Beyond Gut Feel: Predicting Outcomes of Digital Health Companies
Journal
Research Square
Type
working paper
Date Issued
2026-04-06
Author(s)
Abstract
Predicting which digital health companies will succeed is challenging because these firms operate at the intersection of rapid software innovation and slow, regulation- and reimbursement-driven healthcare adoption. We developed a machine learning model, enhanced with large language model (LLM)-derived features from unstructured text, to predict digital health company outcomes and benchmarked it against expert investors. Using data on 10,245 companies founded since 2000 and observed through March 2025, we trained logistic regression, random forest, and XGBoost models, plus an ensemble, to predict successful exits. The ensemble achieved the best discrimination (AUC = 0.934 ± 0.017) and balanced performance (macro-F1 = 0.775 ± 0.031). Adding LLM-derived features improved AUC by 0.007 (P < 0.05). In a blinded benchmark, model accuracy (63%) exceeded mean investor accuracy (52%) and matched the top investor (62%). These results highlight the potential of hybrid human–AI approaches to enhance objectivity and efficiency in digital health investment decisions.
HSG Classification
contribution to practical use / society
Refereed
No
Publisher
Springer Science and Business Media LLC
Subject(s)
Division(s)