Garcia, John P.

In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims to discuss the extent of smartphone (SP) usage in fall detection and prevention across a range of care settings. A computerized search was conducted on six electronic databases to investigate the use of remote sensing technology, wireless technology, and other related MeSH terms for detecting and preventing falls. After applying inclusion and exclusion criteria, 44 studies were included. Most of the studies targeted detecting falls, two focused on detecting and preventing falls, and one only looked at preventing falls. Accelerometers were employed in all the experiments for the detection and/or prevention of falls. The most frequent course of action following a fall event was an alarm to the guardian. Numerous studies investigated in this research used accelerometer data analysis, machine learning, and data from previous falls to devise a boundary and increase detection accuracy. SP was found to have potential as a fall detection system but is not widely implemented. Technology-based applications are being developed to protect at-risk individuals from falls, with the objective of providing more effective and efficient interventions than traditional means. Successful healthcare technology implementation requires cooperation between engineers, clinicians, and administrators.

Abstract High healthcare utilizers are often chronically ill patients who require aggressive hospital and outpatient care. We describe a patient with septic shock who was stabilized in the intensive care unit, then transitioned to a virtual hybrid hospital‐at‐home to complete both inpatient care as well as outpatient wound and rehabilitation therapy.

In 2020, Mayo Clinic established an Advanced Care at Home (ACH) program. ACH is a virtual hybrid hospital-at-home (HaH) program that combines telemedicine with in-home care services by utilizing a state that is software-driven, vendor-mediate medical supply chain. The program initially focused on acute medical diagnosis but has expanded to oversee surgical and postsurgical patients with continued inpatient needs. Here, we report the first case of a small bowel obstruction (SBO) managed under a HaH program. A 52-year-old lady presented to the emergency department with symptoms suggestive of mechanical SBO. The diagnosis was confirmed with an abdominopelvic computed tomography (CT) scan, and the patient was admitted to the hospital. Based on the patient’s presentation and laboratory results, the care team proceeded with conservative treatment including nasogastric tube (NG) placement and suctioning, intravenous (IV) fluid replacement, and daily laboratory studies. She spent the first hospital day in the physical hospital ward so that the surgical team could ensure stability clinically and no urgent need for surgical intervention. On hospital day two, she was transferred home with ACH where the NG suctioning and IV replacement therapy could continue, while the medical team conducted daily virtual visits to ensure continued improvement. Additionally, a paramedic and a nurse performed an in-person, head-to-toe assessment and administered medications to the patient twice daily. She spent 5 days in ACH getting acute care and then was discharged into a postacute phase equivalent to outpatient monitoring called the restorative phase. She was monitored remotely for the duration of the restorative phase for 10 more days, and then she recovered fully. This case highlights that high-acuity patients with SBO can receive invasive treatments like NG tube suction as well as be appropriately monitored for clinical decompensation by a virtual hybrid home hospital program which combines virtual care providers with an in-home vendor-mediated supply chain.

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.

Chronically ill patients with superimposed acute illness requiring hospitalization are more likely to develop an extended length of stay, hospital-acquired infections, and adverse events throughout their hospitalization. An excellent alternative to managing this population of patients in the traditional bricks-and-mortal (BAM) hospital is the hospital-at-home (HaH) model. The Advanced Care at Home (ACH) program is Mayo Clinic’s HaH model that provides acute and postacute care to high-acuity patients in their homes rather than in the traditional hospital and skilled nursing facility. We report a case of postoperative care through the ACH program of a patient suffering from short gut syndrome, high-output ileostomy, and severe protein-calorie malnutrition in the setting of previously diagnosed triple-negative invasive ductal carcinoma (IDC) of the right breast complicated by lung and brain metastasis. The patient had multiple complications that required repeated scare escalations directed by a multidisciplinary virtual care. Despite these complications, the ACH model of care was able to keep the patient in the home setting the majority of the time, limiting BAM hospital days, and eliminating the need to use the emergency department for acute escalation for 3 months. The patient was able to recover during this time period and proceed to successful take-down of the ileostomy. This case highlights the benefits of the ACH program by offering high-acuity hospital-level care to severely ill patients in the comfort of their homes. Highly qualified providers paired with curated technology in the home allowed for prompt identification of patient decompensation and timely initiation of treatment while avoiding institutionalization.

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.

Background: Breast cancer is the most common malignancy and the second most common cause of death in women worldwide. Axillary lymph node metastasis (ALNM) is the most significant prognostic factor in breast cancer. Under the current guidelines, sentinel lymph node biopsy (SLNB) is the standard of axillary staging in patients with clinically-node negative breast cancer. Despite the minimally invasive nature of SLNB, it can cause short and long-term morbidities including pain, sensory impairment, and upper limb motor dysfunction. However, lymphedema remains the most feared adverse event, and it affects 7% of patients within 36 months of follow-up. Recently, we have witnessed the implication of radiomics and artificial intelligence domains in the diagnosis and follow-up of many malignancies with promising results. Therefore, we have conducted a systematic search to investigate the potentials of radiomics and artificial intelligence in predicting ALNM. Methods: Four electronic databases were searched: PubMed, Scopus, CINAHL, and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as our basis of organization. Results: For radiomics, area under the curve (AUC) for the included studies ranged from 0.715 to 0.93. Accuracy ranged from 67.7% to 98%. Sensitivity and specificity ranged from 70.3% to 97.8% and 58.4% to 98.2%, respectively. For other artificial intelligence methods, AUC ranged from 0.68 to 0.98, while accuracy ranged from 55% to 89%. Conclusion: The results of radiomics and artificial intelligence in predicting ALNM are promising. However, validation as a substitute to SLNB requires more substantial evidence from large randomized trials.

Objectives: Traditional hospital at home models often have high patient experience scores. The purpose of this study is to look at the patient experience of a new virtual hybrid model of hospital at home called Advanced Care at Home. Methods: Patients in Mayo Clinic’s Advanced Care at Home program received a survey via email from 1 January–31 May 2021. Each survey consisted of 20 questions divided into 18 multiple-choice and two open-ended questions. Results: Ninety-nine surveys were sent and 41 partially or completely finished surveys were returned for a response rate of 41.4%. Patients responded positively, denoted by answering “strongly agree or somewhat agree,” with regard to the ability to reach the team right away 100% of the time, being kept informed 92% of the time, the command center responding promptly to their needs 95% of the time, the team providing comfort and support 98% of the time, feeling comfortable with interacting with their provider by phone or tablet 95% of the time, the ease of use from the equipment 97% of the time, the virtual and in-person staff working well together 98% of the time, the staff treating patients with courtesy and respect 100% of the time, and the ease of understanding the discharge process and feeling ready to leave the program 100% of the time. All providers received positive responses on listening ⩾88% of the time. Patients gave a top rating in likelihood to recommend the program 100% of the time. Conclusion: Overall, the Advanced Care at Home model of hospital at home was highly recommended by patients. Patients scored the program high on responsiveness, staff engagement and communication, ease of equipment use, and readiness for discharge, strengthening the overall confidence in this novel program.

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.

Abstract Background: Endocrine therapy (ET) reduces breast cancer (BC) recurrence risk and improves BC outcomes. However, patients (pts) may refuse ET, potentially impacting their outcomes. However, the exact incidence and factors associated with ET refusal are largely unknown. Methods: We conducted a retrospective study of BC pts in the National Cancer Data Base between 2003 and 2017. Demographic, socioeconomic, co-morbidity index and tumor-specific data were compared between BC pts who refused ET versus those who agreed to ET using bivariate and multivariate models. Female pts 18 to 89 years of age with stage I, II, and III were included. Logistic regression was used to determine associations with ET refusal. Results: Among 463,228 pts, the incidence of ET refusal was 5.26% (24, 401). Of these pts, 85% were non-Hispanic White, 7.9% were Non-Hispanic Black, 3.1% non-Hispanic Asian/pacific islander, and 3.6% were Hispanic. A majority (56.9%) had government insurance, and 87.2% were from a metro residential area. Infiltrating ductal carcinoma BC subtype comprised 78% of pts. Most pts were 51-89 years of age (80%), and 20% were 18-50 years of age. On multivariable analysis (Table 1), non-Hispanic black (OR 0.82), non-Hispanic Asian (OR 0.72), and Hispanic (OR 0.84) pts had a reduced odds of refusing ET than non-Hispanic white pts. Pts with government insurance (OR 1.6, p&lt;0.001) and noninsured pts (OR 1.3, p&lt;0.001) had increased odds of refusing ET compared to privately insured pts. Estrogen receptor (ER) -/Progesterone (PR) +, and ER +/PR - had increased odds of refusing therapy (OR 2.9, and 1.1 respectively, p&lt;0.001) than ER +/PR + pts. Patients with positive lymph nodes had reduced odds of refusing ET than those with negative lymph nodes (p&lt;0.001). Additionally, pts with a 2.1-5 cm (OR 0.92) or &gt;5 cm (OR 0.87) tumor size had a reduced odd of refusing ET than those with tumors less than 2 cm (p&lt;0.001). Finally, patients with well-differentiated BC (grade I) had increased odds of refusing ET (OR 1.2, p&lt;0.001) than moderately differentiated (grade II) BC. Conclusions: The incidence of ET therapy refusal is relatively low; however, survival outcomes may be negatively impacted. This is the first study to evaluate specific demographic and disease-specific factors associated with ET refusal, which can inform strategies to improve ET acceptance rate. Table 1.Multivariable Logistic Regression – ET Therapy Acceptance (Referent) vs. Refusal.VariableOR95% CIp-valueAge51 - 89 years18 - 50 years0.940.900.970.001Race/EthnicityWhite Non-HispanicBlack Non-Hispanic0.820.760.88&lt;0.001Pacific Islander Non-Hispanic0.720.660.76&lt;0.001Hispanic0.840.790.87&lt;0.001InsurancePrivateGovernment1.61.521.61&lt;0.001Not insured1.31.181.47&lt;0.001ResidenceMetroUrban0.900.870.87&lt;0.001Rural0.870.780.970.014HistologyInfiltrating ductal carcinomaLobular carcinoma0.840.800.87&lt;0.001Infiltrating duct and lobular carcinoma0.900.850.95&lt;0.001Others1.171.101.24&lt;0.001Positive Nodes01 - 30.620.600.65&lt;0.0014 - 90.610.570.66&lt;0.00110 +0.610.550.68&lt;0.001VariableOR95% CIp-valueER/PR ExpressionER +/PR +ER -/PR +2.92.563.18&lt;0.001ER +/PR -1.11.061.14&lt;0.001Tumor size≤ 2 cm2.1 - 5 cm0.920.880.94&lt;0.001&gt; 5 cm0.870.800.93&lt;0.001GradeIII1.211.181.25&lt;0.001III0.980.951.020.518Charlson-Deyo Score010.940.910.980.00421.070.991.150.071≥31.120.981.280.079 Citation Format: Pooja P Advani, Ricardo Torres Guzman, Francisco Avila Verduzco, Karla Maita, John Garcia, Abdullah Eldaly, Antonio Forte, Aaron Spaulding. Factors associated with endocrine therapy refusal in hormone receptor-positive breast cancer patients [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-14-16.