The dynamic development of diagnostic radiology requires updating its approaches to resource management, as well as revising outdated time standards. The purpose of the study is to determine a turnaround time for reporting results of radiological examinations in primary health care for the formation of time standards. Material and Methods. The analytical (elementwise) method of labour rationing was used. The research included image interpretation and writing CT scan reports, image interpretation and writing MRI reports, and the use of telemedicine technologies. Method of obtaining information: Collecting reporting data from the healthcare information system of the Russian Federation. The samples included data on studies conducted in the adult and child populations (studies with contrast enhancement were also included for participants over 18 years of age). Descriptive statistics methods and time series were applied, and calculated values of the 40th and 60th percentiles were used. Results and Discussion. The recommended standards for the turnaround time for reporting CT results (minutes) were established: a study without contrast of patients 18 years or older — 20–30, and patients under 18 years of age — 25–35; a study with contrast of patients 18 years or older — 25–35. It is recommended to use a correction factor of 0.7 for each additional region to calculate standards for the turnaround time for reporting CT results of several anatomical regions. The recommended standards for the turnaround time for reporting MRI results (minutes) were established: a study without contrast of patients 18 years or older — 20–25, and patients under 18 years of age — 0–40; a study with contrast of patients 18 years or older — 30–40. In general, the proposed ranges are consistent with international practice. Conclusions. The recommended standards for the turnaround time for reporting CT and MRI results were substantiated by using the information system. In order to increase efficiency, the need to equip a workplace of radiologists with systems based on artificial intelligence technologies was identified. The obtained results can be used for the development of regulatory documents and government programs guaranteeing free medical care to citizens.
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2. Dora JM, Torres FS, Gerchman M, Fogliatto FS. Development of a local relative value unit to measure radiologists’ computed tomography reporting workload. J Med Imaging Radiat Oncol. 2016; 60(6): 714-719. doi : 10.1111/1754-9485.12492.
3. Nair A, Screaton NJ, Holemans JA, Jones D, Clements L, Barton B, Gartland N, Duffy SW, Baldwin DR, Field JK, Hansell DM, Devaraj A. The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial. Eur Radiol. 2018; 28(1): 226-234. doi : 10.1007/s00330-017-4903-z.
4. Forsberg D, Rosipko B, Sunshine JL. Radiologists’ Variation of Time to Read Across Different Procedure Types. J Digit Imaging. 2017; 30(1): 86-94. doi : 10.1007/s10278-016-9911-z.
5. Zaripov RA. Calculation of the time standards and cost of computed tomography and magnetic resonance imaging for the compulsory health insurance system. Zamestitel ’ glavnogo vracha. 2015; 1(104): 12-23. (In Russ ).
6. Kharbedia ShD , Alkhazishvili AV. Evaluation of the organization of magnetic resonance tomography in children under the conditions of a multidisciplinary stationary. Bulletin of Science and Practice. 2018; 4 (8): 45-52. (In Russ). doi : 10.5281/zenodo.1345133.
7. Basarboliev AV, Cherkasov SN, Kim SYu , Ternavsky AP. Labour operations valuation in assessment of activity planning of radiology department of out-patient establishment. International Research Journal. 20 16; 10-4(52): 64-68. (In Russ). doi : 10.18454/IRJ.2016.52.046.
8. Sveshchinskiy ML, Egorov AS, Basarboliev AV, Polishchuk NS. Operational indicators and characteristics of individual processes of MRI operation in the network of outpatient clinics. The Health Care Mana ger. 2017; 4: 18-29. (In Russ).
9. Ivanova MA, Armashevskaya OV, Lyutsko VV, Sokolovskaya TA. The results of a chronometric study of the time commitment by urologists, oncologists, pulmonologists, orthopedic traumatologists, doctors of functional diagnostics providing medical care to the adult population in outpatient settings. Current problems of health care and medical statistic s. 2019; 2: 197-212. (In Russ).
10. Tolmachev DA, Son IM, Ivanova MA, Reshetnikova OV. Organizational aspects of outpatient functional diagnostics. Cardiovascular Therapy and Prevention. 2019; 18(6): 40-44. (In Russ ). doi : 10.15829/1728-8800-2019-6-40-44.
11. Shipova VM, Yurkin YuYu. Development of time standards for ultrasound examinations: methodology and results. Bulletin of the Semashko National Research Institute of Public Health. 2014: 102-107. (In Russ ).
12. Morozov SP, Vladzimirsky AV, Ledikhova NV. Teleradiology in the Russian Federation: state-of-art. Information Technologies for the Physic ian. 2019; 2: 67-73. (In Russ).
13. Polishchuk NS, Vetsheva NN, Kosarin SP, Morozov SP, Kuz’Mina ES. Unified Radiological Information Service as a Key Element of Organizational and Methodical Work of Research and Practical Center of Medical Radiology. Radiology-Practice. 2018; 1(67): 6-17. (In Russ).
14. Methodology for developing time standards and workload of medical personnel. Federal State Budgetary Central Research Institute of Organization and Informatization of Healthcare, Moscow, 2013. 25 p. (In Russ).
15. Kudryavtsev ND, Sergunova KA, Ivanova GV, Semyonov DS, Horuzhaya AN, Ledikhova NV, et al. Evaluation of the effectiveness of the implementation of speech recognition technology for the preparation of radiological protocols. Physicians and IT. 2020; 1: 58-64. (In Russ).
16. Morozov SP, Vladzimirskiy AV, Gombolevskiy VA, Kuz’mina ES, Ledikhova NV. Artificial intelligence: natural language processing for peer-review in radiology. Journal of radiology and nuclear medicine. 2 018; 99(5): 253-258. (In Russ). doi : 10.20862/0042-4676-2018-99-5-253-258.
17. Donnelly LF, Grzeszczuk R, Guimaraes CV, Zhang W, Bisset Iii GS. Using a Natural Language Processing and Machine Learning Algorithm Program to Analyze Inter-Radiologist Report Style Variation and Compare Variation Between Radiologists When Using Highly Structured Versus More Free Text Reporting. Curr Probl Diagn Radiol. 2019; 48(6): 524-530. doi : 10.1067/j.cpradiol.2018.09.005.
18. Prevedello LM, Ledbetter S, Farkas C, Khorasani R. Implementation of speech recognition in a community-based radiology practice: effect on report turnaround times. J Am Coll Radiol. 2014; 11(4): 402-6. doi : 10.1016/j.jacr.2013.07.008.
19. Segrelles JD, Medina R, Blanquer I, Martí- Bonmatí L. Increasing the Efficiency on Producing Radiology Reports for Breast Cancer Diagnosis by Means of Structured Reports. A Comparative Study. Methods Inf Med. 2017; 56(3): 248-260. doi : 10.3414/ME16-01-0091.
20. Stern C, Boehm T, Seifert B, Kawel -Boehm N. Subspecialized Radiological Reporting Expedites Turnaround Time of Radiology Reports and Increases Productivity. Rofo. 2018; 190(7): 623-629. doi : 10.1055/s-0044-100728.
For citation
Morozov S.P., Vladzymyrskyy A.V., Ledikhova N.V., Trofimenko I.A., Polishchuk N.S., Mukhortova A.N., Shulkin I.M., Klyashtorny V.G. Rationale for the recommended time frames for reporting of ct and mri results. Medical doctor and information technology. 2021; 3: 50-61. (In Russ.). doi : 1025881/18110193_2021_3_50.
Keywords