Home // News // Hospital Readmission Rates Predicted with Automatic Tool

Hospital Readmission Rates Predicted with Automatic Tool

A new development in automated communication technology makes it easier for healthcare professionals to prevent hospital readmissions, and may even help prompt hospitals’ slow transition to ACOs.

As of December 2013, University of Pennsylvania Health System successfully incorporated an automated prediction tool that recognizes newly admitted patients who are at risk for being readmitted within thirty days of their discharge. This tool was developed by researchers at the University’s Perelman School of Medicine and is now an official part of U. Penn’s electronic health record system.

Automated Prediction Tool

The automated prediction tool reads a newly admitted patient’s electronic medical record, looking for just one thing: whether or not that patient had been treated at a hospital two or more times within the past twelve months. If the patient has a history of frequent and recent hospital admissions (two or more within the past year) then the electronic tool will automatically identify the patient as being “high-risk.”

The patient’s high-risk readmission status appears within their electronic medical record, where a flag will appear in the new column titled “readmission risk.” People who have access to the medical records can learn about the patient and how to prevent their readmission by double-clicking the flag. A database of information supporting the reason for the patient’s high-risk status will then appear, providing a history of admissions, former doctors’ contact information, and other helpful information that will provide guidance to healthcare professionals regarding how to best care for the patient and prevent a readmission.

The automated prediction tool was tested for a full year before U. Penn fully integrated it into their electronic record system. During that year, patients who were identified as being at high-risk for readmission in the system were readmitted at a rate of 31%. Patients who were not identified as high-risk using the tool were only admitted 11% of the time—a drastic reduction.

Finding ways to reduce hospital readmissions has been of national concern over the last few years, since Medicaid publicized their budget and began issuing fines to hospitals with high readmission rates. Hundreds of studies have been conducted for this effort, many of which point to increasing the quality of patient care as the most significant measure. The new automated communication tool can help healthcare workers identify which patients will need extra care and attention, and also puts a network in place for healthcare professionals and systems to begin working together toward forming ACOs.

Tom Prose

CEO at General Medicine, P.C.
As founder and CEO of General Medicine PC, the nation’s premier post-hospitalist care company,Tom Prose leads an exceptional team of internal medicine, geriatrics and healthcare administration specialists.