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Pratama, Sony Andik
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Analysis of the effect of hospital management information system implementation on clinical decision-making at PKU Muhammadiyah Purbalingga hospital Pratama, Sony Andik; Handayani, Nining; Efawati, Yen
Science Midwifery Vol 13 No 6 (2026): Health Sciences and related fields
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/midwifery.v13i6.2242

Abstract

Hospital Management Information Systems (HMIS) play an important role in supporting clinical decision making by providing accurate, timely, and integrated information. However, challenges related to system quality, information quality, and workflow efficiency may hinder the optimal utilization of HMIS. This study aimed to analyze the effects of system quality, information quality, and workflow efficiency on clinical decision making at PKU Muhammadiyah Purbalingga Hospital. The research employed a quantitative method with a causal associative design, using a total sampling technique involving 60 respondents consisting of physicians, nurses, and midwives. Data were collected using structured questionnaires and analyzed through multiple linear regression with I BM SPSS Statistics version 26, accompanied by classical assumption tests. The results showed that all variables were categorized as good and that the regression model met all analytical assumptions. Partial test (t-test) results indicated that system quality, information quality, and workflow efficiency had a positive and significant effect on clinical decision making (p < 0.05), with information quality identified as the most dominant variable (β = 0.402). The F-test results showed Fcount = 25.324 > Ftable = 2.70 with p = 0.001, indicating that the independent variables simultaneously had a significant effect. In conclusion, HMIS system quality, HMIS information quality, and workflow efficiency significantly affect clinical decision making at PKU Muhammadiyah Purbalingga Hospital, with information quality as the strongest predictor.