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Predictive Analytics In Healthcare: Medications as a Predictor of Medical Complexity

Roger Higdon1,2,3, Elizabeth Stewart1,3, Jared C Roach4

  • 11 Bioinformatics and High-Throughput Data Analysis Laboratory, Seattle Children's Research Institute , Seattle, Washington.

Big Data
|July 23, 2016
PubMed

Insights

Identifying medically complex children (MCCs) is crucial for healthcare planning. A study found that the number and types of medications accurately predict MCC status, enabling simpler hospital screening for these high-needs pediatric patients.

Area of Science:

  • Pediatric Healthcare
  • Health Services Research
  • Medical Informatics

Background:

  • Children with special healthcare needs (CSHCN) require extensive services.
  • Medically complex children (MCCs) are a growing subset of CSHCN with comorbidities, consuming significant healthcare resources.
  • Rapid identification of MCCs in hospitals is needed for effective planning.

Purpose of the Study:

  • To assess if medication data can predict medically complex children (MCC) status.
  • To develop a simple screening tool for identifying MCCs in a hospital setting.

Main Methods:

  • Retrospective analysis of inpatient medication data at Seattle Children's Hospital.
  • Correlating medication counts (inpatient/outpatient) and drug classes with MCC status.
  • Developing a predictive model based on medication variables.

Main Results:

  • The number of inpatient and outpatient medications significantly correlated with MCC status.
  • A model using outpatient and inpatient drug classes and individual inpatient drug names effectively ranked patient complexity.
  • The model provided a simple, objective method for screening potential MCCs.

Conclusions:

  • Medication data, specifically counts and classes, can effectively identify medically complex children (MCCs).
  • This approach enables the development of simple, objective admission screens for predicting pediatric patient complexity.
  • Implementation of these screens facilitates strategic planning for MCCs' healthcare needs.

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