AI-driven Optimisation of Quality of Recovery (QoR) in Remote Patient Monitoring

2026-06-22

AI Summary

This study addresses the low patient compliance associated with daily postoperative remote monitoring due to the excessive length of the standard QoR-15 questionnaire. To overcome this limitation, the authors exhaustively evaluated all possible five-item subsets of QoR-15, integrating machine learning modeling with AUC-ROC performance assessment. This approach yielded QoR-compact, a lightweight instrument comprising only one-third of the original items. Despite its brevity, QoR-compact preserves both physiological and psychological dimensions of recovery and achieves a mean AUC-ROC of 0.968—comparable to that of the full questionnaire. Furthermore, it effectively tracks readmission events, substantially enhancing the feasibility of routine postoperative monitoring.

Abstract

Remote patient monitoring depends on patient-reported data to capture the subjective dimension of recovery that devices cannot measure. The Quality of Recovery (QoR-15) survey is the gold-standard instrument for this purpose. It was designed and validated for occasional in-hospital assessment, yet remote monitoring now administers it to patients daily. In our own post-surgical deployment, only 55% of patients submitted the survey more than 14 days of 30 monitoring days. We developed QoR-compact, a five-item daily input for the RPM prediction pathway. Setting a deployment-driven target of one-third of the daily items, we exhaustively evaluated all 3,003 five-question subsets of the QoR-15 and tested whether the best of them matches the full instrument in predicting near-term postoperative recovery severity. QoR-compact achieves a mean AUC-ROC of 0.968 (95% CI 0.915-0.988), statistically comparable to the 0.964 baseline obtained with one-third of the items. Patient-level backtesting indicates that it tracks readmission events as faithfully as the full form. Its five items span the physical and psychological axes of recovery: Q3 (feeling rested), Q9 (feeling comfortable and in control), Q10 (general well-being), Q12 (severe pain), and Q14 (feeling worried or anxious). The QoR-15 remains the gold-standard measure of recovery; QoR-compact complements it as a shorter daily input designed for prediction. This parity provides the basis for a prospective study of whether a lighter daily input is, in turn, completed more consistently. External validation on larger cohorts is required before clinical use.

Problem

• Remote Patient Monitoring
• Quality of Recovery
• Patient Compliance
• Postoperative Recovery
• Survey Burden

Innovation

• QoR-compact
• remote patient monitoring
• predictive modeling
• survey reduction
• postoperative recovery