OBJECTIVES: The purpose of this study was to develop clinical decision support systems (CDSS) that are integrated with hospital information systems for the differential diagnosis of idiopathic pulmonary fibrosis (IPF). METHODS: The integrated CDSS were validated and evaluated by physicians. Knowledge modeling for diagnosing IPF was performed by knowledge working groups, composed of radiologists and respiratory specialists. In order to develop the model for CDSS diagnosis, the clinical cases were collected from 290 cases from Seoul National University Hospital and Sevrance Hospital of Yonsei University. For the evaluation of integrated CDSS, interviews were conducted with respiratory specialists and radiologist 2 weeks after applying CDSSs in clinical settings. The CDSS was integrated with the computer vision system (CVS) and diffuse parenchymal lung diseases (DPLD), CDSS developed in our previous project. RESULTS: Eighteen cases diagnosed as IPF were applied to the collection of diagnostic knowledge and the refined knowledge, the former diagnosed 1 case (6%) and the latter diagnosed 14 cases (78%). Therefore, the refined knowledge performed better than collected knowledge. The validation results of integrated CDSSs showed that 81 cases (74.3%) were diagnosed correctly. CONCLUSIONS: There were 109 cases of IPF diagnosed and initiated on treatment. The significance of this study is in developing integrated CDSS with PACS by acquiring and redefining the knowledge needed for IPF diagnosis. In addition, it is significant for the integration of CDSS to verification and clinical evaluation.
OBJECTIVES: The purpose of this study was to develop clinical decision support systems (CDSS) that are integrated with hospital information systems for the differential diagnosis of idiopathic pulmonary fibrosis (IPF). METHODS: The integrated CDSS were validated and evaluated by physicians. Knowledge modeling for diagnosing IPF was performed by knowledge working groups, composed of radiologists and respiratory specialists. In order to develop the model for CDSS diagnosis, the clinical cases were collected from 290 cases from Seoul National University Hospital and Sevrance Hospital of Yonsei University. For the evaluation of integrated CDSS, interviews were conducted with respiratory specialists and radiologist 2 weeks after applying CDSSs in clinical settings. The CDSS was integrated with the computer vision system (CVS) and diffuse parenchymal lung diseases (DPLD), CDSS developed in our previous project. RESULTS: Eighteen cases diagnosed as IPF were applied to the collection of diagnostic knowledge and the refined knowledge, the former diagnosed 1 case (6%) and the latter diagnosed 14 cases (78%). Therefore, the refined knowledge performed better than collected knowledge. The validation results of integrated CDSSs showed that 81 cases (74.3%) were diagnosed correctly. CONCLUSIONS: There were 109 cases of IPF diagnosed and initiated on treatment. The significance of this study is in developing integrated CDSS with PACS by acquiring and redefining the knowledge needed for IPF diagnosis. In addition, it is significant for the integration of CDSS to verification and clinical evaluation.
Entities:
Keywords:
Clinical Decision Support Systems; Computer Vision System; Idiopathic Pulmonary Fibrosis; Radiology Information Systems
Authors: G W Hunninghake; M B Zimmerman; D A Schwartz; T E King; J Lynch; R Hegele; J Waldron; T Colby; N Müller; D Lynch; J Galvin; B Gross; J Hogg; G Toews; R Helmers; J A Cooper; R Baughman; C Strange; M Millard Journal: Am J Respir Crit Care Med Date: 2001-07-15 Impact factor: 21.405
Authors: M Demedts; A U Wells; J M Antó; U Costabel; R Hubbard; P Cullinan; H Slabbynck; G Rizzato; V Poletti; E K Verbeken; M J Thomeer; J Kokkarinen; J C Dalphin; A N Taylor Journal: Eur Respir J Suppl Date: 2001-09
Authors: Shuning Li; Anushri Singh Rajapuri; Grace Gomez Felix Gomez; Titus Schleyer; Eneida A Mendonca; Thankam P Thyvalikakath Journal: Front Digit Health Date: 2022-03-28
Authors: Jin-Hyeok Park; Jeong-Heum Baek; Sun Jin Sym; Kang Yoon Lee; Youngho Lee Journal: BMC Med Inform Decis Mak Date: 2020-09-22 Impact factor: 2.796