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.

Avoiding Escalation to the Emergency Department by Activating an In-Home Rapid Response Team in the 30 Days After Hospital-at-Home Discharge.

BACKGROUND: Mayo Clinic's virtual hybrid hospital-at-home program, Advanced Care at Home (ACH) monitors acute and post-acute patients for signs of deterioration and institutes a rapid response (RR) system if detected. OBJECTIVE: This study aimed to describe Mayo Clinic's ACH RR team and its effect on emergency department (ED) use and readmission rates. METHODS: This was a retrospective review of all post-inpatient (restorative phase) ACH patients admitted from July 6, 2020 through June 30, 2021. If the restorative patient had a clinical decompensation, an RR was activated. All RR activations were analyzed for patient demographic characteristics, admitting and escalation diagnosis, time spent by virtual team on the RR, and whether the RR resulted in transport to the ED or hospital readmission. RESULTS: Three hundred and twenty patients were admitted to ACH during the study interval; 230 received restorative care. Seventy-two patients (31.3%) had events that triggered an RR. Fifty (69.4%) of the RR events were related to the admission diagnosis (p < 0.001; 95% CI 0.59-0.80). Twelve patients (16.7%) required transport to an ED for further treatment and were readmitted and 60 patients (83.3%) were able to be treated successfully in the home by the RR team (p < 0.001; 95% CI 0.08-0.25). CONCLUSIONS: The use of an ACH RR team was effective at limiting both escalations back to an ED and hospital readmissions, as 83% of deteriorating patients were successfully stabilized and managed in their homes. Implementing a hospital-at-home RR team can reduce the need for ED use by providing critical resources and carrying out required interventions to stabilize the patient's condition.

Impact of an Acquisition Advanced Practice Provider on Home Hospital Patient Volumes and Length of Stay.

In July 2020, Mayo Clinic introduced a hospital-at-home program, known as Advanced Care at Home (ACH) as an alternate option for clinically stable medical patients requiring hospital-level care. This retrospective cohort study evaluates the impact of the addition of a dedicated ACH patient acquisition Advanced Practice Provider (APP) on average length of stay (ALOS) and the number of patients admitted into the program between in Florida and Wisconsin between 6 July 2020 and 31 January 2022. Patient volumes and ALOS of 755 patients were analyzed between the two sites both before and after a dedicated acquisition APP was added to the Florida site on 1 June 2021. The addition of a dedicated acquisition APP did not affect the length of time a patient was in the emergency department or hospital ward prior to ACH transition (2.91 days [Florida] vs. 2.59 days [Wisconsin], p = 0.22), the transition time between initiation of the ACH consult to patient transfer home (0.85 days [Florida] vs. 1.16 days [Wisconsin], p = 0.28), or the total ALOS (6.63 days [Florida] vs. 6.34 days [Wisconsin], p = 0.47). The average number of patients acquired monthly was significantly increased in Florida (38.3 patients per month) compared with Wisconsin (21.6 patients per month) (p < 0.01). The addition of a dedicated patient acquisition APP resulted in significantly higher patient volumes but did not affect transition time or ALOS. Other hospital-at-home programs may consider the addition of an acquisition APP to maximize patient volumes.

Rhabdomyolysis Associated With Semaglutide Therapy: A Case Report.

This report describes the case of a 47-year-old woman with myalgias, weakness, and elevated creatine kinase associated with semaglutide therapy prescribed for weight loss. Her symptoms and laboratory markers were consistent with rhabdomyolysis and resolved after discontinuation of semaglutide. Upon rechallenge at a lower dose, symptoms recurred, and urinalysis was consistent with myoglobinuria. Symptoms again rapidly resolved upon discontinuation of the medication. It is imperative for physicians to recognize semaglutide as a possible cause of myalgias and rhabdomyolysis in clinically suspected patients. To the best of our knowledge, this is the first reported case in the literature and may be specific to semaglutide rather than a class effect of glucagon-like peptide 1 (GLP-1) agonists.

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.

Robotics in Simulated COVID-19 Patient Room for Health Care Worker Effector Tasks: Preliminary, Feasibility Experiments.

The coronavirus disease 2019 (COVID-19) pandemic has strained health care systems and personal protective equipment (PPE) supplies globally. We hypothesized that a collaborative robot system could perform health care worker effector tasks inside a simulated intensive care unit (ICU) patient room, which could theoretically reduce both PPE use and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposures. We planned a prospective proof-of-concept feasibility and design pilot study to test 5 discrete medical tasks in a simulated ICU room of a COVID-19 patient using a collaborative robot: push a button on intravenous pole machine when alert occurs for downstream occlusion, adjust ventilator knob, push button on ICU monitor to silence false alerts, increase oxygen flow on wall-mounted flow meter to allow the patient to walk to the bathroom and back (dial-up and dial-down oxygen flow), and push wall-mounted nurse call button. Feasibility was defined as task completion robotically. A training period of 45 minutes to 1 hour was needed to program the system de novo for each task. In less than 30 days, the team completed 5 simple effector task experiments robotically. Selected collaborative robotic effector tasks appear feasible in a simulated ICU room of the COVID-19 patient. Theoretically, this robotic approach could reduce PPE use and staff SARS-CoV-2 exposure. It requires future validation and health care worker learning similar to other ICU device training.

Creation of an optic nerve sheath diameter ultrasound model for NeuroICU education.

BACKGROUND: Using ultrasound to measure optic nerve sheath diameter (ONSD) is an emerging bedside technique to noninvasively assess intracranial pressure (ICP) in patients with brain injury. This technique is unique among bedside ultrasonography and is often performed by providers who have no formal ultrasound training. We sought to create a low-cost, 3D, reusable ONSD model to train neurology, neurosurgery, and critical care providers in measuring ICP. RESULTS: We identified 253 articles, of which 15 were associated with models and 2 with simulation. One gelatin model was reported, upon which we based our initial design. We could not validate the visual findings of this model; however, after constructing multiple beta models, the design most representative of human eye anatomy was a globe made of ballistics gel and either a 3 mm, 5 mm, or 7 mm × 50 mm 3D-printed optic nerve inserted into a platform composed of ballistics gel, all of which sat inside a 3D-printed skull. This model was used to teach ONSD measurements with ultrasound at a continuing medical education event prior to training on a live human model. CONCLUSION: A simple 3D ballistic ONSD model allows learners to practice proper hand placement and pressure, basic landmarks, and ONSD measurement prior to operating on a human eye. This model is replicable and sustainable given that the globe and platform are composed of ballistics gel.

A Colposcopy Trainer to Simulate Visualization and Biopsy of the Cervix.

BACKGROUND: Colposcopy is a common office procedure providing a magnified view of the cervix for the evaluation of an abnormal cytology result. Traditionally the procedure has been replicated in simulation training by using a sausage or hotdog to represent the cervix and allow for target biopsy. This is neither reusable nor sustainable. METHOD: We developed a cervix model comprised of food coloring and ballistics gel. After cooling, the surface was dotted with gel that can be activated by a black light to mimic acetowhite changes. The cervix was placed into an existing pelvic trainer and then piloted by attending physicians and resident trainees in obstetrics and gynecology and family practice. EXPERIENCE: Sixteen physicians and residents were surveyed after a simulated colposcopy. Twelve had performed colposcopies and of those, eight stated that the model was very comparable with the real procedure; four stated the model was somewhat comparable. Three had never performed a colposcopy but stated that model was very or somewhat comparable with textbook or journal photographs. CONCLUSION: Our colposcopy trainer represents continued innovation in the field of simulation while being sustainable and economic. We continue to work on enhancing our model to be even more sophisticated and offering additional training options.