A patient-centered digital cost simulator based on Time-Driven Based-Activity Costing: a proof-of-concept study of knee replacement in two private Portuguese hospitals

doi:10.21203/rs.3.rs-1619416/v1

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Research Article

A patient-centered digital cost simulator based on Time-Driven Based-Activity Costing: a proof-of-concept study of knee replacement in two private Portuguese hospitals

https://doi.org/10.21203/rs.3.rs-1619416/v1

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Background

Decision-makers need accurate information on intervention outcomes and costs to manage the healthcare system more efficiently. Literature proposes Time-Driven Based-Activity Costing (TDABC) as a cost measurement model that accounts for care cycle complexity. However, few studies show how to apply the TDABC model in a real context, considering the existing hospitals' information systems (IS). Our primary goal was to design a patient-centered digital cost simulator for assessing the provider's cost of care delivery in knee replacement surgeries. Additionally, to synthesize the learning experience of implementing it.

Methods

The digital cost simulator was designed on a set of spreadsheets, in Portugal based on the TDABC model and implemented in two Portuguese private hospitals located in two cities by two multidisciplinary TDABC operational teams. The cost simulator development and implementation followed the TDABC implementation steps proposed in the literature. Finally, we developed comprehensive dashboards to analyze the costing information by conducting two focus group sessions. Through one unstructured focus group, teams evaluated the implementation and development of the digital cost simulator.

Results

The cost simulator and the data structure for collecting the most relevant cost data were developed and tested. From the analysis of the developed dashboards, one can visualize the duration of the activities, the ones that can take the most time, the costliest activities, and resources, and how costs are distributed before, during, and after the surgery. Costs with Human Resources represent most costs during pre- and post-hospital surgery. However, during surgery, consumables are the costliest resources.

Conclusion

The developed simulator enlightened the healthcare provider's need for future improvements in the IS. From the simulator implementation, we concluded that the patient journey must drive data collection and analyses and breaking the activities into operations helps obtain time estimations and allocate resources better. Additionally, listing of the used resources should follow the financial IS classification. Moreover, Implementing the cost simulator requires the hospitals' IS to collect and interoperate with different data sources along the patient pathway. Finally, everyone agreed that its implementation involves the commitment of the healthcare provider's clinical and non-clinical teams.

Time-driven activity-based costing

TDABC

Microcosting

Cost analysis

Healthcare costs

Implementation

Implementation process

Process analysis

Organizational learning

Experience-based learning

Costs in healthcare have been increasing worldwide due to population aging, rising healthcare expectations, and the usage of new technologies (1). In addition, the Organization for Economic Co-operation and Development (OECD) estimates that around 30% of healthcare spending is wasted (2, 3). A healthcare service or process is considered to be wasteful if they are harmful, do not deliver benefits, or can be replaced by cheaper options that lead to, at least, similar results (2). These concerns highlight the importance of understanding, controlling, and accurately assessing healthcare costs (1, 4). However, computing costs in this sector is particularly challenging due to activities’ complexity and patient variation (5).

Several costing methods have been used in healthcare, yet there are two main categories: top-down and bottom-up (1, 5). The former assigns the total costs of a healthcare system to an individual service based on hospital performance metrics, not considering time differences or patient variability (6, 7). Oppositely, the bottom-up approach considers the costs associated with the care delivery to the individual patient (1) and infers the cost to the whole targeted population by appraising different patient profiles. It helps decision-makers understand if variations in unit costs or variations in resource use intensity explain cost disparities between different services or the same service conducted in various sites. This category allows identifying all relevant costs for each patient and increases the likelihood of more accurate cost estimations (6). Within the bottom-up costing category, Time-Driven Activity-Based Costing (TDABC) assigns costs according to each resource's time devoted to delivering an individual service (8, 9). TDABC can help reduce inefficiencies and unused capacity by allowing to identify opportunities for process improvement through resource reallocation. When applied in healthcare, studies conclude that TDABC measures the costs more accurately than the traditional hospital accounting systems since the latter generally overestimates them (8, 10–12).

TDABC usage in health economics has expanded over the last five years (13). Most of the studies focus on the results of its implementation in different settings (14). However, TDABC's practical application raises several hurdles as its implementation is complex, costly, and time-consuming (1, 7). It demands a considerable effort to collect and organize costs (4) and the engagement of the hospital’s clinical and non-clinical staff (6). Moreover, few information technology systems sustain TDABC (4). Despite reporting these issues, previous studies are not explicit on how to operationalize this method in mainstream practice and differ in its implementation (13). Therefore, the literature suggests that studies should describe the incurred challenges and clarify the necessary steps to implement the TDABC model (9, 15).

In response to the above difficulties, our primary goal was to design a patient-centered digital cost simulator and test it to assess the cost of knee replacement surgery in two Portuguese private hospitals. The secondary objective was to synthesize the learning experience of implementing it.

2.1. Population and Sampling

The study was conducted in two Portuguese private hospitals that belong to one of the largest private hospital groups in Portugal. Hospital A, located in Lisbon, and Hospital B in Santarém provide hospitalization, outpatient, and emergency services. Hospital A has 161 beds and five operating rooms (ORs), while Hospital B has 22 beds and three ORs. In 2021, Hospital A had roughly 470.000 outpatient appointments, 17.000 surgeries, 100.000 urgencies, and 50.000 hospitalizations. Hospital B reported in 2021 101.000 outpatients’ appointments, 3.000 surgeries, 18.000 urgencies, and 7.000 hospitalizations. Both hospitals performed 325 knee replacement surgeries in 2019 (Hospital A: 170; Hospital B: 155). Both hospitals were already assessing clinical and patient-reported outcomes using the ICHOM Standard Set for Hip and Knee Osteoarthritis (16). Moreover, Hospital B performed perioperative activities while Hospital A did not, so the hospital administrative and clinical staff was interested in comparing the two hospitals’ costs and outcomes.

As the goal of this study was to design and test a new digital cost simulator, not to determine each hospital cost of delivering knee replacement surgery, nine patients were followed in different care process segments (six in Hospital A and three in Hospital B) to provide the simulator with actual patient data. These patients were chosen by the hospital clinical staff as they followed the most used treatment strategies in knee replacement surgery. Patients readmitted to the hospital were excluded. Study participants signed an informed consent before participating in the study.

2.2. TDABC model and digital cost simulator design and implementation

Developing and implementing the TDABC cost simulator is a multidisciplinary task that involves clinical and non-clinical expertise (15). One multidisciplinary TDABC operational team was assembled at each hospital composed of one orthopedic surgeon, one anesthetist, two nurses, and two engineers from the Data Analytics and Value-Based Healthcare (VBHC) team, all familiar with the knee replacement surgery procedure and the hospital information system (IS). Additionally, the authors, from now on referred to as the research team, led the design of the patient-centered digital cost simulator, and guaranteed the data collection process.

The patient-centered digital cost simulator development and implementation followed the following steps (17): mapping the patient pathway with the main performed activities, identification of the main resources used in each activity and department, estimation of the total cost of each resource group and department, estimation of the capacity of each resource and calculate capacity cost rate (CCR), analyzing the time estimates for each resource used in an activity, and calculation of the total cost of patient care.

2.2.1. Step 1 - Mapping the patient pathway with the main performed activities

To map the patient pathway, three focus group sessions were conducted with each hospital's multidisciplinary TDABC operational team. In the first session, as both hospitals were already collecting health outcomes over the care pathway by following the ICHOM standard set (16), we used the existing data collection timestamps to guide the first session (18). The research team presented a chronological list of all patient pathway activities in the second focus group session based on the hospital's staff input in the previous one. Each hospital's multidisciplinary TDABC operational team made corrections and suggestions until everyone agreed on a final version of the process maps. The research team mapped both in-and-out-patient pathways using business process modeling in Visual Paradigm©. Then the multidisciplinary TDABC operational team validated the patient pathway in the last focus group session. Activities conducted outside the hospital were not included.

2.2.2. Step 2 - Identification of the main resources used in each activity and department

During Step 1 last focus group session, the research team collected one element that defines each patient pathway activity - the used resources (18). The research team only included the hospital resources directly involved in the patient pathway, to reduce the initial complexity of the proof-of-concept for the digital cost simulator. Resources were categorized into four modules based on Kaplan's terminology (8): employees, spaces, equipment and technology, and consumables. The research team developed a database that includes all collected resources. The research team matched the activities defined in Step 1 with the used resources in a new spreadsheet. Besides, the research team validated and added missing resources through in situ observation while conducting Step 5.

2.2.3. Step 3 - Estimation of the total cost of each resource group and department

First, the research team conducted a literature search to determine how to calculate the cost of each resource. Second, asked the financial and human resources (HR) department for costing information. Third, since the cost estimation for resources depends on their type and the provided information, the digital cost simulator contains four cost analysis modules in individual spreadsheets for each group of resources.

2.2.4. Step 4 - Estimation of the capacity of each resource and calculate capacity cost rate (CCR).

The CCR calculus requires the computation of the supplied resources' practical capacity (minutes of effective work per year (19)) apart from its cost (8). The research team assumed an 80% effective working time for all resources, as suggested in the literature (9). CCR is presented in euros/minute. First, the research team conducted a literature search to determine the time categories that should be included. Then, based on the search results, the research team asked the financial and HR departments for that information. For this step's operationalization, costa data was added to the spreadsheets created in Step 3. Spreadsheets were dived into annual cost and effective annual working time calculations. This structure enabled the automatic computation of each CCR by dividing the two outputs.

2.2.5. Step 5 - Analyzing the time estimates for each resource used in an activity

Two members of the research team measured process time, that is, the necessary time to perform each activity, through in situ observation in each hospital's real context (chrono-analysis (19)), by following different patients, as suggested in the literature (8). Considering that the main goal was to design a patient-centered digital cost simulator, not to determine the pathway costs accurately, this step was only performed to collect real data. So, a single measure for each activity was obtained at each hospital. The research team matched activities and their duration by adding one column to the spreadsheet developed in Step 2.

2.2.6. Step 6 - Calculation of the total cost of patient care

The research team used the information collected in all previous steps to compute the cost of patient care. The cost simulator was developed to calculate one patient care's total cost by summing all the time dedicated by each resource in each activity multiplied by the respective CCRs (8). For simplicity, it is assumed that there are no deviations from the patient pathway and no dependency between activities. However, activities and resources can be added or removed from the simulator, and the CCRs and activities' duration can be changed.

Two focus group meetings were conducted with both hospitals’ multidisciplinary TDABC operational teams to develop comprehensive dashboards with information that they considered valuable. In the first meeting, the research team asked which information they would like to visualize, and in the second one, dashboards containing that information were presented and validated.

2.3. TDABC and digital cost simulator assessment

There was a last meeting where the research team conducted one unstructured focus group (20) with the two hospitals’ multidisciplinary TDABC operational teams. The goal was to explore the features of the cost simulator that they would improve or do differently. The main ideas are included in each implementation step result.

The research team developed a computational tool that simulates knee replacement surgery's care delivery costs to the healthcare provider. This tool complements the methodology followed in both hospitals to assess clinical and patient-reported outcomes in specific clinical conditions in a VBHC program. This section also describes the learning experience of developing and implementing the digital cost simulator in two Portuguese private hospitals by summarizing the main results of the unstructured focus group in each step.

3.1. Step 1 - Mapping the patient pathway with the main performed activities

In the first focus group, Hospital A multidisciplinary TDABC operational team started by mentioning the difficulty of controlling the costs using the current accounting system as it did not allow for the allocation of costs to the patients that incurred on them, and different hospital departments had different systems. Furthermore, the multidisciplinary TDABC operational team members believed it was important to allocate the incurred costs to each patient. All members of each hospital’s multidisciplinary TDABC operational team participated in the focus group sessions physically held at each hospital.

Hospital A process map (Fig. 1) is an example of the outcome of this step. It was presented and validated by the multidisciplinary TDABC operational team in the third focus group session. It includes all the conducted activities from the patient signalization to surgery to the one-year post-operative appointment. Process mapping allowed the comparison among similar services in both hospitals concerning allocated resources and oriented the implementation of the next steps. The multidisciplinary TDABC operational team, with the graphical representation, recognized the complementarity between the outcomes and cost analysis. Furthermore, they stressed the importance of breaking each activity into smaller operations, as they believe it would be easier to adapt to each patient pathway.

3.2. Step 2 - Identification of the main resources used in each activity and department

The identified resources in Step 1 were allocated to each activity of the patient pathway according to their function. The sheet that resulted from matching the activities with the resources was then used to validate the allocation and add any missing ones in Step 5.

3.3. Step 3 - Estimation of the total cost of each resource group and department

Due to COVID-19, the hospitals had limited time to provide all the required information. Therefore, the research team only collected employee-related costs from the hospital IS (Table 1) and conducted a literature review to collect the remaining ones.

Table 1

Collected costs for each type of employee contract
Type of contract	Employees	Collected costs
Fixed-price contract	Administrative staff, cleaning action assistants, nurses, physiotherapists.	Annual gross salary, including fixed and variable remuneration, holidays, Christmas and meal allowances, and fiscal charges.
Service fee contract	Anesthetist, orthopedic surgeons.	Pre-agreed payment per surgery.
Outsourcing contract	Laboratory technicians.	Literature search to obtain an estimation.

When the research team started to assess cost-related information in the patient records and financial system, several incongruities related to the employees' classification collected in Step 2 were found. To accurately collect cost-related data from hospitals' IS, the research team first manually matched the job titles to the employees' category registered in the financial IS. This data categorization issue delayed data collection. The cost estimation model was structured to integrate the cost information directly. Like this, an analyst can simply modify and update the cost-related data.

For equipment and technology resources, the annual depreciation rate was calculated based on the Portuguese legislation (21). The yearly maintenance cost was computed as a percentage of the acquisition value (22). The annual equipment cost was the sum of its yearly depreciation rate and maintenance cost (12, 23). Regarding consumable goods, only their acquisition value was considered (12). To compute space costs, the research team used the hospital group's Annual Report and Accounts to retrieve the following costs: electricity, water, rent, maintenance, repair, building insurance, and cleaning, as suggested in the literature (12). The cost per square meter was computed by dividing the total cost by the hospital's gross square meters. According to official guidelines, the research team assumed the minimum required dimensions for each space (24, 25). All monetary values were collected in Euros as of 2020.

3.4. Step 4 - Estimation of the capacity of each resource and calculate capacity cost rate

The capacity estimation of each employee's category depends on their type of contract: fixed-price, service-fee, or outsourcing (Fig. 2). Regarding fixed-price contracts, we computed the available working days by subtracting weekends, holidays, vacations, personal leave, and days dedicated to research/education from the total number of days per year, 365 days (26). The number of working hours per day was extracted from each employee category's labor contract in the hospital IS. The yearly practical capacity was computed by multiplying the available working days by the available clinical minutes (26). For employees with the service-fee contract, CCR was calculated by dividing their surgery fee by the average surgery duration (9). Lastly, for employees with outsourcing contracts, the practical capacity was computed by multiplying the number of procedures conducted in the hospital by their duration. Equipment and technology and spaces’ CCR was calculated by dividing their annual cost by the minutes of actual use per year (23). The total cost of consumable resources results from multiplying its acquisition value by the number of used units (12).

To minimize the preprocessing of data collected from the hospital IS and optimize the data exchange with the digital cost simulator, the calculus of the CCR was computed on top of a framework that different data sources and departments could feed (financial, HR, logistics, or sales). This framework was designed as a proof-of-concept for middleware software.

3.5. Step 5 - Analyzing the time estimates for each resource used in an activity

The time estimates are based on in situ observation of each operation or time measures provided by the Hospital IS. As already mentioned, the goal of this study is not to accurately obtain time estimates but to design a patient-centered digital cost simulator using realistic values of activities’ duration (Fig. 3). This approach enables the teams to change the activity’s duration to simulate different scenarios.

3.6. Step 6 - Calculation of the total cost of patient care

Two dashboards were designed based on what the multidisciplinary TDABC operationalization team believed to be more useful (Figs. 4 and 5, the dashboard for Hospital A). The first one contains information on the individual patient pathway: number and duration of the activities and operations performed, the total patient pathway time, resources used, the total cost of the used resources, time devoted by resources to the patient pathway, and the most expensive resources. The second one, a general dashboard, contains cost information on all patients, allowing for comparison, analysis of the cost distribution across different activities, and the evolution of costs through the patient pathway. It allows for identifying the patient pathway’s most costly activities.

From the dashboard analysis we can conclude that HR represents most costs before and after surgery (71,6% and 86,1%, respectively). However, during surgery, consumables represent most of the costs (52,1%). Space resources represent the minority of costs for pre, during, and post-surgery (1,1%, 3,2%, and 0,1%, respectively). Knee replacement surgery is the most expensive activity.

This step's operationalization was straightforward because the teams had a clear vision of what outcomes they wanted to assess and how they wanted to visualize them.

The patient-centered digital cost simulator allows the hospital clinical and non-clinical staff better understand knee replacement surgery costs at a patient and a population level using a bottom-up costing approach - TDABC. From the dashboard analysis, the staff can visualize the costliest activities and resources. In this case, HR represent the highest cost throughout the patient pathway, and space costs the lowest. However, during surgery, the costs of consumable goods are the highest. The obtained results are similar to other studies (27).

Overall, we highlight the relevance of having a multidisciplinary team implementing the digital cost simulator, as stated in the literature (28). Furthermore, selecting the multidisciplinary TDABC operational teams’ members is an important issue, as they should have the authority to implement the necessary changes to put the digital cost simulator into practice and have access to the required data (29). Healthcare professionals' engagement was a critical success factor for accurate and realistic care pathways characterization (29). Another crucial lesson learned is the importance of semantic interoperability between clinical and financial structures to develop a patient-centered costing model. When mapping the pathway, listing the involved resources, and obtaining time estimations, the match to the financial IS resources classification is critical to accelerating data analysis.

When developing the patient-centered digital cost simulator, it is essential to be aware of the time needed to dedicate to it. It requires several meetings with hospital teams and following patients through the care service to complement missing information. The inadequacy of the hospital's efficiency-oriented IS infrastructure for patient-centered processes further complicates this process. For example, to implement Step 3, manual work was necessary. However, nowadays, hospitals are trying to evolve their costing and capacity analysis by improving their data collection mechanisms (30). For example, by using technologies such as radio frequency identification (RFID), which uses electronic tags to exchange data over wireless frequencies (30), enables a more exact workflow design to save time in resource allocation, bottleneck identification, and patient time computation. Moreover, Huang proposed a hospital IS designed at a patient level, capable of collecting activities, mapping processes, assessing resources, and measuring operational time (28). According to the author, this model allows an efficient implementation of TDABC institution wide. Our cost simulator follows this principle and includes a dataset with the required minimum variables to identify different hospital events. Although it is still a proof of concept, the authors believe it will enlighten the healthcare providers about future improvements in the existing IS infrastructure. Nonetheless, recent research suggests that replacing the current IS with a novel TDABC-based IS should be gradual to allow cost-effective incorporation into financial systems (9).

A limitation of this proof-of-concept study is related to the fact that some cost data were collected from the literature (e.g., space- and consumables-related data), so we are not aware of the difficulties of collecting this specific type of data from the hospital IS, probably missing some key insights. Another limitation lies in the conduction of the unstructured focus group to evaluate the learning experience of implementing the cost simulator. Although promoting discussion between researchers and hospital teams about the implementation issues is essential for developing practical solutions (29), the fact that our focus group included the team leader may have caused participants not to feel comfortable sharing their insights (31). However, allowing researchers and hospital teams to discuss the implementation issues is essential for developing practical solutions. Third, the digital cost simulator implementation only considered standard patient pathways with no readmissions and clinical variability. Finally, we only focused on the surgical departments, so we may have missed some important aspects of other hospital departments. Therefore, hospitals should test a second iteration of the simulator to allow variability analysis in patient pathways before implementing the model in their IS. The simulator as we proposed is ready to be adopted by clinical staff as a tool to assess knee replacement surgical care pathways. Future studies should focus on two major improvements to be adopted as a daily clinical practice tool: (i) integrate outcome analysis in the simulator to allow the investigation of how different patient pathways for the knee replacement surgical procedure impact costs; (ii) create a web-application that will interact directly with the simulation model and the required hospitals’ databases.

TDABC has been proved to be more accurate than traditional accounting systems. However, its implementation raises several issues that may compromise its results. To help overcome this hurdle, we developed a patient-centered digital cost simulator. In the context of knee replacement surgery in two hospitals of the same private group in Portugal, we tested it, describing the main lessons learned from its implementation.

The proof-of-concept demonstration enlightened the healthcare provider on future improvements in the existing IS infrastructure to facilitate the TDABC application. Implementing this costing methodology requires that the hospital IS collect and interoperate different data sources (clinical, financial, and logistics) along the patient pathway. Therefore, there is an opportunity to develop effective and efficient Application programming interfaces (APIs) that import process, activity, resources, and time information from the existing IS and calculate care costs per patient.

API: Application programming interface; CCR: Capacity cost rate; CDVC: Care delivery value chain; HR: Human resources; ICHOM: International Consortium for Health Outcomes Measurement; ICU: Intensive Care Unit; IS: Information Systems; KO: Knee osteoarthritis; OECD: Organization for Economic Co-operation and Development; ORs: Operation Rooms; TDABC: Time-Driven Activity-Based Costing

Acknowledgements

Not applicable.

Ethics approval and consent to participate

This study was approved by the CUF Hospital Ethical Committee. All methods of this study followed the guidelines of the Helsinki declaration and the European General Data Protection Regulation. Furthermore, all participants signed informed consent.

Consent for publication

Not applicable

Availability of data and materials

The data that support the findings of this study are available from the Hospitals. Still, restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. However, data may be available from the corresponding author upon reasonable request and with the hospitals' permission.

Competing interests

The authors declare that they have no competing interests.

Funding

SPA, FVH, and ARL acknowledge funding from the Portuguese National Funding Agency for Science, Research, and Technology (FCT) and public ESF funding with reference LISBOA-05-3559-FSE-000003.

Authors' contributions

All authors contributed to proof-of-concept study design and implementation, data analysis, and manuscript writing and editing.

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No competing interests reported.

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A patient-centered digital cost simulator based on Time-Driven Based-Activity Costing: a proof-of-concept study of knee replacement in two private Portuguese hospitals

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Abstract

Background

Methods

Results

Conclusion

Figures

1. Background

2. Methods

2.1. Population and Sampling

2.2. TDABC model and digital cost simulator design and implementation

2.2.1. Step 1 - Mapping the patient pathway with the main performed activities

2.2.2. Step 2 - Identification of the main resources used in each activity and department

2.2.3. Step 3 - Estimation of the total cost of each resource group and department

2.2.5. Step 5 - Analyzing the time estimates for each resource used in an activity

2.2.6. Step 6 - Calculation of the total cost of patient care

2.3. TDABC and digital cost simulator assessment

3. Results

3.1. Step 1 - Mapping the patient pathway with the main performed activities

3.2. Step 2 - Identification of the main resources used in each activity and department

3.3. Step 3 - Estimation of the total cost of each resource group and department

3.4. Step 4 - Estimation of the capacity of each resource and calculate capacity cost rate

3.5. Step 5 - Analyzing the time estimates for each resource used in an activity

3.6. Step 6 - Calculation of the total cost of patient care

4. Discussion

5. Conclusion

Abbreviations

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Acknowledgements

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

References

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