A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care

TL;DR

This paper presents a hybrid recommender system that matches patients with family doctors in primary care, leveraging consultation histories and trust modeling to improve recommendation accuracy.

Contribution

It introduces a novel trust-based hybrid recommendation approach tailored for healthcare matchmaking, considering temporal dynamics and large-scale consultation data.

Findings

Higher predictive accuracy than heuristics and collaborative filtering

Trust measure enhances model performance

Effective for multiple primary care use cases

Abstract

We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels of available information about patients. Then, we adopt a hybrid recommender system to present each patient a list of family doctor recommendations. In particular, we model patient trust of family doctors using a large-scale dataset of consultation histories, while accounting for the temporal dynamics of their relationships. Our proposed approach shows higher predictive accuracy than both a heuristic baseline and a collaborative filtering approach, and the proposed trust measure further improves model performance.