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Postsurgical Pain Risk Stratification to Enhance Pain Management Workflow in Adult Patients: Design, Implementation, and Pilot Evaluation

  • 0Department of Anesthesiology, Pharmacology & Therapeutics, The University of British Columbia, Vancouver, BC, Canada.
Jmir Perioperative Medicine +

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July 2, 2024

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July 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

The Perioperative Opioid Quality Improvement (POQI) platform

Area Of Science

  • Digital health solutions in perioperative care.
  • Opioid use disorder prevention strategies.
  • Postoperative pain management innovations.

Background

  • Opioid exposure post-surgery can lead to chronic opioid use disorder.
  • Effective postoperative pain management with reduced opioid reliance is crucial.
  • The POQI program developed a digital platform and risk algorithm for personalized perioperative care.

Purpose Of The Study

  • To evaluate the screening performance of the POQI risk algorithm.
  • To quantify the utilization of the POQI platform.
  • To assess clinician and patient perceptions of the platform's utility and benefits.

Main Methods

  • Pilot implementation of the POQI platform prototype in a Canadian tertiary care center.
  • Application of a risk stratification algorithm to patient health history data.
  • Retrospective extraction of demographic, procedural, and medication data.
  • Assessment of algorithm performance using postoperative opioid use (>90 MME/day).
  • Evaluation of POQI platform use via patient surveys and qualitative interviews.

Main Results

  • The risk algorithm identified 26.4% of patients as high-risk.
  • High-risk patients were younger and more often female.
  • The algorithm demonstrated reasonable specificity (72%) but low sensitivity (32%).
  • Patient and clinician feedback highlighted usability issues and inadequate platform education.
  • Significant loss to follow-up was observed in postdischarge survey completion.

Conclusions

  • The initial POQI platform prototype showed promise but requires iterative redesign.
  • The risk algorithm's poor sensitivity necessitates improvement for broader clinical utility.
  • Addressing platform design and implementation shortcomings is crucial for future success.
  • Further reevaluation and feature enhancement are needed before widespread adoption.

Keywords:

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