Predictive Modeling of Hospital Readmission: Challenges and Solutions

TL;DR

This paper systematically reviews computational models for hospital readmission prediction, highlighting key challenges, solutions, and resources to improve model effectiveness, interpretability, and deployment in healthcare settings.

Contribution

It provides a comprehensive taxonomy of challenges and summarizes existing methods and datasets, guiding future research in hospital readmission modeling.

Findings

Identifies four main categories of challenges in readmission prediction.

Summarizes technical solutions addressing data complexity, imbalance, and interpretability.

Provides a curated list of datasets and resources for researchers.

Abstract

Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.