Health Services Research最新文献

Objective: To examine the association between COVID-19-related hardship and 1-year adjuvant endocrine therapy (AET) adherence among women with early-stage hormone-receptor-positive breast cancer.

Study setting and design: This post hoc analysis utilized data from the THRIVE trial, which tested a 6-month remote monitoring intervention on 1-year AET adherence, measured using an electronic pillbox. The 1-year follow-up survey included questions about pandemic-related hardship, including financial loss, changes/gaps in health insurance, and difficulty accessing basic needs. Participants reporting any of these were categorized as experiencing pandemic-related hardship. Logistic regressions estimated the association between patient characteristics and pandemic-related hardship, and between hardship and AET adherence (≥ 80% proportion of days covered), controlling for patient characteristics and randomization group.

Data sources and analytic sample: We included 217 women diagnosed with early-stage breast cancer prescribed AET at a large cancer center who enrolled in THRIVE between April 2019 and June 2021.

Principal findings: Overall, 39.6% of participants reported any pandemic-related hardship: 34.6% reported financial loss, 10.6% reported changes/gaps in insurance, and 11.1% reported difficulty accessing basic needs. In adjusted analyses, having an income ≤ 100% of federal poverty level or prior chemotherapy or radiation was associated with a 41.4 (95% CI: 9.8-73.0) and 13.8 (95% CI: 0.3-27.2) percentage-point higher likelihood, respectively, of having any pandemic-related hardship. Over half (52%) of participants were AET adherent. In adjusted analyses, 40.1% of those with any pandemic-related hardship were AET adherent, compared with 59.5% of those without hardship, a 19.3 percentage-point lower likelihood (95% CI: -33.0 to -5.7).

Conclusions: Pandemic-related hardship was more common among individuals with lower income or prior radiation or chemotherapy, and was associated with lower AET adherence, with possible impacts on cancer progression and survival. These findings highlight the need for routine financial screening and targeted support, particularly among lower-income patients on long-term AET.

Trial registration: NCT03592771.

Objective: To examine whether patient sociodemographic and clinical characteristics and prior interactions with the healthcare system were associated with opening patient portal messages related to cancer genetic services and beginning services.

Study setting and design: The trial was conducted in the University of Utah Health (UHealth) and NYU Langone Health (NYULH) systems. Between 2020 and 2023, 3073 eligible primary care patients aged 25-60 years meeting family history-based criteria for cancer genetic evaluation were randomized 1:1 to receive a patient portal message with a hyperlink to a pretest genetics education chatbot or information about scheduling a pretest standard of care (SOC) appointment.

Data sources and analytic sample: Primary data were collected. Eligible patients had a primary care visit in the previous 3 years, a patient portal account, no prior cancer diagnosis except nonmelanoma skin cancer, no prior cancer genetic services, and English or Spanish as their preferred language. Multivariable models identified predictors of opening patient portal messages by site and beginning pretest genetic services by site and experimental condition.

Principal findings: Number of previous patient portal logins (UHealth average marginal effect [AME]: 0.32; 95% CI: 0.27, 0.38; NYULH AME: 0.33; 95% CI: 0.27, 0.39), having a recorded primary care provider (NYULH AME: 0.15; 95% CI: 0.08, 0.22), and more primary care visits in the previous 3 years (NYULH AME: 0.09; 95% CI: 0.02, 0.16) were associated with opening patient portal messages about genetic services. Number of previous patient portal logins (UHealth AME: 0.14; 95% CI: 0.08, 0.21; NYULH AME: 0.18; 95% CI: 0.12, 0.23), having a recorded primary care provider (NYULH AME: 0.08; 95% CI: 0.01, 0.14), and more primary care visits in the previous 3 years (NYULH AME: 0.07; 95% CI: 0.01, 0.13) were associated with beginning pretest genetic services. Patient sociodemographic and clinical characteristics were not significantly associated with either outcome.

Conclusions: As system-level initiatives aim to reach patients eligible for cancer genetic services, patients already interacting with the healthcare system may be most likely to respond. Addressing barriers to accessing healthcare and technology may increase engagement with genetic services.

Objective: To explore how aspects of patient-centered communication (PCC) may directly or indirectly predict patients' preferences for artificial intelligences (AIs) versus human medical professionals, based on the stimulus-organism-response model.

Study setting and design: As AI gains popularity and researchers explore its application in the medical context, it is important to understand how current patient-provider dynamics involving high technology (e.g., telehealth communication) may shape patients' perceptions of future use of AI, especially in the context of cancer care where patient satisfaction and sense of care continuity are important. Participants were recruited from an online panel in China (June 2024). Structural equation modeling analyzed the relationships among variables, including six PCC dimensions (i.e., exchanging information, fostering healing relationships, making decisions, managing uncertainty, responding to emotions, and enabling patient self-management), communication outcomes (i.e., patient satisfaction, sense of care continuity), and patients' preference of AIs vs. human medical professionals.

Data sources and analytic sample: Primary data were collected from an online panel of 495 Chinese cancer patients in China, representative of the gender and age distribution of the overall Chinese population due to quota sampling.

Principal findings: Direct predictors of preference for replacing human medical professionals with AIs included lower patient satisfaction (β = -11, p < 0.05), lower ease of use (β = -0.1, p < 0.05), better care continuity (β = 0.15, p < 0.01), providers' attending to emotions (β = 0.17, p < 0.05), and less enablement in self-management (β = -0.17, p < 0.01). Patient satisfaction, ease of use, and care continuity mediated the relationships between different PCC dimensions and patients' preferences for AI use.

Conclusions: PCC and communication outcomes are associated with cancer patients' preferences in future AI use. Our study sheds light on how clinicians may improve their communication to educate patients on navigating the cancer care continuum using AI technology.

Objective: To evaluate whether the association between receiving care at an accredited hospital and timely treatment initiation varies by county income level.

Study setting and design: This cross-sectional study compared days from diagnosis to treatment initiation among patients receiving care at CoC-accredited hospitals with patients receiving care at non-accredited hospitals. We estimated distributional effects with a quantile regression model. We stratified patients into low (median household-income < 80k) and high-income (median household-income ≥ 80k) counties.

Data sources and analytic sample: We analyzed population-based Surveillance, Epidemiological, and End Results case data (2018-2021). We excluded cancer cases that did not receive treatment. All analyses were adjusted for tumor and patient characteristics, treatment received, and geographic factors.

Principal findings: Among 2,107,188 patients receiving cancer treatment, 73.65% received care at an accredited hospital. Median time-to-treatment was 27 days (interquartile range = 1-52). Care at an accredited hospital was associated with longer median time-to-treatment (+2.6 days) in low-income counties but not high-income counties. Similarly, care at an accredited hospital was associated with widening the time-to-treatment interquartile range (+1.8 days) in low-income but not high-income counties. The magnitude of these associations was highest in patients aged 65+, unmarried, diagnosed at an early stage, and in less common cancers. Only among patients diagnosed with distant-stage cancer was accreditation associated with reduced median time-to-treatment in both low and high-income counties.

Conclusions: Treatment at an accredited hospital appeared to increase time-to-treatment differences between high-and low-income counties and low-income counties. This heterogeneity may reflect access challenges facing low-income cancer patients. Health systems seeking to provide high quality, timely care must overcome these access challenges as they navigate patients through the cancer care continuum. While a 2.6-day delay in treatment may not impact outcomes, future research should understand why patients from lower-resource communities wait longer than patients in affluent communities.