Perancangan Model Seleksi Dokter Terbaik Menggunakan Metode SMART sebagai Basis Penetapan Bonus
DOI:
https://doi.org/10.35969/g6hymz48Keywords:
sistem pendukung keputusan, metode SMART, kinerja dokter, alokasi bonus, manajemen kesehatanAbstract
Sistem insentif berbasis kinerja untuk dokter sering menghadapi tantangan objektivitas, transparansi, dan konsistensi yang dapat memengaruhi kualitas layanan dan motivasi profesional. Tujuan: Penelitian ini bertujuan mengembangkan dan memvalidasi sistem pendukung keputusan menggunakan metode Simple Multi-Attribute Rating Technique (SMART) untuk menyeleksi dokter berkinerja terbaik sebagai dasar pemberian bonus bulanan. Studi kasus kuantitatif deskriptif-evaluatif dilakukan di Rumah Sakit XYZ periode Januari–Juni 2025, melibatkan 15 dokter spesialis yang dipilih melalui purposive sampling. Lima kriteria evaluasi ditetapkan melalui Focus Group Discussion dan divalidasi menggunakan Content Validity Ratio (CVR > 0,75): kualitas pelayanan medis (30%), kedisiplinan (20%), produktivitas (20%), kepuasan pasien (20%), dan pengembangan kompetensi (10%). Hasil: Model SMART berhasil merangking dokter secara objektif, dengan D7 mencapai skor tertinggi (88,45). Validasi konkuren dengan metode SAW menunjukkan korelasi sangat tinggi (rs=0,982, p<0,01), dengan kesesuaian 93,3% terhadap penilaian manajemen. Analisis sensitivitas mengonfirmasi stabilitas model terhadap variasi bobot ±10%. Ditemukan korelasi positif kuat antara kualitas pelayanan medis dan kepuasan pasien (r=0,78, p<0,01). Sistem pendukung keputusan berbasis SMART menyediakan mekanisme objektif, transparan, dan andal untuk evaluasi kinerja dokter dan penetapan bonus, berkontribusi pada peningkatan kualitas layanan kesehatan melalui tata kelola sumber daya manusia yang lebih baik.
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[1] Garengo P, Sardi A, Nudurupati SS (2022), "Human resource management (HRM) in the performance measurement and management (PMM) domain: a bibliometric review". International Journal of Productivity and Performance Management, Vol. 71 No. 7 pp. 3056–3077, doi: https://doi.org/10.1108/IJPPM-04-2020-0177
[2] R. A. Berenson and T. Rice, "Beyond measurement and reward: methods of motivating quality improvement and accountability," Health Serv. Res., vol. 50, Suppl 2, pp. 2155-2186, Dec. 2015, doi: 10.1111/1475-6773.12413.
[3] Dewettinck, K., & van Dijk, H. (2013). Linking Belgian employee performance management system characteristics with performance management system effectiveness: exploring the mediating role of fairness. The International Journal of Human Resource Management, 24(4), 806–825. https://doi.org/10.1080/09585192.2012.700169.
[4] C. Maley et al., "Employee performance appraisal: a critical review of research and practice," Asia Pac. J. Hum. Resour., vol. 58, no. 4, pp. 461-480, 2020, doi: 10.1111/1744-7941.12265.
[5] Widianta, W., & Wening, N. (2025). Decision Support System For Selecting The Best Employee: A Literature Review. Oikonomia : Journal of Management Economics and Accounting, 2(3), 1–9. https://doi.org/10.61942/oikonomia.v2i3.335.
[6] Mardani, A., Jusoh, A., MD Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja, 28(1), 516–571. https://doi.org/10.1080/1331677X.2015.1075139.
[7] Băcescu Ene GV, Stoia MA, Cojocaru C, Todea DA. SMART Multi-Criteria Decision Analysis (MCDA)-One of the Keys to Future Pandemic Strategies. J Clin Med. 2025 Mar 13;14(6):1943. doi: 10.3390/jcm14061943. PMID: 40142750; PMCID: PMC11943005..
[8] Najjar, A., Amro, B., & Macedo, M. (2021). An Intelligent Decision Support System For Recruitment: Resumes Screening And Applicants Ranking. Informatica, 45(4). https://doi.org/10.31449/inf.v45i4.3356.
[9] Wang G, Shao Q. Design of a smart medical service quality evaluation system based on a hybrid multi-criteria decision model. Sci Rep. 2024 Nov 2;14(1):26407. doi: 10.1038/s41598-024-71224-6. PMID: 39488546; PMCID: PMC11531580.
[10] Kuvaas, B. (2006). Performance appraisal satisfaction and employee outcomes: mediating and moderating roles of work motivation. The International Journal of Human Resource Management, 17(3), 504–522. https://doi.org/10.1080/09585190500521581
[11] Russell Ackoff, A concept of corporate planning, Long Range Planning, Volume 3, Issue 1, 1970, Pages 2-8, ISSN 0024-6301, https://doi.org/10.1016/0024-6301(70)90031-2
[12] Hobiana, S., & Rofiqoh, I. (2025). Decision Support System for Employee Recruitment Using Various Methods: A Literature Review. OPSearch: American Journal of Open Research. https://doi.org/10.58811/opsearch.v4i3.182.
[13] J. Figueira, S. Greco, and M. Ehrgott, Multiple Criteria Decision Analysis: State of the Art Surveys. New York: Springer, 2005.
[14] Ward Edwards, F.Hutton Barron, SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement, Organizational Behavior and Human Decision Processes,Volume 60, Issue 3, 1994, Pages 306-325, ISSN 0749-5978, https://doi.org/10.1006/obhd.1994.1087.
[15] Overeem, Karlijn & Wollersheim, Hub & Arah, Onyebuchi & Cruijsberg, Juliette & Grol, Richard & Lombarts, Kiki. (2012). Evaluation of physicians' professional performance: An iterative development and validation study of multisource feedback instruments. BMC health services research. 12. 80. 10.1186/1472-6963-12-80.
[16] R. K. Yin, Case Study Research and Applications: Design and Methods, 6th ed. Thousand Oaks, CA: SAGE Publications, 2018.
[17] R. B. Johnson and L. Christensen, Educational Research: Quantitative, Qualitative, and Mixed Approaches, 7th ed. Thousand Oaks, CA: SAGE, 2019.
[18] C. V. Lawshe, “A quantitative approach to content validity,” Pers. Psychol., vol. 7, no. 4, pp. 251–255, 1975.
[19] W. N. Dunn, Public Policy Analysis: An Introduction, 6th ed. New York: Routledge, 2021.
[20] A. Ishizaka and P. Nemery, Multi-Criteria Decision Analysis: Methods and Software. Chichester, UK: Wiley, 2013.























