White Paper

Predictive Analytics In Healthcare: Medications As A Predictor Of Medical Complexity

Source: Big Data In Healthcare

By Eugene Kolker, Chief Data Officer, Seattle Children’s Hospital

Children with special healthcare needs (CSHCN) require health and related services that exceed those required by most hospitalized children. A small but growing and important subset of the CSHCN group includes medically complex children (MCCs). MCCs typically have comorbidities and disproportionately consume healthcare re- sources. To enable strategic planning for the needs of MCCs, simple screens to identify potential MCCs rapidly in a hospital setting are needed. We assessed whether the number of medications used and the class of those medications correlated with MCC status. Retrospective analysis of medication data from the inpatients at Seattle Children’s Hospital found that the numbers of inpatient and outpatient medications significantly correlated with MCC status. Numerous variables based on counts of medications, use of individual medications, and use of combinations of medications were considered, resulting in a simple model based on three different counts of medications: outpatient and inpatient drug classes and individual inpatient drug names. The combined model was used to rank the patient population for medical complexity. As a result, simple, objective admission screens for predicting the complexity of patients based on the number and type of medications were implemented.

Download the complete paper below to learn more.


Get unlimited access to:

Trend and Thought Leadership Articles
Case Studies & White Papers
Extensive Product Database
Members-Only Premium Content
Welcome Back! Please Log In to Continue. X

Enter your credentials below to log in. Not yet a member of Health IT Outcomes? Subscribe today.

Subscribe to Health IT Outcomes X

Please enter your email address and create a password to access the full content, Or log in to your account to continue.


Subscribe to Health IT Outcomes