Literature DB >> 30860895

Automated Tracking of Follow-Up Imaging Recommendations.

Thusitha Mabotuwana1,2, Christopher S Hall1,2, Vadiraj Hombal3, Prashanth Pai1, Usha Nandini Raghavan1,4, Shawn Regis4, Brady McKee4, Sandeep Dalal3, Christoph Wald4, Martin L Gunn2.   

Abstract

OBJECTIVE: Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to poor patient outcomes, complications, and legal liability. As such, the primary objective of this research was to determine adherence rates to follow-up recommendations.
MATERIALS AND METHODS: Radiology-related examination data, including report text, for examinations performed between June 1, 2015, and July 31, 2017, were extracted from the radiology departments at the University of Washington (UW) and Lahey Hospital and Medical Center (LHMC). The UW dataset contained 923,885 examinations, and the LHMC dataset contained 763,059 examinations. A 1-year period was used for detection of imaging recommendations and up to 14-months for the follow-up examination to be performed.
RESULTS: On the basis of an algorithm with 97.9% detection accuracy, the follow-up imaging recommendation rate was 11.4% at UW and 20.9% at LHMC. Excluding mammography examinations, the overall follow-up imaging adherence rate was 51.9% at UW (range, 44.4% for nuclear medicine to 63.0% for MRI) and 52.0% at LHMC (range, 30.1% for fluoroscopy to 63.2% for ultrasound) using a matcher algorithm with 76.5% accuracy.
CONCLUSION: This study suggests that follow-up imaging adherence rates vary by modality and between sites. Adherence rates can be influenced by various legitimate factors. Having the capability to identify patients who can benefit from patient engagement initiatives is important to improve overall adherence rates. Monitoring of follow-up adherence rates over time and critical evaluation of variation in recommendation patterns across the practice can inform measures to standardize and help mitigate risk.

Entities:  

Keywords:  follow-up imaging; follow-up imaging adherence; medical informatics applications; radiology reports

Year:  2019        PMID: 30860895     DOI: 10.2214/AJR.18.20586

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  4 in total

1.  Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model.

Authors:  Kevin Lybarger; Aashka Damani; Martin Gunn; O Zlem Uzuner; Meliha Yetisgen
Journal:  AMIA Annu Symp Proc       Date:  2022-05-23

2.  Analysis of Radiology Report Recommendation Characteristics and Rate of Recommended Action Performance.

Authors:  Tiantian White; Mark D Aronson; Scot B Sternberg; Umber Shafiq; Seth J Berkowitz; James Benneyan; Russell S Phillips; Gordon D Schiff
Journal:  JAMA Netw Open       Date:  2022-07-01

3.  Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.

Authors:  Wilson Lau; Kevin Lybarger; Martin L Gunn; Meliha Yetisgen
Journal:  J Digit Imaging       Date:  2022-10-17       Impact factor: 4.903

4.  Automatic Fully-Contextualized Recommendation Extraction from Radiology Reports.

Authors:  Jackson Steinkamp; Charles Chambers; Darco Lalevic; Tessa Cook
Journal:  J Digit Imaging       Date:  2021-02-10       Impact factor: 4.056

  4 in total

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