Enhancing Workforce Stability through Data-Driven Selection: An AHP-TOPSIS Approach for Healthcare HRM
Enhancing Workforce Stability through Data-Driven Selection
DOI:
https://doi.org/10.63841/iue32673Keywords:
HRM, AHP-TOPSIS Integration, Employee Selection and Healthcare Sector.Abstract
Recruitment challenges in private healthcare, particularly in Sulaimani, Iraq, stem from manual hiring processes that introduce bias and inefficiency, compromising service delivery and increasing turnover. This study proposes an integrated AHP-TOPSIS framework to enhance objectivity in competency-based selection. Three criteria—Medical Knowledge, Clinical Experience, and Patient Care—were weighted via AHP using expert pairwise comparisons, with Medical Knowledge (weight = 0.769) emerging as the most critical for doctors (CR = 0.062, validating consistency). TOPSIS then ranked candidates, prioritizing practical skills (e.g., interpreting lab results, CCi = 0.873; leadership, CCi = 0.934) over conventional metrics like board certification (CCi = 0.131). However, Patient Care (weight = 0.127) and Patient Satisfaction Scores (CCi = 0.064) scored poorly, revealing a gap in human-centric evaluation. The methodology section delineates a rigorous framework of quantitative methodologies, embedding AHP and TOPSIS under the umbrella of MCDM methods. For empirical validation, 10 private hospitals in Sulaimani were selected out of 15, excluding very small facilities functioning more like clinical centers; ultimately, only 7 hospitals agreed to participate and provided responses to the administered questionnaires. The findings of this study establish that, as a framework, it serves to reduce subjective bias, enhance candidate job alignment, and finally, stabilize retention of the workforce. This data driven model offered a replicable solution for HRM in healthcare besides the fact that future applications should integrate psychosocial variables and public health systems for alignment with patient-centered care paradigms. Such efforts contribute to the emerging practices that focus on addressing both technical competencies and systemic inefficiencies, thereby balancing organizational needs with quality care delivery.
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