Radiologist Use of and Perceived Need for Patient Data Access
Open accessAbstract
Given the increasing volume of radiological exams, the decreasing frequency of direct communication with the referring provider, and the distribution of patient data over many clinical systems, radiologists often do not have adequate clinical information at the time of interpretation. We have performed a survey of radiologists to determine the need and actual utilization of patient data at the time of image interpretation. Our findings demonstrate that most radiologists want more clinical information when interpreting images and that this information would impact their report, but they are discouraged by the time it takes to access this information. In addition, current mechanisms for monitoring necessary patient follow-up are inadequate.
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In a survey of radiologists, 87% stated that clinical information had a significant impact on interpretation.
“In a survey of radiologists, the majority (87%) stated that clinical information had a significant impact on interpretation3.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [3]
The Results report that more than 87% of surveyed radiologists considered additional clinical information important and said it could change or modify their interpretive report. Table 1 records 87.0% as willing to modify or change a diagnosis based on such information. These are judgments about its potential impact, not reports that it had already significantly affected their interpretations. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAI/ML systems aimed to improve clinical efficiency and patient outcomes will require knowledge from multiple data sources and various input modalities.
“Such anticipated utility, particularly in AI/ML systems aimed to improve clinical efficiency and patient outcomes, will require knowledge from multiple data sources and various input modalities2–4.”
From Integrated multimodal artificial intelligence framework for healthcare applications (2022) · cites this paper as [4]
The Conclusion calls for an application that retrieves pertinent patient information from multiple clinical systems and presents it efficiently to radiologists, with expected benefits for interpretation and quality of care. The Results identify several useful information sources. The paper does not establish that AI/ML systems require multiple input modalities; that part extends beyond its discussion of radiologists’ access to clinical data. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPertinent and accurate non-imaging data based on clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes.
“Pertinent and accurate non-imaging data based on the clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [3]
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Experimental — accuracy not yet measuredA majority of radiologists (87%) stated in a survey that clinical information impacts image interpretation significantly.
“A majority of radiologists (87%) stated in a survey that clinical information impacts image interpretation significantly9,10.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [9]
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“Such anticipated utility, particularly in AI/ML systems aimed to improve clinical ef fi ciency and patient outcomes, will require knowledge from multiple data sources and various input modalities 2 – 4 .”
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- Authors
- Boonn, W. W., & Langlotz, C. P.
- Affiliations
- Hospital of the University of Pennsylvania (US)
- Corresponding author
- Boonn, William W.
- ORCID
- Langlotz, Curtis P.
- Venue
- Journal of Digital Imaging
- Published
- 2008-05-06
- Volume
- 22
- Issue
- 4
- Pages
- 357-362
- ISSN
- 0897-1889 (ISSN-L)
- 1618-727X
- PMC
- PMC3043710
- OpenAlex
- W1990078252
- Semantic Scholar
- ac0d01455a65c1fee1dac02e938e95e23eae76e5
- Cited by
- 60
- References
- 2
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 6 · 2023: 2 · 2022: 8 · 2021: 6 · 2020: 2 · 2019: 3 · 2018: 4 · 2016: 4 · 2015: 2 · 2014: 5 · 2012: 2
- FWCI
- 1.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Imaging Informatics in Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- radiology report quality, clinical data integration
- MeSH terms
- Humans, Medical Records, Surveys and Questionnaires, Radiology Information Systems, Interdisciplinary Communication
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