Journal of Modern Medical

Introduction
The hemodialysis department has a special position in the health care system due to the provision of vital services for patients with end stage renal disease, where the provided services must be in accordance with clinical guidelines. Therefore, the performance of this department should be evaluated based on clinical outcomes, optimal management of resources, and identification of risk factors to obtain sufficient information for clinical and managerial decisions. Today, management dashboards are one of the best tools for measuring the performance of organizations. A management dashboard is a visualization tool that is used to display up-to-date, accurate, and understandable information about the organization’s performance. The correct determination of key performance indicators (KPIs) is one of the main requirements for designing management dashboards.
Considering the effect of service quality in the hemodialysis departments on the survival rate and quality of life of patients, it is vital to monitor the performance of this department. Identification of KPIs can be useful in monitoring the quality of services in this department. To categorize KPIs in the healthcare system, the Donabedian model is mostly applied, in which the indicators are classified into three main parts: structure, process and outcome. The purpose of this study is to determine the KPIs in the dialysis management dashboard.

Methods
This is a descriptive cross-sectional study that was conducted in 2022. The study population consists of all nephrology specialists with at least 3 years of work experience in the hemodialysis department in Iran (n=346). These experts were identified with the cooperation of Iranian Society of Nephrology. The data collection tool was a researcher-made questionnaire which was created using Google Forms. A total of 40 KPIs were extracted by reviewing available papers and sources. They were categorized into three groups of structure, process and outcome according to the Donabedian model. A 5-point Likert scale (From 1=not important to 5=very important) was used to determine the degree of importance for KPIs. Those with a total average score of >3.75 were added to the final list. The content validity of the questionnaires was determined based on the opinions of a panel of expert including nephrologists, dialysis department nurses, and health information management specialists). Its reliability was determined by calculating Cronbach’s alpha which was obtained 0.89. The electronic questionnaire was sent to the experts via Email. Data were analyzed in SPSS software, version 16.

Results 
Out of 346 questionnaires, 183 were completed (Response rate=52.89%). Participants were 98(53.55%) males and 85(46.45%) females. Their mean age and work experience were 41.53±8.78 and 9.40±4.53 years, respectively. Among the structure-related KPIs related to the time before admission to the dialysis department, underlying cause of kidney failure (5), comorbidity diseases (5), blood type (5), history of viral diseases (5), dry weight (4.65), number of nurses for each patient (4.41), average age (4.32), and number of doctors for each patient (3.86) had the highest scores. Among the process-related KPIs which show how to provide services to patients, the most important KPIs were: Type of filtration (5), type of vascular access (5), duration of dialysis (5), blood pressure before and after dialysis (4.44), heparin rate (4.24), amount of patient education (4.07), and filtration rate (4.06). Among the outcome-related KPIs that are related to the effect of care on the patient, Kt/V (5), urea reduction ratio (5), fistula infection (4.81), mortality rate (4.73), normalized protein catabolic rate (4.66), hemoglobin level (4.65), patient satisfaction (4.26), hospitalization length (4.14), patient survival rate (4.15), and nosocomial infection rate (4.07) were the most important KPIs in the dialysis department.

Discussion 
Accurate identification of KPIs and their use in continuous monitoring of dialysis department performance can play an important role in improving patient safety, promoting the quality of services, optimal management of resources, and increasing patient satisfaction. In the present study, which was conducted to determine KPIs in dialysis management dashboard, out of 40 KPIs, 31 were identified in three main categories (12 structure-related, 8 process-related and 11 outcome-related KPIs). Displaying these indicators in the management dashboard and analyzing the relationship between different KPIs (e.g. between the type of vascular access and infection, between the type of filtration and adequacy of hemodialysis, between comorbidity diseases and mortality rate) can help hospital managers and doctors to make correct decisions and implement timely interventions.

Ethical Considerations
Compliance with ethical guidelines
This study was approved by the Research Ethics Committee of Urmia University of Medical Sciences (Code: IR.UMSU.REC.1400.410).

Funding
This study was funded by Urmia University of Medical Sciences (Grant No.: 2929).

Authors' contributions
Conceptualization and design: Mohamad Jebraeily and Mehrdad Dehghani; Data curation and analysis: Khadijeh Makhdomi and Bahlol Rahimi; Writing the initial draft, reviewing and approving the final draft: All authors.

Conflicts of interest
The authors declared no conflict of interest.

Acknowledgements
The authors would like to thank the Iranian Society of Nephrology and nephrology specialists who participated in this study.

References

1. Byrne CD, Targher G. NAFLD as a driver of chronic kidney disease. J Hepatol. 2020; 72(4):785-801. [DOI:10.1016/j.jhep.2020.01.013] [PMID]
2. Jager KJ, Kovesdy C, Langham R, Rosenberg M, Jha V, Zoccali C. A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases. Kidney Int. 2019; 96(5):1048-50. [DOI:10.1016/j.kint.2019.07.012] [PMID]
3. Yang CW, Harris DCH, Luyckx VA, Nangaku M, Hou FF, Garcia Garcia G, et al. Global case studies for chronic kidney disease/end-stage kidney disease care. Kidney Int Suppl (2011). 2020; 10(1):e24-48. [DOI:10.1016/j.kisu.2019.11.010] [PMID] [PMCID]
4. Dalrymple LS, Katz R, Kestenbaum B, Shlipak MG, Sarnak MJ, Stehman-Breen C, et al. Chronic kidney disease and the risk of end-stage renal disease versus death. J Gen Intern Med. 2011; 26(4):379-85. [DOI:10.1007/s11606-010-1511-x] [PMID] [PMCID]
5. Nafar M, Aghighi M, Dalili N, Alipour Abedi B. Perspective of 20 years hemodialysis registry in Iran, on the road to progress. Iran J Kidney Dis. 2020; 14(2):95-101. [PMID]
6. Robinson BM, Akizawa T, Jager KJ, Kerr PG, Saran R, Pisoni RL. Factors affecting outcomes in patients reaching end-stage kidney disease worldwide: Differences in access to renal replacement therapy, modality use, and haemodialysis practices. Lancet. 2016; 388(10041):294-306. [DOI:10.1016/S0140-6736(16)30448-2] [PMID]
7. Ronco C, Davenport A, Gura V. The future of the artificial kidney: Moving towards wearable and miniaturized devices. Nefrología. 2011; 31(1):9-16. [DOI:10.3265/Nefrologia.pre2010.Nov.10758] [PMID]
8. Lv JC, Zhang LX. Prevalence and disease burden of chronic kidney disease. Adv Exp Med Biol. 2019; 1165:3-15. [DOI:10.1007/978-981-13-8871-2_1] [PMID]
9. Grangé S, Hanoy M, Le Roy F, Guerrot D, Godin M. Monitoring of hemodialysis quality-of-care indicators: Why is it important? BMC Nephrol. 2013; 14(1):1-10. [DOI:10.1186/1471-2369-14-109] [PMID] [PMCID]
10. Liu HC, Itoh K. Conceptual framework for holistic dialysis management based on key performance indicators. Ther Apher Dial. 2013; 17(5):532-50. [DOI:10.1111/1744-9987.12019] [PMID]
11. Himmelfarb J, Vanholder R, Mehrotra R, Tonelli M. The current and future landscape of dialysis. Nat Rev Nephrol. 2020; 16(10):573-85. [DOI:10.1038/s41581-020-0315-4] [PMID] [PMCID]
12. Klarenbach SW, Tonelli M, Chui B, Manns BJ. Economic evaluation of dialysis therapies. Nat Rev Nephrol. 2014; 10(11):644-52. [DOI:10.1038/nrneph.2014.145] [PMID]
13. Donca IZ, Wish JB. Systemic barriers to optimal hemodialysis access. Semin Nephrol. 2012; 32(6):519-29. [DOI:10.1016/j.semnephrol.2012.10.002] [PMID]
14. Gu X, Itoh K. Performance measures for a dialysis setting. J Ren Care. 2018; 44(1):52-9. [DOI:10.1111/jorc.12229] [PMID]
15. Tokola H, Gröger C, Järvenpää E, Niemi E. Designing manufacturing dashboards on the basis of a key performance indicator survey. Procedia CIRP. 2016; 57:619-24. [DOI:10.1016/j.procir.2016.11.107]
16. Pestana M, Pereira R, Moro S. Improving health care management in hospitals through a productivity dashboard. J Med Syst. 2020; 44(4):87. [DOI:10.1007/s10916-020-01546-1] [PMID]
17. Twohig PA, Rivington JR, Gunzler D, Daprano J, Margolius D. Clinician dashboard views and improvement in preventative health outcome measures: A retrospective analysis. BMC Health Serv Res. 2019; 19(1):475. [DOI:10.1186/s12913-019-4327-3] [PMID] [PMCID]
18. Jebraeily M, Valizade Hasanloei MA, Rahimi B, Saeidi S. Design of a management dashboard for the intensive care unit: Determining key performance indicators and their required capabilities. Appl Med Inform. 2019; 41(3):111-21. [Link]
19. Harvey HB, Hassanzadeh E, Aran S, Rosenthal DI, Thrall JH, Abujudeh HH. Key performance indicators in radiology: You can't manage what you can't measure. Curr Probl Diagn Radiol. 2016; 45(2):115-21. [DOI:10.1067/j.cpradiol.2015.07.014] [PMID]
20. Moore L, Lavoie A, Bourgeois G, Lapointe J. Donabedian's structure-process-outcome quality of care model: Validation in an integrated trauma system. J Trauma Acute Care Surg. 2015; 78(6):1168-75. [DOI:10.1097/TA.0000000000000663] [PMID]
21. Martinez DA, Kane EM, Jalalpour M, Scheulen J, Rupani H, Toteja R, et al. An electronic dashboard to monitor patient flow at the Johns Hopkins Hospital: Communication of key performance indicators using the Donabedian model. J Med Syst. 2018; 42(8):133. [DOI:10.1007/s10916-018-0988-4] [PMID]
22. Martin N, Bergs J, Eerdekens D, Depaire B, Verelst S. Developing an emergency department crowding dashboard: A design science approach. Int Emerg Nurs. 2018; 39:68-76. [DOI:10.1016/j.ienj.2017.08.001] [PMID]
23. Coetzee LM, Tepper ME, Perelson L, Glencross DK, Cassim N. Timely delivery of laboratory efficiency information, part II: Assessing the impact of a turn-around time dashboard at a high-volume laboratory. African J Lab Med. 2020; 9(2):1-8. [DOI:10.4102/ajlm.v9i2.948]
24. Karami M. Development of key performance indicators for academic radiology departments. Int J Healthc Manag. 2017; 10(4):275-80. [DOI:10.1080/20479700.2016.1268350]
25. Kliger AS. Quality measures for dialysis: Time for a balanced scorecard. Clin J Am Soc Nephrol. 2016; 11(2):363-8. [DOI:10.2215/CJN.06010615] [PMID] [PMCID]
26. Fischer MJ, Kourany WM, Sovern K, Forrester K, Griffin C, Lightner N, et al. Development, implementation and user experience of the veterans health administration (VHA) dialysis dashboard. BMC Nephrol. 2020; 21(1):136. [DOI:10.1186/s12882-020-01798-6] [PMID] [PMCID]
27. Pongpirul K, Kanjanabuch T, Puapatanakul P, Chuengsaman P, Dandecha P, Kingwatanakul P, et al. National feasibility survey of peritoneal dialysis key performance indicators in Thailand from provider perspective. Nephrology. 2020; 25(6):483-90. [DOI:10.1111/nep.13668] [PMID]
28. Plantinga LC, Pastan SO, Wilk AS, Krisher J, Mulloy L, Gibney EM, et al. Referral for kidney transplantation and indicators of quality of dialysis care: A cross-sectional study. Am J Kidney Dis. 2017; 69(2):257-65. [DOI:10.1053/j.ajkd.2016.08.038] [PMID] [PMCID]