A validated digital twin of your discharge process, calibrated to your own data. See where 30-day readmissions come from across the conditions Medicare penalizes, then test a change before you commit staff and budget to it.
After a decade of effort, most hospitals have captured the straightforward improvements. What remains is stubborn: which specific breakdown in your discharge process is driving your readmissions, and which change would actually move the number? The answer must work not only in a controlled trial, but also in your building, with your staff and your patients.
The interventions are well known. The hard part isn’t the playbook. It’s knowing which play to run, in what order, for which patients, and being confident it will hold up before you spend on it.
PRISM mirrors how patients actually move through your discharge process. Calibrated to your own data, it does three things.
Identifies and ranks the care-transition failures driving your 30-day readmissions.
Models a candidate change and estimates its likely effect on readmissions and cost, so you can compare options before committing.
One engine tunes to a different hospital and extends from one penalized condition to the next without starting over.
We build a working model of your current discharge and transition process from your operational and claims data, and quantify where and how often it breaks down.
We model candidate improvements against your own baseline and run the process thousands of times to estimate each option’s likely impact on readmissions and cost.
The chosen change is piloted against your baseline, so you confirm the lift is real before scaling it, turning an estimate into evidence.
Most tools tell you your readmission risk. PRISM lets you simulate the redesign, comparing interventions before you spend and using your data rather than a generic average.
Effects are followed across the care journey, not treated as isolated checkboxes, so you see where a well-intentioned step quietly fails.
The interventions PRISM models are drawn from established, peer-reviewed transitional-care research, applied to your operational reality.
One validated engine, reusable across hospitals and across every condition CMS penalizes.
PRISM is the healthcare initiative of GTC Analytics a metro-Atlanta R&D firm working at the intersection of engineering, modeling & simulation, and data science.
We bring the same model-based systems-engineering rigor GTC has applied for 25 years to the problem of avoidable readmissions. We model a complex system, test changes in simulation, and validate them before deployment.
Know which interventions are most likely to reduce readmissions before you invest.
PRISM uses AI, Digital Twins, and Systems Engineering to identify the operational drivers of avoidable readmissions and simulate the expected impact of evidence-based interventions using your hospital’s own data.
We’re offering early access to a limited number of hospitals interested in improving patient outcomes while reducing unnecessary costs.