Several Ministry of Health (MOH) initiatives of the Medical Programme are proactive anticipation of further reforms to the health care system in Malaysia. This endeavor includes changing existing processes to improve hospital admission or discharge and patient flow [1-3]. These processes include the concept of clustered hospitals, lean thinking in the organization structures of hospitals, hospital information system (HIS) improvement, and using the Casemix system to measure the performance and financials [1,2]. In the case of the MOH Medical Programme, which encompasses the strategic framework for the 12th Malaysia Plan (2021-2025), this is considered one of the critical areas of concern [2]. This approach aims to enhance health system efficiency and planning with an expectation of a 13% increase in bed occupancy by 2020 [1,2].

The Medical Programme has also commenced developing and launching the Casemix system application, commonly known as the Malaysian Diagnosis-Related Group (MalaysianDRG) Casemix system. This tool sorts patients into categories by care costs, leading to improved information flow and resources [2,4,5]. It is used for cost estimation and fund allocation, and 71 hospitals have implemented it for inpatient and daycare services under the 11th Malaysia Plan, as stated in the strategic framework for the 12th Malaysia Plan [2]. Other outputs, including diagnosis-related group, severity of illness, average cost per disease, and Casemix Index can be accessed from the executive information system module [2,4,5]. The Casemix system is an imperative tool in the formulation of resource evidentiary budgets, especially for hospitals [2,4,5].

Health care finance innovations such as the Madani Medical Scheme and medical equipment rental services were designed to better the health care financing processes and application of health care services and encourage more investors to invest in the health care sector under the 12th Malaysia Plan [6]. This initiative faces a significant challenge, procuring medical equipment for major advancements in health care is costly, with high expenses related to acquisition, maintenance, and potential replacement due to ongoing innovation [6].

Hence, in order to enhance health care quality, clinical decision-making, and patient safety while reducing costs, the MOH has allocated funds for HIS and other health care information technology [7,8]. Integrating patient information facilitates the prescribing, filling, and dispensing of drugs, eliminating the need for physical prescription slips [9-11]. Studies indicate improvements in patient registration, appointment management, and ward processes, significantly reducing waiting times [12,13]. The national telehealth policy, part of the Multimedia Super Corridor initiative established 25 years ago to facilitate the country’s transformation into a high-income nation by 2020, aims to enhance patient care and expand health care information availability [3,14-16].

The HIS is a comprehensive system that integrates clinical, administrative, and financial functions to enrich service productivity [17,18]. In Malaysia, the HIS is classified into 3 types: Total HIS (THIS), Intermediate HIS, and Basic HIS [19]. Despite the importance of the HIS, only 15.2% of public hospitals in Malaysia have implemented the system within the categories of THIS, Intermediate HIS, and Basic HIS. These data indicate a low level of adoption of the HIS in the country [20].

The THIS is a comprehensive software solution that integrates patient data, administrative tasks, financial transactions, and appointment management into a single system within a hospital [1,21,22]. The THIS was developed and implemented in Selayang Hospital (1999) and Putrajaya Hospital (2000) and aimed to create a paperless digital environment [13,23]. It encompasses various applications for managing clinical notes, nursing information systems, laboratory information systems, picture archiving communication systems, radiology information systems, and pharmacy information systems [7,8,18]. The MOH of Malaysia has implemented the HIS at 37 hospitals supported by various systems and vendors (n=22). THIS hospitals use systems such as Cerner, FiSiCien, iSOFT, and ProfDoc. ProfDoc is exclusively used in a single hospital, meanwhile, Sistem Pengurusan Pesakit and SPPD are used in 9 and 10 hospitals, respectively [21]. The remaining 105 MOH hospitals operate using manual methods [21].

In addition to that, HIS@KKM, developed by the Medical Development Division, manages patient information comprehensively as part of the National Electronic Medical Record project [24]. This initiative aims to provide efficient, transparent, and prompt delivery of government health services, establishing the lifetime health record for continuous health care [24]. These initiatives are implemented to improve health care delivery by boosting productivity and efficiency, involving patients in the decision-making process, and reducing errors. Hence, strategies such as HIS and Casemix System have been introduced to support these goals [20,25,26]. Casemix has been implemented in all 149 MOH hospitals, with 19 equipped with THIS facilities since 2022 [4,5,24,27,28]. Therefore, the Casemix system within THIS facilities is either fully or partially integrated with the HIS depending on whether the vendor-supported HIS can support full integration.

Therefore, adopting and accepting new technologies in health care presents challenges due to patient vulnerability and data confidentiality concerns [29]. Various studies have identified factors affecting IT acceptability, leading to the adaptation of frameworks such as the technology acceptance model (TAM) and the human, organization, and technology-fit (HOT-Fit) evaluation framework to address these issues effectively [30-36]. Therefore, we decided to use the HOT-Fit and TAM frameworks as the conceptual basis for this study to meet its specific goals, scope, and context (Figure 1). The HOT-Fit framework provides a detailed approach to examining how human, organizational, and technological factors align, whereas the TAM focuses specifically on how individuals accept technology [31,33,37-39]. In this study, the HOT-Fit evaluation framework emphasizes technological elements such as system, information, service quality (SQ), and organizational factors, whereas the TAM addresses human aspects such as perceived ease of use (PEOU), perceived usefulness (PU), intention to use (ITU), and acceptance. Combining these frameworks is essential for meeting both specific and broad study objectives, which makes them appropriate for this research. Conversely, other models such as the Unified Theory of Acceptance and Use of Technology were deemed unsuitable due to their broad scope and complexity, and the information system success model by Delone and McLean [40] was not chosen because it is too simplistic [32,41,42].

Several studies have explored individual elements, such as system quality (SY), information quality (IQ), SQ, organizational factors, PEOU, PU, and ITU in health care information systems. However, no such studies have been known to be conducted on the Casemix system implementation within the THIS environment in Malaysia or even worldwide. A previous study was conducted on the knowledge, attitude, and practice of Casemix implementation in Turkish health care settings [43,44]. Nevertheless, many studies have been conducted in Malaysia on the HIS or even on the THIS [33-35,45-50]. Similar studies on the HIS have also been observed generally in other countries. However, no such study solely examines the THIS, which might be because the terms used in other countries differ from the HIS categories in Malaysia [10,51-56]. However, there is no specific study on Casemix integration into the THIS; hence, this constitutes the novelty of this study and the research gap [57]. Therefore, it is important to recognize that due to cultural, organizational, and structural differences in hospitals under the MOH of Malaysia, findings from other regions may not be directly applicable. Thus, a context-specific study of Casemix within the THIS is necessary to tailor the understanding to the Malaysian setting. This study involves exploring the interrelationships among these factors and their relative importance in influencing acceptance. Identifying which factors have the most significant impact and how they interact can provide deeper insights for effective implementation [57].

The critical factors in this study were predominantly adapted from the TAM and the HOT-Fit frameworks [30-36]. In addition, incorporating these elements is essential to understand their interplay and relative importance in influencing acceptance. Previous studies may have used models that partially address these factors in relation to the HIS. However, there is a need for a model that incorporates all dimensions comprehensively. Therefore, this study seeks to integrate all critical determinants and constructs into a single model based on 2 frameworks [57].

Many studies concentrate on the initial stages of system implementation, highlighting the need for longitudinal research to examine how these factors influence acceptance over time. This involves considering changes in perceptions and attitudes as users become more familiar with the system. Current researchers combine quantitative measures (eg, surveys) with qualitative insights (eg, interviews) to understand the perspectives, barriers, and challenges faced by hospital administrators and Casemix coordinators. This mixed methods approach can significantly enhance the understanding and implementation of the Casemix system in hospitals of the MOH of Malaysia, providing a model adaptable to similar contexts in other regions [57].

Exploring the critical success factors (CSFs) and acceptance of the Casemix system within Malaysia’s THIS is essential for several reasons, hence the significance of this study. First, it is imperative to stress that the presence of such factors is vital in optimizing health care processes. A smooth integration improves business continuity; therefore, practice relocation makes it possible for health care providers to spend more time treating patients than on paperwork. Gathering comprehensive patient information through an efficient and effective Casemix system is essential to ensure accurate diagnoses, effective treatment, and better patient outcomes [58]. Secondly, improved resource utilization is another advantage, ensuring that all available resources are efficiently managed. The Casemix system can help properly allocate resources as the MOH can use its financial resources more strategically [26,59,60]. This is essential when planning for the future budget and managing the costs involved. In addition, encouraging user adoption decreases the probability of apprehensions and increases satisfaction among health care employees, optimizing the use of people [48].

Third, informed decision-making is greatly improved through accurate and comprehensive data from the Casemix system. Health care administrators and policy makers can make more informed decisions regarding health care services, policies, and strategies. Continuous monitoring and enhancement of data quality leads to better clinical outcomes and patient safety, ensuring a higher standard of care [58]. In addition, improved patient outcomes result from accurate data that enable more personalized and effective patient care plans. This leads to better health outcomes and reduces the likelihood of medical errors and misdiagnoses. An efficient health information system enhances patient satisfaction by providing timely and effective health care services [48].

On top of that, this study also aids the government, especially the MOH in developing health policies and supports academic and clinical research. Insights gained from examining CSFs and acceptance can inform policies that promote the effective use of health information systems. Comprehensive data support research efforts, contributing to medical advancements and evidence-based practices [48]. On the other hand, stakeholder satisfaction, encompassing health care providers and patients, is significantly enhanced. Ensuring that the system meets the needs of health care providers increases job satisfaction and reduces turnover. For patients, the benefits of timely and effective health care services lead to higher satisfaction levels [48,58].

Furthermore, comprehending CSFs can guide future health care information technology system (HITS) implementation in other hospitals or regions. An effective implementation model can also be fine-tuned to different health care contexts and, thus, maximize positive outcomes [58]. Moreover, this study area also promotes acceptance of technological change. By calling attention to such areas, the potential for improvement and innovation in one’s own country can be achieved within HIS environment. Evidence of the ability of the Casemix system to be integrated into the health care system if the other HITS can be incorporated in the same way goes further to support the argument for developing a more synchronous HITS [48].

Finally, Casemix also has a vital role in the issue of reimbursement and financial distribution. Due to the nature of medical procedures and interventions, it also affords accurate monitoring of delivered health care services in a bid to enhance billing and reimbursement. This creates an intellectual framework that supports proper hospital reimbursement, thereby enhancing financial viability [58]. These factors collectively advance the health care system in Malaysia [48,58]. In addition, Casemix system aids in the early detection of cheaper treatment approaches and the use of available resources for a better and more economical distribution of the available financial resources [59-61]. Cost data of health care services and their demonstrable impact aid in effective financial planning to manage the hike in expenditure for health systems while maintaining or even enhancing the quality of services [59-61].

Hence, identifying CSFs and cultivating acceptance from the user group is crucial for implementing the Casemix system in the context of the health care system in the MOH of Malaysia. This way, it maintains the positive results achieved by the system, fosters better health care, manages the resources more adequately, empowers decision makers with valuable data, strengthens the positive changes in patients’ statuses, increases the satisfaction of the involved stakeholders, and positively influences financial reimbursement and allocation. These factors collectively advance the health care system in Malaysia [48,58].

Thus, the main goal of this study was to assess the CSFs and acceptance of the Casemix system implementation in the THIS of the MOH, Malaysia. Therefore, this study aimed to evaluate the appropriateness of the items using underlying constructs and investigate the reliability and validity of the study instrument (questionnaire), adapted from the HOT-Fit and TAM frameworks, in preparation for the next steps of the research process [31-35,37]. Specifically, this study had two objectives:

By the end of this paper, the ultimate goal is to develop a valid and reliable instrument to assess the critical factors influencing the acceptance of successful Casemix system implementation within the THIS environment, in preparation for a field study.

The theories of the HOT-Fit highlight the significance of implementing the HIS [31,37]. According to this framework, the HIS implementation consists of 3 primary influencing dimensions: human, organization, and technology [31,37]. These components were analyzed in relation to the net benefits of the HIS implementation. The HOT-Fit evaluation framework was established and developed by Yusof et al [33,34] comprises 8 interrelated elements that determine the effectiveness of an HIS: SY, IQ, SQ, system use, user satisfaction, organizational structure, organizational environment, and net benefits. Furthermore, the TAM framework was established and developed by Davis and Venkatesh [30-32]. The TAM is supported by both theoretical and empirical evidence, suggesting that the PU and PEOU factors mitigate the impact of external variables on the ITU, assisting organizations in encouraging user adoption and use of new technologies [30-32,37].

Consequently, we selected 8 significant subdimensions from the work by Yusof et al [33-35] and Davis and Venkatesh [30,31] to create a tool gauging the CSFs and acceptance of the Casemix implementation in the THIS [30-34]. The 8 constructs include SY, IQ, SQ, organizational factors, PU, PEOU, ITU, and acceptance. The conceptual framework for this study is shown in Figure 1.

The study population of “Hospital W” consisted of 775 medical doctors by profession, encompassing hospital directors, deputy directors (medical division), consultants or specialists, medical officers, and house officers. The pilot study population possessed characteristics similar to the participants or samples involved in the subsequent quantitative field study. However, these respondents were excluded from participation in the quantitative field study. For this quantitative pilot study, the investigators purposely selected 2 major clinical departments, general surgery, and obstetrics and gynecology, to avoid the same respondents participating in the field study. These medical doctors should fulfill the inclusion criteria, similar to those of the final field study, as shown in Multimedia Appendix 5.

The pilot study was conducted using proportionate stratified random sampling, a probability sampling method that divides the total population into homogeneous groups [22,43,94,95]. The target population was selected from a hospital in Malaysia’s central or west regions, a federal territory hospital equipped with THIS facilities since 2000 and that has implemented the Casemix system since 2020. To ensure valid results, a minimum of 100 participants was used for the EFA [93,96]. The initial sample size, including 125 medical doctors from 2 major clinical departments, was chosen using proportionate stratified random sampling [97]. The final field study did not include these respondents.

Some studies suggest that 10% of the projected sample size would be enough for a pilot study, with a minimum of 10 to 30 people [98,99]. The pilot study was analyzed using the EFA method, and the researchers adopted a minimum of 100 responses for this pilot test. The researchers omitted the samples or responses from the pilot test research in the field study and conducted the research without participant or public involvement in the design, conduct, reporting, or dissemination strategies.

This voluntary SAQ for the quantitative pilot study was conducted on the web via Google Forms, with data automatically collected in Google Sheets to avoid issues such as low response rates and manual transcription associated with paper surveys [100,101]. The developed questionnaire can be viewed in Multimedia Appendix 6. The target population was provided access to the survey via a QR code or link through department coordinators and heads, ensuring that the survey was closed to a specific sample.

Ethical considerations were taken into account in this study, including ethics approval from the Medical Research Ethics Committee of the Faculty of Medicine, Universiti Kebangsaan Malaysia (JEP-2022-777); the Medical Research and Ethics Committee of the MOH of Malaysia (NMRR ID-22-02621-DKX); the Hospital Financing Unit (Casemix subunit) of the MOH of Malaysia; and the directors of the participating hospitals. The data collection process included a participant information sheet and consent form, ensuring anonymity and confidentiality according to the Helsinki Declaration. The participant information sheet form and the consent form can be found in Multimedia Appendices 7 and 8, respectively. Participants had 2 weeks to complete the survey, which took 10 to 15 minutes, with their data secured in the investigators’ password-protected systems. No incentives were offered for participation.

This study used EFA to analyze 106 quantitative pilot responses from the preliminary questionnaire. Some researchers have advocated for EFA to determine whether the items under investigation have distinct dimensions compared to those in previous studies [93,96,105,106,108,109,113,129]. The EFA procedure involved grouping 42 items into 9 components, each representing a distinct set of measured items. The rotated component matrix elucidated the assignment of items to specific components [105,106,108,109,113]. The scree plot provided corroboration for identifying these components, and the specific allocation of items can be found in Figure 2. This study’s findings provide valuable insights into the construct under investigation.

The analysis indicates that 9 components with eigenvalues of >1.0 were identified. These components exhibited values ranging between 6.6 and 14.155. The variance explained by each component was as follows: 33.187% for the first component, 47.852% for the second component, 58.507% for the third component, 65.76% for the fourth component, 71.357% for the fifth component, 75.25% for the sixth component, 78.853% for the seventh component, 81.673% for the eighth component, and 84.07% for the ninth component. The TVE for this construct was 84.07%, which meets and exceeds the minimum requirement of 60%, thus making it an acceptable result [93,105,106,109,113,116,117]. If the TVE is <60%, the researcher must consider using more items to measure the constructs, which did not occur in this study. Put simply, if the TVE derived in Table 2 is <60%, the current items are insufficient for measuring the constructs [93,105,106,109,116].

The PCA extraction method with varimax rotation was used to identify 9 components across all constructs, deviating from the initial conceptual framework of 8. This finding is expected to unveil novel dimensions due to its execution within a new environment [105,106,108,109,113]. The factor loading of EFA with PCA and varimax rotation was chosen based on its widespread use as an orthogonal factor rotation approach and its ability to clarify factor analysis [93,96,105,106,108,127].

The initial organizational factors (O) construct was divided into 2 components: component 4 (items O6-O9) and component 7 (items O2-O5). Another item, O1, with the statement “Organizational capacity to allocate resources for the implementation of the Casemix System in THIS context” was eliminated from the instrument as its factor loading was <0.6. The construct previously referred to as “organizational factors (O)” was renamed “organizational characteristics (ORG)” in the measurement model. The individual components within this construct were also renamed based on their specific measurements. They are consistent with the components of the related construct “organization” outlined in the HOT-Fit evaluation framework [33,34].

The remaining 7 constructs, namely, SY, IQ, SQ, PEOU, PU, ITU, and acceptance, did not form a new component and did not have any items removed. The total number of constructs remained unchanged at 8. Among the 8 constructs, 98% (41/42) of the items were retained in the measurement model. The researcher proceeded to reorganize the items into their appropriate constructs and components and then began collecting data in the field study. The PCA using varimax rotation is shown in Table 3.

Multimedia Appendix 9 presents the EFA results, indicating the number of items for each construct before and after the study. The initial 42 items were reduced to 41 (98%) following the elimination of item O1 due to a low factor loading <0.60, culminating in a final set of 41 items for the quantitative instrument.

This study assessed the internal reliability of the retained items using the Cronbach α [130]. The Cronbach α is used to assess the internal consistency of measurement items and how well they measure the same underlying concept [93,96,105,106,108,109,128]. In this study, a Cronbach α value of ≥0.7 was necessary for assessment [121]. All studied constructs exhibited values of >0.7, indicating satisfactory internal reliability. PEOU had the highest Cronbach α at 0.969, whereas PU had the lowest at 0.914. This evaluation highlights the robustness of the measurement items in capturing the intended underlying constructs. Table 4 presents the Cronbach α score for each construct.

This study aimed to improve the quality and validity of a questionnaire used for measuring the acceptance of Casemix in the THIS setting [114,131-133]. The pilot study was conducted at one of the THIS MOH facilities in Malaysia using an SAQ based on multiple questions and adapted from previous instruments. The results showed that the SAQ is reliable and valid for assessing the critical factors and acceptance of Casemix implementation in THIS MOH hospitals in Malaysia.

Conclusively, the focus of this study was to explore items based on the constructs of the system, information and SQ, organizational characteristics, PEOU, PU, ITU, and acceptance. This was to develop a quantitative instrument measuring the acceptance of the Casemix system implementation within the THIS setting. All the constructs met the Bartlett test requirements (significant with P<.001) and had satisfactory KMO scores (>0.6), indicating that the instrument is suitable for factor loading. EFA is crucial for developing and evaluating study instruments. In this study, EFA was based on pilot data, enhancing its practical use. By analyzing various criteria, including scree plots and item-total correlations, EFA suggested a 9-factor solution. Of 42 items, 41 (98%) were retained for the final instrument, explaining 84.07% of the TVE. Using EFA with PCA and varimax rotation, 9 components were extracted, with only 1 construct splitting into 2 components. These components, related to organizational characteristics, were identified as structure and environment. A total of 42 items were used to measure these constructs, with 41 (98%) items showing factor loadings exceeding the minimum value of 0.6. One item (O1) was removed due to a low factor loading of <0.6. The remaining items for all constructs demonstrated high Cronbach α values, indicating strong internal reliability. The results confirmed the relevance of the items for this study. The final questionnaire was validated and is considered reliable for field studies. The refinement and validation of the preliminary instrument ensured its internal consistency and validity across the sample. Further research should broaden the target population and increase the sample size for more robust findings. This study aimed to guide policy makers, health care professionals, and administrators regarding strategic service expansion and solutions to identified challenges. Worldwide, this study provides a tool for assessing the effectiveness of technology-driven solutions. Evaluating the Casemix system’s quality and usefulness is vital for making informed decisions and ensuring its long-term acceptability and sustainability in the THIS setting.