AI-based assessment of pulmonology inpatient consultation note completeness: predicting documentation gaps and response delays.

Journal: International journal of medical informatics
Published Date: Aug 2, 2025

Abstract

OBJECTIVES: Inpatient consultation notes frequently suffer from incomplete documentation, which may delay clinical decision-making and compromise patient care. Although consultations are central to multidisciplinary coordination, there is still no widely adopted framework to ensure standardized documentation across specialties. This study aims to evaluate the relationship between the completeness of referring physician documentation and consultation response time in a high-volume inpatient setting and to develop an artificial intelligence (AI) tool for detecting missing information in real time.

Authors

  • Damla Azakli Yazici
    Basaksehir Cam and Sakura City Hospital, Department of Pulmonology, Istanbul, Turkey. Electronic address: damla.azakli@saglik.gov.tr.
  • Celal Satici
    University of Health Sciences, Yedikule Chest Diseases and Thoracic Surgery Training and Research Hospital, Department of Pulmonology, Istanbul, Turkey.
  • Ayse Bahadir
    Basaksehir Cam and Sakura City Hospital, Department of Pulmonology, Istanbul, Turkey.
  • Sibel Yurt
    Basaksehir Cam and Sakura City Hospital, Department of Pulmonology, Istanbul, Turkey.
  • Mehmet Akif Özgül
    Basaksehir Cam and Sakura City Hospital, Department of Pulmonology, Istanbul, Turkey.
  • Inanc Yazici
    Basaksehir Cam and Sakura City Hospital, Department of Thoracic Surgery, Istanbul, Turkey.
  • Furkan Atasever
    University of Health Sciences, Yedikule Chest Diseases and Thoracic Surgery Training and Research Hospital, Department of Pulmonology, Istanbul, Turkey.

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