Health System Reduces Readmissions, Lowers Cost of Care, and Builds Cost-Effective Preferred Care Network

According to Scott Rifkin, interoperability and care collaboration continues to drive the future of healthcare. Learn how a major health system in South Central Pennsylvania utilized an interventional analytics software platform to build a collaborative preferred care network with their skilled nursing facilities – eliminating unnecessary hospital readmissions, reducing length of stay, and improving quality measures.

BACKGROUND
A major health system within South Central Pennsylvania was seeking innovative ways to impact cost and quality of their post-acute care network. With skilled nursing costs above the national average, this four hospital, ACO, 786-licensed bed health system was continuing to encounter roadblocks in obtaining line of sight to their patients’ care when discharged to skilled nursing facilities.

Discharging nearly 35,000 patients per year, with an average of 4,000 patients being transitioned to skilled nursing facilities from their hospital/ACO, standardized care plans and clinical alerts were used to monitor patients more closely. Length of stay was managed by monitoring the patient’s functional status to determine appropriate guidelines and timing of discharge. Outpatient follow-up care was embedded in the clinical standards to maintain the patient’s connection with the health system.

CHALLENGES
The current case management program in place was becoming increasingly labor intensive. When tracking patients care after hospital discharges, the hospital’s care management team found telephone communication to be slow and unreliable. Calls to update patient information were often returned too late to be able to impact the care and prevent hospital readmissions.

  • Cost control and utilization of care in the post-acute care arena
  • ACO accountability for post-acute quality and cost containment
  • Managing post-acute care network of 10+ skilled nursing facilities
  • Average 3,000 to 4,000 discharges per year to skilled nursing facilities
  • Impact of readmission rates and lengths of stays on total cost of care

“The biggest challenge faced was the ability to obtain line of sight into patients care once discharged to the SNF.“
– Director of Post-Acute Care (former)

SOLUTION
Real Time Medical Systems was identified by the health system as a solution that would provide direct line of sight into the care continuum post-hospital discharge and enable them to work collaboratively with the skilled nursing team to effect better outcomes, control costs of care, and prevent unnecessary hospital readmissions.
Real Time’s Interventional Analytics platform was deployed to the skilled nursing facilities hospital’s preferred care network – enabling seamless collaboration with the clinical teams in the skilled nursing facilities based on clinical, functional, and financial outcomes. The hospital’s care transitions team was able to easily monitor their progression of their patients care, as well as readmissions by facility, diagnosis, and by length of stay. Clinical alerts provided the team information on what was happening with patients in real time, which helped to improve relationships and bridge collaboration efforts between the hospitals, skilled nursing facilities, and providers. Since Real Time allows alerts to be customized, the hospital was able to set alerts based on their clinical plans and pathways.

Interventional Analytics
Unlike predictive analytics, which predicts trends based on static data, Interventional Analytics assesses live data and alerts you in real-time to intervene in the patient’s care before an adverse situation occurs.

RESULTS

  • Readmission rates dropped from an average of 18% prior to Real Time Medical Systems implementation to 8.55% in the first performance year, and 8.20% in the second performance year.*
  • Length of stay decreased by 43% in the first performance year.*
  • Decrease in utilization led to a cost savings of $4 million for the total cost of care for that performance year.*

Interventional Analytics
Unlike predictive analytics, which predicts trends based on static data, Interventional Analytics assesses live data and alerts you in real-time to intervene in the patient’s care before an adverse situation occurs.

*Claims data.