Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Analysis of Medicaid Claims Data

Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Analysis of Medicaid Claims Data https://t.co/TPGaJCPo75

RT @JournalGIM: Using machine learning to identify subgroups of high-need high-cost #Medicaid patients, with expected (mental health) + une…

Our paper @ArnholdInst using clustering to better ID high-need high-cost Medicaid pts @MountSinaiNYC. Top groups: Mental health/substance use, chronic dz, pregnancy-related complications. Not catch all — Need personalized programs for different populations

RT @JournalGIM: Using machine learning to identify subgroups of high-need high-cost #Medicaid patients, with expected (mental health) + une…

Using machine learning to identify subgroups of high-need high-cost #Medicaid patients, with expected (mental health) + unexpected (pregnancy) results @sudhakarnuti @patrickdoupe @_bicv @em_bruze @aaronibaum @ArnholdInst https://t.co/W1OibVnyQN https://t.c

RT @JournalGIM: Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Anal…

Characterizing Subgroups of High-Need, High-Cost Patients Based on Their Clinical Conditions: a Machine Learning-Based Analysis of Medicaid Claims Data https://t.co/WIXw1eojPb