Citations

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.

AbstractBackgroundHospital‐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.ObjectiveTo 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 ParticipantsWe 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.InterventionThe 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 MeasuresThe 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.ResultsDuring 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.

Key Clinical Messages This case report demonstrates a virtual hybrid hospital‐at‐home program can provide inpatient‐level postoperative and rehabilitative care after total knee arthroplasty to a medically complex patient in the comfort of their own home. Abstract Advanced Care at Home combines virtual providers with in‐home care delivery. We report a case of virtual postoperative and rehabilitative care in a medically complex patient who underwent a total knee arthroplasty. This new model of care delivery allows effective patient‐provider communication and meets patient needs in the postoperative period.

Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

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.

Background: Multiple myeloma (MM) is the second most common hematologic malignancy, with 34,470 estimated new cases in 2022. High-dose therapy followed by autologous hematopoietic cell transplantation (auto-HCT) remains a standard treatment for MM even in the era of novel therapies. This is usually performed in hospital-based settings, either in the inpatient or outpatient units. Advanced Care at Home (ACH) represents a virtual hybrid hospital-at-home program that combines a virtual provider-staffed command center with a vendor-mediated supply chain capable of delivering high-acuity care in the comfort of the patients’ own homes. In our program, we used the existing ACH platform to deliver post-HCT care for recipients of auto-HCT. Patients and methods: Four patients (female = 2, 50%) with MM, with a median age of 60 (range, 40–74) years, were admitted to the inpatient Blood and Marrow Transplant (BMT) unit. The conditioning regimen consisted of melphalan 200 mg/m 2 , administered on day –2. All patients received stem cell infusion (day 0) in the inpatient setting, with a median dose of 3.64 (range, 2.92–8.22) × 10 6 /kg CD34 cells. Results: Patients were discharged to their homes after completing the infusion on day 0 or day +1 at the latest. Postinfusion care was provided by the ACH team in coordination with the BMT team. The median time intervals to absolute neutrophil count and platelet engraftment were 12 (range, 11–13) and 11 (range, 9–16) days, respectively. All patients were successfully discharged from the ACH program at a median of day +14 (range, day +14 to day +15). Conclusions: Our results highlight the feasibility of delivering post-HCT care for auto-HCT recipients in the home setting and confirm the generalizability of this approach.

Technology-enhanced hospital-at-home (H@H), commonly referred to as hybrid H@H, became more widely adopted during the COVID-19 pandemic. We conducted focus group interviews with Mayo Clinic staff members (n = 14) delivering hybrid H@H in three separate locations—a rural community health system (Northwest Wisconsin), the nation’s largest city by area (Jacksonville, FL), and a desert metropolitan area (Scottsdale, AZ)—to understand staff experiences with implementing a new care delivery model and using new technology to monitor patients at home during the pandemic. Using a grounded theory lens, transcripts were analyzed to identify themes. Staff reported that hybrid H@H is a complex care coordination and communication initiative, that hybrid H@H faces site-specific challenges modulated by population density and state policies, and that many patients are receiving uniquely high-quality care through hybrid H@H, partly enabled by advances in technology. Participant responses amplify the need for additional qualitative research with hybrid H@H staff to identify areas for improvement in the deployment of new models of care enabled by modern technology.