METHODS: All patients admitted to UKMMC in 2011 were recruited in this study. Combination of Step-down and Bottom-up costing methodology has been used in this study. The drug and supplies cost; the cost of staff; the overhead cost; and the equipment cost make up the four components of pharmacy. Direct costing approach has been employed to calculate Drugs and supplies cost from electronic-prescription system; and the inpatient pharmacy staff cost, while the overhead cost and the pharmacy equipments cost have been calculated indirectly from MY-DRG data base. The total pharmacy cost was obtained by summing the four pharmacy components' cost per each MY-DRG. The Pharmacy service weight of a MY-DRG was estimated by dividing the average pharmacy cost of the investigated MY-DRG on the average of a specified MY-DRG (which usually the average pharmacy cost of all MY-DRGs).
RESULTS: Drugs and supplies were the main component (86.0%) of pharmacy cost compared o overhead cost centers (7.3%), staff cost (6.5%) and pharmacy equipments (0.2%) respectively. Out of 789 inpatient MY-DRGs case-mix groups, 450 (57.0%) groups were utilized by the UKMMC. Pharmacy service weight has been calculated for each of these 450 MY-DRGs groups. MY-DRG case-mix group of Lymphoma & Chronic Leukemia group with severity level three (C-4-11-III) has the highest pharmacy service weight of 11.8 equivalents to average pharmacy cost of RM 5383.90. While the MY-DRG case-mix group for Circumcision with severity level one (V-1-15-I) has the lowest pharmacy service weight of 0.04 equivalents to average pharmacy cost of RM 17.83.
CONCLUSION: A mixed approach which is based partly on top-down and partly on bottom up costing methodology has been recruited to develop MY-DRG case-mix pharmacy service weight for 450 groups utilized by the UKMMC in 2011.
Patients and methods: An observational study was conducted at four different intensive care units of an academic medical institution. Demographic characteristics, disease-management casemix information, cost and outcome of the high costing decile, and the rest of the cases were compared.
Results: A total of 3,220 discharges were included in the study. The high-cost group contributed 35.4% of the ICU stays and 38.8% of the total ICU expenditure. Diseases of the central nervous system had higher odds to be in the top decile of costly patients whereas the cardiovascular system was more likely to be in the non-high cost category. The high-cost patients were more likely to have death as an outcome (19.2% vs 9.3%; p<0.001). The most common conditions that were in the high-cost groups were craniotomy, other ear, nose, mouth, and throat operations, simple respiratory system operations, complex intestinal operations, and septicemia. These five diagnostic groups made up 43% of the high-cost decile.
Conclusion: High-cost patients utilized almost 40% of the ICU cost although they were only 10% of the ICU patients. The chances of admission to the ICU increased with older age and severity level of the disease. Central nervous system diseases were the major problem of patients aged 46-69 years old. In addition to cost reduction strategies at the treatment level, detailed analysis of these cases was needed to explore and identify pre-event stage prevention strategies.
OBJECTIVE: This study aims to develop and refine items of a quantitative instrument measuring the critical success factors influencing acceptance of Casemix system implementation within the Ministry of Health's Total Hospital Information System (THIS).
METHODS: A cross-sectional pilot study collected data from medical doctors at a hospital equipped with the THIS in the federal territory of Putrajaya, Malaysia. This pilot study's minimum sample size was 125, achieved through proportionate stratified random sampling. Data were collected using a web-based questionnaire adapted from the human, organization, and technology-fit evaluation framework and the technology acceptance model. The pilot data were analyzed using exploratory factor analysis (EFA), and the Cronbach α assessed internal reliability. Both analyses were conducted in SPSS (version 25.0; IBM Corp).
RESULTS: This study obtained 106 valid responses, equivalent to an 84.8% (106/125) response rate. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.859, and the Bartlett test of sphericity yielded statistically significant results (P0.6, leading to the removal of 1 item for the final instrument for the field study. EFA ultimately identified 8 main constructs influencing Casemix implementation within the THIS: system quality, information quality, service quality, organizational characteristics, perceived ease of use, perceived usefulness, intention to use, and acceptance. Internal reliability measured using the Cronbach α ranged from 0.914 to 0.969, demonstrating high reliability.
CONCLUSIONS: This study provides insights into the complexities of EFA and the distinct dimensions underlying the constructs that influence Casemix system acceptance in the THIS. While the findings align with extensive technology acceptance literature, the results accentuate the necessity for further research to develop a consensus regarding the most critical factors for successful Casemix adoption. The developed instrument is a substantial step toward better understanding the multidimensional challenges of health care system transformations in Malaysia, postulating an underpinning for future fieldwork and broader application across other hospitals.