Data Collaborative Taps Predictive Analytics to Coordinate Care
For example, hospitals are now penalized for certain "preventable" readmissions—yet the collaborative seeks to prevent "all-cause" readmissions, anticipating that hospitals and health systems will have to transform even further the way they provide clinical care.
Often, patients are readmitted because they've failed to follow their drug regimens post discharge, or because they don't have the support to take care of themselves after they are discharged. This is a huge source of waste in healthcare because of lack of capability.
For example, the 30-day readmission rate for Medicare beneficiaries is around 20%, which equates to about 2 million people a year. Those dollars quickly add up. The collaborative aims to change that by being able first to predict based on a variety of disparate sources of data, who is likely to be readmitted, so that hospital can address issues that may cause a readmission proactively.
The DAC pharmacy compliance model is designed to remind patients and their caregivers about the importance of follow-up medications. Analytics in the module will work to notify providers within 24 hours who has not filled their prescriptions, for example, and immediately intervene.
With its readmissions module, the DAC model will analyze both EMR and administrative data to identify patients who are most likely to be readmitted before they are discharged, and will identify potential risk factors for particular patients, tying patients to evidence-based checklists based on their condition.
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