A narrative review of telemedicine and its adoption across specialties.

Background and Objective: Telemedicine and video consultation are crucial advancements in healthcare, allowing remote delivery of care. Telemedicine, encompassing various technologies like wearable devices, mobile health, and telemedicine, plays a significant role in managing illnesses and promoting wellness. The corona virus disease 2019 (COVID-19) pandemic accelerated the adoption of telemedicine, ensuring convenient access to medical services while maintaining physical distance. Legislation has supported its integration into clinical practice and addressed compensation issues. However, ensuring clinical appropriateness and sustainability of telemedicine post-expansion has gained attention. We south to identify the most friendly and resistant specialties to telemedicine and to understand areas of interest within those specialties to grasp potential barriers to its use. Methods: We aimed to identify articles that incorporated telemedicine in any medical or surgical specialty and determine the adoption rate and intent of this new form of care. Additionally, a secondary search within these databases was conducted to analyze the advantages, disadvantages, and implementation of telemedicine in the healthcare system. Non-English articles and those without full text were excluded. The study selection and data collection process involved using search terms such as "medicine", "surgery", "specialties", "telemedicine", and "telemedicine". Key Content and Findings: Telemedicine adoption varies among specialties. The pandemic led to increased usage, with telemedicine consultations comprising 30.1% of all visits, but specialties like mental health, gastroenterology, and endocrinology showed higher rates of adoption compared to optometry, physical therapy, and orthopedic surgery. Conclusions: The data shows that telemedicine uptake varies by specialty and condition due to the need for physical exams. In-person visits still dominate new patient visits despite increased telemedicine use. Telemedicine cannot fully replace in-person care but has increased visit volume and is secure. The adoption of telemedicine is higher in medical practices than in surgical practices, with neurosurgery and urology leading. Further research is needed to assess telemedicine's suitability and effectiveness in different specialties and conditions.

The usefulness of artificial intelligence in breast reconstruction: a systematic review.

BACKGROUND: Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS: A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction. RESULTS: A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification. CONCLUSIONS: In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.

"Toward Breast Reinnervation- What is our Endpoint" A systematic review of normal breast sensibility.

BACKGROUND: To restore breast sensibility, some centers are offering nerve reconstruction as a component of implant and flap-based breast reconstruction. To interpret and contextualize the results of these procedures, it is necessary to understand the normal range of breast sensibility, the factors that affect it, and the best methods for its objective measurement. METHODS: We conducted systematic and comprehensive searches across PubMed, Web of Science, and Cochrane Library databases using keywords and controlled vocabulary for the concepts of the breast, nipple, areola, and measurement. The search results were imported into Rayyan QCRI for a blinded screening of titles and abstracts. Studies were evaluated for bias using RevMan 5 software. The results of sensory measurements were pooled, and a quantitative summary of breast sensibility was generated. RESULTS: A total of 36 articles were identified, including retrospective, cross-sectional, and prospective studies. Although there were some consistent findings across studies, such that breast sensibility is inversely related to breast volume, there was wide variability in the following parameters: population, breast condition, measurement modality, anatomic areas of measurement, and sensibility findings. This heterogeneity precluded the generation of normative breast sensibility measurements. Furthermore, we detected a high degree of bias in most studies, due to self-selection of participants and failure to record patient characteristics that may alter sensibility. CONCLUSIONS: The literature lacks consistent data delineating normative values for breast sensibility. Standardized measurements of healthy volunteers with various breast characteristics are necessary to elucidate normative values and interpret efforts to restore sensibility in breast reconstruction.

In Vitro Enhanced Osteogenic Potential of Human Mesenchymal Stem Cells Seeded in a Poly (Lactic-co-Glycolic) Acid Scaffold: A Systematic Review.

Study Design: Human bone marrow stem cells (hBMSCs) and human adipose-derived stem cells (hADSCs) have demonstrated the capability to regenerate bone once they have differentiated into osteoblasts. Objective: This systematic review aimed to evaluate the in vitro osteogenic differentiation potential of these cells when seeded in a poly (lactic-co-glycolic) acid (PLGA) scaffold. Methods: A literature search of 4 databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted in January 2021 for studies evaluating the osteogenic differentiation potential of hBMSCs and hADSCs seeded in a PLGA scaffold. Only in vitro models were included. Studies in languages other than English were excluded. Results: A total of 257 studies were identified after the removal of duplicates. Seven articles fulfilled our inclusion and exclusion criteria. Four of these reviews used hADSCs and three used hBMSCs in the scaffold. Upregulation in osteogenic gene expression was seen in all the cells seeded in a 3-dimensional scaffold compared with 2-dimensional films. High angiogenic gene expression was found in hADSCs. Addition of inorganic material to the scaffold material affected cell performance. Conclusions: Viability, proliferation, and differentiation of cells strongly depend on the environment where they grow. There are several factors that can enhance the differentiation capacity of stem cells. A PLGA scaffold proved to be a biocompatible material capable of boosting the osteogenic differentiation potential and mineralization capacity in hBMSCs and hADSCs.

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 Algorithms Predict Long-Term Postoperative Opioid Misuse: A Systematic Review.

INTRODUCTION: A steadily rising opioid pandemic has left the US suffering significant social, economic, and health crises. Machine learning (ML) domains have been utilized to predict prolonged postoperative opioid (PPO) use. This systematic review aims to compile all up-to-date studies addressing such algorithms' use in clinical practice. METHODS: We searched PubMed/MEDLINE, EMBASE, CINAHL, and Web of Science using the keywords "machine learning," "opioid," and "prediction." The results were limited to human studies with full-text availability in English. We included all peer-reviewed journal articles that addressed an ML model to predict PPO use by adult patients. RESULTS: Fifteen studies were included with a sample size ranging from 381 to 112898, primarily orthopedic-surgery-related. Most authors define a prolonged misuse of opioids if it extends beyond 90 days postoperatively. Input variables ranged from 9 to 23 and were primarily preoperative. Most studies developed and tested at least two algorithms and then enhanced the best-performing model for use retrospectively on electronic medical records. The best-performing models were decision-tree-based boosting algorithms in 5 studies with AUC ranging from .81 to .66 and Brier scores ranging from .073 to .13, followed second by logistic regression classifiers in 5 studies. The topmost contributing variable was preoperative opioid use, followed by depression and antidepressant use, age, and use of instrumentation. CONCLUSIONS: ML algorithms have demonstrated promising potential as a decision-supportive tool in predicting prolonged opioid use in post-surgical patients. Further validation studies would allow for their confident incorporation into daily clinical practice.

Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review.

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

Acute postoperative and rehabilitation care managed in a virtual hybrid hospital-at-home after a total knee arthroplasty: A case report.

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.

Mesenchymal Stromal Cell Healing Outcomes in Clinical and Pre-Clinical Models to Treat Pressure Ulcers: A Systematic Review.

BACKGROUND: Despite numerous measures used to prevent pressure ulcers, their growing prevalence in recent years is expected to continue as the population ages. This review aims to report the outcomes of the regenerative potential of MSCs in treating pressure ulcers, assessing the effectiveness of MSCs in treating pressure ulcers. METHODS: A computerized search for articles on animal models that use MSCs as primary therapy to treat pressure ulcers, published from conception to present, was conducted using PubMed, MEDLINE, Embase, and CINAHL. Our search yielded 52 articles, narrowed to 44 after excluding duplicates. RESULTS: Out of 52 articles collected from four databases, 11 met the inclusion criteria. A total of 11 articles published between 2008 and 2020 met the inclusion criteria. Eight studies were observational descriptive papers in animal models, and three were prospective. Six studies used autologous MSCs, while five used allogenic MSCs. Three studies were conducted in humans, and the remaining eight were conducted in animals. The most common method of cell delivery was an intradermal injection in the margins of the ulcer. All studies reported positive results, including improved wound healing, reduced inflammation, and improved tissue regeneration. CONCLUSIONS: MSCs have shown promising results in treating pressure ulcers in animal and clinical trials. The combination of MSCs and scaffold materials has also been studied and found to be effective in wound healing. A standardized human wound model has been proposed further to investigate the efficacy of cell-based therapies for chronic wounds. However, more research is needed to determine the best quantity of cells to apply for pressure ulcers and to ensure the safety and efficacy of these treatments in clinical settings.

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications.

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.

Benefits and Challenges of Remote Patient Monitoring as Perceived by Health Care Practitioners: A Systematic Review.

BACKGROUND: Remote patient monitoring (RPM), or telemonitoring, offers ways for health care practitioners to gather real-time information on the physiological conditions of patients. As telemedicine, and thus telemonitoring, is becoming increasingly relevant in today's society, understanding the practitioners' opinions is crucial. This systematic review evaluates the perspectives and experiences of health care practitioners with telemonitoring technologies. METHODS: A database search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for the selection of articles measuring health care practitioners' perspectives and experiences with RPM technologies published between 2017 and 2021. Only articles written in English were included. No statistical analysis was performed and thus this is a qualitative review. RESULTS: A total of 1605 studies were identified after the initial search. After applying the inclusion and exclusion criteria of this review's authors, 13 articles were included in this review. In all, 2351 practitioners' perspectives and experience utilizing RPM technology in a variety of medical specialties were evaluated through close- and open-ended surveys. Recurring themes emerged for both the benefits and challenges. Common benefits included continuous monitoring of patients to provide prompt care, improvement of patient self-care, efficient communication, increased patient confidence, visualization of health trends, and greater patient education. Challenges comprised increased workload, higher patient anxiety, data inaccuracy, disorienting technology, financial issues, and privacy concerns. CONCLUSION: Health care practitioners generally believe that RPM is feasible for application. Additionally, there is a consensus that telemonitoring strategies will become increasingly relevant. However, there are still drawbacks to the technology that need to be considered.

Patient Satisfaction With a Multisite, Multiregional Remote Patient Monitoring Program for Acute and Chronic Condition Management: Survey-Based Analysis.

BACKGROUND: Remote patient monitoring (RPM) is an option for continuously managing the care of patients in the comfort of their homes or locations outside hospitals and clinics. Patient engagement with RPM programs is essential for achieving successful outcomes and high quality of care. When relying on technology to facilitate monitoring and shifting disease management to the home environment, it is important to understand the patients' experiences to enable quality improvement. OBJECTIVE: This study aimed to describe patients' experiences and overall satisfaction with an RPM program for acute and chronic conditions in a multisite, multiregional health care system. METHODS: Between January 1, 2021, and August 31, 2022, a patient experience survey was delivered via email to all patients enrolled in the RPM program. The survey encompassed 19 questions across 4 categories regarding comfort, equipment, communication, and overall experience, as well as 2 open-ended questions. Descriptive analysis of the survey response data was performed using frequency distribution and percentages. RESULTS: Surveys were sent to 8535 patients. The survey response rate was 37.16% (3172/8535) and the completion rate was 95.23% (3172/3331). Survey results indicated that 88.97% (2783/3128) of participants agreed or strongly agreed that the program helped them feel comfortable managing their health from home. Furthermore, 93.58% (2873/3070) were satisfied with the RPM program and ready to graduate when meeting the program goals. In addition, patient confidence in this model of care was confirmed by 92.76% (2846/3068) of the participants who would recommend RPM to people with similar conditions. There were no differences in ease of technology use according to age. Those with high school or less education were more likely to agree that the equipment and educational materials helped them feel more informed about their care plans than those with higher education levels. CONCLUSIONS: This multisite, multiregional RPM program has become a reliable health care delivery model for the management of acute and chronic conditions outside hospitals and clinics. Program participants reported an excellent overall experience and a high level of satisfaction in managing their health from the comfort of their home environment.

Post-Autologous Hematopoietic Cell Transplant Care in the "Home Sweet Home" Setting: A Treatment Paradigm Shift.

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/m2, 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) × 106/kg CD34 cells. RESULTS: Patients were discharged to their homes after completing the infusion on day 0 or day +1 at the latest. Post-infusion 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.

Bone Morphogenic Protein and Mesenchymal Stem Cells to Regenerate Bone in Calvarial Defects: A Systematic Review.

BACKGROUND: The use of bone morphogenic protein and mesenchymal stem cells has shown promise in promoting bone regeneration in calvarial defects. However, a systematic review of the available literature is needed to evaluate the efficacy of this approach. METHODS: We comprehensively searched electronic databases using MeSH terms related to skull defects, bone marrow mesenchymal stem cells, and bone morphogenic proteins. Eligible studies included animal studies that used BMP therapy and mesenchymal stem cells to promote bone regeneration in calvarial defects. Reviews, conference articles, book chapters, and non-English language studies were excluded. Two independent investigators conducted the search and data extraction. RESULTS: Twenty-three studies published between 2010 and 2022 met our inclusion criteria after a full-text review of the forty-five records found in the search. Eight of the 23 studies used mice as models, while 15 used rats. The most common mesenchymal stem cell was bone marrow-derived, followed by adipose-derived. BMP-2 was the most popular. Stem cells were embedded in Scaffold (13), Transduction (7), and Transfection (3), and they were delivered BMP to cells. Each treatment used 2 × 104-1 × 107 mesenchymal stem cells, averaging 2.26 × 106. Most BMP-transduced MSC studies used lentivirus. CONCLUSIONS: This systematic review examined BMP and MSC synergy in biomaterial scaffolds or alone. BMP therapy and mesenchymal stem cells in calvarial defects, alone, or with a scaffold regenerated bone. This method treats skull defects in clinical trials. The best scaffold material, therapeutic dosage, administration method, and long-term side effects need further study.

Perceived Age in Patients Exposed to Distinct UV Indexes: A Systematic Review.

Photodamage is caused by chronic sun exposure and ultraviolet radiation and presents as wrinkles, sagging, and pigmented spots. An increase in the ultraviolet index can increase a person's perceived age by worsening skin photodamage. However, since the ultraviolet index varies considerably between geographical regions, perceived age might vary substantially among them. This review aims to describe the differences in chronological and perceived age in regions of the world with different ultraviolet indexes. A literature search of three databases was conducted for studies analyzing perceived age and its relationship to sun exposure. Ultraviolet indexes from the included studies were retrieved from the National Weather Service and the Tropospheric Emission Monitoring Internet Service. Out of 104 studies, seven fulfilled the inclusion criteria. Overall, 3,352 patients were evaluated for perceived age. All studies found that patients with the highest daily sun exposures had the highest perceived ages for their chronological age ( p < 0.05). People with high sun exposure behaviors living in regions with high ultraviolet indexes will look significantly older than same-aged peers living in lower ultraviolet index regions.

Using AI to Detect Pain through Facial Expressions: A Review.

Pain assessment is a complex task largely dependent on the patient's self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area.

Evaluation of the Aging Effect on Peripheral Nerve Regeneration: A Systematic Review.

INTRODUCTION: Peripheral nerve injuries have been associated with increased healthcare costs and decreased patients' quality of life. Aging represents one factor that slows the speed of peripheral nervous system (PNS) regeneration. Since cellular homeostasis imbalance associated with aging lead to an increased failure in nerve regeneration in mammals of advanced age, this systematic review aims to determine the main molecular and cellular mechanisms involved in peripheral nerve regeneration in aged murine models after a peripheral nerve injuries. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search of 4 databases was conducted in July 2022 for studies comparing the peripheral nerve regeneration capability between young and aged murine models. RESULTS: After the initial search yielded 744 publications, ten articles fulfilled the inclusion criteria. These studies show that age-related changes such as chronic inflammatory state, delayed macrophages' response to injury, dysfunctional Schwann Cells (SCs), and microenvironment alterations cause a reduction in the regenerative capability of the PNS in murine models. Furthermore, identifying altered gene expression patterns of SC after nerve damage can contribute to the understanding of physiological modifications produced by aging. CONCLUSIONS: The interaction between macrophages and SC plays a crucial role in the nerve regeneration of aged models. Therefore, studies aimed at developing new and promising therapies for nerve regeneration should focus on these cellular groups to enhance the regenerative capabilities of the PNS in elderly populations.

Diagnosis Related Groups of Patients Admitted from an Urban Academic Medical Center to a Virtual Hybrid Hospital-at-Home Program.

Background: The diagnosis related group (DRG) is used as an economic patient classification system based on clinical characteristics, hospital stay, and treatment costs. Mayo Clinic's virtual hybrid hospital-at-home program, advanced care at home (ACH), offers high-acuity home inpatient care for a variety of diagnosis. This study aimed to determine the DRGs admitted to the ACH program at an urban academic center. Methods: A retrospective study was performed on all patients discharged from the ACH program at Mayo Clinic Florida from July 6, 2020, to February 1, 2022. DRG data were extracted from the Electronic Health Record (EHR). Categorization of DRG was done by systems. Results: The ACH program discharged 451 patients with DRGs. Categorization of the DRG demonstrated that the most frequent code assigned corresponded to respiratory infections (20.2%), followed by septicemia (12.9%), heart failure (8.9%), renal failure (4.9%), and cellulitis (4.0%). Conclusion: The ACH program covers a wide range of high-acuity diagnosis across multiple medical specialties at its urban academic medical campus, including respiratory infections, severe sepsis, congestive heart failure, and renal failure, all with major complications or comorbidities. The ACH model of care may be useful in taking care of patients with similar diagnosis at other urban academic medical institutions.