Abstract

Implementation of quality management is very important for hospitals to improve processes, solve problems, and reduce variations and errors in service, including through the implementation of popular Total Quality Management (TQM) and Six Sigma (SS) as new quality management strategies to increase profitability, effectiveness and efficiency of the organization's operations to meet customer’s needs. This study aims to develop an integrated hospital quality management model from the practice of TQM and SS to provide synergy in improving hospital performance. The study design was cross sectional through a survey using a questionnaire on 863 respondents, namely all employees ranging from doctors to administrative personnel at 8 hospitals. The TQM and SS practice integration model identified as “Quality Management Alliance Model (QMA)” consists of 6 variable constructs, namely: Management Practice (MP); TQM Infrastructure Practice (IPTQM); SS Infrastructure Practice (IPSS); Core Practice TQM (CPTQM); Core Practice SS (CPSS); and Hospital Performance (KRS) with 12 structural equations hypothesized. Data analysis are performed using Structural Equation Model through 2 tests, namely analysis of measurement models using confirmatory factor analysis (CFA) second order approach and structural model analysis. The results of the first order confirmatory factor analysis (CFA) analysis, after issuing invalid indicators (SLF≤0.5 and t≤1.96), obtained constructs of latent variables with models fit, valid, and reliable. Then in the second order CFA analysis on the overall model after being simplified through LVS (latent variable score) the study obtained construct model fit, valid and reliable. The results of the structural model analysis obtained a model fit with 11 structural equations that are positively and significantly related (t> 1.96). This study proves that the QMA model is feasible and can be applied to measure the implementation of hospital quality management. Hospital management is recommended to implement the QMA Model optimally to improve performance.

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