in advanced cancer using patient-generated health data: study protocol for the PROStep randomised controlled trial.

Introduction: Patients with advanced cancers often face significant symptoms from their cancer and adverse effects from cancer-associated therapy. Patient-generated health data (PGHD) are routinely collected information about symptoms and activity levels that patients either directly report or passively record using devices such as wearable accelerometers. The objective of this study was to test the impact of an intervention integrating remote collection of PGHD with clinician and patient nudges to inform communication between patients with advanced cancer and their oncology team regarding symptom burden and functional status.

Clinician perspectives on machine learning prognostic algorithms in the routine care of patients with cancer: a qualitative study.

Purpose: Oncologists may overestimate prognosis for patients with cancer, leading to delayed or missed conversations about patients' goals and subsequent low-quality end-of-life care. Machine learning algorithms may accurately predict mortality risk in cancer, but it is unclear how oncology clinicians would use such algorithms in practice.

Methods: The purpose of this qualitative study was to assess oncology clinicians' perceptions on the utility and barriers of machine learning prognostic algorithms to prompt advance care planning.

Impact of Behavioral Nudges on the Quality of Serious Illness Conversations Among Patients With Cancer: Secondary Analysis of a Randomized Controlled Trial.

Purpose: Serious Illness Conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Although behavioral interventions may prompt earlier or more frequent SICs, their impact on the quality of SICs is unclear.

Methods: This was a secondary analysis of a randomized clinical trial (NCT03984773) among 78 clinicians and 14,607 patients with cancer testing the impact of an automated mortality prediction with behavioral nudges to clinicians to prompt more SICs.