ORIGINAL_ARTICLE
Cost and Delay Time Analytics in OR: A Simulation-based Approach
Background: Operating room (OR) is one of the main hospital parts and management of time and cost are very important in this essential unit. Also, due to the close relationship with other departments, improving its service quality and performance, significantly increases the efficiency of hospital. OR is a complex system in which each lack of coordination effects on all hospital departments. So it is important to identify and categorize the factors that caused loss of OR orchestration and analyze the cost and delay times that imposed by this loss of orchestrations. Method: Computer simulation is a useful technique for modelling system and its behavior. OR is a complex system which has lots of agents interacting with each other, so the agent based simulation method is a suitable technique for modelling OR agents, relationships, defining loss of orchestrations and analyzing the results on the system performance. Results: By identifying OR non-orchestration factors, the most frequencies are related to the lack of recovery beds, emergency surgery, surgeon delay, lack of patient transferor, prolongation of other surgical procedures, anesthesia and pediatric surgery; and the less frequencies are for Clinical changes in the patient status, inadequate testing, and patient's cancellation or lack of readiness. Also, the most delayed and lost time were due to the inadequacy of patient tests, anesthesia and pediatric surgery, prolongation of other surgical procedures, and lack of recovery beds. Conclusion: Surgery procedure is not just a surgical technique, but has many aspects that should be addressed and resolved. The results indicated that the most effective factor in hospital delay and costs is the shortage of resources and lack of planning, which can be improved by interconnecting communication and on-time information sharing.
https://ijhr.iums.ac.ir/article_90647_a5188a51cbce2e52e97aedb2dee45b39.pdf
2018-05-01
1
20
Simulation
Operating room
delay time
Cost management
Masoumeh
Saeedian
saeedian68@gmail.com
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran.
AUTHOR
Mohammad Mehdi
Sepehri
mehdi.sepehri@modares.ac.ir
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran; Hospital Management Research Center (HMRC), Iran University of Medical Sciences, Tehran 1969714713, Iran
LEAD_AUTHOR
Pejman
Shadpour
shadpour.p@iums.ac.ir
3
Hospital Management Research Center (HMRC), Hasheminejad Kidney Center (HKC), Iran University of Medical Sciences, Tehran 1969714713, Iran.
AUTHOR
Ammar
jalalimanesh
jalalimanesh@irandoc.ac.ir
4
Iranian Research Institute for Information Science and Technology (IRANDOC), Tehran 1315773314, Iran
AUTHOR
Sheida
HayatBakhsh
hayat2028@yahoo.com
5
Hasheminejad Kidney Center (HKC), Tehran 1969714713, Iran
AUTHOR
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46
ORIGINAL_ARTICLE
Measuring Performance, Estimating Most Productive Scale Size, and Benchmarking of Hospitals Using DEA Approach: A Case Study in Iran
Background and Objectives: The goal of current study is to evaluate the performance of hospitals and their departments. This manuscript aimed at estimation of the most productive scale size (MPSS), returns to scale (RTS), and benchmarking for inefficient hospitals and their departments.Methods: The radial and non-radial data envelopment analysis (DEA) approaches under variable returns to scale (VRS) assumption are applied for performance assessment of hospitals. Also, the MPSS model in DEA is employed to identify hospital with optimal scale size. Furthermore, the benchmarking for inefficient decision making units (DMUs) is introduced using the slack based measure (SBM) model.Results: In this manuscript, the DEA approaches are implemented at macro and micro levels in health care. At macro level, the performance of 15 Iranian hospitals is assessed and at micro level, the performance of 15 departments of one hospital is evaluated. It should be noted that the number of staff, the number of beds, location & infrastructures, and equipment & facilities were considered as the input variables and number of patients and number of surgeries were selected as output variables. According to the results, six hospitals at macro level and seven hospital departments at micro level were efficient. As a result, these hospitals and departments can be considered as a benchmark for other DMUs. Notably, only four hospitals at macro level and four hospital departments at micro level have the most productive scale size.Conclusions: The current study presents a functional pattern to managers at macro and micro levels in health care systems to better planning for capacity development and resource saving.
https://ijhr.iums.ac.ir/article_92261_b772f76108c92d673cba8c9f3969a10d.pdf
2018-05-01
Hospital Performance Evaluation
Data envelopment analysis (DEA)
Health care
Most productive scale size (MPSS)
Returns to Scale (RTS)
Pejman
Peykani
pejman.peykani@yahoo.com
1
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Emran
Mohammadi
e_mohammadi@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Fatemeh Sadat
Seyed Esmaeili
rozita.esmaeeli@gmail.com
3
Faculty of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran
AUTHOR
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71
Ghatreh Samani M, Hosseini-Motlagh SM. A hybrid algorithm for a two-echelon location-routing problem with simultaneous pickup and delivery under fuzzy demand. International Journal of Transportation Engineering. 2017;5(1):59-85.
72
Hosseini-Motlagh SM, Ghatreh Samani MR, Cheraghi S. Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences. 2019.
73
Hosseini-Motlagh SM, Ghatreh Samani MR, Homaei S. Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing. 2019:1-20.
74
ORIGINAL_ARTICLE
An Assessment of Chemotherapy Drugs with Incomplete Information using the Analytic Hierarchy Process and Choquet Integral
Background and Objectives: Obviously, cancer is one of the most prevalent deadly health problems that have seriously impacted societies. Although experts have been able to treat many patients, choosing the right therapeutic strategy and right medication for patients is still a challenge. Chemotherapy is one of the most common therapeutic strategies for cancer, which could be combined with radiotherapy or surgery. Since various chemotherapy drugs are available, depending on different criteria, oncologists may prescribe one chemotherapy medication or another.Methods: Analytic Hierarchy Process (AHP) as one of the most effective decision-making methods, is applied in this paper. AHP relies on pairwise comparison matrix (PCM) that offers preferential relationships between alternatives. However, due to inaccurate and uncertain information, the revised geometric mean method (RGM) is applied in PCM. Also, considering the importance of interactions between criteria in the investigated issue, Choquet integral was employed for ranking alternatives.Findings: Antimetabolites with weight 0.473868421 is the most preferred alternative. Plant alkaloids with weight 0.232740616, Alkylating agents with weight 0.17723893 and Anti-Tumor Antibiotics with weight 0.11819451, are alternative priorities for a chemotherapy drug, respectively.Conclusion: In this paper, 10 questionnaires have been completed by oncologists in the hospital. According to the received results, Antimetabolites are the most preferred alternative among other chemotherapy drugs.
https://ijhr.iums.ac.ir/article_92699_a31894ff45af0355a6904f9c994be5ce.pdf
2018-05-01
Multi-criteria decision making
Analytic Hierarchy Process
Revised geometric mean method
Choquet fuzzy integral
Chemotherapy
Maryam
Bagherifard
maryambagherifard4@gmail.com
1
School of Industrial Engineering, Iran University of Science and Technology
AUTHOR
Nazanin
Maleki
nazaninmaleki7474@gmail.com
2
School of Industrial Engineering, Iran University of Science and Technology
AUTHOR
Mohammad Reza
Gholamian
gholamian@iust.ac.ir
3
School of Industrial Engineering, Iran University of Science and Technology (IUST)
AUTHOR
Benítez J, Delgado-Galván X, Gutiérrez-Pérez JA, Izquierdo J. Balancing consistency and expert judgment in AHP. Mathematical and Computer Modelling, 2011; 54:1785–1790.
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Ghatreh Samani MR, Hosseini-Motlagh SM, Ghannadpour SF.A multilateral perspective towards blood network design in an uncertain environment: methodology and implementation. Computers & Industrial Engineering. 2018; online.
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Ghatreh Samani MR, Hosseini-Motlagh SM. An enhanced procedure for managing the blood supply chain under disruptions and uncertainties. Annals of Operations Research. 2018; online.
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40
ORIGINAL_ARTICLE
An augmented data envelopment analysis approach for designing a health service network
Background: In the healthcare systems, health centers are taken into consideration as the most important sector due to providing health care services to people. In this respect, the assessing of this center is of great importance. Therefore, there is a need for a performance evaluation system to evaluate both efficiency and effectiveness of human resource, processes, and programs of health centers to improve the competitive power. Methods: To measure the efficiency and productivity of Decision Making Units (DMUs), Data Envelopment Analysis (DEA), which is a nonparametric technique, is considered as the most common tool and can be applied to compare the performance of health centers. However, being DMUs homogenous is one of the underlying assumptions of DEA which prevent us from devising this technique because health centers provide different services, and thus, they are incommensurable. To overcome this barrier, a novel DEA technique is developed to select the best locations for health centers of Iran’s healthcare system. Results: A practical case study, that is designing the health service network for urban residents’ health center (towns) in Fars province, is incorporated into the proposed technique. Finally, the candidate locations for health centers are ranked in terms of efficiency using novel DEA technique, and then, the sensitivity analysis is conducted on final results. Conclusion: The obtained results imply the high performance of the proposed technique in the ranking of efficient health centers in health care systems. Moreover, this technique introduces a comprehensive performance evaluation tool for health centers and also aids managers and decision-makers to more accurately plan for selecting the best candidate location for health centers along with saving the resources.
https://ijhr.iums.ac.ir/article_92735_cd8e5007a844d22d6ebc09f68e976cee.pdf
2018-05-01
62
80
Health service network design
Healthcare Systems
health centers
Data Envelopment Analysis
Mahdyeh
Shiri
m.shiri1991@yahoo.com
1
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Fardin
Ahmadizar
f.ahmadizar@uok.ac.ir
2
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Zahiri B, Mousazadeh M, Bozorgi-Amiri A. A robust stochastic programming approach for blood collection and distribution network design. International Journal of Research in Industrial Engineering. 2014; 3(2):1.
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Ghatreh Samani M, Hosseini-Motlagh SM. A hybrid algorithm for a two-echelon location-routing problem with simultaneous pickup and delivery under fuzzy demand. International Journal of Transportation Engineering. 2017; 5(1):59-85.
26
Samani MR, Hosseini-Motlagh SM. An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research. 2018; 1-50.
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58
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59
ORIGINAL_ARTICLE
Evaluation of the Effect of Cognitive-behavioral Therapy on Adherence to Treatment and General Health in HIV Positive Patients.
Background and Objective: The aim of this study was to evaluate the effect of cognitive-behavioral therapy on adherence to treatment and general health of HIV positive patients.
Method: In a quasi-experimental design, 30 HIV positive patients referred to Imam Khomeini Hospital for treatment were randomly selected, and then, they were randomly assigned into two groups of the experiment (n = 15) and control (n = 15). Pre-test was performed for both groups before intervention. The experimental group received 12 sessions of cognitive-behavioral therapy, 1 session per week, but the control group received no intervention. Then, both groups completed post-test and finally, both groups completed the research questionnaires after 3 months (3 months follow-up). Data were collected using the General Health Questionnaire (GHQ 28) and Modanlou Adherence to Treatment Questionnaire. The collected data were analyzed by covariance analysis.
Results: The results showed a significant difference between experimental and control groups in terms of adherence to treatment and general health in the pre-test, post-test and follow-up stages (P <0.05).
Conclusion: The results of this study suggest that cognitive-behavioral therapy can improve adherence to treatment and general health in HIV positive patients.
https://ijhr.iums.ac.ir/article_94051_f5f85c2228d9b79ddbe0e8c96439a7a0.pdf
2018-05-01
HIV positive
cognitive-behavioral therapy
Adherence to treatment
General health
Bahram
Mirzaeian
alirasouli863@gmail.com
1
Department of Psychology, Islamic Azad University, Sari Branch, Sari, Iran
AUTHOR
Hamid Reza
Talebi
livelybabak@yahoo.com
2
Department of Psychology, Islamic Azad University, Sari Branch, Sari, Iran
AUTHOR
yarali
doosti
doosti.yaraliio92@gmail.com
3
Department of Psychology, Islamic Azad University, Sari Branch, Sari, Iran
AUTHOR
Posht Chaman, Z, Jadid Milani, M, Atashzadeh Shourideh, F, and Akbarzadeh Baghban, A (2014). Evaluation of adherence to treatment of patients after coronary artery bypass graft surgery in Tehran hospitals in 2014. Journal of Sabzevar University of Medical Sciences. Volume 22 / Issue 4 / October & November 2015.
1
Tarkashvand, F. Asadpour, M, Sheikh Fatollahi, M, Sheikhi, E., Salehi, MH. (2015). Frequency of high-risk behaviors in HIV-infected people referring to the Kerman and Rafsanjan Behavioral Therapy Centers in 2012, Third University of Tehran. Volume 14, October 2015, 587-598.
2
Taghavi, SMR (2001), examining the reliability and validity of General Health Questionnaire (GHQ), Journal of Psychology, 20, 98-381.
3
Razaghian, A (2015). The effect of cognitive-behavioral therapy on mental health and social adjustment in addicts. Master thesis, Sari Azad University.
4
Selghi, Z., Hashemi, K, and Saeedipour, B (2007). The effect of group cognitive therapy on reducing depression in HIV-positive male patients. Psychological Studies of Al-Zahra School of Education and Psychology, Volume 3, Issue 4.
5
Shams, M., Karimzadeh Shirazi, K., Fararuei., M, and Shariatinia, S (2016). Developing AIDS Literacy Assessment Tool for Iranian Society. Journal of Ilam University of Medical Science, Volume 24, Issue 5.
6
Shushtari, A., Rezaei, AM. Taheri, E (2016). The Effectiveness of the cognitive-behavioral therapy emotional adjustment, meta-cognitive beliefs, and rumination of divorced women, Journal of Mental Health Principles. 95, 8–321.
7
Shokr Beighi, A, Yasminejad, P (2012). Comparing the parenting styles, self-esteem, and general health of male young offender and normal people in Kermanshah, Journal of Family Counseling and Psychotherapy, Volume, 2, Issue 2
8
Samadzadeh, N, Poursharifi, H, And Babapour, J (2015). The Effectiveness of Cognitive Behavioral Therapy on Self-Care and Symptoms of Depression and Anxiety in Women with Type 2 Diabetes: A Case Study. Feyz Journal of Research, Volume 19, Issue 3, 255-264
9
Gholamali, B, Karimi, A, Roshanaei, Gh., and Rezapour Shahkalaei, F (2015). Adherence to drug therapy and its related factors in type 2 diabetic patients, Volume 4, Winter 2015, 3-12.
10
White, Creek, A. (2010). Cognitive-behavioral therapy for chronic medical patients: A practical guide for evaluation and treatment. (Translators: Moloudi, R., Fatahi, K). Tehran: Arjmand Publications, (Published in Original Language, 2001).
11
Antoni, M. H., Lechner, S., Kazi, A., Wimberly, S., Sifre, T., Urcuyo, K., & Carver, C. S. (2006). How stress management improves quality of life after treatment for breast cancer. Journal of Consulting and Clinical Psychology, 74, 1143–1152.
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Blashill, A. J., Safren, S. A., Wilhelm, S., Jampel, J., Taylor, S. W., O'Cleirigh, C., & Mayer, K. H. (2017). Cognitive behavioral therapy for body image and self-care (CBT-BISC) in sexual minority men living with HIV: A randomized controlled trial. Health Psychology, 36(10), 937.
14
Brandt, C. P., Paulus, D. J., Garza, M., Lemaire, C., Norton, P. J., & Zvolensky, M. J. (2017). A Novel Integrated Cognitive-Behavioral Therapy for Anxiety and Medication Adherence Among Persons Living With HIV/AIDS. Cognitive and Behavioral Practice.
15
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Collins, P. Y., Holman, A. R., Freeman, M. C., & Patel, V. (2006). What is the relevance of mental health to HIV/AIDS care and treatment programs in developing countries? A systematic review. AIDS (London, England), 20(12), 1571.
17
Carrico, A. W., Antoni, M. H., Pereira, D. B., Fletcher, M. A., Klimas, N., Lechner, S. C., & Schneiderman, N. (2005). Cognitive behavioral stress management effects on mood, social support, and a marker of antiviral immunity are maintained up to 1 year in HIV-infected gay men. International Journal of Behavioral Medicine, 12(4), 218-226.
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Dale, S. K., & Safren, S. A. (2017). Striving Towards Empowerment and Medication Adherence (STEP-AD): A Tailored Cognitive Behavioral Treatment Approach for Black Women Living With HIV. Cognitive and Behavioral Practice.
20
Esposito-Smythers, C., Brown, L. K., Wolff, J., Xu, J., Thornton, S., & Tidey, J. (2014). Substance abuse treatment for HIV infected young people: An open pilot trial. Journal of substance abuse treatment, 46(2), 244-250.
21
Fauci, A. S., & Folkers, G. K. (2012). Toward an AIDS-free generation. Jama, 308(4), 343-344.
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Gulliksson,M., Burell,G., Vessby, B., Lundin, L., Toss, H.,& Svärdsudd, K. (2011). Randomized controlled trial of cognitive behavioral therapy vs standard treatment to prevent recurrent cardiovascular eventsin patients with coronary heart disease: Secondary Prevention in Uppsala Primary Health Care project (SUPRIM). Archives of Internal Medicine, 171(2), 134–140.
23
Huggins, J. L., Bonn-Miller, M. O., Oser, M. L., Sorrell, J. T., & Trafton, J. A. (2012). Pain anxiety, acceptance, and outcomes among individuals with HIV and chronic pain: A preliminary investigation. Behaviour Research and Therapy, 50(1), 72–78.
24
HIV/AIDS JUNPo. Gap report 2013: UNAIDS; 2014. Available at:http://www.unaids.org/en/media/unaids/contentassets/documents/unaidspubli cation/2014/UNAIDS_Gap_report_en.pdf
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Hart, T. A., Tulloch, T. G., & O’Cleirigh, C. (2014). Integrated cognitive behavioral therapy for social anxiety and HIV prevention for gay and bisexual men. Cognitive and Behavioral Practice, 21(2), 149-160.
26
Jayasvasti, I., Hiransuthikul, N., Pityaratstian, N., Lohsoonthorn, V., Kanchanatawan, B., & Triruangworawat, B. (2011). The effect of cognitive behavioral therapy and changes of depressive symptoms among Thai adult HIV-infected patients. World Journal of AIDS, 1(02), 15.
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Jin J, sklar GE, Min Sen Oh V, Li SC.Factors affecting therapeutic compliance: A review from the patients perspective. Therapeutics perspective. Ther Clin Risk Manag. 2008; 4(1): 269-86.
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45
ORIGINAL_ARTICLE
Echocardiography appointment scheduling through better utilization of resources
Background and Objectives: Appointment scheduling systems are applied in a broad variety of healthcare environments to reduce costs, increase resource utilization, and facilitate patients’ access to care. This study strives to present efficient scheduling models for the Echocardiography Department of Tehran Heart Center (THC). These models seek to optimize both patient and hospital utility by maximizing the weighted number of performed echos and minimizing overtime. Methods: There are two major problems in developing such models: shift scheduling problem and capacity allocation problem.In this paper, two mixed integer linear programming models are presented based on two different sets of assumptions. The first model is developed according to the current routines of the hospital.In this model, it is assumed that the assignment of specialists to echocardiography laboratories in different shifts is predetermined. Thus this model merely allocates the available capacity of specialists and labs to different types of patients. However, the second model is more comprehensive, as it schedules the shifts of the specialists and allocates the capacity to the patients simultaneously. Results: The efficiency of the proposed models is evaluated using the real data of the Echocardiography Department of THC. The results showed that both models increased the utility (12.35% and 19.14%, respectively) in comparison with the current status of the department. The first model improved the performance of the department significantly through better utilization of resources; however, the second model improved the performance much more than the first one through creating more capacity and utilizing the capacity efficiently. Conclusion: Although both models showed significant improvements, the second model was found to be more efficient. The reason is that the first model assumes the specialists' shift assignment to be predetermined, while the second model finds the best shift assignment itself.
https://ijhr.iums.ac.ir/article_94062_b563816c69dd5f8b956709d55e11524b.pdf
2018-05-01
94
111
echocardiography
Appointment scheduling
Resource Utilization
Shift scheduling
Capacity allocation
optimization
mathematical model
delaram
chaghazardy
d.chaghazardy@modares.ac.ir
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran.
AUTHOR
Seyed Hessameddin
Zegordi
zegordi@modares.ac.ir
2
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran.
AUTHOR
Hassan
Aghajani
aghajanih@tums.ac.ir
3
Department of Cardiology Tehran Heart Centre Tehran University of medical sciences Tehran Iran
AUTHOR
1. Trang A, Kampangkaew J, Fernandes R, Tiwana J, Misra A, Hamzeh I, et al. Understanding by General Providers of the Echocardiogram Report. The American Journal of Cardiology. 2019;124(2):296-302.
1
2. Lancellotti P, Price S, Edvardsen T, Cosyns B, Neskovic AN, Dulgheru R, et al. The use of echocardiography in acute cardiovascular care: recommendations of the European Association of Cardiovascular Imaging and the Acute Cardiovascular Care Association. European Heart Journal-Cardiovascular Imaging. 2014;16(2):119-46.
2
3. Gandhi R. Increasing the Daily Throughput of Echocardiogram Patients using Discrete Event Simulation [Master's thesis]: University of Toronto; 2013.
3
4. Munt B, O’Neill B, Koilpillai C, Gin K, Jue J, Honos G, et al. Treating the right patient at the right time: Access to echocardiography in Canada. Canadian Journal of Cardiology. 2006;22(12):1029-33.
4
5. Castro E, Petrovic S. Combined mathematical programming and heuristics for a radiotherapy pre-treatment scheduling problem. Journal of Scheduling. 2012;15(3):333-46.
5
6. Saure A, Patrick J, Tyldesley S, Puterman ML. Dynamic multi-appointment patient scheduling for radiation therapy. European Journal of Operational Research. 2012;223(2):573-84.
6
7. Pena SM, Lawrence N. Analysis of wait times and impact of real-time surveys on patient satisfaction. Dermatologic Surgery. 2017;43(10):1288-91.
7
8. Murray M, Berwick DM. Advanced access: reducing waiting and delays in primary care. JAMA. 2003;289(8):1035-40.
8
9. Gupta D, Denton B. Appointment scheduling in health care: Challenges and opportunities. IIE Transactions. 2008;40(9):800-19.
9
10. Cayirli T, Veral E. Outpatient scheduling in health care: a review of literature. Production and Operations Management. 2003;12(4):519-49.
10
11. about Tehran Heart Center. Available at: http://thc.tums.ac.ir/en-1118/About-tehran-Heart-center Accessed July 17, 2019.
11
12. Batun S, Begen MA. Optimization in healthcare delivery modeling: Methods and applications. Handbook of Healthcare Operations Management: Springer; 2013: 75-119.
12
13. Ahmadi-Javid A, Jalali Z, Klassen KJ. Outpatient appointment systems in healthcare: A review of optimization studies. European Journal of Operational Research. 2017;258(1):3-34.
13
14. Bailey NT. A study of queues and appointment systems in hospital out‐patient departments, with special reference to waiting‐times. Journal of the Royal Statistical Society: Series B (Methodological). 1952;14(2):185-99.
14
15. Hong Y-C, Cohn A, Epelman MA, Alpert A. Creating resident shift schedules under multiple objectives by generating and evaluating the Pareto frontier. Operations Research for Health Care,. 2018, https://doi.org/10.1016/j.orhc.2018.08.001.
15
16. Volland J, Fügener A, Brunner JO. A column generation approach for the integrated shift and task scheduling problem of logistics assistants in hospitals. European Journal of Operational Research. 2017;260(1):316-34.
16
17. Brunner JO, Bard JF, Kolisch R. Flexible shift scheduling of physicians. Health Care Management Science. 2009;12(3):285-305.
17
18. Nguyen TBT, Sivakumar AI, Graves SC. A network flow approach for tactical resource planning in outpatient clinics. Health care management science. 2015;18(2):124-36.
18
19. Choi S, Wilhelm WE. On capacity allocation for operating rooms. Computers & Operations Research. 2014;44:174-84.
19
20. LaGanga LR, Lawrence SR. Appointment overbooking in health care clinics to improve patient service and clinic performance. Production and Operations Management. 2012;21(5):874-88.
20
21. Aringhieri R, Landa P, Soriano P, Tànfani E, Testi A. A two-level metaheuristic for the operating room scheduling and assignment problem. Computers & Operations Research. 2015;54:21-34.
21
22. Marchesi JF, Pacheco MAC, editors. A genetic algorithm approach for the master surgical schedule problem. Evolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on; 2016: IEEE.
22
23. Fairley M, Scheinker D, Brandeau ML. Improving the efficiency of the operating room environment with an optimization and machine learning model. Health care management science. 2018, https://doi.org/10.1007/s10729-018-9457-3.
23
24. Guido R, Ielpa G, Conforti D. Scheduling outpatient day service operations for rheumatology diseases. Flexible Services and Manufacturing Journal. 2019, https://doi.org/10.1007/s10696-019-09354-7.
24
25. M'Hallah R, Visintin F. A stochastic model for scheduling elective surgeries in a cyclic Master Surgical Schedule. Computers & Industrial Engineering. 2019;129:156-68.
25
26. Hamid M, Nasiri MM, Werner F, Sheikhahmadi F, Zhalechian M. Operating room scheduling by considering the decision-making styles of surgical team members: a comprehensive approach. Computers & Operations Research. 2019;108:166-81.
26
27. Atighehchian A, Sepehri MM, Shadpour P. Operating room scheduling in teaching hospitals: a novel stochastic optimization model. International Journal of Hospital Research. 2015;4(4):171-6.
27
28. Sadeghzadeh H, Sadat S. Increasing Operating Room Profits and Decreasing Wait Lists by Use of a Data-Driven Overbooking Model. International Journal of Hospital Research. 2018;7(2):1-20.
28
29. Holm LB, Bjornenak T, Kjaeserud GG, Noddeland H, editors. Using discrete event simulation and soft systems methodology for optimizing patient flow and resource utilization at the surgical unit of radiumhospitalet in Oslo, NORWAY. 2017 Winter Simulation Conference (WSC); 2017: IEEE.
29
30. Durán G, Rey PA, Wolff P. Solving the operating room scheduling problem with prioritized lists of patients. Annals of Operations Research. 2017;258(2):395-414.
30
31. Najjarbashi A, Lim GJ. A variability reduction method for the operating room scheduling problem under uncertainty using CVaR. Operations Research for Health Care. 2019;20:25-32.
31
32. Katsi VK, Vrachatis DA, Politi A, Papageorgiou M, Koumoulidis A, Vlasseros I, et al. Cardiac echo-lab productivity in times of economic austerity. SpringerPlus. 2014;3(1):703.
32
33. Bakshi S. Business process re-engineering a cardiology department. World Hospitals and Health Services: The Official Journal of the International Hospital Federation. 2013;50(2):40-5.
33
34. Geronimo R. Improving Stress Echocardiogram Access for Patients with Low-Risk Chest Pain in the Emergency Department Clinical Decision Unit [Master's thesis]: University of San Francisco; 2017.
34
ORIGINAL_ARTICLE
Employment of multi criteria decision making techniques and mathematical formulation for Construction of the sustainable hospital
Background and objective: One of the most prominent factors in the success of medical systems is finding a proper location to build hospitals and other medical care centers. On the other hand, sustainable development is illustrated an important concept for both private and public sectors which focuses on three aspects of development: social, environmental and economical. Hence in order to find the best location, taking in to account sustainable criteria, can pave the path of meeting triple bottom line requirements in the field of hospitals construction. Methods: Focus in this paper is on identifying the best location for the hospital construction with the help of best-worst method to find weights of each criteria and then The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the possible locations. After applying TOPSIS method, with the use of additive utility function we analyze the initial ranking. In the next step Mathematical formulation has been applied in order to find the proper locations to open hospital. The objective functions consist of two equations; the first one is minimizing the opening cost and penalty cost (because of the fact that the proximity of hospitals to patients are of high importance); the second one is related to maximizing the utilities obtained from las step. Results and conclusion: according to the case study which was implemented in Tehran, the best locations for hospital construction considering penalty cost, construction cost and utilities of each hospital to offer better service, have been identified.
https://ijhr.iums.ac.ir/article_95265_93f23c2048569b97aab06bcddefe434b.pdf
2018-05-01
112
127
best location
healthcare management
sustainable development
best-worst method
TOPSIS
Multi-objective Programming
Zahra
Mohammadnazari
z_mohammad@ind.iust.ac.ir
1
Department of industrial engineering, Iran University of science and technology, 16846-13114.
AUTHOR
Seyed Farid
Ghannadpour
ghannadpour@iust.ac.ir
2
Department of industrial engineering, Iran University of science and technology, 16846-13114
AUTHOR
ORIGINAL_ARTICLE
A study of the Relationship Between Job Satisfaction, Job Motivation and Organizational Commitment Among Employees of Ministry of Health, Treatment and Medical Education (MHTME)
Bacground and Objective: Today, it is crucial that organizations pay special attention to their human resources in order to achieve maximum effectiveness, performance and efficiency. Employees’ attitude regarding their jobs, is what affects their performance and effectiveness at work more than any other factor. Due to the importance of employees’ attitude and perception in improving efficiency and achieving organizational goals, the aim of the current study is to investigate the correlation between job satisfaction, job motivation and organizational commitment among employees of Ministry of Health, Treatment and Medical Education (MHTME).
Study method: This is a descriptive – analytical study which was carried out using cross-sectional approach in 2017. The statistical sample included 327 employees of MHTME which were selected using Stratified sampling and suitable sample size. Data gathering tools include Job Descriptive Index (JDI), Lodahl – Kushner Job Motivation Inventory and Organizational Commitment Inventory of Allen and Mayer. Data were then entered into SPSS-20 software and analyzed using independent T-Test, ANOVA, Person Correlation and Regression Tests.
Findings: Job satisfaction and motivation had a direct significant correlation with organizational commitment (P < 0.05). The results of regression analysis also indicated that organizational commitment can be proper predicted based on job satisfaction and motivation.
Conclusion: According to the results of this study, it is suggested that human resource managers use proper employee selection, timely incentives based on real performance evaluations, promote employees based on their abilities. Holding motivational seminars and creating appropriate job advancement opportunities increase satisfaction and motivation and therefore organizational commitment among their employees.
https://ijhr.iums.ac.ir/article_96865_376cdb92fc9d021d2f47e435966354d7.pdf
2018-05-01
128
139
Job Satisfaction
job motivation
organizational commitment
Efficiency
Employees
Ministry of health
Treatment and Medical Education (MHTME)
Ali
Ebraze
ebraze1880@yahoo.com
1
Ministry of Health and Medical Education, Tehran, Iran .
AUTHOR
Fahimeh
Rabbanikhah
f_rbbani@yahoo.com
2
Ministry of Health and Medical Education, Tehran, Iran .
AUTHOR
Amir
Kazemi
amirkazemi439@yahoo.com
3
Ministry of Health and Medical Education, Tehran, Iran .
AUTHOR
Maryam
Safarnavadeh
dr.safarnavadeh@gmail.com
4
Ministry of Health and Medical Education, Tehran, Iran .
AUTHOR
Amir Hossein
Eskandari
dr_a_eskandari@yahoo.com
5
Ministry of Health and Medical Education, Tehran, Iran .
AUTHOR
Reza
Moradi
r.moradi@behdasht.gov.ir
6
Department of Health Economics and Management, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR