Rocky Mountain Pain Solutions: risk-based care and fewer claim denials with cliexa

The challenge.

Rocky Mountain Pain Solutions (RMPS) is a multi-provider chronic pain practice. Before cliexa, it faced several problems that are common in pain management.

• Time-consuming case review. Clinicians spent visit time manually gathering and reviewing each patient’s data.
• No systematic opioid risk stratification. Treatment options were not guided by a consistent measure of each patient’s opioid risk, so there was no data-driven way to identify at-risk patients early.
• Missed ancillary services. Clinicians were not systematically identifying which additional services each patient’s risk profile called for, such as mental health screening and physical therapy.
• Insufficient claim documentation. A 13% claim rejection and denial rate left significant revenue uncaptured.

How cliexa worked at RMPS.

cliexa deployed its integrated pain management platform with EMR integration. The core insight was that risk identification and ancillary service channeling are not two separate problems. They are one workflow.

1. 01

   Personalized risk identification

   cliexa combined scores from seven validated clinical assessments into a composite risk score. The score mapped to risk tiers, with a clinical action protocol for each tier.

   SOAPP-14Screener and Opioid Assessment for Patients with Pain

   DAST-10Drug Abuse Screening Test

   AUDITAlcohol Use Disorders Identification Test

   GAD-7Generalized anxiety disorder screening

   PHQ-9Patient Health Questionnaire for depression

   PDIPain Disability Index

   COMMCurrent Opioid Misuse Measure
2. 02

   Risk-based channeling of ancillary services

   Based on each patient’s risk and clinical profile, cliexa’s protocol engine determined which ancillary services were medically necessary and surfaced those recommendations inside the EMR workflow.

   • High-risk patients were channeled to mental health screening, behavioral health therapy, frequent urinalysis monitoring, and chronic care management programs.
   • Moderate-risk patients received targeted referrals for physical therapy, imaging, and regular urine drug monitoring.
   • All patients had their chief complaint mapped to procedure eligibility, including injection procedures, X-ray, and MRI orders.

   Clinicians no longer had to remember which services each patient qualified for. The system surfaced them based on the risk outcome.

3. 03

   Real-time insight in the EMR

   Clinicians saw a unified view of each patient inside the EMR:

   • A calculated opioid risk level, shown as a clear risk badge
   • A radar chart of all assessment scores at once
   • Assessment score trends over time
   • An interactive body map of the chief complaint, such as back pain location and intensity
   • Policy-driven eligibility recommendations, surfaced automatically

The results.

Over the six-month deployment, combined claim rejections and denials fell sharply. Estimated revenue also grew.

Administrative and financial

From a clinical pilot to the OUD risk model.

The 1,200-patient dataset collected during the RMPS deployment became the training foundation for the opioid use disorder (OUD) risk model that was later qualified on the Mayo Clinic Platform.

1. 2018: Clinical pilot at RMPS

   A six-month deployment with 1,200 chronic pain patients captured structured assessment, medication, urine toxicology, and behavioral data during active care.

2. 2020: University partnership and peer presentation

   cliexa partnered with the University of Colorado Denver’s Department of Computer Science and Engineering on a capstone project called Prospect, a deep neural network built on the RMPS data to classify OUD risk. It was handed off to cliexa on April 24, 2020. The results were also presented to medical practice administrators in an MGMA-accredited CME session.

3. 2022: Mayo Clinic Platform_Accelerate

   cliexa was among the first companies selected, gaining access to de-identified patient data, bias and fairness evaluation, and clinical validation infrastructure.

4. January 2026: Qualified on the Mayo Clinic Platform

   The OUD risk model qualified as cliexaAI Opioid Use Disorder (OUD) Solution. Read the press release.