ABSTRACT
Advances in image quality produced by computed tomography (CT) and the growth in the number of image studies currently performed has made the management of incidental pulmonary nodules (IPNs) a challenging task. This research aims to identify IPNs in radiology reports of chest and abdominal CT by Natural Language Processing techiniques to recognize IPN in sentences of radiology reports. Our preliminary analysis indicates vastly different pulmonary incidental findings rates for two different patient groups.
Subject(s)
Decision Support Systems, Clinical/organization & administration , Diagnosis, Computer-Assisted/methods , Machine Learning , Natural Language Processing , Radiography, Abdominal/statistics & numerical data , Radiology Information Systems/supply & distribution , Data Mining/methods , Humans , Illinois/epidemiology , Incidental Findings , Pilot Projects , Radiography, Abdominal/classification , Radiology Information Systems/classification , Reproducibility of Results , Sensitivity and Specificity , Terminology as Topic , Vocabulary, ControlledABSTRACT
The management of follow-up recommendations is fundamental for the appropriate care of patients with incidental pulmonary findings. The lack of communication of these important findings can result in important actionable information being lost in healthcare provider electronic documents. This study aims to analyze follow-up recommendations in radiology reports containing pulmonary incidental findings by using Natural Language Processing and Regular Expressions. Our evaluation highlights the different follow-up recommendation rates for oncology and non-oncology patient cohorts. The results reveal the need for a context-sensitive approach to tracking different patient cohorts in an enterprise-wide assessment.
Subject(s)
Decision Support Systems, Clinical/organization & administration , Diagnosis, Computer-Assisted/methods , Natural Language Processing , Radiography, Abdominal/statistics & numerical data , Radiology Information Systems/supply & distribution , Referral and Consultation/statistics & numerical data , Data Mining/methods , Humans , Illinois/epidemiology , Incidental Findings , Machine Learning , Pilot Projects , Radiography, Abdominal/classification , Radiology Information Systems/classification , Reproducibility of Results , Sensitivity and Specificity , Terminology as Topic , Vocabulary, ControlledSubject(s)
Diagnostic Imaging/instrumentation , Health Care Sector/trends , Radiology Information Systems/supply & distribution , Consumer Behavior , Data Collection , Diagnostic Imaging/trends , Diffusion of Innovation , Health Services Needs and Demand , Humans , Systems Integration , Technology, Radiologic/instrumentation , Technology, Radiologic/trends , United StatesSubject(s)
Diagnostic Imaging/standards , Radiology Department, Hospital/organization & administration , Radiology Information Systems , Hospitals, Teaching , Humans , Minnesota , Radiology Department, Hospital/supply & distribution , Radiology Information Systems/organization & administration , Radiology Information Systems/standards , Radiology Information Systems/supply & distribution , Systems Integration , Teleradiology , User-Computer InterfaceSubject(s)
Radiology Information Systems/economics , Technology Transfer , Commerce , Computer Communication Networks , Contract Services , Diffusion of Innovation , Hospitals, University/economics , Hospitals, University/organization & administration , Managed Care Programs/economics , Managed Care Programs/organization & administration , Purchasing, Hospital , Radiology Information Systems/supply & distribution , Teleradiology , United StatesABSTRACT
For any standardization activities, cooperation with other standardization activities is important. Cooperation may be both within a country and internationally scaled. This paper briefly reviews some examples (good and bad) in other fields, and then illustrates some strategies to promote cooperation.
Subject(s)
Diagnostic Imaging/standards , International Cooperation , Radiology Information Systems/standards , Forecasting , Health Services Needs and Demand/trends , Humans , Marketing of Health Services , Politics , Radiology Information Systems/supply & distributionABSTRACT
This paper describes the file format of the IS & C standard and the results of IS & C scientific exhibition at Yokohama. The English version of IS & C standard Version 1.0 was published in the spring of 1992 by the IS & C committee. On the basis of this document, the IS & C data format was explained. The enhanced description of color images, wave data and the laboratory data is still under discussion. The data format of the color images was established in March 1993. In April 1992, the IS & C scientific exhibition was held at the general assembly of the Japanese Radiological Society. At this exhibition, the demonstration of the compatibility among IS & C workstations was successful. The consensus on IS & C system was investigated. IS & C system was well understood by observers and the interchangeability of radiological images was recognized as very feasible.
