Volume 6, Issue 2 (5-2022)
EBHPME 2022, 6(2): 100-109 |
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Mokhtari A, Nabilou B, Aghlmand S, Yusefzadeh H. Productivity Analysis of Medical Record Department in Hospitals Affiliated with Urmia University of Medical Sciences. EBHPME 2022; 6 (2) :100-109
URL: http://jebhpme.ssu.ac.ir/article-1-386-en.html
URL: http://jebhpme.ssu.ac.ir/article-1-386-en.html
Department of Health Economics and Management, School of Public Health, Urmia University of Medical Sciences, Urmia, Iran , yusefzadeh.h@umsu.ac.ir
Abstract: (1409 Views)
Background: The importance of paying attention to improving productivity in different health system departments has become increasingly evident. This is due to increased costs of health care goods and services, limited resources and production facilities. This study aims to measure the productivity of hospitals' Medical Records Departments (MRD) affiliated with Urmia University of Medical Sciences to provide a comprehensive analysis of their performance.
Methods: In this study, total productivity and total factor productivity changes of medical record departments in hospitals affiliated with Urmia University of Medical Sciences were calculated. This was conducted through Kendrick-Creamer and Malmquist indexes in 2016-2020, and has examined the trend of each one of them. Input variables included the number of personnel and equipment. Output variables encompassed the number of outpatient and inpatient medical records prepared for patients, responses to letters, and patients referred to the studied MRD. After estimating the production function, the elasticity of production factors and the return to the scale of medical record departments were calculated.Total productivity changes and production function were estimated via Deap2,1 and Frontier4,1 softwares by applying input minimization and variable returns to scale assumptions.
Results: The results of the Malmquist index indicated that the average total productivity changes of medical records departments of hospitals during the study period were equal to 1,096. This is greater than one; therefore, it can be concluded that productivity has decreased by about 9.600 % during the study period. Changes in technological efficiency have had the greatest impact on reducing productivity, to other factors. In addition, the results of Kendrick-Creamer index during the years under review demonstrated that the average total productivity of production factors obtained, was equal to 18725.530 services.
Conclusion: There is decreased productivity due to technological changes in the studied departments. This is because of the lack of sufficient knowledge of the medical records department staff in the use of technology and equipment for providing efficient services . Holding training courses for the appropriate use of technology by personnel can be useful in this regard.
Methods: In this study, total productivity and total factor productivity changes of medical record departments in hospitals affiliated with Urmia University of Medical Sciences were calculated. This was conducted through Kendrick-Creamer and Malmquist indexes in 2016-2020, and has examined the trend of each one of them. Input variables included the number of personnel and equipment. Output variables encompassed the number of outpatient and inpatient medical records prepared for patients, responses to letters, and patients referred to the studied MRD. After estimating the production function, the elasticity of production factors and the return to the scale of medical record departments were calculated.Total productivity changes and production function were estimated via Deap2,1 and Frontier4,1 softwares by applying input minimization and variable returns to scale assumptions.
Results: The results of the Malmquist index indicated that the average total productivity changes of medical records departments of hospitals during the study period were equal to 1,096. This is greater than one; therefore, it can be concluded that productivity has decreased by about 9.600 % during the study period. Changes in technological efficiency have had the greatest impact on reducing productivity, to other factors. In addition, the results of Kendrick-Creamer index during the years under review demonstrated that the average total productivity of production factors obtained, was equal to 18725.530 services.
Conclusion: There is decreased productivity due to technological changes in the studied departments. This is because of the lack of sufficient knowledge of the medical records department staff in the use of technology and equipment for providing efficient services . Holding training courses for the appropriate use of technology by personnel can be useful in this regard.
Type of Study: Original article |
Subject:
Health Economics
Received: 2022/02/26 | Accepted: 2022/05/20 | Published: 2022/07/3
Received: 2022/02/26 | Accepted: 2022/05/20 | Published: 2022/07/3
References
1. Khayeri F, Goudarzi L, Meshkini A, Khaki A. Implementation of "reducing the outpatient payments" program in hospitals. National Conference of the eleventh governments performance review in the field of health. Ministry of Health and Medical Education, Tehran, Iran. Tehran; May 5-6 2015; p. 12-3. [In Persian]
2. Ghaderi H, Goudarzi Gh, Gohari MR. Determination technical efficiency of hospitals affiliated with Iran University of Medical Science by data envelopment analysis (2000-2004). Journal of Health Administration. 2007; 9(26): 39-44.
3. Rezapour A, Ebadifard Azar F, Yousefzadeh N, Roumiani Y, Bagheri Faradonbeh S. Technical efficiency and resources allocation in university hospitals in Tehran, 2009-2012. Medical Journal of the Islamic Republic of Iran. 2015; 29(1): 839-50.
4. Hadian M, Rahimi B, Valinejadi A, Yusefzadeh H, Faghisolouk F. An analysis of productivity in hospitals affiliated to Iran and Tehran Universities Of Medical Sciences: 2006-2011. Journal of Health Administration. 2015; 17(58): 72-84. [In Persian]
5. Rahimi H, Khammar-nia M, Kavosi Z, Eslahi M. Indicators of hospital performance evaluation: A systematic review. International Journal of Hospital Research. 2014; 3(4): 199-208.
6. Nghiem S, Coelli T, Barber S. Sources of productivity growth in health services: A case study of Queensland public hospitals. Economic Analysis and Policy. 2011; 41(1): 37-48. [DOI:10.1016/S0313-5926(11)50003-7]
7. Raeisi AR, Yarmohammadian M, Mohammadi Bakhsh R, Gangi H. Performance evaluation of Al-Zahra academic medical center based on Iran balanced scorecard model. Journal of Education and Health Promotion. 2012; 1(1): 1. [DOI:10.4103/2277-9531.94408]
8. Yusefzadeh H, Ghaderi H, Bagherzade R, Barouni M. The efficiency and budgeting of public hospitals: Case study of Iran. Iranian Red Crescent Medical Journal. 2013; 15(5): 393-9. [DOI:10.5812/ircmj.4742]
9. Ajami S, Ketabi S, Torabiyan F. Performance improvement indicators of the medical records department and iInformation technology (IT) in hospitals. Pakistan Journal of Medical Sciences. 2015; 31(3): 717-20. [DOI:10.12669/pjms.313.8005]
10. Dargahi H, Masoori N, Safdari R, Fazaeli S, Yousefi M. Investigating productivity changes in medical records departments of Tehran University of Medical Sciences hospitals using malmquist index. Health Information Management. 2011; 8(3): 395-404. [In Persian]
11. Kuntz L, Scholtes S, Vera A. Incorporating efficiency in hospital-capacity planning in Germany. The European Journal of Health Economics. 2007; 8(3): 213-23. [DOI:10.1007/s10198-006-0021-6]
12. Emami Meybodi A. Measurement principles of efficiency and productivity. Second Edition. Tehran: Institute for Trade Studies and Research; 2005. [In Persian]
13. Li N-N, Wang C-H, Ni H, Wang H. Efficiency and productivity of county-level public hospitals based on the data envelopment analysis model and Malmquist index in Anhui, China. Chinese Medical Journal. 2017; 130(23): 2836-43. [DOI:10.4103/0366-6999.219148]
14. Mehregan MR. Data envelopment analysis: quantitative models in organizational performance evaluation. Tehran, Iran: Ketabedaneshgahi Publications; 2014. [In Persian]
15. Mujasi PN, Asbu EZ, Puig-Junoy J. How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach. BMC Health Services Research. 2016; 16: 230. [DOI:10.1186/s12913-016-1472-9]
16. Nabilou B, Yusefzadeh H, Rezapour A, Ebadi Fard Azar F, Salem Safi P, Sarabi Asiabar A, et al. The productivity and its barriers in public hospitals: Case study of Iran. Medical Journal of the Islamic Republic of Iran. 2016; 30 (1): 36-43.
17. Silwal PR, Ashton T. Productivity of public hospitals in Nepal: A data envelopment analysis. BMJ Open. 2017; 7(7): e015327. [DOI:10.1136/bmjopen-2016-015327]
18. Valadkhani A, Dudu Moffat B, Harvie Ch. Malmquist indices of productivity change in Botswana's financial institutions. Global Business and Economics Review. 2009; 11(1): 28-43. [DOI:10.1504/GBER.2009.025380]
19. Nouraei Motlagh S, Ghasempour S, Yusefzadeh H, Lotfi F, Astaraki P, Saki K. Evaluation of the productivity of hospitals affiliated to Lorestan University of Medical Sciences using the malmquist and the kendrick-creamer indices. Shiraz E Medical Journal. 2019; 20(7): e85222. [In Persian] [DOI:10.5812/semj.85222]
20. Dargahi H, Masuri N, Safdari R, Fazaeli S, Yousefi M. The efficiency analysis of medical records departments of Tehran University of Medical Sciences hospitals with data envelopment analysis (2007-2009). Journal of Payavard Salamat. 2010; 4 (2 and 1) :9-17. [In Persian]
21. Sahin I, Ozcan YA, Ozgen Narci H. Assessment of hospital efficiency under health transformation program in Turkey. Central European Journal of Operations Research. 2009; 19(1): 19-37. [DOI:10.1007/s10100-009-0121-3]
22. Torabipour A, Bahmani T. Efficiency and productivity analysis of schools in Ahvaz University of Medical Sciences during 2011-2015. Journal of Medical Education Development. 2018; 11(31): 30-42. [In Persian] [DOI:10.29252/edcj.11.31.30]
23. Kirigia JM, Emrouznejad A, Cassoma B, Zere E, Barry S. A performance assessment method for hospitals: The case of municipal hospitals in Angola. Journal of Medical Systems. 2009; 32(6): 509-19. [DOI:10.1007/s10916-008-9157-5]
24. Yawe BL. Total factor productivity growth in Uganda's district referral hospitals. Tanzanian Economic Trends, TET: A Biannual Review of the Economy; 2006; 19(2): 26-42.
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