Papers for

health insurance companies

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Patient surveys boost prediction of opioid use disorder diagnoses

Patient-Reported Survey Data Improve Prediction of Opioid Use Disorder

Abstract: Electronic health records (EHRs) may incompletely capture patient-reported factors associated with opioid use disorder (OUD). We evaluated whether survey data improve prediction of a first recorded OUD diagnosis among 267,747 All of Us participants with documented opioid exposure, including 15,287 OUD cases. We compared EHR-only and EHR+survey models across 6-, 12-, and 24-month look-back windows using logistic regression, random forest, XGBoost, LightGBM, multilayer perceptron, LSTM, GRU, and Transformer. Survey augmentation improved PR-AUC across all 24 model-window combinations by 0.0087-0.0505; the best 24-month LightGBM model improved from 0.6219 to 0.6603. Survey coverage increased with longer windows and differed by OUD status (24 months: 21.7% OUD-positive vs. 60.7% OUD-negative). Permutation analysis ranked survey features as the second most important information domain at 24 months in both evaluated models. Patient-reported data provide complementary predictive signals beyond structured EHRs while highlighting the importance of survey availability.

Thu 10 SeptMachine Learning

The gist

Doctors often use electronic health records (EHRs) to predict who might develop opioid use disorder (OUD), but these records may miss important patient details. The authors studied over 267,000 patients with opioid exposure and found that adding patient survey answers improved prediction accuracy. This means surveys can catch information that EHRs miss, helping identify patients at risk of OUD earlier. They also noticed survey availability varied by time and patient status, showing the importance of collecting these patient-reported data.

Open → 2609.12224v1