Development of the PAIN (Pain AI iNtervention) Platform for Patients at Home - PROJECT SUMMARY Pain measurement relies on patient-reported scales, prone to four crucial obstacles: high subjectivity, the need for consciousness, the evaluation at only one point, and the inability to describe pain’s complexity. The core innovation within this project centers on deploying our previously developed and validated deep learning algorithms leveraging continuous physiologic data captured using a wearable sensor. We hypothesize that the recognition of physiologic patterns driven by pain responses (i.e., physiological data such as heart rate, respiratory rate, and heart rate variability (HRV)) will allow for accurate and safe therapeutic intervention in a closed-loop individualized sensor-based artificial intelligence (AI)-enabled system. This project will allow the creation of a real-time, at-home continuous physiologic monitoring database from which a pain biomarker will be created using artificial intelligence. The overarching goal is to personalize pain medication regimens leveraging sensor-driven, digital ecosystems that can recognize subtle markers of pain and over-medication. Multiple sensors in a single device (ECG, PPG, EDA, body movement, temperature, barometric pressure, and oxygen saturation) will be used, supplemented with a blood pressure cuff and finger pulse oximeter, thereby building a composite and objective profile to determine pain needs and safety indicators. To test the hypothesis, we plan on 1) characterizing multimodal physiologic and psychosocial signatures of postoperative pain, 2) validating machine learning/artificial intelligence (ML/AI) models to estimate pain intensity and opioid response, and 3) evaluating the acceptability, appropriateness, feasibility, and integration potential of the Omni-AI ecosystem in outpatient care. A total of 500 patients will be recruited at their initial preoperative consultation and general anxiety levels and pain catastrophizing attitudes will be measured using the State-Trait Anxiety Inventory (STAI) and Pain Catastrophizing Scale (PCS). The patients will evaluate their pain intensity using the numerical rating score (NRS) concomitantly. After the surgery, the wearable Omni devices will continuously record physiologic signals, which will be stored on-device during the 5-day home monitoring period and securely transferred to Mayo Clinic servers after device return. During daytime hours (8 AM, 12 PM, 4 PM, and 8 PM), patients will be prompted by a Mayo Clinic-developed mobile app to submit an NRS pain evaluation and record medication consumption, with the option to enter additional reports at any time when they experience pain. We will create a data bank with subject-specific time series, including physiological variables and self-reported pain scores. To measure the acceptability, appropriateness, and feasibility of our proposed intervention, we will use the validated Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and the Feasibility of Intervention Measure (FIM) surveys. This will be achieved by performing a phone interview five days after the study discharge. The Consolidated Framework for Implementation Research (CFIR) will also be deployed to assess integration potential.
