COVID-19 and Diagnosis-Related Group in one of the university hospitals in Malaysia: patient classifications and associated hospital costs

Introduction

Background

The coronavirus disease 2019 (COVID-19) pandemic has been associated with a considerable health burden on many healthcare systems worldwide. The pandemic resulted in significant economic and financial implications in addition to the clinical burden. The country was forced to prepare to cope with the additional pressure on the healthcare system to ensure that the services are not interrupted during the crucial time (1). Nevertheless, there were many uncertainties during the pandemic and the magnitude of the economic consequences to the healthcare system in Malaysia, particularly in the university hospital is unknown.

The COVID-19 pandemic impacted healthcare systems and patients in many aspects, with the most immediate need being the increased healthcare burden and the increased demand for healthcare resources including healthcare personnel, healthcare facilities, and an increased load of clinical episodes. To effectively contain the pandemic at a national level, Malaysia continued to invest heavily in the healthcare system (2). To date, scarce data is estimating the clinical and economic burden associated with the COVID-19 pandemic in Malaysia. Many epidemiologic analyses estimated a medium to long-term timeline of implications related to this pandemic, which is expected to result in increased healthcare needs nationally and corresponding budget (3-5). The increased pressure on the healthcare system to ensure that the health system capacity can continue to provide routine health services during the pandemic will require considerable funding and human and medical resources. The changes to the delivery of usual clinical care alongside the clinical management of COVID-19 patients can have a substantial impact on healthcare resource use and therefore healthcare funding (2).

The costs of managing COVID-19 cases are varied, including direct medical costs such as hospitalizations, intensive care unit (ICU) admissions, and treatments, direct non-medical costs such as out-of-pocket expenses for transportation to clinic visits and hospitalizations and indirect costs such as loss of income, absenteeism and presenteeism. Furthermore, intangible costs related to pain and anxiety due to the disease, which were less widely reported and analyzed, were also significant (6,7).

Several studies have documented the considerable economic burden imposed by COVID-19 hospitalizations on healthcare systems. In the United States, it was estimated that total direct medical costs ranged from $163.4 billion to $654.0 billion. This substantial variation is attributed to factors such as sociodemographic characteristics of patients, clinical manifestations, and disease severity. The study highlighted that hospital admission at the ICU and utilisations of mechanical ventilation were the primary cost drivers (8). Similarly, in Italy, it was estimated that an average cost of approximately €9,500 per hospitalized patient, with ICU admissions significantly inflating this figure (9). Studies from China echoed these concerns. The average cost for hospitalized patients was ¥21,500, with ICU admissions accounting for a considerable portion of the expenses (10). Another research conducted in Spain reported that the direct medical costs for hospitalized patients ranged from €2,000 to €15,000, depending on illness severity and the level of care required (11).

Studies in India and Indonesia estimated the healthcare costs associated with COVID-19 hospitalizations ranged from Indian rupee (INR) 90,000 to INR 150,000 and Indonesian rupiah (IDR) 50 million respectively, with ICU care significantly increasing costs (12,13). Studies from the Philippines and Bangladesh further emphasize the financial strain on healthcare systems. The average cost per patient in the Philippines was around Philippine peso (PHP) 200,000, while Bangladesh reported an average of Bangladesh taka (BDT) 150,000, with ICU admissions again a major cost factor (14,15).

These studies highlight the considerable costs associated with managing COVID-19 cases particularly hospital admission for the severe stage of disease requiring intensive care and mechanical ventilations. The costs varied by country due to the differences in healthcare systems, health policy, resource availability, and patient demographics. Understanding these economic consequences is critical for policymakers and healthcare providers to reduce the financial impact of future pandemics and ensure the sustainability of healthcare systems.

Due to the high burden related to COVID-19, there is a need for Malaysia to generate data to estimate the downstream economic and budgetary consequences of the pandemic to inform national COVID-19 management strategies and beyond the pandemic, to prepare for the subsequent outbreaks (5). Robust estimates of the economic consequences can inform the development and selection of national clinical strategies as well as optimise patient outcomes in our local settings by ensuring that funding for healthcare remains sufficient for pandemic control.

Following the detection of the first case of COVID-19, Malaysia’s government has ramped up steps to meet the challenges of the pandemic. University Malaya Medical Centre (UMMC) has been gazetted as a hybrid hospital (a tertiary care centre that was responsible for providing care for COVID-19 and non-COVID-19 cases) and has geared up the increase in the number of hospital beds and patient workload to cater for the need of the nation (6,7). One of the strategies used by the UMMC to facilitate decision-making on resource allocation was the case-mix implementation. Case-mix provides the healthcare system with a consistent method of classifying types of patients, their treatment episodes and associated costs. Conditions and treatments are often captured using a medical classification system, in a process called clinical coding. The coded data can be grouped further into Diagnosis-Related Groups (DRG), which are useful in the billing process, budget estimation and management of healthcare resources. It involves developing and implementing a patient classification system, usually through the DRG that categorizes patients according to their clinical conditions and healthcare resources used (16,17).

Through the case-mix system, a treatment episode of a patient will be classified into a specific code or group, which reflect the clinical characteristics and resource used for that particular code or group. The case-mix code or group will have its medical costs related to its clinical management. Therefore, through this system, the hospital can identify and control its spending and expenses for unnecessary resources used by clinicians and the hospital could also optimize the resources efficiently (16,17). Hospitals may also use the information from the case-mix system to determine the performance of service delivery based on health outcomes achieved with the costs of achieving those outcomes. The application of case-mix will improve transparency on services provided and also standardise the treatment care. Case-mix will help the hospital to enhance the quality of care and deliver cost-efficient services to patients. (18).

Case-mix has become more crucial during the pandemic. Studies in Cape Town and the UK showed a 15% decrease in emergency visits during lockdown in 2020 compared to 2019 (19,20). These findings provide input to the hospitals to allocate their resources accordingly based on the workload. In Milan, a case-mix system helped predict disease patterns, patient outcomes, and manage healthcare resources during the COVID-19 peak (21). Studies also found that case-mix analysis could anticipate outcomes and resource utilization for patients, enabling hospitals to prepare effectively for managing cases (19-22). These findings highlight the importance of utilizing case-mix data for efficient healthcare resource allocation during emergencies like pandemics.

Rationale and knowledge gap

Nevertheless, there is no study on the DRG and case-mix of COVID-19 in Malaysia. Therefore, this study was conducted to determine the DRG and the hospital costs associated with the management of COVID-19 cases in one of the university hospitals in Malaysia, which is the UMMC among patients admitted at the centre in 2021.

Objective

This study aimed to determine the DRG and the hospital costs of COVID-19 cases in the largest university hospital in Malaysia to understand the magnitude of economic implication related to the pandemic.

Methods

The methodology used in this study is designed to achieve the study objective. There were mainly two study designs used, which were a cross-sectional study design, and a descriptive cost-of-illness study. These study designs used several approaches and methods of data collection, study tools and data analyses. The details of the methodology will be described accordingly in the subsequent sub-sections.

DRGs for COVID-19 cases at UMMC

To obtain the DRGs for COVID-19 cases, patients’ data from the Health Information System (HIS) were extracted. This data was gathered among patients who were admitted to UMMC in 2021. Data for our study were gathered from hospital records between January 1, 2021, and December 27, 2021. The patient hospitalized on December 27, 2021, was the last COVID-19 patient admitted to UMMC in 2021. Subsequent patients were admitted from January 2022 onward. This 12-month timeframe was chosen to provide a thorough perspective of the DRG distribution for COVID-19 cases over the course of a full calendar year, allowing for a study that takes into account probable epidemiological fluctuations as well as the pandemic’s many stages over severity that year. Also, as one of the objectives of this study was to calculate the total annual costs associated with COVID-19 management, by using annual data, we ensured that all costs incurred throughout the year were included, providing a complete financial assessment.

To give perspective regarding Malaysia’s situation on COVID-19 in 2021, there were significant and dynamic changes in COVID-19 hospitalisation and policy changes. We also chose a one-year distribution to reflect important policy changes and public health initiatives adopted in 2021, such as lockdowns, vaccination rollouts, and changes in treatment protocols. These parameters had a considerable impact on the frequency and severity of COVID-19 cases.

While we recognize the relevance of temporal analysis, we did not conduct a comprehensive examination of DRG distribution across weeks or months for a variety of reasons. First, national public domain websites provide information on the temporal trends of COVID-19 instances and severity, including weekly and monthly fluctuations. These statistics give precise information on the temporal distribution of COVID-19 cases throughout the country. Furthermore, our first comparisons revealed that the trends seen at the national level were comparable to those identified at UMMC. Only data about in-care patients were extracted while data on outpatient visits were excluded. This is because, at the moment, Malaysia only has a DRG system for in-patient care and does not have any patient classification system for outpatient care and daycare visits (8-10). During the study period, the case-mix system was not fully implemented at UMMC; therefore, most of the data were not coded and entered into the HIS as a free-type format. The HIS works, as a tool to document and store all patients’ data in soft copy and has a very minimal analytical component, which is not capable of generating DRGs.

Data that were extracted from HIS consisted of sociodemographic characteristics of patients such as patient identification, date of birth, age, admission date, discharge date, gender and discharge status. All diagnoses recorded through International Classification of Diseases, 10^th Revision (ICD-10) codes were extracted for both primary and secondary diagnoses for each patient admission. While clinical procedures were all in a free-type format, all of the procedures were also extracted.

For the diagnosis code (ICD-10), all codes selected by the clinicians were checked to ensure that they were accurate. If the code was found to be wrong, the patient’s case notes were re-read and re-assessed to confirm the diagnosis. Then, the correct ICD-10 codes will be given and correction of the codes will be made in the system if the codes were wrongly assigned. This activity was done by four trained coders monitored by two medical officers and confirmed by two case-mix experts in the centre. For clinical procedures, all the free text clinical procedures were coded accordingly with ICD, 9^th Revision, Clinical Modification (ICD-9-CM). Also, the ICD-9-CM codes selected by the trained coders based on the descriptions of the procedure were double-checked by the two medical officers and confirmed by the two case-mix experts in the centre. Any missing data found in the HIS were clarified with the clinicians involved in treating the patients and were double-checked with the hard copy documents accordingly. Before the DRG codes were generated, all required data were ensured to be completed.

Statistical analysis

All the coded data (both ICD-10 and ICD-9-CM) and patient information were entered into the grouper and the system did the grouping and produced DRG as outputs. If the DRG output was an error, the input was identified and re-checked. Based on the output generated by the grouper, the DRG codes used for COVID-19 were A-4-13-I, A-4-13-II and A-4-13-III for mild, moderate and severe respectively. In the DRG code, the final digit, which is written as a Roman number reflects the resource intensity level, which is mild, moderate or severe. The bigger the number, the greater the healthcare resource utilization required, and therefore the higher the costs (8-10).

Other than generating DRG codes, the grouper is also capable of providing information on the expected average cost of treating patients for every DRG through statistical analysis and mathematical algorithm matrix in the system. The costs were determined through several case-mix parameters such as hospital base rate, disease cost weight and case-mix index. Nevertheless, when the study was conducted, those case-mix parameters were not available at UMMC. Due to this limitation, the data that can be used from the grouper were only the DRG without the cost for each DRG.

While the objective of this study was to determine the DRG and the hospital costs associated with the management of COVID-19 cases, the output generated from the grouper was only used to determine the DRG. For the hospital costs, the estimations were conducted through different approaches as described below.

Cost of COVID-19 treatment and management at UMMC

As mentioned earlier, the unavailability of data on case-mix parameters at UMMC during the study has limited us from generating the DRG costs for the identified codes, which are A-4-13-I, A-4-13-II and A-4-13-III. Therefore, to overcome this limitation, a descriptive cost-of-illness study was conducted using a combined approach of top-down and micro-costing methods to capture the hospital costs of COVID-19 at UMMC. The costing analysis was conducted from the hospital’s perspective, UMMC, the largest teaching hospital in Malaysia that has been gazetted as a designated COVID-19 centre. The time horizon was one year and the costs reported were the direct medical costs for hospitalizations and ICU admissions due to COVID-19 with and without intubation. Identification of major healthcare cost categories was carried out through discussions with experts, and these categories included UMMC’s general overheads, hospitalizations, ICU admissions and COVID-19-related medicines. In addition, special allowance for healthcare workers for COVID-19 services, costs of decanting for non-COVID-19 patients to other healthcare facilities and expenditures for consumables and equipment related to COVID-19 management were also inputted to obtain the cumulative costs incurred by UMMC for COVID-19 management in 2021. The cost of outpatient visits and COVID-19 vaccinations were not within the scope of this analysis.

The costs were estimated based on healthcare resource utilization from clinical pathways gathered through face-to-face interviews with experts and from the national guidelines for COVID-19 treatment (11,12). In addition to the information obtained through clinical pathways, we have also compiled data on the total numbers of COVID-19 hospitalizations (number of admissions and number of bed days) and ICU bed days (with and without ventilation) at UMMC in 2021. All related unit costs were estimated from various sources from UMMC such as previous costing analysis studies (13-15), previous presentations done by UMMC staff (16-19), the hospital’s annual report (20), published studies (21,22) and expert opinions. The price year used in this study was 2021 [USD 1 = ringgit Malaysia (RM) 4.48]. Discounting and inflation of cost data were based on local guidelines and official rates.

Statistical analysis

The hospital costs were estimated for costs of (I) admission to the general medical ward and (II) admission to ICU with and without intubation per patient. Then, costs for all admissions were calculated by multiplying the cost per patient with the statistics or volume of patients. For patients who were admitted to the ICU, it was assumed that they spent some days in a general medical ward before and after the ICU admission. This is because, based on the treatment pathways and fundamental concept of medical costing, the ICU is the intermediate cost centre whereby, in any treatment episode, the patient will neither be admitted straight to the ICU nor discharged from the ICU (10).

To estimate the total hospital costs in 2021 for COVID-19, all cost components (general overheads, hospitalizations, ICU admissions, COVID-19-related medicines, special allowance for healthcare workers for COVID-19 services, costs of decanting for non-COVID-19 patients to other healthcare facilities and expenditure for consumables and equipment related to COVID-19 management) were added to get the cumulative cost.

Ethical consideration

This study was reviewed and approved by the University of Malaya Medical Centre-Medical Research Ethics Committee (UMMC-MREC) (approval No. 202133-9908). Consent to participate was waived by the UMMC-MREC. The study was performed according to the relevant guidelines and regulations. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Results

Based on the data obtained, a total of 4,889 patients with COVID-19 diagnosis were admitted in 2021 from January to December, at UMMC. There were approximately equal distributions of male (n=2,456, 50.2%) and female (n=2,433, 49.8%) patients. The median [interquartile range (IQR)] age of the patients was 51.0 [30.0] years old. The details of the distribution of sociodemographic characteristics of COVID-19 patients were presented in Table 1.

Of that, 98% (4,813/4,889) had a primary diagnosis of COVID-19 while the remaining 2% (76/4,889) of the patients were admitted for other medical reasons, and were found to be positive for COVID-19 during the admission. The details of the distribution of COVID-19 patients were presented based on DRG as frequencies and percentages in Table 2.

Among the patients, 3,909 of them were admitted with mild COVID-19 (A-4-13-I), 630 had severe COVID-19 (A-4-13-III) and the remaining 274 were admitted for moderate COVID-19 (A-4-13-II). The mild, moderate and severe COVID-19 in this context refers to the intensity of resource utilisation rather than the clinical categories of the disease. Further analysis showed that the distribution of gender by the intensity of resource utilisation were 50% male and 50% female for mild cases, 34% male and 66% female for moderate cases and 58% male and 42% female for severe cases. While for age distribution, the mean age of patient in the mild, moderate and severe cases were 41.1, 44.5 and 61.2 years, respectively. Nevertheless, there were no significant associations between gender and age with the intensity of resource utilization. Among the mild COVID-19 patients (A-4-13-I), the mean ± standard deviation (SD) and median [IQR] for the length of stay were 9.46 (9.152) and 8±6 days respectively. For moderate COVID-19 patients (A-4-13-II), the mean ± SD and median [IQR] for the length of stay were 13.05±21.005 and 9 [7] days respectively. While for the severe COVID-19 patients (A-4-13-III), the mean ± SD was 13.89±12.643 days and median [IQR] for the length of stay was 10 [12] days. The majority of the patients with COVID-19 as the secondary diagnosis were admitted for cardiac catheterisation and other elective procedures. They were found to be positive upon screening before the elective procedures. Based on the data obtained, of the 4,889 patients admitted, 88% were discharged well, 11.4% died during the admission, 0.2% were unknown and the remaining 0.1% were left against medical advice.

From the hospital database, it was found that 78.0% (3,814/4,889) of the patients were admitted to the general medical ward only, while 22.0% (1,075/4,889) required ICU admission during the hospital stay. The average length of stay (ALOS) for patients admitted to the general medical ward was 9 days, with the minimum and maximum ALOS of 1 and 12 days, respectively. For the ICU stay, the average ALOS was 8 days with the minimum and maximum stay of 1 and 17 days, respectively. If the patients were admitted to the ICU, it was assumed that the patient would spend 13 days in the general medical ward before and after the ICU admission. The assumption was made based on the information obtained from the clinical pathways.

In this study, it was estimated that the mean costs of medical ward admission were RM 8,824.23 and ranged from RM 980.47 to 11,768.88. For patients requiring ICU admission, the mean cost for ICU stay only was RM 44,848.56 and ranged from RM 5,606.07 to 96,303.19.

In this analysis, the COVID-19-specific medications were only given to those admitted to the ICU as in the clinical pathways. The medications include common anesthesiology drugs for intubation, hydroxychloroquine, tocilizumab, antibiotics, diuretics, prophylaxis for deep vein thrombosis, Tamiflu and medications for comorbidities such as diabetic drugs and anti-hypertensive. The rest of the patients treated in the general medical ward received minimal medications as symptomatic therapy only. Table 3 describes the details of the direct medical costs included in the analysis.

The total direct medical costs for admission to the medical ward per patient were approximately RM 8,992.51 (Table 3). When the total number of admissions, which was 3,814, multiplied this amount, the cumulative costs were RM 34,297,433.14. The direct medical cost to manage patients with ICU admission was higher. It was estimated that the cost per patient per admission was RM 77,436.58 (Table 3). When the total number of ICU admissions, which was 1,075, was multiplied by the estimated amount, the cumulative costs were RM 83,252,923.50. Overall, the total direct medical costs incurred by UMMC alone for COVID-19 management in Malaysia was almost RM 117.5 million in 2021 (Table 4).

Apart from the direct medical costs of COVID-19 management, the details of other cost components are presented in Table 4. The additional costs include a special allowance for healthcare workers for COVID-19 services, costs of decanting non-COVID-19 patients to other healthcare facilities and extra consumables and equipment related to COVID-19 management. The special allowance for COVID-19 services was funded through internal UMMC funds and a reserved hospital fund from previous years. The costs of decanting non-COVID-19 patients were incurred by the Ministry of Health (MoH) while the costs for extra consumables and equipment related to COVID-19 management were done through donations from various institutions and personnel. The total cost incurred by UMMC in 2021 to manage COVID-19 was RM 131,836,935.81, which was approximately 12% of the annual UMMC budget.

Discussion

Key findings

This study helps to comprehensively estimate the potential consequences of the pandemic on the healthcare system, and use UMMC as an example of a healthcare centre in Malaysia that manages COVID-19 in-patient cases. In addition, an economic analysis helps to estimate and forecast different strategies that would help to inform healthcare decision-making concerning the development of policies for preparedness and management of any outbreaks or pandemics.

In this study, it was found that UMMC incurred a huge financial implication in managing COVID-19 alone. Based on the cost estimates, 12% of the hospital budget was required for COVID-19 alone (23), which resulted in disruption to other clinical services (24-26). The major cost driver was the admission to ICU stay. Patients with COVID-19 could be presented with acute respiratory distress syndrome (ARDS) that need mechanical ventilation and require them to be treated in the ICU. Some of the patients manifest with cytokine storm, a condition in which cytokines are released into the bloodstream damaging body organs and potentially leading to multiple organ failure. This condition requires intensive care including dialysis for kidney failure and certain drugs that are available only in ICUs (10). The amount is disproportionately large considering it is a single disease, compared to the other chronic diseases and malignancies treated at the centre (27-34). According to the information obtained from the experts and the hospital’s financial report, should there be no reserved funds and financial assistance from the institutions and personnel, the hospital budget at that time might be challenging to manage the pandemic on top of other non-COVID cases (23).

The sudden increase and ongoing COVID-19 cases have resulted in considerable challenges to the UMMC to provide high-quality and effective healthcare services as a hybrid hospital for COVID-19 and non-COVID patients. At the same time, as a university hospital, UMMC plays a role in providing training and education to people and healthcare workers. The hospital has developed a comprehensive COVID-19 management structure based on the updated guidelines on preparedness and readiness. Human and budget priorities are crucial for UMMC to ensure the sustainability of the services and therefore a substantial and optimised budget allocation is important for the hospitals to function at their maximum, especially during the pandemic. One of the important tools to measure and justify more financial needs and hospital budgets is through the case-mix system (23).

In this study, the case-mix analysis that was conducted focussed on the descriptions and distribution of the hospital’s workload rather than the estimation of the hospital tariffs. It was found that the majority of the patients admitted at UMMC were in the mild case-mix group, followed by the severe and moderate groups. There are several possible reasons for the patterns of the patients’ distributions seen. Firstly, as a tertiary hospital in Kuala Lumpur, UMMC complemented the roles of the other public hospitals and received referrals from nearby primary healthcare facilities. At that time, symptomatic and vulnerable patients were required to be admitted regardless of the stages of the COVID-19 disease. While the vaccination program was ongoing, there were still many more patients with lower clinical stages of disease who required admission and were transferred to UMMC since other public hospitals at Klang Valley were fully occupied (6,7). Secondly, the severe stage of COVID-19 disease (A-4-13-III) admitted at UMMC was higher compared to the moderate stage found in this analysis. This was because UMMC is the largest teaching hospital in Malaysia with comprehensive intensive care facilities with various clinical specialties. UMMC can rapidly convert general wards to ICUs for critically ill patients with COVID-19 there, which is capable of managing more severe cases (23).

Even though the estimation of direct medical costs was conducted through a traditional approach, rather than the case-mix tariff in this study, a discussion with a case-mix expert from the MoH, Malaysia found that the costs estimated in this study were consistent with the case-mix costing analysis done by the MoH. An unofficial report by the MoH found that the tariff for mild COVID-19 case per patient was approximately RM 6,960.00 to RM 8,000.00 (the price year 2021), depending on the hospital, which was similar to our findings for admission to the ward. While the tariff for moderate and severe cases ranged from RM 15,000.00 to RM 25,000.00, which was lower than the estimated cost in this study (35-37). The possible reason for this observation is because of the economies of scale whereby, UMMC has several new buildings and more professors and academicians, which leads to higher personnel costs and also sophisticated clinical equipment with a lesser number of patients compared to the public hospitals. In addition, the financial reporting from the MoH hospitals might not compute comparable costs as in UMMC, because some of the costs might have been absorbed under a different budget. For example, a COVID-19 patient who required dialysis in the MoH hospitals might have been treated and cost under the renal unit budget rather than the COVID-19 budget (38). Nevertheless, when a comparison was made to the other university hospital in Malaysia, the University Kebangsaan Malaysia Medical Centre (UKMMC), the tariff for A-4-13-III was estimated at RM 69,964.00 (39), which was close to our estimation. It shows that the nature of the workload and facilities provided by UMMC and UKMMC are similar. Table 5 illustrates the costs of various severity levels of DRG at other university hospital (UKMMC) and the MoH hospitals as a reference. This additional information provides context on the cost and economic impact of healthcare resources related to COVID-19 in this study.

This study highlights that using case-mix in classifying various types of COVID-19 patients at the UMMC has helped in estimating the workload and patients’ distributions. Application of case-mix will help for better management with cost optimization in treating patients; hence will improve the quality and quantity of service in UMMC. This study has provided new data on the analysis of COVID-19 with case-mix analysis; however, it has several limitations. Due to the unavailability of the case-mix tariffs at the time of the analysis in the case-mix database at UMMC, the estimation of the direct medical costs was conducted through a traditional approach. Therefore, the comparison of case-mix tariffs with the public hospitals and other university hospitals in Malaysia is rather challenging. In addition, the economic implications found in this study only reflect the burden at UMMC and could not be generalized to the whole country. Nevertheless, the methods can be used and replicable in other healthcare settings in Malaysia. The economic implications related to COVID-19 at national level is currently being conducted by the research team from the MoH Malaysia and several authors in this study involved actively for the report.

Conclusions

In conclusion, sufficient resources and financial commitment will need to be available to successfully navigate our way through the pandemic, also in the post-pandemic era. Case-mix system is effective and helps to increase the efficiency and quality of hospital care, improves transparency in hospital activities and encourages cost-containment initiatives. The system can also be used to assess the performance of the hospital in general, or individual clinicians in specific, in terms of quality of care, resource utilization and clinical outcomes. Findings from this study can help to inform national policy planning and resource allocation in the context of limited economic resources to optimise benefits for the country, and also can help healthcare facilities to prepare, financially, for future outbreaks or pandemics.

Acknowledgments

None.

Footnote

Data Sharing Statement: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-65/dss

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-65/prf

Funding: This study was part of a project “Developing a new intelligent healthcare system assisted by the diagnosis-related group (DRG) coding and clinical pathway in Malaysia” under the Fundamental Research Grant Scheme (FRGS) funded by the Department of Higher Education, Ministry of Higher Education Malaysia (No. FRGS/1/2022/SS06/USIM/02/1; FRGS USIM Vote. USIM/FRGS/FPSK/KPT/50622), and COVID-19 Epidemiological Analysis and Strategies (CEASe) Project with funding from the Ministry of Science, Technology and Innovation (No. UM.0000245/HGA.GV). Both are government organizations and they provide us with funding to hire enumerators to collect the data. The fund also supported the design of the study and collection, analysis, and interpretation of data and manuscript publication.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-65/coif). All authors report that this study was part of a project “Developing a new intelligent healthcare system assisted by the diagnosis-related group (DRG) coding and clinical pathway in Malaysia” under the Fundamental Research Grant Scheme (FRGS) funded by the Department of Higher Education, Ministry of Higher Education Malaysia (No. FRGS/1/2022/SS06/USIM/02/1; FRGS USIM Vote. USIM/FRGS/FPSK/KPT/50622), and COVID-19 Epidemiological Analysis and Strategies (CEASe) Project with funding from the Ministry of Science, Technology and Innovation (No. UM.0000245/HGA.GV). Both are government organizations and they provide us with funding to hire enumerators to collect the data. The fund also supported the design of the study and collection, analysis, and interpretation of data and manuscript publication. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was reviewed and approved by the University of Malaya Medical Centre-Medical Research Ethics Committee (UMMC-MREC) (approval No. 202133-9908). Consent to participate was waived by the UMMC-MREC. The study was performed according to the relevant guidelines and regulations. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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