SOAR
Project status
Innovation leads
Awards
UPHS Quality and Patient Safety Award, Sustainable Impact, 2019
Funding
Innovation Accelerator Program, Penn Medicine
Independence Blue Cross
Research Institute for Home Care
Opportunity
Older adults are frequently hospitalized. In fact, approximately 22 percent of Medicare beneficiaries are hospitalized at least once per year, and for those over the age of 85, the rate increases to 34 percent.
Most older adults desire to return home after hospitalization. However, fragmented handoffs across siloed care settings can lead to lengthy hospital stays and delays in home care services. Paired with the fact that patients are often physically compromised after hospitalization and their caregivers underprepared, this can result in riskier recovery at home, readmissions, and increased costs.
Transitional care models in the United States have several limitations. They often rely on specialized roles, are costly, and have limited capacity. In Sheffield, England, a hospital system developed what is known as the Discharge to Assess (D2A) model. This novel intervention “flips” post-acute care planning from a hospital-based activity to one that takes place in the patient’s home. The flipped discharge approach leverages existing care team members to execute a more patient-centered approach. It enables patients who are medically ready to be discharged sooner and receive personalized support at home.
Intervention
Supporting Older Adults at Risk (SOAR) adapts the D2A concept to the context of the U.S. health system and customizes it for the unique needs of the Penn Medicine community in a sustainable way.
SOAR’s mission is to get older adults home sooner and help them recover at home safely. The program comprises three phases.
- Prepare: A custom dashboard automatically identifies patients eligible for the program. Once patients are identified, geriatric nurse consultants (GNCs) perform comprehensive assessments and create specialized patient care plans. GNCs keep the care team, patients, and caregivers informed using standardized communication templates and checkpoints throughout the preparation phase.
- Transition: Hospital and home care providers participate in a collaborative call on the day of discharge to enable seamless and timely handoffs between care teams. Discharge time is defaulted to 10 AM, and patients receive transportation home aligned with caregiver availability. Most importantly, SOAR patients receive same or next-day nursing visits and medication delivery to ensure that they have all of the support and resources they need to begin recovery at home.
- Support: SOAR patients are defaulted to receive home evaluations for physical therapy, occupational therapy, and social work, with an option to add on speech therapy if necessary. They also have access to telemedicine support for vital monitoring and virtual case managers who can assist with geriatric concerns, care navigation, and connection to community resources for services like home health aides, meal delivery, and adult day care services.
SOAR has since expanded to include other hospital-based support models that advance senior-focused care: a geriatric nurse practitioner primary care co-management model and the American Geriatric Society’s CoCare: HELP program.
Impact
SOAR’s phased approach ensures continuity of care for complex patients across settings so that patients can receive expedited, comprehensive, and compassionate care tailored to their specific needs and home recovery goals.
During initial pilots on three units at the Hospital of the University of Pennsylvania, the implementation of SOAR resulted in better patient flow, improved care delivery and patient outcomes, enhanced patient and caregiver experience, and a positive return on investment (ROI).
- Patient flow: Length of stay for SOAR patients was reduced by more than one day on average, and discharges before noon increased from 9 to 76 percent among older patients.
- Care delivery: On average, SOAR patients were seen by a Penn Medicine Home Health nurse six hours after discharge - compared to an average of two days with the traditional care model. Medication reconciliation decreased from an average of up to six days to just six hours.
- Patient outcomes: There was a 30 percent reduction in 30-day readmissions, a 35 percent reduction in 30-day emergency department visits among older patients, and 80 percent of SOAR patients met or made progress toward their documented home recovery goals.
- Patient and caregiver experience: SOAR patients and caregivers rated the program highly. One patient in the pilot noted, “There’s something about the magic of being home that helps me recover.”
- ROI: By discharging patients earlier, SOAR increased the availability of patient beds on hospital care units, which can result in a positive ROI for health systems.
The SOAR team is actively working to scale the program to new units and service lines with a goal for it to become the system-wide standard of care for older adult patients seeking to return home after hospitalization.
Innovation Methods
Mini-pilot
Mini-pilot
Mini-pilot
High-fidelity learning can come from low-fidelity deployment.
Mini-pilots will allow you to learn by doing, usually by deploying a fake back end. You might try a new intervention with ten patients over two days in one clinic, using manual processes for what might ultimately be automated.
Running a "pop-up" novel clinic or offering a different path to a handful of patients will enable you to learn what works and what doesn't more quickly. And, limiting the scope can help you gain buy-in from stakeholders to get your solution out into the world with users and test safely.
Mini-pilot
When the time came to test the SOAR model, we decided to work with just one attending and their care team and with only one patient at a time.
Limiting the roll-out scope helped us gain buy-in from stakeholders to test the model with actual patients.
In running a series of mini-pilots, we learned by doing - piloting close to 15 different iterations of SOAR in one month. With each patient, we validated features that worked and identified pitfalls that needed to be addressed.