A data-driven scoring framework for personalized employee health check-ups: Integrating historical laboratory trends and evidence-based prevalence
Abstract
INTRODUCTION: Rising healthcare costs and growing demand for personalized preventive care have highlighted the need for data-driven approaches to optimize health check-ups, particularly in corporate settings. This study presents a scoring-based platform designed to prioritize laboratory tests for individual employees by integrating historical health data with condition prevalence, aiming to improve the precision and efficiency of routine health assessments.
METHODS: The platform integrates two main components. First, a prevalence model was developed through a systematic review and meta-analysis of 266 studies (from an initial 28,558), providing prevalence estimates for various conditions detectable through laboratory testing. Second, theIndividual Historical Lab Score (IHLS)model was built using employee health records. IHLS combines three metrics: (1) prevalence scores for each test, (2) abnormality scores based on current lab values relative to reference ranges, and (3) trend scores derived from linear trend estimation using least squares error across prior years. These components are heuristically combined to rank check-up items for each individual.
RESULTS: The model was evaluated using six years (2016-2022) of longitudinal health check-up data from 3,198 employees across seven business entities (7,518 total records; mean follow-up: 3.4 years; mean age: 39.3 ± 9.6 years; 29.3 % male). Model performance was assessed using Receiver Operating Characteristic (ROC) curve…
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