Bypassing the Brick-and-Mortar Hospital: Increasing Direct Admissions from the Emergency Department to Inpatient Hospital-at-Home.

Home hospital programs continue to grow across the United States. There are limited studies around the process of patient selection and successful acquisition from the emergency department. The article describes how an interdisciplinary team used quality improvement methodology to significantly increase the number of admissions directly from the emergency department to the Advanced Care at Home program.

Surgical service and distance traveled drive patient preference for Care Hotel: a retrospective cohort study.

Mayo Clinic's Care Hotel is a virtual hybrid care model which allows postoperative patients to recover in a comfortable environment after a low-risk procedure. Hospitals need to understand the key patient factors that promote acceptance of the Care Hotel if they are to benefit from this innovative care model. This study aims to identify factors that can predict whether a patient will stay at Care Hotel. Materials and methods: This retrospective chart review of 1065 patients was conducted between 23 July 2020, and 31 December 2021. Variables examined included patient age, sex, race, ethnicity, Charlson comorbidity index, distance patient travelled to hospital, length of surgery, day of the week of surgery, and surgical service. Associations of patient and surgery characteristics with the primary outcome of staying at the Care Hotel were assessed using unadjusted and multivariable logistic regression models. Results: Of the 1065 patients who met criteria for admission to the Care Hotel during the study period, 717 (67.3%) chose to stay at the Care Hotel while 328 (32.7%) choose to be admitted to the hospital. In multivariable analysis, there was a significant association between surgical service and staying at the Care Hotel (P<0.001). Specifically, there was a higher likelihood of staying at the Care Hotel for patients from Neurosurgery [odds rato (OR)=1.86, P=0.004], Otorhinolaryngology (OR=2.70, P<0.001), and General Surgery (OR=2.75, P=0.002). Additionally, there was a higher likelihood of staying at the Care Hotel with distance travelled over 110 miles [OR (per each doubling)=1.10, P=0.007]. Conclusion: When developing a post-surgical care model for patients following outpatient procedures, the referring surgical service is a primary factor to consider in order to ensure patient acceptance, along with patient distance. This study can assist other healthcare organizations considering this model, as it provides guidance on which factors are most indicative of acceptance.

Implementation of a virtual and in-person hybrid hospital-at-home model in two geographically separate regions utilizing a single command center: a descriptive cohort study.

BACKGROUND: As providers look to scale high-acuity care in the patient home setting, hospital-at-home is becoming more prevalent. The traditional model of hospital-at-home usually relies on care delivery by in-home providers, caring for patients in urban communities through academic medical centers. Our objective is to describe the process and outcomes of Mayo Clinic's Advanced Care at Home (ACH) program, a hybrid virtual and in-person hospital-at-home model combining a single, virtual provider-staffed command center with a vendor-mediated in-person medical supply chain to simultaneously deliver care to patients living near an urban hospital-at-home command center and patients living in a rural region in a different US state and time zone. METHODS: A descriptive, retrospective medical records review of all patients admitted to ACH between July 6, 2020, and December 31, 2021. Patients were admitted to ACH from an urban academic medical center in Florida and a rural community hospital in Wisconsin. We collected patient volumes, age, sex, race, ethnicity, insurance type, primary hospital diagnosis, 30-day mortality rate, in-program mortality, 30-day readmission rate, rate of return to hospital during acute phase, All Patient Refined-Diagnosis Related Groups (APR-DRG) Severity of Illness (SOI), and length of stay (LOS) in both the inpatient-equivalent acute phase and post-acute equivalent restorative phase. RESULTS: Six hundred and eighty-six patients were admitted to the ACH program, 408 in Florida and 278 in Wisconsin. The most common diagnosis seen were infectious pneumonia (27.0%), septicemia / bacteremia (11.5%), congestive heart failure exacerbation (11.5%), and skin and soft tissue infections (6.3%). Median LOS in the acute phase was 3 days (IQR 2-5) and median stay in the restorative phase was 22 days (IQR 11-26). In-program mortality rate was 0% and 30-day mortality was 0.6%. The mean APR-DRG SOI was 2.9 (SD 0.79) and the 30-day readmission rate was 9.7%. CONCLUSIONS: The ACH hospital-at-home model was able to provide both high-acuity inpatient-level care and post-acute care to patients in their homes through a single command center to patients in urban and rural settings in two different geographical locations with favorable outcomes of low mortality and hospital readmissions.

Improving Patient Selection and Prioritization for Hospital at Home Through Predictive Modeling.

Hospital at home is designed to offer patients hospital level care in the comfort of their own home. The process by which clinicians select eligible patients that are clinically and socially appropriate for this model of care requires labor-intensive manual chart reviews. We addressed this problem by providing a predictive model, web application, and data pipeline that produces an eligibility score based on a set of clinical and social factors that influence patients' success in the program. Providers used this predictive model to prioritize the order in which they perform chart reviews and patient screenings. Training performance area under the curve (AUC) was 0.77. Testing 'in production' had an AUC of 0.75. Admission criteria in training rapidly changed over the course of the study due to the novelty of the clinical model. The current algorithm successfully identified many inconsistencies in enrollment and has streamlined the process of patient identification.