Implementation of a virtual and in-person hybrid hospital-at-home model in two geographically separate regions utilizing a single command center: a descriptive cohort study.

BACKGROUND: As providers look to scale high-acuity care in the patient home setting, hospital-at-home is becoming more prevalent. The traditional model of hospital-at-home usually relies on care delivery by in-home providers, caring for patients in urban communities through academic medical centers. Our objective is to describe the process and outcomes of Mayo Clinic's Advanced Care at Home (ACH) program, a hybrid virtual and in-person hospital-at-home model combining a single, virtual provider-staffed command center with a vendor-mediated in-person medical supply chain to simultaneously deliver care to patients living near an urban hospital-at-home command center and patients living in a rural region in a different US state and time zone. METHODS: A descriptive, retrospective medical records review of all patients admitted to ACH between July 6, 2020, and December 31, 2021. Patients were admitted to ACH from an urban academic medical center in Florida and a rural community hospital in Wisconsin. We collected patient volumes, age, sex, race, ethnicity, insurance type, primary hospital diagnosis, 30-day mortality rate, in-program mortality, 30-day readmission rate, rate of return to hospital during acute phase, All Patient Refined-Diagnosis Related Groups (APR-DRG) Severity of Illness (SOI), and length of stay (LOS) in both the inpatient-equivalent acute phase and post-acute equivalent restorative phase. RESULTS: Six hundred and eighty-six patients were admitted to the ACH program, 408 in Florida and 278 in Wisconsin. The most common diagnosis seen were infectious pneumonia (27.0%), septicemia / bacteremia (11.5%), congestive heart failure exacerbation (11.5%), and skin and soft tissue infections (6.3%). Median LOS in the acute phase was 3 days (IQR 2-5) and median stay in the restorative phase was 22 days (IQR 11-26). In-program mortality rate was 0% and 30-day mortality was 0.6%. The mean APR-DRG SOI was 2.9 (SD 0.79) and the 30-day readmission rate was 9.7%. CONCLUSIONS: The ACH hospital-at-home model was able to provide both high-acuity inpatient-level care and post-acute care to patients in their homes through a single command center to patients in urban and rural settings in two different geographical locations with favorable outcomes of low mortality and hospital readmissions.

Severity of illness and risk of mortality in Mayo Clinic's virtual hybrid advanced care at home program: a retrospective cohort study.

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.

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies.

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

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

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

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

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

Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review.

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.

Psychologic wellness of PA, NP, and physician hospitalists during the COVID-19 pandemic.

OBJECTIVES: Hospitalists, comprising PAs, NPs, and physicians, manage patients hospitalized with COVID-19. To guide the development of support programs, this study compared the psychologic wellness of hospitalist PAs, NPs, and physicians during the COVID-19 pandemic. METHODS: We surveyed hospitalists in 16 hospitals at Mayo Clinic, from May 4 to 25, 2020. We used PROMIS surveys for self-reported global well-being (two single-item measures), anxiety, social isolation, and emotional support, before and during the pandemic. Linear and logistic regression models were adjusted for personal and professional factors. RESULTS: The response rate was 52.2% (N = 154/295). In adjusted linear regression models, the change in scores (before minus during pandemic) for anxiety, social isolation, and emotional support was similar for PAs and NPs compared with physicians. In adjusted logistic regression models, physicians, compared with PAs and NPs, had a higher odds of top global well-being for mental health (adjusted odds ratio [95% confidence interval]: 2.82 [1.12, 7.13]; P = .03) and top global well-being for social activities and relationships (adjusted odds ratio 4.08 [1.38, 12.08]; P = .01). CONCLUSIONS: During the COVID-19 pandemic, global well-being was lower for PAs and NPs compared with physician hospitalists. These results can guide support programs for hospitalists.

Home Telemonitoring to Reduce Readmission of High-Risk Patients: a Modified Intention-to-Treat Randomized Clinical Trial.

BACKGROUND: Home telemonitoring has been used with discharged patients in an attempt to reduce 30-day readmissions with mixed results. OBJECTIVE: To assess whether home 30-day telemonitoring after discharge for patients at high risk of readmission would reduce readmissions or mortality. DESIGN: Prospective, randomized controlled trial. PATIENTS: We compared 30-day readmission rates and mortality for patients at high risk for readmission who received home telemonitoring versus standard care between November 1, 2014, and November 30, 2018, in 2 tertiary care hospitals. INTERVENTIONS: The intervention group received home-installed equipment to measure blood pressure, heart rate, pulse oximetry, weight if heart failure was present, and glucose if diabetes was present. Results were transmitted daily and reviewed by a nurse. Both groups received standard care. MAIN MEASURES: The primary outcome was a composite end point of hospital readmission or death within 30 days after discharge. The secondary outcome was an emergency department visit within 30 days after discharge. KEY RESULTS: A total of 1380 participants (mean [SD] age, 66 [14] years; 722 [52.3%] men and 658 [47.7%] women) participated in this study. Using a modified intention-to-treat analysis, the risk of readmission or death within 30 days among patients at high readmission risk was 23.7% (137/578) in the control group and 18.2% (87/477) in the telemonitoring group (absolute risk difference, - 5.5% [95% CI, - 10.4 to - 0.6%]; relative risk, 0.77 [95% CI, 0.61 to 0.98]; P = .03). Emergency department visits occurred within 30 days after discharge in 14.2% (81/570) of patients in the control group and 8.6% (40/464) of patients in the telemonitoring group (absolute risk difference, - 5.6% [95% CI, - 9.4 to - 1.8%]; relative risk, 0.61 [95% CI, 0.42 to 0.87]; P = .005). CONCLUSIONS: Thirty days of postdischarge telemonitoring may reduce readmissions of high-risk patients. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02136186.

Older adults on involuntary hold status in the emergency department.

BACKGROUND: As the United States' population ages, the health care system will experience overall change. This study aims to identify factors in the older adult that may contribute to involuntary hold status in the ED. METHODS: This study is a retrospective review conducted at a suburban acute-care hospital ED of adult patients evaluated while on involuntary hold from January 1, 2014, through November 30, 2015. Older adults (patients born on or before 06/31/1964) were compared to younger adults (born on or after 07/01/1964) according to demographic and clinical variables including medical comorbidity, ED length of stay, reason for involuntary hold, psychiatric disorder, suicide attempt, substance use disorder, serum alcohol level, urine drug testing, medical comorbidity, violence in the ED, 30-day ED readmission, and 30-day mortality. RESULTS: Of 251 patients, 90 (35.9%) were older adults. The most common reason for involuntary hold in both cohorts was suicidal ideation. Medical comorbidities were more prevalent in older adults [60 (66.7%) vs. 64 (39.8%), P ≤.0001]. Older adults were less likely to report current drug abuse [31 (34.4%) vs. 77 (47.8%), P = .04]. The most commonly misused substance in both groups was alcohol; however, despite similar rates, blood alcohol levels (BAC) and urine drug screen (UDS) were performed less often in older adults. Cohorts were not significantly different with respect to sex, race, violence in the ED, psychiatric diagnosis, and ED LOS. CONCLUSIONS: Involuntary older adult patients present with medical comorbidities that impact mental health. In the ED, they are less likely report substance use, and drug screening may be underutilized. Medical needs make their care unique and may present challenges in transfer of care to inpatient psychiatric facilities.

Involuntary patient length-of-stay at a suburban emergency department.

BACKGROUND: Patients who may be a danger to themselves or others often are placed on involuntary hold status in the Emergency Department (ED). Our primary objective was to determine if there are demographic and/or clinical variables of involuntary hold patients which were associated with an increased ED LOS. METHODS: Records of ED patients evaluated while on involuntary hold from January 1, 2014 through November 30, 2015 at a suburban acute-care hospital ED were reviewed. Data collected included demographics information, LOS, suicidal or homicidal ideation, suicide attempt, blood alcohol concentration (BAC), urine drug test (UDT), psychiatric disorder, substance use, medical illness, violence in the ED, and hospital admission. Linear regression based on the log of LOS was used to identify factors associated with increased LOS. RESULTS: Two-hundred and fifty-one patients were included in the study. ED LOS (median) was 6 h (1, 49). Linear regression analysis showed increased LOS was associated with BAC (p = 0.05), urine drug test (UDT) (p = 0.05) and UDT positive for barbiturates (p = 0.01). There was no significant difference in ED LOS with respect to age, gender, housing, psychiatric diagnosis, suicidal or homicidal ideation, suicide attempt, violence, medical diagnosis, or admission status. CONCLUSIONS: Involuntary hold patients had an increased ED LOS associated with alcohol use, urine drug test screening, and barbiturate use. Protocol development to help stream-line ED evaluation of alcohol and drug use may improve ED LOS in this patient population.

Patients on Involuntary Hold Status in the Emergency Department.

OBJECTIVES: Patients requiring involuntary holds are frequently seen in the emergency department (ED). Much of what is known comes from studies of patients at urban academic centers. Our aim was to describe the demographic and clinical characteristics of patients who were evaluated while on involuntary status at a suburban ED. METHODS: The medical records of patients seen in the ED requiring involuntary hold status between January 1, 2014 and November 30, 2015 were reviewed. Demographic and clinical variables including medical and psychiatric comorbidity were collected. A subanalysis was performed comparing patients who attempted suicide with all other involuntary patients. RESULTS: Two hundred fifty-one patient records were reviewed; 215 patients (85.3%) had psychiatric disorders-depression was the most common (57%)-and 108 patients (43%) had substance use disorders. Only 13 patients (5.2%) had neither a psychiatric disorder nor a history of substance use. Twenty-two patients (8.8%) were violent in the ED. Thirteen patients (5.2%) were readmitted, and 1 patient died within 30 days of discharge from the ED. One hundred twenty-four patients (49.4%) had medical disorders. Suicidal ideation was the most common reason for involuntary hold (n = 185, 73.7%); 63 patients (25.1%) attempted suicide. Compared with other involuntary patients, the patients who attempted suicide were less likely to use opiates (odds ratio 0.27, 95% confidence interval 0.08-0.94, P = 0.04) and to have medical disorders (odds ratio 0.52, 95% confidence interval 0.28-0.98, P = 0.04). CONCLUSIONS: Patients in this study differed from those in urban centers with respect to sex and psychiatric disorder; however, substance misuse was common in both settings. Suicidal ideation including suicide attempt was the most common reason for involuntary status. Patients who attempted suicide were similar to other patients on involuntary hold with respect to demographic and clinical variables.

Patients Threatening Harm to Others Evaluated in the Emergency Department under the Florida Involuntary Hold Act (Baker Act).

OBJECTIVES: This study describes the specific threats of harm to others that led to the use of the Baker Act, the Florida involuntary hold act for emergency department (ED) evaluations. The study also summarizes patient demographics, concomitant psychiatric diagnoses, and emergent medical problems. METHODS: This is a retrospective review of 251 patients evaluated while on involuntary hold from January 1, 2014 through November 30, 2015 at a suburban acute care hospital ED. The data that were collected included demographic information, length of stay, reason for the involuntary hold, psychiatric disorder, substance use, medical illness, and violence in the ED. The context of the homicidal threat also was collected. RESULTS: We found that 13 patients (5.2%) were homicidal. Three patients had homicidal ideations alone, whereas 10 made homicidal threats toward others. Of the 10 making homicidal threats, 7 named a specific person to harm. Ten of the 13 homicidal patients (76.9%) also were suicidal. Eleven patients (84.6%) had a psychiatric disorder: 9 patients (69.2%) had a depressive disorder and 8 patients (61.5%) had a substance use disorder. Eight patients had active medical problems that required intervention in the ED. CONCLUSIONS: We found that three-fourths of patients expressing homicidal threats also were suicidal. The majority of patients making threats of harm had a specific plan of action to carry out the threat. It is important to screen any patient making homicidal threats for suicidal ideation. If present, there is a need to implement immediate management appropriate to the level of the suicidal threat, for the safety of the patient. Eighty-five percent of patients making a homicidal threat had a previously documented psychiatric disorder, the most common being a depressive disorder. This finding differs from previous studies in which psychosis predominated. More than 60% of homicidal patients had an unrelated medical disorder requiring intervention. It is important not to overlook these medical disorders while focusing on the psychiatric needs of the patient; most of our homicidal patients proved to be cooperative in the ED setting.

Violent behavior by emergency department patients with an involuntary hold status.

BACKGROUND: Violence against health care workers has been increasing. Health care workers in emergency departments (EDs) are highly vulnerable because they provide care for patients who may have mental illness, behavioral problems, or substance use disorders (alone or in combination) and who are often evaluated during an involuntary hold. Our objective was to identify factors that may be associated with violent behavior in ED patients during involuntary holds. METHODS: Retrospective review of patients evaluated during an involuntary hold at a suburban acute care hospital ED from January 2014 through November 2015. RESULTS: Of 251 patients, 22 (9%) had violent incidents in the ED. Violent patients were more likely to have a urine drug screen positive for tricyclic antidepressants (18.2% vs 4.8%, P=0.03) and to present with substance misuse (68.2% vs 39.7%, P=0.01), specifically with marijuana (22.7% vs 9.6%, P=0.06) and alcohol (54.5% vs 24.9%, P=0.003). ED readmission rates were higher for violent patients (18.2% vs 3.9%, P=0.02). No significant difference was found between violent patients and nonviolent patients for sex, race, marital status, insurance status, medical or psychiatric condition, reason for involuntary hold, or length of stay. CONCLUSION: Violent behavior by patients evaluated during an involuntary hold in a suburban acute care hospital ED was associated with tricyclic antidepressant use, substance misuse, and higher ED readmission rates.

Association of Anemia with Outcomes of Acute Heart Failure.

OBJECTIVES: Anemia is common in patients presenting with acute congestive heart failure (CHF); when hemoglobin (HGB) declines to low levels, it can result in worse outcomes. The aim of this study was to determine a level of HGB on admission or discharge that affects outcomes in patients with CHF and then to evaluate the effect of the low HGB levels on these outcomes. METHODS: We conducted a retrospective cohort study of 756 patients admitted with acute CHF during the period January 1, 2011-December 31, 2014. We used multivariable regression analysis to evaluate the relation among HGB levels and three major outcomes: 3-year mortality, 30-day readmission rate, and length of stay (LOS). RESULTS: Compared with patients with HGB ≥10 g/dL, patients with HGB <10 g/dL on discharge from the hospital had higher mortality (3-year survival 46% vs 33%, P = 0.023) and 30-day readmission rates (23% vs 14%; P = 0.008) and increased LOS (4.8 vs 3.2 days, P < 0.001). Patients with admission HGB <10 g/dL had higher mortality rates (3-year survival 45% vs 32%, P = 0.019) and increased LOS (4.5 vs 3.4 days, P = 0.014). A lower admission HGB value was associated with higher 30-day readmission rates, but it was not statistically significant (P = 0.06). CONCLUSIONS: An HGB level <10 g/dL on admission or discharge in patients hospitalized with acute CHF is associated with a significantly worse outcome.

A multidisciplinary approach to reducing alarm fatigue and cost through appropriate use of cardiac telemetry.

BACKGROUND: Alarm fatigue (AF) is a distressing factor for staff and patients in the hospital. Using cardiac telemetry (CT) without clinical indications can create unnecessary alarms, and increase AF and cost of healthcare. We sought to reduce AF and cost associated with CT monitoring. METHODS: After implementing a new protocol for CT placement, data were collected on telemetry orders, alarms and bed cost for 13 weeks from 1 January 2015 through 31 March 2015. We also retrospectively collected data on the same variables for the 13 weeks prior to the intervention. A survey was administered to nurses to assess past and present perceptions of AF. Interventions included protocol creation and education for participants. RESULTS: At baseline, 77% of patients were monitored with CT. A total of 145 (31%) order discrepancies were discovered during data collection, of which 72% had no indication for CT, so CT was discontinued. The other 28% had indications, so orders were placed. A total of 8336 alarms were recorded during 4 weeks of data collection, of which 333 (4%) were classified as true actionable alarms. Postintervention data showed 67% CT assignment with 10% reduction in CT usage, with no increase in mortality (p<0.001 and >0.05, respectively). A 42% cost reduction was achieved after adjusting the patient status. Nurses reported 27% perceived reduction in AF. One-year follow-up revealed that 69% of patients were being monitored by CT, and the rate of order discrepancies due to lack of indication was 9%. CONCLUSION: All hospital units may benefit from the protocols created during this study. If applied appropriately, these protocols can lead to reduced AF and cost per episode of care.

Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data.

The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.

Individual- and Community-Level Predictors of Hospital-at-Home Outcomes.

Advanced Care at Home is a Mayo Clinic hospital-at-home (HaH) program that provides hospital-level care for patients. The study examines patient- and community-level factors that influence health outcomes. The authors performed a retrospective study using patient data from July 2020 to December 2022. The study includes 3 Mayo Clinic centers and community-level data from the Agency for Healthcare Research and Quality. The authors conducted binary logistic regression analyses to examine the relationship among the independent variables (patient- and community-level characteristics) and dependent variables (30-day readmission, mortality, and escalation of care back to the brick-and-mortar hospital). The study examined 1433 patients; 53% were men, 90.58% were White, and 68.2% were married. The mortality rate was 2.8%, 30-day readmission was 11.4%, and escalation back to brick-and-mortar hospitals was 8.7%. At the patient level, older age and male gender were significant predictors of 30-day mortality (P-value <0.05), older age was a significant predictor of 30-day readmission (P-value <0.05), and severity of illness was a significant predictor for readmission, mortality, and escalation back to the brick-and-mortar hospital (P-value <0.01). Patients with COVID-19 were less likely to experience readmission, mortality, or escalations (P-value <0.05). At the community level, the Gini Index and internet access were significant predictors of mortality (P-value <0.05). Race and ethnicity did not significantly predict adverse outcomes (P-value >0.05). This study showed promise in equitable treatment of diverse patient populations. The authors discuss and address health equity issues to approximate the vision of inclusive HaH delivery.

Initial Experience With Inpatient Hospital Care at Home for Urologic Disease.

INTRODUCTION: In 2020, Mayo Clinic launched Advanced Care at Home (ACH), a hospital-at-home program that cares for high-acuity inpatients via remote monitoring and in-person care. Herein, we describe our initial experience utilizing ACH for patients with urologic problems. METHODS: We identified ACH patients treated at Mayo Clinic Florida from July 2020 to August 2022. Records were reviewed to identify those with urologic problems, defined as genitourinary infections, urinary tract obstruction, bleeding, or complications following urologic surgery within 90 days of admission. Demographics, Charlson Comorbidity Index, ACH interventions, length of stay, and hospital readmission were assessed. RESULTS: We identified 563 ACH admissions involving 537 patients, of whom 51 (9%) had illnesses with urologic etiology and 3 (0.6%) were admitted for nonurologic postoperative complications following urologic surgery. Admitting diagnoses included pyelonephritis (n = 51, 91%) and epididymoorchitis (n = 2, 4%). Postoperative diagnoses included cellulitis (n = 1, 2%), congestive heart failure (n = 1, 2%), and diverticulitis (n = 1, 2%). Median Charlson Comorbidity Index of admitted patients was 4 (interquartile range: 3-6.8). Twenty-five patients (46%) underwent 38 urologic procedures within 90 days of admission. Interventions included IV antibiotics (n = 51, 91%), IV fluids (n = 12, 21%), IV antifungals (n = 2, 4%), and oral diuretic therapy (n = 1, 2%). Median length of stay was 3 days (interquartile range: 2-4), and 9 patients (16%) were readmitted within 30 days. A total of 216 inpatient hospital days were saved by utilizing ACH. CONCLUSIONS: ACH appeared to be a feasible alternative to brick-and-mortar inpatient care for patients with genitourinary infections requiring IV antimicrobials.

Bypassing the Brick-and-Mortar Hospital: Increasing Direct Admissions from the Emergency Department to Inpatient Hospital-at-Home.

Home hospital programs continue to grow across the United States. There are limited studies around the process of patient selection and successful acquisition from the emergency department. The article describes how an interdisciplinary team used quality improvement methodology to significantly increase the number of admissions directly from the emergency department to the Advanced Care at Home program.

Risk factors associated with escalation of care in a quaternary academic hospital at home program.

BACKGROUND: Hospital-at-home has become a more recognized way to care for patients requiring inpatient hospitalization. At times, these patients may require escalation of care (transfer from home back to the brick-and-mortar (BAM) hospital for ongoing hospitalization care needs), a process that has not been extensively studied. OBJECTIVE: To evaluate what patient factors contribute to escalations of care in the hospital-at-home delivery model. DESIGNS, SETTINGS, AND PARTICIPANTS: We conducted a retrospective review of all patients admitted to Mayo Clinic's Advanced Care at Home (ACH) program from January 1, 2022 to December 31, 2022. INTERVENTION: None. MAIN OUTCOMES AND MEASURES: Patient information was collected via electronic health record including demographic, socioeconomic, and clinical status. The primary outcome was the of occurrence of an escalation. RESULTS: A total of 904 patients were included, of whom 80 (8.8%) required an escalation of care. In multivariable analysis, risk of an escalation was significantly higher for patients who were married or had a life partner (HR: 1.82, 95% CI: 1.05-3.23, p = .033) for patients admitted with procedure-related disorders (HR: 2.61, 95% CI: 1.35-5.05, p = .005) and patients with an increased mortality risk score (HR [per each 1-category increase] = 1.86, 95% CI: 1.39-2.50, p < .001).