Q&A: How Baptist Health saved $13M using AI to reduce readmissions

The largest hospital group in Montgomery, Ala. deployed artificial intelligence with its EHR system; the information gleaned has helped cut unnecessary admissions by 18% over the past two years.

blockchain in healthcare / doctor accesses one block in a chain of digital medical records
Leo Wolfert / Getty Images

Baptist Health is a three-hospital, nonprofit system serving Montgomery, Ala. and the surrounding region. It has 680 beds, 550 affiliated physicians and is the largest private employer in the area.

Like most healthcare facilities, Baptist Health has been working to reduce unnecessary admissions and readmissions by using massive data stores in electronic health record systems (EHRs) — in this case, Cerner EHR system.

Baptist Health had been using a LACE index tool, a widely used predictive analytics tool healthcare facilities often deploy within their existing EHR systems. LACE — it  stands for Length of stay, Acuity of admission, Co-morbidities and Emergency room visits — ranks patients: the higher the scores, the higher the risk of returning to the hospital.

Five years ago, Baptist Health piloted an AI software tool from Jvion to bolster its data analytics results.

The Jvion Machine is a combination of Eigen-based mathematics, a dataset of more than 16 million patients, and software that can be applied to 50+ preventable harm vectors without the need to create new models or to have perfect data. More recently, Baptist Health added two additional vectors to its AI platform to determine a patient’s general risk of readmission and find ways to lower those risks.

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