A trained model to create an echo system that continuously monitors lab results, or other relevant health metrics leading up to surgery. It aims to provide early warnings of potential health risks so that healthcare providers can take proactive measures. The ML based time series algorithm continuously monitors the historical healthcare provider/lab records of the multiple patients to compare the similarities of the lab test results, treatment durations, diagnosis relations information to predict and warn the Providers on potential outcomes of surgery needs of a Patient.

The input to this model is :

• Patient ID
• Patient’s historical claims data for last 6 months to 1 year (or even beyond)
• Claim type (Inpatient/Out Patient)
• Patient’s Age
• Patient’s Gender
• Rev codes used on each claim
• Diagnosis codes used on each claim
• Place of Service on each claim
• Length of Stay Medication details

The Model generates an early prediction analysis/report for the Provider/Payer to warn the potential surgery need situations for the Patients by monitoring the similarities of the Patient treatment records over a period of time