Guideline-based tobacco treatment is infrequently offered. Electronic health record-enabled patient-generated health data (PGHD) has the potential to increase patient treatment engagement and satisfaction.

We evaluated outcomes of a strategy to enable PGHD in a medical oncology clinic from July 1, 2021 to December 31, 2022. Among 12,777 patients, 82.1% received a tobacco screener about use and interest in treatment as part of eCheck-in via the patient portal.

We attained a broad reach (82.1%) and moderate response rate (30.9%) for this low-burden PGHD strategy. Patients reporting current smoking (n = 240) expressed interest in smoking cessation medication (47.9%) and counseling (35.8%). As a result of patient requests via PGHD, most tobacco treatment requests by patients were addressed by their providers (40.6–80.3%). Among patients with active smoking, those who received/answered the screener (n = 309 ) were more likely to receive tobacco treatment compared with usual care patients who did not have the patient portal (n = 323) (OR = 2.72, 95% CI = 1.93–3.82, P < 0.0001) using propensity scores to adjust for the effect of age, sex, race, insurance, and comorbidity. Patients who received yet ignored the screener (n = 1024) compared with usual care were also more likely to receive tobacco treatment, but to a lesser extent (OR = 2.20, 95% CI = 1.68–2.86, P < 0.0001). We mapped observed and potential benefits to the Translational Science Benefits Model (TSBM).

PGHD via patient portal appears to be a feasible, acceptable, scalable, and cost-effective approach to promote patient-centered care and tobacco treatment in cancer patients. Importantly, the PGHD approach serves as a real world example of cancer prevention leveraging the TSBM.

Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features associated with successful participant recruitment measured by accrual percentage and (2) compare the characteristics of the RCTs by assessing the most and least successful recruitment, which are indicated by varying thresholds of accrual percentage such as ≥ 90% vs ≤ 10%, ≥ 80% vs ≤ 20%, and ≥ 70% vs ≤ 30%.

Data from the internal research registry at Columbia University Irving Medical Center and Aggregated Analysis of ClinicalTrials.gov were collected for 393 randomized interventional treatment studies closed to further enrollment. We compared two regularized linear regression and six tree-based machine learning models for accrual percentage (i.e., reported accrual to date divided by the target accrual) prediction. The outperforming model and Tree SHapley Additive exPlanations were used for feature importance analysis for participant recruitment. The identified features were compared between the two subgroups.

CatBoost regressor outperformed the others. Key features positively associated with recruitment success, as measured by accrual percentage, include government funding and compensation. Meanwhile, cancer research and non-conventional recruitment methods (e.g., websites) are negatively associated with recruitment success. Statistically significant subgroup differences (corrected p-value < .05) were found in 15 of the top 30 most important features.

This multi-source retrospective study highlighted key features influencing RCT participant recruitment, offering actionable steps for improvement, including flexible recruitment infrastructure and appropriate participant compensation.

Integration of clinical skills during graduate training in dual-degree programs remains a challenge. The present study investigated the availability and self-perceived efficacy of clinical continuity strategies for dual-degree trainees preparing for clinical training.

Survey participants were MD/DO-PhD students enrolled in dual-degree-granting institutions in the USA. The response rate was 95% of 73 unique institutions surveyed, representing 56% of the 124 MD-PhD and 7 DO-PhD recognized training programs. Respondents were asked to indicate the availability and self-perceived efficacy of each strategy.

Reported available clinical continuity strategies included clinical volunteering (95.6%), medical grand rounds (86.9%), mentored clinical experiences (84.2%), standardized patients/ practice Objective Structured Clinical Examinations (OSCEs) (70.3%), clinical case reviews (45.9%), clinical journal clubs (38.3%), and preclinical courses/review sessions (37.2%). Trainees rated standardized patients (µ = 6.98 ± 0.356), mentored clinical experiences (µ = 6.94 ± 0.301), clinical skills review sessions (µ = 6.89 ± 0.384), preclinical courses/review sessions (µ = 6.74 ± 0.482), and clinical volunteering (µ = 6.60 ± 0.369), significantly (p < 0.050) higher than clinical case review (µ = 5.34 ± 0.412), clinical journal club (µ = 4.75 ± 0.498), and medicine grand rounds (µ = 4.45 ± 0.377). Further, 84.4% of respondents stated they would be willing to devote at least 0.5–1 hour per week to clinical continuity opportunities during graduate training.

Less than half of the institutions surveyed offered strategies perceived as the most efficacious in preparing trainees for clinical reentry, such as clinical skills review sessions. Broader implementation of these strategies could help better prepare dual-degree students for their return to clinical training.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Hospital palliative care has been shown to improve quality of life and optimize hospital utilization for seriously ill patients who need intensive care. The present review examined whether hospital palliative care in intensive care (ICU) and non-ICU settings will influence hospital length of stay and in-hospital mortality.

A systematic search of CINAHL/EBSCO, the Cochrane Library, Google Scholar, MEDLINE/Ovid, PubMed, and the Web of Science through 12 October 2016 identified 16 studies that examined the effects of hospital palliative care and reported on hospital length of stay and in-hospital death. Random-effects pooled odds ratios and mean differences with corresponding 95% confidence intervals were estimated. Heterogeneity was measured by the I2 test. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was utilized to assess the overall quality of the evidence.

Of the reviewed 932 articles found in our search, we reviewed the full text of 76 eligible articles and excluded 60 of those, which resulted in a final total of 16 studies for analysis. Five studies were duplicated with regard to outcomes. A total of 18,330 and 9,452 patients were analyzed for hospital length of stay and in-hospital mortality from 11 and 10 studies, respectively. Hospital palliative care increased mean hospital length of stay by 0.19 days (pooled mean difference = 0.19; 95% confidence interval [CI95%] = –2.22–2.61 days; p = 0.87; I2 = 95.88%) and reduced in-hospital mortality by 34% (pooled odds ratio = 0.66; CI95% = 0.52–0.84; p < 0.01; I2 = 48.82%). The overall quality of evidence for both hospital length of stay and in-hospital mortality was rated as very low and low, respectively.

Hospital palliative care was associated with a 34% reduction of in-hospital mortality but had no correlation with hospital length of stay.