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1AbstractPurposePredicting 30-day readmission risk is paramount to improving the quality of patient care. Previous studies have examined clinical risk factors associated with hospital readmissions. In this study, we compare sets of patient, provider, and community-level variables that are available at two different points of a patient’s inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models in order to identify and target appropriate actionable interventions that can potentially reduce avoidable readmissions.MethodsUsing EHR data from a retrospective cohort of 2460 oncology patients, two sets of binary classification models predicting 30-day readmission were developed; one trained on variables that are available within the first 48 hours of admission and another trained on data from the entire hospital encounter. A comprehensive machine learning analysis pipeline was leveraged including preprocessing and feature transformation, feature importance and selection, machine learning modeling, and post-analysis.ResultsLeveraging all features, the LGB (Light Gradient Boosting Machine) model produced higher, but comparable performance: (AUROC: 0.711 and APS: 0.225) compared to Epic (AUROC: 0.697 and APS: 0.221). Given features in the first 48-hours, the RF (Random Forest) model produces higher AUROC (0.684), but lower AUPRC (0.18) and APS (0.184) than the Epic model (AUROC: 0.676). In terms of the characteristics of patients flagged by these models, both the full (LGB) and 48-hour (RF) feature models were highly sensitive in flagging more patients than the Epic models. Both models flagged patients with a similar distribution of race and sex; however, our LGB and random forest models more inclusive flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, cancer type), provider (winter discharge, hospital admission type), community (zip income, marital status of partner).ConclusionWe demonstrated that we could develop and validate models comparable to existing Epic 30-day readmission models, but provide several actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.

Introduction: Studies have shown aggressive cancer care at the end of life is associated with decreased quality of life, decreased median survival, and increased cost of care. This study describes the patients most likely to receive systemic anticancer therapy at the end of life in a community cancer institute. Materials and Methods: We performed a retrospective cohort study of 201 patients who received systemic anticancer therapy in our institution and died between July 2016 and April 2017. Data collected included primary malignancy, hospice enrollment, healthcare utilization, Oncology Care Model (OCM) enrollment, and clinical assessments at last office visit prior to a treatment decision before death. We defined our outcome variable as the receipt of anticancer treatment in the last 14 days of a patient’s life. We evaluated 20 clinical exposure variables with respect to the outcome classes. Risk ratios along with their associated confidence intervals and P values were calculated. Significance was determined using the Benjamini-Hochberg procedure to account for multiple testing. Results: Of the 201 patients who died of cancer, 36 (17%) received anticancer therapy within the last 14 days of life. Several risk factors were significantly positively associated with receiving anticancer therapy at the end of life including hospitalization within 30 days of end of life, number of hospitalizations per patient (≥2), death in hospital, enrollment in OCM, and a diagnosis of hematologic malignancy. Conclusion: Our findings demonstrate those enrolled in the OCM and those with hematologic malignancies have a higher risk of receiving anticancer therapy in the last 14 days of life. These observations highlight the need for better identifying the needs of high-risk patients and providing good quality care throughout the disease trajectory to better align end-of-life care with patients’ wishes.

Background: There is a growing patient population using yoga as a therapeutic intervention, but little is known about how yoga interfaces with health care in clinical settings. Purpose: To characterize how yoga is documented at a large academic medical center and to systematically identify clinician-derived therapeutic use cases of yoga. Methods: We designed a retrospective observational study using a yoga cohort (n = 30,976) and a demographically matched control cohort (n = 92,919) from the electronic health records at Penn Medicine between 2006 and 2016. We modeled the distribution of yoga notes among patients, clinicians, and clinical service departments, built a multinomial Naive Bayes classifier to separate the notes by context-dependent use of the word yoga, and modeled associations between clinician recommendations to use yoga and 754 diagnostic codes with Fisher9s exact test, setting an false discovery rate (FDR)-adjusted P-value ≤ .05 (ie, q-value) as the significance threshold. Results: Yoga mentions in the electronic health record have increased 10.4-fold during the 10-year study period, with 2.6% of patients having at least 1 mention of yoga in their notes. In total, 30,976 patients, 2398 clinicians, and 41 clinical service departments were affiliated with yoga notes. The majority of yoga notes are in primary care. Nine diagnoses met the significance criteria for having an association with clinician recommendations to use yoga including Parkinson9s disease (Odds ratio [OR], 6.3 [3.7 to 11.4]; q-value Conclusions: There is a widespread and growing trend to include yoga as part of the clinical record. In practice, clinicians are recommending yoga as a nonpharmacological intervention for a subset of common chronic diseases.

66 Background: Studies have shown that aggressive cancer care at end of life is associated with decreased quality of life, decreased median survival, and increased cost of care. This study describes the patients most likely to receive aggressive anti-cancer therapy at the end of life in a community cancer institute. Methods: We performed a retrospective review of 213 patients who received anti-cancer therapy in our institution and died between July 2016-April 2017. Data collected included primary malignancy, death date, date of last anti-cancer treatment, hospice enrollment, healthcare utilization, Oncology Care Model (OCM) enrollment, and clinical assessments at last office visit prior to a treatment decision before death. Data were analyzed using univariate logistic regression to determine feature importance. Results: Of the 201 patients who died of cancer, 36 (17%) received anti-cancer therapy within the last 14 days of life. Several factors were significantly positively correlated with receiving anti-cancer therapy at end of life, including enrollment in OCM (p < 0.001), frequency of hospital utilization (p < 0.001), death in hospital (p