Valider et évaluer l'algorithme de flux de travail numérique de la douleur de la douleur (PGS 1.0)

Background and Rationale:

Chronic pain is a global epidemic. On average, 20% of the Western population suffers from chronic pain, and in some countries, such as the U.S., the number reaches up to 30%. When chronic pain is not properly diagnosed and treated, it leads not only to patient suffering but also to serious comorbidities, increased use of healthcare services, and, in growing numbers, addiction and misuse of prescription drugs.

Pain medicine, a relatively young medical field, offers evidence-based diagnosis and treatment tailored to the patient's pain mechanisms. However, there is a major gap between the large number of patients and the very limited number of pain specialists (around 4,000 in the U.S. and about 80 in Israel). This shortage leads to extremely long waiting times (5-6 months) while patients continue to suffer.

To help close this gap, PG Medical Systems, together with Dr. Simon Wolfson (Head of the Pain Institute at Rambam Health Care Campus), developed an algorithm that analyzes patient symptoms and provides recommendations for pain diagnosis and treatment pathways.

Study Objective:

The study aims to prospectively validate the algorithm's performance by collecting patient data via an online questionnaire at the Rambam Pain Institute. The results will compare the algorithm's diagnoses to those of pain specialists.

Study Procedures:

Patients visiting the Rambam Pain Institute (first-time or follow-up appointments) will be invited to join the study. After signing informed consent, they will complete an online questionnaire with the help of a research assistant. This is a one-time activity, following their medical consultation. The study does not involve additional clinical follow-up. Participation will not affect the patient's diagnosis or treatment.

Duration:

Each participant will spend about 45-60 minutes, including the consent process and questionnaire completion.

Compensation:

Participants will receive 100 NIS for their time.

Data Management and Privacy:

The study will collect only non-identifiable patient data. Each participant will be assigned a code number. Identifying details will remain securely stored at Rambam, linked only by the consent form. This allows for possible follow-up research but ensures that the sponsor company has access only to de-identified data.

Data Security:

All data is stored on Amazon's secure cloud with advanced encryption. Access requires strict authentication, servers are closed to public internet, and only approved private networks can connect. All data is encrypted in transfer and storage. Importantly, the system does not collect identifiable patient information.