Clinical Coders' Perspectives on Pressure Injury Coding in Acute Care Services in Victoria, Australia

Carolina Dragica Weller1, Louise Turnour1, Elizabeth Connelly2

  • 1Faculty of Medicine, Nursing and Health Sciences, School of Nursing and Midwifery, Monash University, Clayton, VIC, Australia.

Insights

Hospital coders need better clinical documentation to accurately report pressure injuries (PIs). Improving nursing notes and providing ongoing training are key to enhancing PI data quality and patient care.

Area of Science:

  • Healthcare Quality Improvement
  • Clinical Documentation
  • Medical Coding

Background:

  • Pressure injuries (PIs) significantly affect patient care quality in hospitals.
  • Accurate reporting of PIs is crucial for quality indicators, yet challenges persist.
  • Coders' perspectives on PI data quality assurance remain under-explored.

Purpose of the Study:

  • To investigate challenges faced by hospital coders in accurately coding and reporting PI data.
  • To explore reasons for variations in PI data reporting across different sources.
  • To identify quality assurance methods vital for accurate PI reporting.

Main Methods:

  • 16 semi-structured phone interviews were conducted with clinical coders.
  • Participants were recruited from four health services in Melbourne, Australia.
  • Data collection was part of a larger collaborative project on PI prevention.

Main Results:

  • Coders frequently lacked essential information in clinical records for accurate PI coding and quality indicator reporting.
  • Improved nursing documentation is vital for coders.
  • Inter-professional workshops enhanced coders' PI classification knowledge and clinicians' documentation practices.
  • Key quality assurance methods identified: initial training, continued education, and audit/feedback.

Conclusions:

  • Enhancing clinical documentation quality is essential for accurate PI data reporting.
  • Collaborative training between nurses and coders improves understanding and documentation.
  • Robust quality assurance processes, including training and feedback, are critical for reliable PI data.

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