The implementation of Electronic Medical Records (EMRs) in hospital emergency units is essential to improve the speed, accuracy, and quality of services. However, various challenges hinder optimal system utilization. This study aimed to assess the adoption of EMR at the Emergency Unit of RSUD A.M Parikesit Tenggarong using the Human-Organization-Technology Fit (HOT-FIT) framework. A qualitative descriptive approach was employed involving seven purposively selected informants, including health professionals, administrative staff, and IT personnel. Data were collected through interviews, observations, and document reviews, then analyzed using the Miles and Huberman interactive model. The findings indicate that from the human aspect, the system was considered easy to use (85%), though user satisfaction remained moderate (67.14%) due to system errors and incomplete CPPT data entries. Organizationally, the hospital provided adequate infrastructure and support, though some policies require stronger enforcement. Technological aspects revealed limitations in system stability, lack of pharmacy integration, and low service quality due to frequent downtime. The study concludes that EMR implementation in the Emergency Unit has not yet achieved full effectiveness across all HOT-FIT domains. Improvements are needed in system reliability, information consistency, and inter-unit integration to optimize EMR benefits and enhance patient care delivery in critical settings.

The implementation of health information systems such as SIMKES (Sistem Informasi Manajemen Kesehatan) plays a crucial role in enhancing administrative efficiency and service delivery in primary healthcare centers. However, user satisfaction remains a key indicator of system effectiveness, especially in outpatient service units. This study aims to evaluate the satisfaction level of health personnel toward the implementation of SIMKES at the outpatient unit of Teguhan Health Center, Ngawi, Indonesia. Employing a descriptive quantitative method, this research involved a census of 17 respondents using a validated questionnaire based on the End-User Computing Satisfaction (EUCS) model, which comprises five dimensions: content, accuracy, format, ease of use, and timeliness. The data were analyzed using frequency distribution and mean scoring to categorize satisfaction levels. Results showed that the overall satisfaction score was 73.58, indicating that users were generally satisfied with the system. All five EUCS dimensions were categorized as satisfactory, with the highest rating in the format dimension and the lowest in timeliness. Despite the overall positive assessment, users reported issues related to system speed, technical bugs, and limited guidance for system navigation. The findings suggest that while SIMKES supports outpatient service delivery effectively, improvements are needed in system responsiveness and user support to enhance satisfaction and usability. This study contributes to the ongoing evaluation and development of health information systems in resource-limited primary care settings.

process standardization, and system optimization. The study concludes that the HOT-FIT model is This study evaluates the performance of the outpatient registration information system at RSUD Komodo using the Human-Organization-Technology Fit (HOT-FIT) framework. Hospital Information Systems, particularly in the outpatient registration process, are crucial for supporting service efficiency and data accuracy. However, RSUD Komodo has experienced several challenges in the implementation of its SIMRS module, including system slowdowns, sudden monitor failures, and unstable internet connectivity during service hours. These issues hinder operational effectiveness and risk compromising service quality. The objective of this research is to assess system performance comprehensively across human, organizational, and technological dimensions. A qualitative descriptive design was employed, involving in-depth interviews with five key informants: registration staff, IT personnel, coder, head of the medical records unit, and head of the casemix team. The findings show that in the human dimension, users lacked sufficient training and adaptation strategies. In the organizational aspect, weak coordination and the absence of standardized procedures were identified. In the technology dimension, hardware malfunctions and slow system performance significantly disrupted services. These interconnected issues reveal the need for capacity buildingan effective tool for evaluating hospital information systems, offering a structured approach to identifying and resolving performance gaps in outpatient service modules.

The implementation of Electronic Medical Records (EMR) in healthcare institutions aims to improve the efficiency, accuracy, and accessibility of medical data. RSUD Weda, a regional hospital in Indonesia, has adopted an EMR system in its outpatient services to replace conventional manual processes. However, this transition has faced several challenges, including inadequate training, unstable network infrastructure, and inconsistent adherence to Standard Operating Procedures (SOPs). This study aims to evaluate the performance of the EMR implementation using a qualitative descriptive method based on the 5M framework—Man, Material, Machine, Method, and Money. Data were collected through interviews, direct observation, and document analysis involving stakeholders such as medical staff, administrators, and IT personnel. The findings indicate that while users acknowledge improvements in data accuracy and accessibility, key issues remain, particularly in training quality, infrastructure readiness, and system reliability. The study also observed gradual improvements in SOP compliance and reduced system failures over time. These results suggest that successful EMR adoption requires a balance of technical infrastructure, human resource readiness, financial support, and continuous system monitoring. The use of the 5M framework proved effective in identifying multidimensional challenges and offering holistic insights into the implementation process. This study contributes to the broader literature on EMR evaluation by providing contextualized evidence from a district-level hospital and offers practical recommendations for enhancing EMR system performance in similar healthcare settings.
 
 

Diagnostic codification must fulfill 10 characteristics of quality data, namely one of which is accurate, namely the truth and validity of data values. External Causes are external causes of fracture cases which classify based on the place and activity of the incident. It is very important that external causes codes are documented in medical record files for financing analysis, health services and reporting. The purpose of this study was to determine the accuracy of the fracture diagnosis code and external causes at Mardi Waluyo Hospital, Blitar City in 2021. The research method used a retrospective descriptive. The sampling technique is simple random sampling, with 52 samples. The implementation of coding at Mardi Waluyo Hospital, Blitar City, was carried out by coder officers and was in accordance with the policies and standard operating procedures at the hospital. The level of accuracy of the fracture diagnosis code with a percentage of 85% is included in the category that needs improvement and external causes with a percentage of 6% is included in the unsatisfactory category. The inaccuracy of the external causes code is caused by the use of the 5th character or the activity code that has not yet been coded. The inaccuracy of the officers in coding caused the resulting code to be inaccurate, so it was suggested that filling out medical record documents must be filled in completely and clearly to support proper and accurate coding of fractures and external causes so as to produce a better percentage of accuracy.

The detection of computer network attacks is becoming increasingly important as the complexity of cyber-attacks threatening information systems and infrastructure continues to rise. To address these threats, artificial intelligence techniques have become a primary focus in the development of more effective attack detection systems. One algorithm that has proven reliable in this context is the C4.5 decision tree. This study aims to apply the C4.5 algorithm in network attack detection using a dataset that includes various types of attacks and network activities. The process includes data preprocessing, decision tree model building, and model performance evaluation. The results show that the C4.5 decision tree algorithm is effective in classifying network activities into attacks and normal activities with a satisfactory level of accuracy. The model successfully recognizes attack-related patterns, and further analysis identifies key factors influencing attack detection. This research provides a significant contribution to the development of reliable and efficient attack detection systems in computer networks. By applying the C4.5 decision tree algorithm, it is expected to help enhance information security and protect network infrastructure from increasingly complex cyber threats