Case Study ยท RadOrder Helper

Reducing radiology insurance denials for PET/CT orders.

A clinical workflow intelligence case study showing how structured order logic, payer-ready indication support, and AI-assisted guidance can improve PET/CT order quality before authorization delays occur.

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Problem Incomplete or inconsistent PET/CT orders can trigger clarification loops, authorization delays, and payer denials.
Solution RadOrder Helper structures indication, modality, region, and context before the order reaches scheduling.
Direction Future pilots can measure denial rate, rework time, order completeness, and time-to-scan.
Executive Summary

Why this case matters

PET/CT ordering is not just a form entry task. It sits at the intersection of clinical intent, cancer history, payer rules, modality protocols, documentation, scheduling, and patient access. When the order language is unclear or mismatched, the downstream effect is predictable: phone calls, rework, prior authorization delays, rescheduling, and sometimes denial.

OraDigit addresses this problem by turning real imaging workflow knowledge into a structured clinical AI product. RadOrder Helper guides the order toward cleaner clinical indication language, more consistent study selection, and better readiness for authorization review.

The workflow challenge

  • Free-text indications are often incomplete or inconsistent.
  • Ordering intent may not match the requested imaging study.
  • Schedulers and imaging teams spend time clarifying orders.
  • Payer documentation may not show medical necessity clearly.
  • Patients can experience delays before imaging is completed.

OraDigit design goals

  • Improve order quality before submission.
  • Reduce avoidable clarification loops.
  • Support PET/CT, CT, and MRI workflow logic.
  • Generate payer-ready indication language.
  • Keep human review in control of final clinical decisions.

How the workflow works

  1. The user selects modality, region, clinical context, and condition.
  2. RadOrder Helper uses structured rules to adapt available options.
  3. The system generates an editable clinical indication.
  4. Order guidance can help clarify missing or weak documentation.
  5. The user reviews, copies, exports, or sends the improved order text into the next workflow.
OraDigit does not replace clinical judgment. The system supports workflow quality, documentation clarity, and operational consistency.

Connected OraDigit product

This case study connects directly to RadOrder Helper, the OraDigit product focused on imaging order validation, clinical indication support, and denial-reduction workflow logic.

Future versions can integrate payer-specific rules, audit trails, EMR-ready export, and utilization analytics.

Expected pilot measurements

  • Order completeness rate
  • Prior authorization rework rate
  • Clarification call volume
  • Initial denial rate
  • Time from order to scheduled scan
  • User confidence and usability feedback

Current status

Active product development. RadOrder Helper already supports structured imaging order workflows and can be expanded with deeper rule sets, specialty-specific ordering scenarios, and pilot data capture.

Future document area

This section can later include downloadable case-study documents, pilot summaries, workflow diagrams, implementation notes, and performance reports.

  • PDF case summary
  • Before / after workflow diagram
  • Pilot metric report
  • Implementation checklist

Next development path

The next step is to connect RadOrder Helper to measurable operational outcomes: how many orders are clarified, how many are ready for authorization, where denials occur, and which rules produce the greatest workflow improvement.

Discuss a Pilot View Related Lab Work