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
Abstract: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-… Show more
“…Previous studies have looked at trying to address hospital capacity issues by setting up basic home services in order to move low acuity patients home [ 14 , 15 ]. Our hybrid model of care [ 12 ] is able to combine virtual providers with an in-home medical support that can care for many different moderate to complex diagnosis as seen in Table 2 . This model could be used by other institutions to address capacity issues by shifting moderately complex, clinically stable patients to an alternate setting for their care.…”
Section: Discussionmentioning
confidence: 99%
“…Our findings of the high SOI and ROM in the majority of our ACH patients coupled with our in-program mortality rate of 0% should help reassure both patients and clinicians that a properly built HaH program can provide safe and effective inpatient care for high-acuity patients at home. This low mortality rate can mostly be attributed to the careful clinically screening process that is done prior to admitting a patient into ACH [ 12 ]. This clinical screening process (Additional file 1 : Appendix A) detects patients that may either require higher acuity medical services to treat their primary diagnosis or who may be at high risk for rapid decompensation.…”
Section: Discussionmentioning
confidence: 99%
“…The ACH model of care has been described in detail previously [ 11 , 12 ]. Briefly, ACH is a virtual hybrid model where patients receive inpatient care in the comfort of their own homes.…”
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.
“…Previous studies have looked at trying to address hospital capacity issues by setting up basic home services in order to move low acuity patients home [ 14 , 15 ]. Our hybrid model of care [ 12 ] is able to combine virtual providers with an in-home medical support that can care for many different moderate to complex diagnosis as seen in Table 2 . This model could be used by other institutions to address capacity issues by shifting moderately complex, clinically stable patients to an alternate setting for their care.…”
Section: Discussionmentioning
confidence: 99%
“…Our findings of the high SOI and ROM in the majority of our ACH patients coupled with our in-program mortality rate of 0% should help reassure both patients and clinicians that a properly built HaH program can provide safe and effective inpatient care for high-acuity patients at home. This low mortality rate can mostly be attributed to the careful clinically screening process that is done prior to admitting a patient into ACH [ 12 ]. This clinical screening process (Additional file 1 : Appendix A) detects patients that may either require higher acuity medical services to treat their primary diagnosis or who may be at high risk for rapid decompensation.…”
Section: Discussionmentioning
confidence: 99%
“…The ACH model of care has been described in detail previously [ 11 , 12 ]. Briefly, ACH is a virtual hybrid model where patients receive inpatient care in the comfort of their own homes.…”
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.
“…Further detailed information on the ACH program, clinical and social screening criteria, and the delivery on in-home services/rapid response program has been previously published. 16 Figure 1 Common Diagnoses Considered for ACH Admission. …”
Background
The diagnosis related group (DRG) is used as an economic patient classification system based on clinical characteristics, hospital stay, and treatment costs. Mayo Clinic’s virtual hybrid hospital-at-home program, advanced care at home (ACH), offers high-acuity home inpatient care for a variety of diagnosis. This study aimed to determine the DRGs admitted to the ACH program at an urban academic center.
Methods
A retrospective study was performed on all patients discharged from the ACH program at Mayo Clinic Florida from July 6, 2020, to February 1, 2022. DRG data were extracted from the Electronic Health Record (EHR). Categorization of DRG was done by systems.
Results
The ACH program discharged 451 patients with DRGs. Categorization of the DRG demonstrated that the most frequent code assigned corresponded to respiratory infections (20.2%), followed by septicemia (12.9%), heart failure (8.9%), renal failure (4.9%), and cellulitis (4.0%).
Conclusion
The ACH program covers a wide range of high-acuity diagnosis across multiple medical specialties at its urban academic medical campus, including respiratory infections, severe sepsis, congestive heart failure, and renal failure, all with major complications or comorbidities. The ACH model of care may be useful in taking care of patients with similar diagnosis at other urban academic medical institutions.
“…Patient identification in hospital-at-home (HaH) is complex, with numerous variables, such as clinical and social stability, insurance compatibility, and home address to consider for safely hospitalizing patients in the home setting. 1 Identifying appropriate HaH patients can require burdensome manual chart review of potential candidates. This process is time intensive, concerning in a hospital environment where every minute matters for hospital throughput and patient care.…”
BackgroundHospital‐at‐home (HaH) is a growing model of care that has been shown to improve patient outcomes, satisfaction, and cost‐effectiveness. However, selecting appropriate patients for HaH is challenging, often requiring burdensome manual screening by clinicians. To facilitate HaH enrollment, electronic health record (EHR) tools such as best practice advisories (BPAs) can be used to alert providers of potential HaH candidates.ObjectiveTo describe the development and implementation of a BPA for identifying HaH eligible patients in Mayo Clinic's Advanced Care at Home (ACH) program, and to evaluate the provider response and the patient characteristics that triggered the BPA.Design, Setting, and ParticipantsWe conducted a retrospective multicenter study of hospitalized patients who triggered the BPA notification for ACH eligibility between March and December 2021 at Mayo Clinic in Jacksonville, FL and Mayo Clinic Health System in Eau Claire, WI. We extracted demographic and diagnosis data from the patients as well as characteristics of the providers who received the BPA notification.InterventionThe BPA was developed based on the ACH inclusion and exclusion criteria, which were derived from clinical guidelines, literature review, and expert consensus. The BPA was integrated into the EHR and displayed a pop‐up message to the provider when a patient met the criteria for ACH eligibility. The provider could choose to refer the patient to ACH, dismiss the notification, or defer the decision.Main Outcomes and MeasuresThe main outcomes were the number and proportion of BPA notifications that resulted in a referral to ACH, and the number and proportion of referrals that were accepted by the ACH clinical team and transferred to ACH. We also analyzed the factors associated with the provider's decision to refer or not refer the patient to ACH, such as the provider's role, location, and specialty.ResultsDuring the study period, 8962 notifications were triggered for 2847 patients. Providers opted to refer 711 (11.4%) of the total notifications linked to 324 unique patients. After review by the ACH clinical team, 31 of the 324 referrals (9.6%) met clinical and social criteria and were transferred to ACH. In multivariable analysis, Wisconsin nurses, physician assistants, and in‐training personnel had lower odds of referring the patients to ACH when compared to attending physicians.
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