Individual- and Community-Level Predictors of Hospital-at-Home Outcomes.

Advanced Care at Home is a Mayo Clinic hospital-at-home (HaH) program that provides hospital-level care for patients. The study examines patient- and community-level factors that influence health outcomes. The authors performed a retrospective study using patient data from July 2020 to December 2022. The study includes 3 Mayo Clinic centers and community-level data from the Agency for Healthcare Research and Quality. The authors conducted binary logistic regression analyses to examine the relationship among the independent variables (patient- and community-level characteristics) and dependent variables (30-day readmission, mortality, and escalation of care back to the brick-and-mortar hospital). The study examined 1433 patients; 53% were men, 90.58% were White, and 68.2% were married. The mortality rate was 2.8%, 30-day readmission was 11.4%, and escalation back to brick-and-mortar hospitals was 8.7%. At the patient level, older age and male gender were significant predictors of 30-day mortality (P-value <0.05), older age was a significant predictor of 30-day readmission (P-value <0.05), and severity of illness was a significant predictor for readmission, mortality, and escalation back to the brick-and-mortar hospital (P-value <0.01). Patients with COVID-19 were less likely to experience readmission, mortality, or escalations (P-value <0.05). At the community level, the Gini Index and internet access were significant predictors of mortality (P-value <0.05). Race and ethnicity did not significantly predict adverse outcomes (P-value >0.05). This study showed promise in equitable treatment of diverse patient populations. The authors discuss and address health equity issues to approximate the vision of inclusive HaH delivery.

Development and evaluation of best practice advisory alert for patient eligibility in a hospital-at-home program: A multicenter retrospective study.

BACKGROUND: Hospital-at-home (HaH) is a growing model of care that has been shown to improve patient outcomes, satisfaction, and cost-effectiveness. However, selecting appropriate patients for HaH is challenging, often requiring burdensome manual screening by clinicians. To facilitate HaH enrollment, electronic health record (EHR) tools such as best practice advisories (BPAs) can be used to alert providers of potential HaH candidates. OBJECTIVE: To describe the development and implementation of a BPA for identifying HaH eligible patients in Mayo Clinic's Advanced Care at Home (ACH) program, and to evaluate the provider response and the patient characteristics that triggered the BPA. DESIGN, SETTING, AND PARTICIPANTS: We conducted a retrospective multicenter study of hospitalized patients who triggered the BPA notification for ACH eligibility between March and December 2021 at Mayo Clinic in Jacksonville, FL and Mayo Clinic Health System in Eau Claire, WI. We extracted demographic and diagnosis data from the patients as well as characteristics of the providers who received the BPA notification. INTERVENTION: The BPA was developed based on the ACH inclusion and exclusion criteria, which were derived from clinical guidelines, literature review, and expert consensus. The BPA was integrated into the EHR and displayed a pop-up message to the provider when a patient met the criteria for ACH eligibility. The provider could choose to refer the patient to ACH, dismiss the notification, or defer the decision. MAIN OUTCOMES AND MEASURES: The main outcomes were the number and proportion of BPA notifications that resulted in a referral to ACH, and the number and proportion of referrals that were accepted by the ACH clinical team and transferred to ACH. We also analyzed the factors associated with the provider's decision to refer or not refer the patient to ACH, such as the provider's role, location, and specialty. RESULTS: During the study period, 8962 notifications were triggered for 2847 patients. Providers opted to refer 711 (11.4%) of the total notifications linked to 324 unique patients. After review by the ACH clinical team, 31 of the 324 referrals (9.6%) met clinical and social criteria and were transferred to ACH. In multivariable analysis, Wisconsin nurses, physician assistants, and in-training personnel had lower odds of referring the patients to ACH when compared to attending physicians.

Machine Learning-Based Predictive Models for 90-Day Readmission of Total Joint Arthroplasty Using Comprehensive Electronic Health Records and Patient-Reported Outcome Measures.

Background: The Centers for Medicare & Medicaid Services currently incentivizes hospitals to reduce postdischarge adverse events such as unplanned hospital readmissions for patients who underwent total joint arthroplasty (TJA). This study aimed to predict 90-day TJA readmissions from our comprehensive electronic health record data and routinely collected patient-reported outcome measures. Methods: We retrospectively queried all TJA-related readmissions in our tertiary care center between 2016 and 2019. A total of 104-episode care characteristics and preoperative patient-reported outcome measures were used to develop several machine learning models for prediction performance evaluation and comparison. For interpretability, a logistic regression model was built to investigate the statistical significance, magnitudes, and directions of associations between risk factors and readmission. Results: Given the significant imbalanced outcome (5.8% of patients were readmitted), our models robustly predicted the outcome, yielding areas under the receiver operating characteristic curves over 0.8, recalls over 0.5, and precisions over 0.5. In addition, the logistic regression model identified risk factors predicting readmission: diabetes, preadmission medication prescriptions (ie, nonsteroidal anti-inflammatory drug, corticosteroid, and narcotic), discharge to a skilled nursing facility, and postdischarge care behaviors within 90 days. Notably, low self-reported confidence to carry out social activities accurately predicted readmission. Conclusions: A machine learning model can help identify patients who are at substantially increased risk of a readmission after TJA. This finding may allow for health-care providers to increase resources targeting these patients. In addition, a poor response to the "social activities" question may be a useful indicator that predicts a significant increased risk of readmission after TJA.

Why U.S. Patients Declined Hospital-at-Home during the COVID-19 Public Health Emergency: An Exploratory Mixed Methods Study.

To understand why US patients refused participation in hospital-at-home (H@H) during the coronavirus disease 2019 Public Health Emergency, eligible adult patients seen at 2 Mayo Clinic sites, Mayo Clinic Health System-Northwest Wisconsin region (NWWI) and Mayo Clinic Florida (MCF), from August 2021 through March 2022, were invited to participate in a convergent-parallel study. Quantitative associations between H@H participation status and patient baseline data at hospital admission were investigated. H@H patients were more likely to have a Mayo Clinic patient portal at baseline (P-value: .014), indicating a familiarity with telehealth. Patients who refused were more likely to be from NWWI (P-value < .001) and have a higher Epic Deterioration Index score (P-value: .004). The groups also had different quarters (in terms of fiscal calendar) of admission (P-value: .040). Analyzing qualitative interviews (n = 13) about refusal reasons, 2 themes portraying the quantitative associations emerged: lack of clarity about H@H and perceived domestic challenges. To improve access to H@H and increase patient recruitment, improved education about the dynamics of H@H, for both hospital staff and patients, and inclusive strategies for navigating domestic barriers and diagnostic challenges are needed.

Nasal Embryonal Rhabdomyosarcoma in the Pediatric Population: Literature Review and Report of Midline Presentation.

BACKGROUND: Congenital midline nasal masses are rare anomalies and are typically benign nasal dermoid sinus cysts (NDSCs). Rhabdomyosarcomas (RMSs) are even less common, and only a fraction affect sites like the external nose, nasal cavity, nasopharynx, and paranasal sinuses. We review the clinical presentation and treatment of nasal, nasopharyngeal, and paranasal RMSs and report the first documented midline presentation. METHODS: We queried PubMed for articles with titles containing the terms rhabdomyosarcoma or sarcoma botryoides and nose, nasal, paranasal, sinonasal, nasopharynx, or nasopharyngeal. We then searched the references of each included article using the same parameters and continued this process iteratively until no new articles were found. RESULTS: The paranasal sinuses were the most commonly affected site, followed by the nasopharynx, nasal cavity, and external nose. Two patients presented with involvement of the external nose, but each presented with involvement of the right ala rather than a midline mass. The rates of intracranial extension and/or skull base involvement were comparable to those of NDSCs. The alveolar subtype was most common, followed by the embryonal subtype. CONCLUSIONS: Most midline nasal masses are benign; however, we report the first documented presentation of an RMS as a midline nasal mass. Accordingly, RMS should be included in the differential diagnosis of midline nasal masses in the pediatric population. Surgery for midline nasal masses is sometimes delayed due to the risks of interfering with developing structures and early anesthesia. However, early surgical treatment should be considered given this new differential and its predilection for early metastasis.