PURPOSE: The purpose of this study was to evaluate the use of telemedicine amid the SARS-CoV-2 pandemic in patients with cancer and assess barriers to its implementation. PATIENTS AND METHODS: Telehealth video visits, using the Houston Methodist MyChart platform, were offered to patients with cancer as an alternative to in-person visits. Reasons given by patients who declined to use video visits were documented, and demographic information was collected from all patients. Surveys were used to assess the levels of satisfaction of treating physicians and patients who agreed to video visits. RESULTS: Of 1,762 patients with cancer who were offered telehealth video visits, 1,477 (83.8%) participated. The patients who declined participation were older (67.7 v 60.2 years; P < .0001), lived in significantly lower-income areas ( P = .0021), and were less likely to have commercial insurance ( P < .0001) than patients who participated. Most participating patients (92.6%) were satisfied with telehealth video visits. A majority of physicians (65.2%) were also satisfied with its use, and 74% indicated that they would likely use telemedicine in the future. Primary concerns that physicians had in using this technology were inadequate patient interactions and acquisition of medical data, increased potential for missing significant clinical findings, decreased quality of care, and potential medical liability. CONCLUSION: Oncology/hematology patients and their physicians expressed high levels of satisfaction with the use of telehealth video visits. Despite recent advances in technology, there are still opportunities to improve the equal implementation of telemedicine for the medical care of vulnerable older, low-income, and underinsured patient populations.

Purpose Only 34% of breast cancer survivors engage in the recommended level of physical activity because of a lack of accountability and motivation. Methodist Hospital Cancer Health Application (MOCHA) is a smartphone tool created specifically for self-reinforcement for patients with cancer through the daily accounting of activity and nutrition and direct interaction with clinical dietitians. We hypothesize that use of MOCHA will improve the accountability of breast cancer survivors and help them reach their personalized goals. Patients and Methods Women with stages I to III breast cancer who were at least 6 months post–active treatment with a body mass index (BMI) greater than 25 kg/m2 were enrolled in a 4-week feasibility trial. The primary objective was to demonstrate adherence during weeks 2 and 3 of the 4-week study period (14 days total). The secondary objective was to determine the usability of MOCHA according to the system usability scale. The exploratory objective was to determine weight loss and dietitian-participant interaction. Results We enrolled 33 breast cancer survivors who had an average BMI of 31.6 kg/m2. Twenty-five survivors completed the study, and the average number of daily uses was approximately 3.5 (range, 0 to 12) times/day; participants lost an average of 2 lbs (+4 lbs to −10.6 lbs). The average score of usability (the second objective) was 77.4, which was greater than the acceptable level. More than 90% of patients found MOCHA easy to navigate, and 84% were motivated to use MOCHA daily. Conclusion This study emphasizes the importance of technology use to improve goal adherence for patients by providing real-time feedback and accountability with the health care team. MOCHA focuses on the engagement of the health care team and is integrated into clinical workflow. Future directions will use MOCHA in a long-term behavior modification study.

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Background We present data on a cohort of patients diagnosed with sepsis over a 10‐year period comparing outcomes in solid organ transplant (SOT) and non–solid organ transplant (non–SOT) recipients. Methods This is a retrospective single‐center study of patients with diagnosis of sepsis from 1/1/06 to 6/30/16. Cases and controls were matched by year of sepsis diagnosis with propensity score matching. Conditional logistic regression and repeated measurement models were performed for binary outcomes. Trends over time for in‐hospital mortality were determined using the Cochran‐Armitage test. A gamma‐distributed model was performed on the continuous variables. Results Overall, there were 18 632 admission encounters with a discharge diagnosis of sepsis in 14 780 unique patients. Of those admissions, 1689 were SOT recipients. After 1:1 matching by year, there were three thousand three hundred and forty patients (1670 cases; 1670 controls) diagnosed with sepsis. There was a decreasing trend for in‐hospital mortality for sepsis over time in SOT patients and non–SOT patients (P < .05) due to early sepsis recognition and improved standard of care. Despite higher comorbidities in the SOT group, conditional logistic regression showed that in‐hospital mortality for sepsis in SOT patients was similar compared with non–SOT patients (odds ratio [OR] =1.14 [95% confidence interval {CI}, 0.95‐1.37], P = .161). However, heart and lung SOT subgroups had higher odds of dying compared with the non–SOT group (OR = 1.83 [95% CI, 1.30‐2.57], P < .001 and OR = 1.77 [95% CI, 1.34‐2.34], P < .001). On average, SOT patients had 2 days longer hospital length of stay compared with non–SOT admissions (17.00 ± 19.54 vs 15.23 ± 17.07, P < .05). Additionally, SOT patients had higher odds of hospital readmission within 30 days (OR = 1.25 [95% CI, 1.06‐1.51], P = .020), and higher odds for DIC compared with non–SOT patients (OR = 1.76 [95% CI, 1.10‐2.86], P = .021). Conclusion Sepsis in solid organ transplants and non–solid organ transplant patients have similar mortality; however, the subset of heart and lung transplant recipients with sepsis has a higher rate of mortality compared with the non–solid organ transplant recipients. SOT with sepsis as a group has a higher hospital readmission rate compared with non–transplant sepsis patients.

PURPOSE The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the application of the BI-RADS lexicon, the decision to biopsy varies greatly and results in overdiagnosis and excessive biopsies. The false-positive rate from mammograms is estimated to be 7% to approximately 10% overall, but within the BI-RADS 4 category, it is greater than 70%. Therefore, we developed the Breast Cancer Risk Calculator (BRISK) to target a well-characterized and specific patient subgroup (BI-RADS 4) rather than a broad heterogeneous group in assessing breast cancer risk. METHODS BRISK provides a novel precise risk assessment model to reduce overdiagnosis and unnecessary biopsies. It was developed by applying natural language processing and deep learning methods on 5,147 patient records archived in the Houston Methodist systemwide data warehouse from 2006 to May 2015, including imaging and pathology reports, mammographic images, and patient demographics. Key characteristics for BI-RADS 4 patients were collected and computed to output an index measure for biopsy recommendation that is clinically relevant and informative and improves upon the traditional BI-RADS 4 scores. RESULTS For the validation set, we assessed data from 1,247 BI-RADS 4 patients, including mammographic images and medical reports. The BRISK model sensitivity to predict malignancy was 100%, whereas the specificity was 74%. The total accuracy of our implemented model in BRISK was 81%. Overall area under the curve was 0.93. CONCLUSION BRISK for abnormal mammogram uses integrative artificial intelligence technology and has demonstrated high sensitivity in the prediction of malignancy. Prospective evaluation is under way and can lead to improvement in patient-physician engagement in making informed decisions with regard to biopsy.

Patient falls during hospitalization can lead to severe injuries and remain one of the most vexing patient-safety problems facing hospitals. They lead to increased medical care costs, lengthened hospital stays, more litigation, and even death. Existing methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries. Here, a retrospective cohort study was designed and performed to predict the severity of inpatient falls, based on a machine learning classifier integrating multi-view ensemble learning and model-based missing data imputation method. As input, over two thousand inpatient fall patients’ demographic characteristics, diagnoses, procedural data, and bone density measurements were retrieved from the HMH clinical data warehouse from two separate time periods. The predictive classifier developed based on multi-view ensemble learning with missing values (MELMV) outperformed other three baseline models; achieved a cross-validated AUC of 0.713 (95% CI, 0.701–0.725), an AUC of 0.808 (95% CI, 0.740–0.876) on the separate testing set. Our studies show the efficacy of integrative machine-learning based classifier model in dealing with multi-source patient data, which in this case delivers robust predictive performance on the severity of patient falls. The severe fall index provided by the MELMV classifier is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard-of-care procedure for all high-risk fall patients.

This paper presents a novel Lasso Logistic Regression model based on feature-based time series data to determine disease severity and when to administer drugs or escalate intervention procedures in patients with coronavirus disease 2019 (COVID-19). Advanced features were extracted from highly enriched and time series vital sign data of hospitalized COVID-19 patients, including oxygen saturation readings, and with a combination of patient demographic and comorbidity information, as inputs into the dynamic feature-based classification model. Such dynamic combinations brought deep insights to guide clinical decision-making of complex COVID-19 cases, including prognosis prediction, timing of drug administration, admission to intensive care units, and application of intervention procedures like ventilation and intubation. The COVID-19 patient classification model was developed utilizing 900 hospitalized COVID-19 patients in a leading multi-hospital system in Texas, United States. By providing mortality prediction based on time-series physiologic data, demographics, and clinical records of individual COVID-19 patients, the dynamic feature-based classification model can be used to improve efficacy of the COVID-19 patient treatment, prioritize medical resources, and reduce casualties. The uniqueness of our model is that it is based on just the first 24 hours of vital sign data such that clinical interventions can be decided early and applied effectively. Such a strategy could be extended to prioritize resource allocations and drug treatment for future pandemic events.

Patients with inflammatory bowel disease often present to the emergency department due to the chronic relapsing nature of the disease. Previous studies have shown younger patients to have an increased frequency of emergency department visits, resulting in repeated exposure to imaging studies and steroids, both of which are associated with risks. We performed a retrospective cohort analysis of inflammatory bowel disease patients seen at Houston Methodist Hospital’s emergency department from January 2014 to December 2017 using ICD codes to identify patients with Crohn’s disease, ulcerative colitis, or indeterminate colitis from the electronic medical record. Data were collected on demographics, medications, and imaging. Five hundred and fifty-nine patients were randomly selected for inclusion. Older age was associated with decreased risk of CT scan or steroid use. Patients with ulcerative colitis compared to Crohn’s had decreased risk of CT scan, while there was an increased risk of CT in patients on a biologic, immunomodulator, or when steroids were given. Steroid use was also more common in those with inflammatory bowel disease as the primary reason for the visit. Patients in our study frequently received steroids and had CT scans performed. The increased risk of CT in those on a biologic, immunomodulator, or steroids suggests more severe disease may contribute. Guidelines are needed to reduce any unnecessary corticosteroid use and limit repeat CT scans in young inflammatory bowel disease patients to decrease the risk of radiation-associated malignancy over their lifetime.