Open AccessFirst study of the RuPatient health information system with optical character recognition of medical records based on machine learning
Author(s) -
А.А. Комков,
В.П. Мазаев,
S.V. Ryazanova,
D.N. Samochatov,
E. V. Koshkina,
Е. V. Bushueva,
О. М. Drapkina
Publication year - 2022
Publication title -
kardiovaskulârnaâ terapiâ i profilaktika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.158
H-Index - 16
eISSN - 2619-0125
pISSN - 1728-8800
DOI - 10.15829/1728-8800-2021-3080
Subject(s) - anamnesis , medicine , documentation , correctness , medical record , artificial intelligence , medical emergency , family medicine , computer science , algorithm , programming language
RuPatient health information system (HIS) is a computer program consisting of a doctor-patient web user interface, which includes algorithms for recognizing medical record text and entering it into the corresponding fields of the system. Aim. To evaluate the effectiveness of RuPatient HIS in actual clinical practice. Material and methods . The study involved 10 cardiologists and intensivists of the department of cardiology and сardiovascular intensive care unit of the L. A. Vorokhobov City Clinical Hospital 67 We analyzed images (scanned copies, photos) of discharge reports from patients admitted to the relevant departments in 2021. The following fields of medical documentation was recognized: Name, Complaints, Anamnesis of life and illness, Examination, Recommendations. The correctness and accuracy of recognition of entered information were analyzed. We compared the recognition quality of RuPatient HIS and a popular optical character recognition application (FineReader for Mac). Results. The study included 77 pages of discharge reports of patients from various hospitals in Russia from 50 patients (men, 52%). The mean age of patients was 57,7±7,9 years. The number of reports with correctly recognized fields in various categories using the program algorithms was distributed as follows: Name — 14 (28%), Diagnosis — 13 (26%), Complaints — 40 (80%), Anamnesis — 14 (28%), Examination — 24 (48%), Recommendations — 46 (92%). Data that was not included in the category was also recognized and entered in the comments field. The number of recognized words was 549±174,9 vs 522,4±215,6 (p=0,5), critical errors in words — 2,1±1,6 vs 4,4±2,8 (p<0,001), non-critical errors — 10,3±4,3 vs 5,6±3,3 (p<0,001) for RuPatient HIS and optical character recognition application for a personal computer, respectively. Conclusion. The developed RuPatient HIS, which includes a module for recognizing medical records and entering data into the corresponding fields, significantly increases the document management efficiency with high quality of optical character recognition based on neural network technologies and the automation of filling process.