Search

Francisco Avila, Ricardo Torres-Guzman, Karla Maita, John P Garcia, Gioacchino D De Sario, Sahar Borna, Olivia A Ho, Antonio J Forte Division of Plastic Surgery, Department of Surgery, Mayo Clinic, Jacksonville, FL, USACorrespondence: Antonio J Forte, Mayo Clinic Florida, 4500 San Pablo Roads, Jacksonville, FL, 32224, USA, Tel +1 904-953-2073, Email ajvforte@yahoo.com.brAbstract: Postmastectomy pain syndrome (PMPS) is a common and debilitating form of postsurgical pain with neuropathic characteristics, presenting as burning, stabbing, or pulling sensations after mastectomy, lumpectomy, or other breast procedures. With a prevalence of 31%, the risk factors for PMPS include younger age, psychosocial factors, radiotherapy, axillary lymph node dissection, and a history of chronic pain. This review evaluates the pharmacological and surgical options for managing PMPS. Pharmacological treatment options include antidepressants, gabapentinoids, levetiracetam, capsaicin, and topical lidocaine. Procedural and surgical options include fat grafting, nerve blocks, radiofrequency ablation, peripheral nerve surgery, serratus plane block, and botulinum toxin injections. Despite the variety of therapeutic options available for patients, further randomized trials are required to conclude whether these treatments reduce the intensity of neuropathic pain in patients with PMPS. In particular, comparative studies and the inclusion of patients across a range of pain intensities will be essential to developing a treatment algorithm for PMPS. In conclusion, current management for these patients should be tailored to their individual requirements.Keywords: postmastectomy pain syndrome, postsurgical pain, neuropathic pain, neuralgia, breast neoplasms, Mastectomy

Michael J Maniaci,1 J Colt Cowdell,1 Karla Maita,2 Francisco R Avila,2 Sagar B Dugani,3 Ricardo A Torres-Guzman,2 John P Garcia,2 Antonio J Forte,2 Margaret R Paulson4 1Division of Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL, USA; 2Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA; 3Division of Hospital Internal Medicine, Mayo Clinic, Rochester, MN, USA; 4Division of Hospital Internal Medicine, Mayo Clinic Health Systems, Eau Claire, WI, USACorrespondence: Michael J Maniaci, Division of Hospital Internal Medicine, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA, Tel +1 904-956-0081, Fax +1 904-953-2848, Email Maniaci.Michael@mayo.eduBackground: 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.Keywords: hospital at home, home care, home acute care, diagnosis related group, inpatient prospective payment system

Michael J Maniaci,1 Karla Maita,2 Ricardo A Torres-Guzman,2 Francisco R Avila,2 John P Garcia,2 Abdullah Eldaly,2 Antonio J Forte,2 Gautam V Matcha,1 Ricardo J Pagan,1 Margaret R Paulson3 1Division of Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL, USA; 2Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL, USA; 3Division of Hospital Internal Medicine, Mayo Clinic Health Systems, Eau Claire, WI, USACorrespondence: Michael J Maniaci, Division of Hospital Internal Medicine, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA, Tel +1 904-956-0081, Fax +1904-953-2848, Email Maniaci.Michael@mayo.eduBackground: Healthcare provider’s experience with new models of care is crucial for long-term success. In July 2020, Mayo Clinic implemented a novel virtual hybrid hospital at home program called Advanced Care at Home (ACH). This model allows virtual providers in a command center to care for high-acuity patients in the home setting through collaboration with a vendor-mediated supply chain. This study aims to describe the outcomes obtained from a survey applied to the ACH providers to determine their acceptance of the quality and safety of the virtual hybrid care model, their perception towards the decision-making and teamwork between the command center and supplier network, and determine if the overall experience with ACH was rewarding.Methods: A 15-question anonymous survey was distributed via email quarterly to all the physicians and nurse practitioners registered in ACH program at Mayo Clinic. The survey encompassed questions related to the overall experience in ACH concerning work environment, quality of care, service reliability, teamwork, decision-making, and satisfaction. All the questions were Likert-like scale choice, and a descriptive analysis using frequency distribution and percentages of the data was performed.Results: Between September 1, 2020 and April 30, 2021, three quarterly surveys were sent to a total of 21 physicians and nurse practitioners caring for patients virtually in ACH. The response rate reported was 72%, 33%, and 66%, respectively, at the first, second, and third quarters. Eighty percent or more of providers consistently gave positive scores to all three areas analyzed throughout the 8-month study.Conclusion: Providers found the ACH virtual hybrid model of home hospital care very rewarding. They were able to deliver high-quality and safe care to their patients through positive teamwork with a vendor-mediated supply chain. This novel model of hospital at home has the potential to be a great provider satisfier moving forward.Keywords: telemedicine, virtual hybrid, hospital home, home healthcare, provider satisfaction

Digital health tools can improve health care access and outcomes for individuals with limited access to health care, particularly those residing in rural areas. This scoping review examines the existing literature on using digital tools in patients with limited access to health care in rural areas. It assesses their effectiveness in improving health outcomes. The review adopts a comprehensive search strategy to identify relevant studies from electronic databases, and the selected studies are analyzed descriptively. The findings highlight the advantages and barriers of digital health interventions in rural populations. The advantages include increased access to health care practitioners through teleconsultations, improved health care outcomes through remote monitoring, better disease management through mobile health applications and wearable devices, and enhanced access to specialized care and preventive programs. However, limited internet connectivity and a lack of familiarity with digital tools are barriers that must be addressed to ensure equitable access to digital health interventions in rural areas. Overall, digital tools improve health outcomes for individuals with limited health care access in rural areas., Competing Interests: Conflicts of Interest None declared

Chronic pain affects over 50 million people in the United States, particularly older adults, making effective assessment and treatment essential in primary care. Actigraphy, which monitors and records limb movement to estimate wakefulness and sleep, has emerged as a valuable tool for assessing pain by providing insights into activity patterns. This review highlights the non-invasive, cost-effective nature of actigraphy in pain monitoring, along with its ability to offer continuous, detailed data on patient movement. However, actigraphy's reliance on physical activity as a proxy for pain, and its inability to directly measure pain intensity, limit its applicability to certain pain types, such as neuropathic pain. Further research is needed to overcome these limitations and to improve the effectiveness of actigraphy in diverse clinical settings.

Background and Objective: While significant sensation recovery improvements in neurotized breasts following reconstruction have been reported, sensation testing methods and surgical techniques have been widely variable. This narrative review aims to summarize available literature on current neurotization practices and sensory recovery outcomes in patients undergoing innervated breast reconstruction., Methods: A comprehensive literature search of PubMed Medline, Web of Science, and Embase was conducted to identify all studies reporting outcomes of neurotization in breast reconstruction surgeries. Data analyzed included operative times, neurotization techniques, sensory outcomes, and methods as well as patient reported outcomes., Key Content and Findings: Despite the heterogeneity of various studies reviewed, all forms of neurotization achieved earlier and superior sensory recovery throughout the reconstructed breast skin compared to non-innervated breasts. In absence of randomized controlled trials or high-quality comparative studies, further evidence is required to objectively confirm this technique offers better sensory recovery., Conclusions: Neurotization at the time of breast reconstruction may lead to improved sensation and patient reported outcomes delineating improved quality of life compared to non-innervated breasts. Future studies need to standardize the way that breast sensation is measured and determine pre-operative variables leading to expected changes in final sensation recovery to help manage surgical outcome expectations of both the surgeon and the patient., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-23-504/coif). The series “Breast Reconstruction” was commissioned by the editorial office without any funding or sponsorship. O.J.M. served as the unpaid Guest Editor of the series and serves as an unpaid editorial board member of Annals of Translational Medicine from July 2022 to June 2024. The authors have no other conflicts of interest to declare., (2024 Annals of Translational Medicine. All rights reserved.)

Introduction: Vascularized composite allotransplantation (VCA) is the transplantation of multiple tissue types as a solution for devastating injuries. Despite the highly encouraging functional outcomes of VCA, the consequences of long-term immunosuppression remain the main obstacle in its application. In this review, we provide researchers and surgeons with a summary of the latest advances in the field of cell-based therapies for VCA tolerance., Methods: Four electronic databases were searched: PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature , and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as the basis of our organization., Results: Hematopoietic stem cells prolonged VCA survival. A combination of immature dendritic cells and tacrolimus was superior to tacrolimus alone. T cell Ig domain and mucin domain modified mature dendritic cells increased VCA tolerance. Bone marrow-derived mesenchymal stem cells prolonged survival of VCAs. A combination of adipose-derived mesenchymal stem cells, cytotoxic T-lymphocyte antigen 4 immunoglobulin, and antilymphocyte serum significantly improved VCA tolerance. Ex-vivo allotransplant perfusion with recipient's bone marrow-derived mesenchymal stem cells increased VCA survival. Recipient's adipose-derived mesenchymal stem cells and systemic immunosuppression prolonged VCA survival more than any of those agents alone. Additionally, a combination of peripheral blood mononuclear cells shortly incubated in mitomycin and cyclosporine significantly improved VCA survival. Finally, a combination of donor recipient chimeric cells, anti-αβ-T cell receptor (TCR), and cyclosporine significantly prolonged VCA tolerance., Conclusions: Evidence from animal studies shows that cell-based therapies can prolong survival of VCAs. However, there remain many obstacles for these therapies, and they require rigorous clinical research given the rarity of the subjects and the complexity of the therapies. The major limitations of cell-based therapies include the need for conditioning with immunosuppressive drugs and radiation, causing significant toxicity. Safety concerns also persist as most research is on animal models. While completely replacing traditional immunosuppression with cell-based methods is unlikely soon, these therapies could reduce the need for high doses of immunosuppressants and improve VCA tolerance., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background:  Breast cancer is one of the most common types of cancer, with around 2.3 million cases diagnosed in 2020. One in five cancer patients develops chronic lymphedema caused by multifactorial triggers and treatment-related factors. This can lead to swelling, skin infections, and limb dysfunction, negatively affecting the patient's quality of life. This retrospective cohort study aimed to determine the associations between demographic and breast cancer characteristics and postoperative cellulitis in breast cancer survivors who underwent lymphovenous bypass surgery (LVB) at Mayo Clinic, Florida., Methods:  We performed a retrospective chart review. Data were collected retrospectively from 2016 to 2022. Sixty adult breast cancer survivors who underwent LVB were included in the final analysis based on specific inclusion and exclusion criteria. Patients were excluded if they did not meet the inclusion criteria or had incomplete follow-up data. Demographic and surgical data were extracted, including body mass index (BMI), type of anastomosis, number of anastomoses, and preoperative cellulitis status. Lymphedema measurements were performed using tape measurements. Fisher's exact test was used to determine statistically significant associations between variables and postoperative cellulitis., Results:  Postoperative cellulitis was more common in patients aged 60 to 69 years (43.2%), whites (75.0%), overweight or obese (90.9%), with one to four anastomoses (81.8%), and nonsmokers (79.5%). The mean International Society of Lymphology (ISL) criteria for both postoperative cellulitis and no postoperative cellulitis was 1.93. Statistically significant associations with postoperative cellulitis were found for the number of anastomoses ( p  = 0.021), smoking status ( p  = 0.049), preoperative cellulitis ( p  = 0.04), and the length of years with lymphedema diagnosis variable ( p  = 0.004)., Conclusion:  Our results suggest that a greater number of anastomoses, smoking, preoperative cellulitis, and years with lymphedema are significantly associated with an increased risk of postoperative cellulitis. Awareness of these risk factors is crucial for monitoring and early treatment of infections following surgery., Competing Interests: None declared., (Thieme. All rights reserved.)

Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.

Background: Immediate lymphatic reconstruction (ILR) has been proposed to decrease lymphedema rates. The primary aim of our study was to determine whether ILR decreased the incidence of lymphedema in patients undergoing axillary lymph node dissection (ALND)., Methods: We conducted a two-site pragmatic study of ALND with or without ILR, employing surgeon-level cohort assignment, based on breast surgeons' preferred standard practice. Lymphedema was assessed by limb volume measurements, patient self-reporting, provider documentation, and International Classification of Diseases, Tenth Revision (ICD-10) codes., Results: Overall, 230 patients with breast cancer were enrolled; on an intention-to-treat basis, 99 underwent ALND and 131 underwent ALND with ILR. Of the 131 patients preoperatively planned for ILR, 115 (87.8%) underwent ILR; 72 (62.6%) were performed by one breast surgical oncologist and 43 (37.4%) by fellowship-trained microvascular plastic surgeons. ILR was associated with an increased risk of lymphedema when defined as ≥10% limb volume change on univariable analysis, but not on multivariable analysis, after propensity score adjustment. We did not find a statistically significant difference in limb volume measurements between the two cohorts when including subclinical lymphedema (≥5% inter-limb volume change), nor did we see a difference in grade between the two cohorts on an intent-to-treat or treatment received basis. For all patients, considering ascertainment strategies of patient self-reporting, provider documentation, and ICD-10 codes, as a single binary outcome measure, there was no significant difference in lymphedema rates between those undergoing ILR or not., Conclusion: We found no significant difference in lymphedema rates between patients undergoing ALND with or without ILR., (© 2024. Society of Surgical Oncology.)

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., (© 2024. The Author(s), under exclusive licence to The Japanese Breast Cancer Society.)

Background and Objectives: Large language models (LLMs) are emerging as valuable tools in plastic surgery, potentially reducing surgeons' cognitive loads and improving patients' outcomes. This study aimed to assess and compare the current state of the two most common and readily available LLMs, Open AI's ChatGPT-4 and Google's Gemini Pro (1.0 Pro), in providing intraoperative decision support in plastic and reconstructive surgery procedures. Materials and Methods: We presented each LLM with 32 independent intraoperative scenarios spanning 5 procedures. We utilized a 5-point and a 3-point Likert scale for medical accuracy and relevance, respectively. We determined the readability of the responses using the Flesch-Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) score. Additionally, we measured the models' response time. We compared the performance using the Mann-Whitney U test and Student's t-test. Results: ChatGPT-4 significantly outperformed Gemini in providing accurate (3.59 ± 0.84 vs. 3.13 ± 0.83, p -value = 0.022) and relevant (2.28 ± 0.77 vs. 1.88 ± 0.83, p -value = 0.032) responses. Alternatively, Gemini provided more concise and readable responses, with an average FKGL (12.80 ± 1.56) significantly lower than ChatGPT-4's (15.00 ± 1.89) ( p < 0.0001). However, there was no difference in the FRE scores ( p = 0.174). Moreover, Gemini's average response time was significantly faster (8.15 ± 1.42 s) than ChatGPT'-4's (13.70 ± 2.87 s) ( p < 0.0001). Conclusions: Although ChatGPT-4 provided more accurate and relevant responses, both models demonstrated potential as intraoperative tools. Nevertheless, their performance inconsistency across the different procedures underscores the need for further training and optimization to ensure their reliability as intraoperative decision-support tools.

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI's ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model's performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise.

Upper and lower limb amputations are frequently associated with phantom limb pain (PLP). Recently, virtual reality (VR) and augmented reality (AR) have been reported as a potential therapy of PLP. We have conducted a systematic review of literature to evaluate the efficacy of VR and AR in managing PLP. Four databases were searched: PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature, and Web of Science. We utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for our organization. The initial search resulted in 164 results. After title, abstract, and full-text screening, 9 studies were included. One study was of good quality and 8 studies were of fair to poor quality. Seven studies utilized VR and 2 studies utilized AR. The number of treatment sessions ranged from 1 to 28 and the duration ranged from 10 minutes to 2 hours. Several pain scales were used to evaluate PLP pre- and postintervention including Numeric Rating Scale, Pain Rating Index, McGill Pain Questionnaire, and Visual Analog Scale. All the studies reported improvement of PLP on one or more of pain scales after one or more sessions of VR or AR. Despite the promising results reported by literature, we cannot recommend using VR or AR for PLP. Most of the studies are of poor design and have limited sample size with high bias levels. Therefore, no substantial evidence can be derived from them. However, we do believe further research with high-quality randomized controlled trials should take place to increase the knowledge of the potential advantages., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Since their release, the medical community has been actively exploring large language models' (LLMs) capabilities, which show promise in providing accurate medical knowledge. One potential application is as a patient resource. This study analyzes and compares the ability of the currently available LLMs, ChatGPT-3.5, GPT-4, and Gemini, to provide postoperative care recommendations to plastic surgery patients. We presented each model with 32 questions addressing common patient concerns after surgical cosmetic procedures and evaluated the medical accuracy, readability, understandability, and actionability of the models' responses. The three LLMs provided equally accurate information, with GPT-3.5 averaging the highest on the Likert scale (LS) (4.18 ± 0.93) ( p = 0.849), while Gemini provided significantly more readable ( p = 0.001) and understandable responses ( p = 0.014; p = 0.001). There was no difference in the actionability of the models' responses ( p = 0.830). Although LLMs have shown their potential as adjunctive tools in postoperative patient care, further refinement and research are imperative to enable their evolution into comprehensive standalone resources.

Background: Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs. Results: The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties. Conclusions : LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.

In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

This study aims to explore how artificial intelligence can help ease the burden on caregivers, filling a gap in current research and healthcare practices due to the growing challenge of an aging population and increased reliance on informal caregivers. We conducted a search with Google Scholar, PubMed, Scopus, IEEE Xplore, and Web of Science, focusing on AI and caregiving. Our inclusion criteria were studies where AI supports informal caregivers, excluding those solely for data collection. Adhering to PRISMA 2020 guidelines, we eliminated duplicates and screened for relevance. From 947 initially identified articles, 10 met our criteria, focusing on AI's role in aiding informal caregivers. These studies, conducted between 2012 and 2023, were globally distributed, with 80% employing machine learning. Validation methods varied, with Hold-Out being the most frequent. Metrics across studies revealed accuracies ranging from 71.60% to 99.33%. Specific methods, like SCUT in conjunction with NNs and LibSVM, showcased accuracy between 93.42% and 95.36% as well as F-measures spanning 93.30% to 95.41%. AUC values indicated model performance variability, ranging from 0.50 to 0.85 in select models. Our review highlights AI's role in aiding informal caregivers, showing promising results despite different approaches. AI tools provide smart, adaptive support, improving caregivers' effectiveness and well-being.

Background : OpenAI's ChatGPT (San Francisco, CA, USA) and Google's Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods : Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t -test, and sensitivity testing. Results : Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p -value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions : Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice.

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.

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., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-23-28/coif). B.M.D. serves as an unpaid editorial board member of mHealth from November 2022 to October 2024. B.M.D. reports Co-PI of a tele neonatology trial was funded by NIH, which is not a direct conflict of interest however a telemedicine topic. The other authors have no conflicts of interest to declare., (2024 mHealth. All rights reserved.)

Introduction: As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice., Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Five electronic databases were searched in October 2023. Eligible studies included surgery-related articles that focused on large language models and contained adequate ethical discussion. Study details, including specialty and ethical concerns, were collected., Results: The literature search yielded 1179 articles, with 53 meeting the inclusion criteria. Plastic surgery, orthopedic surgery, and neurosurgery were the most represented surgical specialties. Autonomy was the most explicitly cited ethical principle. The most frequently discussed ethical concern was accuracy (n = 45, 84.9%), followed by bias, patient confidentiality, and responsibility., Conclusion: The ethical implications of using large language models in surgery are complex and evolving. The integration of these models into surgery necessitates continuous ethical discourse to ensure responsible and ethical use, balancing technological advancement with human dignity and safety.

Primary Care Physicians (PCPs) are the first point of contact in healthcare. Because PCPs face the challenge of managing diverse patient populations while maintaining up-to-date medical knowledge and updated health records, this study explores the current outcomes and effectiveness of implementing Artificial Intelligence-based Clinical Decision Support Systems (AI-CDSSs) in Primary Healthcare (PHC). Following the PRISMA-ScR guidelines, we systematically searched five databases, PubMed, Scopus, CINAHL, IEEE, and Google Scholar, and manually searched related articles. Only CDSSs powered by AI targeted to physicians and tested in real clinical PHC settings were included. From a total of 421 articles, 6 met our criteria. We found AI-CDSSs from the US, Netherlands, Spain, and China whose primary tasks included diagnosis support, management and treatment recommendations, and complication prediction. Secondary objectives included lessening physician work burden and reducing healthcare costs. While promising, the outcomes were hindered by physicians' perceptions and cultural settings. This study underscores the potential of AI-CDSSs in improving clinical management, patient satisfaction, and safety while reducing physician workload. However, further work is needed to explore the broad spectrum of applications that the new AI-CDSSs have in several PHC real clinical settings and measure their clinical outcomes.

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., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

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., (© 2024 Society of Hospital Medicine.)

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background and Objective: Surgical considerations for breast reconstruction (BR) in patients requiring neoadjuvant radiotherapy (NART) or adjuvant radiotherapy (ART) cannot be understated. The management of irradiated tissue leads surgeons to face several challenges. Therefore, it is essential to comprehensively understand the proper patient selection and preoperative planning to ensure the best outcomes and minimize the risk of complications. This narrative review aims to provide an update and summary of the most important technical considerations every breast surgeon must contemplate reconstructing the irradiated breast., Methods: The search strategy was performed on January 10th, 2023. The PubMed, Embase, Cochrane Library, and Web of Science databases were queried to capture all publications regarding surgical considerations in BR of patients undergoing NART and ART., Key Content and Findings: This review shows that the effects of radiotherapy (RT) on BR are still being studied. RT represents an essential factor for overall patient survival, and its use is increasing. However, the range of RT treatments across different cancer centers complicates the creation of a single treatment protocol. BR improves women's quality of life, so finding the proper integration of BR and RT is essential. When deciding on the reconstructive method, there are several factors to consider, such as the patient's body characteristics, tumor stage, RT protocol, and chemotherapy. To achieve the best surgical results and the most satisfied patient, using less aggressive and safer RT methods in the treatment sequence is recommended., Conclusions: The timing of the radiation will influence the selection of the best reconstructive methods to be employed in the breast cancer patient. However, there is clear evidence of preference for immediate autologous-based BR in cases due to the low rate of complications in the long term. But patient individualization is the key. Therefore, the benefits and risks of immediate versus delayed and autologous versus implant-based reconstruction must be weighed in every single case., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-23-1052/coif). The series “Breast Reconstruction” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare., (2023 Annals of Translational Medicine. All rights reserved.)

Background and Objective: Breast cancer-related lymphedema (BCRL) represents a colossal burden in terms of health and patient-reported outcomes. Surgical management plays a prominent role in the psychological and physical well-being of women suffering from BCRL. Therefore, we performed a narrative review of the current surgical management of BCRL and analyzed the postoperative results., Methods: A literature search was conducted across PubMed MEDLINE, Scopus, and Web of Science from database inception through January 2, 2023. We included English-written studies evaluating postoperative outcomes of lymphatic surgery for the management of BCRL., Key Content and Findings: The surgical management of lymphedema can be classified into two approaches: (I) physiologic procedures and (II) debulking or ablative procedures. While ablative procedures are intended to lessen the symptomatic burden of lymphedema via the removal of pathological tissues, physiologic procedures are performed to restore the abnormal lymphatic flow by creating bypasses into the venous or lymphatic circulation, or by creating new lymphatic connections by means of lymphangiogenesis. Physiologic procedures generate better outcomes in the early stages of lymphedema as there is some residual physiologic flow and vessels are less fibrotic, while ablative procedures are regarded to be the best alternative in very advanced lymphedema stages. A combination of physiologic and ablative procedures provides more comprehensive surgical management to BCRL., Conclusions: Lymphedema is a common complication of breast cancer treatment with an extensive incidence range. Postoperative outcomes of the surgical management of BCRL are heterogeneous despite most studies indicating favorable results after lymphatic surgery., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-23-292/coif). The series “Breast Reconstruction” was commissioned by the editorial office without any funding or sponsorship. O.J.M. served as the unpaid Guest Editor of the series, and serves as an unpaid editorial board member of Annals of Translational Medicine from July 2022 to June 2024. The authors have no other conflicts of interest to declare., (2023 Annals of Translational Medicine. All rights reserved.)

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., Competing Interests: Conflicts of Interest None declared

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.

(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.

Introduction: The Care Hotel is a virtual hybrid care model for postoperative patients after low-risk procedures which allow recovery in an outpatient environment. This study aimed to analyze if the American Society of Anesthesiologists Physical Status (ASA PS) Classification System can be used as a predictive factor for staying at Mayo Clinic's Care Hotel., Methods: This retrospective cohort study was conducted between July 23, 2020, and June 4, 2021, at Mayo Clinic in Florida, a 306-bed community academic hospital. ASA PS Class and post-procedure care setting (Care Hotel vs inpatient ward) were collected. Patients were classified into two ASA PS groups (ASA PS Classes 1-2 and 3-4). Pearson's Chi-square test was used to determine if the ASA PS Class and having stayed or not at the Care Hotel were independent and an Odds Ratio (OR) calculated., Results: Out of 392 surgical and procedural patients, 272 (69.39%) chose the Care Hotel and 120 (30.61%) chose the inpatient ward. There was a statistically significant association between ASA PS Class and staying at the Care Hotel, P < .01. The OR of preferring to stay at the Care Hotel in patients with ASA PS Class 1-2 vs ASA PC Class 3-4 was 1.91 ( P = .0041, 95% CI: 1.229-2.982)., Conclusion: Patients with ASA PS Classes 1-2 are almost twice as likely to elect to stay at the Care Hotel compared to those with ASA PS Classes 3-4. This finding may help care teams focus their Care hotel recruitment efforts., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Microsurgery is most commonly used in the anastomosis of vessels, flap harvesting, lymphedema, and nerve reconstruction, among others. During the 1920s the first microscope was invented by Nylen and Holmgren, quickly becoming the gold standard for microsurgery. However, technological advances have come forth in the form of exoscopes., Methods: A search for full-text articles where using an exoscope was compared with a traditional operating microscope was conducted on the databases PubMed, Scopus, Web of Science, and Embase. The following terms were used to guide our search: Microscope AND exoscope AND plastic surgery; Microscope AND exoscope AND microvascular surgery., Results: Our search yielded 69 studies, of which 12 were included. Five exoscope systems were used by the authors. All studies reported the exoscope as a valid alternative to the standard operating microscope. The exoscope was reported as non-inferior to the operating microscope. Although some studies reported lower quality of image, this did not seem to influence the outcome of surgeries. Newer models may have bridged the gap between the operating microscope's image quality and the exoscope. Superior ergonomics was always reported among the included studies., Conclusion: Based on our search, we conclude that the exoscope is a safe and valid alternative to the operating microscope in plastic surgery. As newer models are now commercially available, these have overcome difficulties in image quality and zoom capabilities, responsible for the main drawbacks of the first exoscope systems., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2023 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.)

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.

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., (Copyright © 2023 Elsevier Inc. All rights reserved.)

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., (©Tufia C Haddad, Karla C Maita, Jonathan W Inselman, Francisco R Avila, Ricardo A Torres-Guzman, Jordan D Coffey, Laura A Christopherson, Angela M Leuenberger, Sarah J Bell, Dominick F Pahl, John P Garcia, Lukas Manka, Antonio J Forte, Michael J Maniaci. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.07.2023.)

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 × 10 4 -1 × 10 7 mesenchymal stem cells, averaging 2.26 × 10 6 . 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.

Background: Patients with mild to severe chronic systemic disease undergoing low-risk procedures are often hospitalized for observation. The Care Hotel is a novel virtual medicine hybrid model of care that offers patients a comfortable, out of hospital environment where they can receive both in-person and virtual care after a surgery or procedure. This study aimed to analyze if virtual hybrid post-procedure care in a hotel could be both conducted on and accepted by patients, even those with moderate to severe chronic diseases., Methods: This retrospective cohort study was conducted between July 23, 2020 and June 4, 2021 at Mayo Clinic in Florida, a 306-bed community academic hospital. We collected the sex, age, race, ethnicity, acceptance rate, ASA score, and primary procedure of patients using the Care Hotel., Results: Out of 392 patients, 272 (69.4%) opted for care in the program. Median patient age was 61.5 years, 59.56% were males, and 86.40% were white. We found that 50.37% had an ASA score of 2 and 43.4% had an ASA score of 3. Ten different surgical specialties were able to utilize the Care Hotel for care in 47 different procedure types. Urology had the most patients (n=70, 25.7%). Post-electrophysiologic procedures were the most common procedures (n=39, 14.3%)., Conclusion: Our virtual hybrid Care Hotel program was widely accepted by patients and could care for a multitude of post-operative procedures. Additionally, this novel program can care for patients with both mild and severe systemic diseases.

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. 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.

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.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

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., Competing Interests: None of the authors report any conflicts of interest., (Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc.)

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., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Venous congestion after deep inferior epigastric artery perforator (DIEP) flap breast reconstruction is a complication that may be partially attributable to variations in venous abdominal wall anatomy. In previous work, we have shown that ferumoxytol may be used as a bloodpool contrast agent to perform high-resolution venous imaging. Our current aim was to use this technology to perform a detailed analysis of the venous anatomy among patients undergoing DIEP flap breast reconstruction., Methods: All patients undergoing DIEP flap reconstruction with preoperative ferumoxytol-enhanced magnetic resonance angiography (FE-MRA) were retrospectively reviewed. A detailed anatomic analysis of each abdominal wall on FE-MRAwas performed before review of operative findings. Statistical analysis was used to determine venous characteristics associated with superficial inferior epigastric vein (SIEV) augmentation., Results: From 2012 to 2016, 59 patients underwent preoperative FE-MRA. This resulted in imaging for 118 hemiabdomen and 99 flaps. Superficial-deep communication was identified in 117 of 118 hemiabdomen. Fifty (93%) of 59 patients had greater than 1-mm venous communication of the superficial system across midline. Reconstructed breasts were based on dominant medial row perforators in 82 (83%) of 99 flaps. The mean diameters of the SIEVand dominant venous perforator were 3.8 and 2.8mm, respectively. Anatomic characteristics associated with SIEVaugmentation included SIEVdiameter ( P = 0.01), dominant perforator diameter ( P = 0.04), and the ratio between these 2 variables ( P = 0.001)., Conclusions: Ferumoxytol-enhanced magnetic resonance angiography provides excellent imaging of the venous system. Anatomic characteristics such as the diameter of the SIEVand the diameter of the dominant perforator may be useful in determining which flaps require venous augmentation using the SIEV., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Background: In July 2020, Mayo Clinic launched Advanced Care at Home (ACH), a high-acuity virtual hybrid hospital-at-home model (HaH) of care at Mayo Clinic Florida and Northwest Wisconsin, an urban destination medical center and a rural community practice respectively. This study aims to describe demographic characteristics of ACH patients as well as their acuity of illness using severity of illness (SOI) and risk of mortality (ROM), to illustrate the complexity of patients in the program, taking into account the different diagnostic related groups., Methods: Mayo Clinic uses All Patient Refined-Diagnosis Related Groups (APR-DRG) to calculate SOI and ROM on hospitalized patients. APR-DRG data, including SOI and ROM, were gathered from individual chart reviews from July 6, 2020, to March 31, 2022., Results:  Out of 923 patients discharged from ACH, the average APR-DRG SOI was 2.89 (SD 0.81) and ROM was 2.73. (SD 0.92). Mean age was 70.88 (SD 14.46) years, 54.6% were male patients and the average length of stay was 4.10 days. The most frequent diagnosis was COVID-19 infection with 162 patients (17.6%), followed by heart failure exacerbation (12.7%) and septicemia (10.9%). The 30-day readmission rate after discharge from ACH was 11.2% (n = 103) and the 30-day mortality rate was 1.8% (n = 17). There were no in-program patient deaths., Conclusions: SOI and ROM from patients at the ACH program have been shown to be in the range of "moderate/major" according to the APR-DRG classification. The ACH program is capable of accepting and managing highly complex patients that require advanced therapeutic means. Furthermore, the ACH program has an in-program mortality rate of 0 to date. Therefore, ACH is rising as a capable alternative to the brick-and-mortar hospital., (© 2023. The Author(s).)