The hospitalist model of care has gained favour in many hospital systems for the value, cost-effectiveness and quality of care that hospitalists provide. Hospitalists are experts in high-acuity medical problems of patients and they are intimately knowledgeable about hospital operations that enable efficiency of patient care. This results in tremendous cost-savings for institutions especially since hospitalists are also obligated to be involved in quality and practice improvement initiatives. The University of Texas MD Anderson Cancer Center employs oncology-hospitalists for many of their patients with cancer needing inpatient services. This physician team has expertise in both cancer-related and comorbidity-related reasons for hospitalisation. In September 2015, the thoracic and head and neck medical oncology team started a collaboration with the Oncology Hospitalist team whereby a proportion of patients with thoracic malignancies were directly admitted to hospitalists for inpatient care. To determine the value of this collaboration, a pre- and post- implementation study was done to compare quality outcomes such as readmission rates and length of stay (LOS) between the two groups. Adjusted outcomes showed that readmission rates were similar for both physician groups both at baseline and after implementation of the collaborative (p=0.680 and p=0.840, respectively). Median LOS was similar for both groups at baseline (4 days) and was not significantly different post-implementation (4vs5 days, p=0.07). The adjusted cost of a hospitalisation was also similar for hospitalist encounters and thoracic oncology encounters. This initial study showed that quality of care remained comparable for patients with lung cancer who were admitted to either service. With possibly shorter LOS but comparable readmission outcomes and adjusted cost for patients discharged from the hospitalist service, there is a strong value benefit for the implemented Thoracic Oncology-Hospitalist inpatient collaborative.

PURPOSE Readmissions for the medical treatment of cancer have traditionally been excluded from readmission measures under the Hospital Readmissions Reduction Program. Patients with cancer often have higher readmission rates and may need heightened support to ensure effective care transitions after hospitalization. Estimating readmission risk before discharge may assist in discharge planning efforts and help promote care coordination at time of discharge. PATIENTS AND METHODS We developed and validated a readmission risk scoring system among a cohort of adult cancer patients with solid tumor admitted at a comprehensive cancer center. Multivariate logistic regression analysis was used to develop the model. The model's discriminative capacity was evaluated through a receiver operating characteristic curve analysis. We further compared the performance of the developed score with existing risk scores for 30-day readmission. RESULTS The 30-day unplanned readmission rate in the total cohort was 16.0% (n = 1,078 of 6,720). After multivariate analysis, Cancer site, Recent emergency room visit within 30 days, non-English primary language, Anemia defined as hemoglobin < 10 g/dL, > 4 Days length of stay during the index admission, unmarried Marital status, Increased white blood cell count > 11 × 109/L, and distant Tumor spread were significantly associated with risk of unplanned 30-day readmission. The derived score, which we call the Cancer READMIT score, had modest discriminatory performance in predicting readmissions (area under the curve for the model receiver operating characteristic curve = 0.647). CONCLUSION The Cancer READMIT score was able to predict 30-day unplanned readmissions to our institution with fairly modest performance. External validation of our derived risk scoring system is recommended.

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Purpose Several studies have confirmed increased mortality among patients with both COVID-19 and cancer. It remains important to continue to report observations of morbidity and mortality from COVID-19 in this vulnerable population. The purpose of this study is to describe the hospitalization characteristics and outcomes of patients with both cancer and COVID-19 admitted to our comprehensive cancer center. Methods This was a descriptive study of the first COVID-19-related hospitalization among adult patients with cancer admitted to our institution. Descriptive statistics were used to summarize patient demographics, clinical as well as hospitalization characteristics. Overall survival (OS) was estimated using the Kaplan-Meier method. Results A total of 212 patients were included in our cohort with a mean age of 59 years. Fifty-four percent of patients had history of solid tumor malignancy and 46% had hematologic malignancies. Eighty-five percent of our cohort had active malignancy. The mean length of stay (LOS) for hospitalization was 11.2 days (median LOS of 6 days). Twenty-five percent had severe disease and 10.8% died during their initial hospitalization. Those who had severe disease had worse survival at the end of the observation period. Conclusions COVID-19 among cancer patients causes significant morbidity and mortality as well as repeat hospitalizations. Continued study of COVID-19 in this vulnerable population is essential in order to better inform evolving treatment algorithms, public health policies, and infection control protocols, especially for institutions caring for patients with cancer.

This study evaluated the utility and performance of the LACE index and HOSPITAL score with consideration of the type of diagnoses and assessed the accuracy of these models for predicting readmission risks in patient cohorts from 2 large academic medical centers. Admissions to 2 hospitals from 2011 to 2015, derived from the Vizient Clinical Data Base and regional health information exchange, were included in this study (291 886 encounters). Models were assessed using Bayesian information criterion and area under the receiver operating characteristic curve. They were compared in CMS diagnosis-based cohorts and in 2 non-CMS cancer diagnosis-based cohorts. Overall, both models for readmission risk performed well, with LACE performing slightly better (area under the receiver operating characteristic curve 0.73 versus 0.69; P ≤ 0.001). HOSPITAL consistently outperformed LACE among 4 CMS target diagnoses, lung cancer, and colon cancer. Both LACE and HOSPITAL predict readmission risks well in the overall population, but performance varies by salient, diagnosis-based risk factors.