Optimising preventative health investments: From line items to national budgets

During this valuable session, attendees learned about how Singapore's public healthcare sector utilises rich data captured across its public hospitals to build an accurate picture of the costs consumed by each individual patient - down to the individual x-ray, drug, or doctor consult the patient consumed. 

Modern costing system like Civica's CostMaster, a healthcare cost accounting solution, help organisations analyse and understand their cost data at a granular level.
 
Utilising this level of granularity, the cost of service can then be aggregated and sliced in any way - from instances of patient episodes characterised by disease to its total sum at the national level. By providing an integrated overview of the combined efforts of the three public healthcare clusters in Singapore, it is possible to derive the cost for serving each resident under its care. 

“This process otherwise known as capitation, serves as the basis for Singapore’s Ministry of Health’s method to compute its funding rates for its three public healthcare clusters,” said Chen Yi, Assistant Director of Finance Division for MOH Holdings (MOHH). 

MOHH is the holding company of Singapore's public healthcare institutions, overseeing effective and efficient distribution of resources to deliver value-based healthcare. 

The Ministry Of Health (MOH) converted the way it calculated its budgets for healthcare clusters from a workload-based model to a capitation model in 2022. 

During his fireside chat with Civica’s Market Leader for Singapore, Stanimira Koleva, Chen Yi shared insights on how this kind of costing work is done by MOHH and why it now underpins some of the strategic initiatives for the MOH. 

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From Excel formulas to capitation tools 

Koleva highlighted the role of big data in deriving actionable insights that can be used by government officials to utilise budgets in the most efficient ways to optimise operations. 

“The scale of data generated from costing exercises would be in the region of billions of lines of data. This data is married with other inputs like financial and HR data and is further mapped with survey data to derive actionable insights,” Koleva said.      

Chen Yi explained the two primary purposes of the cost data produced from costing exercises for MOHH.  

The first was for MOH to determine the amount of funding, or subvention, to provide to each public healthcare cluster.   

Under a capitation model, MOH pays its three main public healthcare clusters a fixed rate per resident for the number of residents residing in a particular cluster based on the cost derived.  

The second is for analysis of cost data to minimise unwarranted variation in terms of the cost of services. 

Chen Yi also highlighted how costing exercises used to be a very manual and taxing process for hospitals' finance departments, relying on excel spreadsheets and done on an infrequent basis - about once every five to six years. 

With the advent of sophisticated data tools, Chen Yi told the audience the benefits of automation include its accuracy and speed, whereas excel-driven processes were prone to human error from manual computations and data entry. 

Systems like Civica’s CostMaster, further enabled costing exercises to derive a more granular analysis of the data.

Regarding the previous excel approach, Chen Yi said that cost analysis was limited to only about 10 to 20 categories. MOHH now has the capability to compute upwards of 100 cost categories because such modern systems are able to hold on to the data at a much more granular level and with processing speed much faster than an excel spreadsheet, he said. 

Data’s role in national goals 

Chen Yi explained that MOHH hopes to reduce the manpower needed to perform costing at hospitals as well as allowing costing exercises to be performed more frequently. 

 
“We want to refresh the data as often as possible, so we are able to draw insights from the data,” he said. “This data then feeds back into the operational practices so that we can strive towards continuous improvement.” 

Chen Yi added that MOH aims to drive improvements among the public healthcare clusters not just to meet the acute care needs, but to take care of the whole health of a person as part of the nation’s shift towards preventive care. 

As the nation deals with the effect of an ageing population, Health Minister Ong Ye Kung predicted in a recent interview that government healthcare expenditure would likely be close to S$30 billion a year by 2030, compared to of S$18 billion in 2024. 

To cost or cost not 

When Koleva asked Chen Yi about MOHH’s learnings and how to best navigate such wide-reaching procedural change, he highlighted the need for patience and buy-in from fellow government colleagues. 

“I think really for any of you who work in the government, you realise that you have to work with a lot of stakeholders ... 

“One thing that I learned was really to be patient and to understand that people need time to work through the issues that they have and come to a good conclusion. That’s when the decision is owned by the stakeholders involved in the project,” he said. 
 
Koleva echoed Chen Yi’s sentiment adding – “as data comes from multiple sources of stakeholders, getting cooperation from partners is crucial to ensure the quality and integrity of data for it to be extracted and mapped to different systems.” 

In the same vein, Chen Yi concluded by sharing that the revamp of costing exercises was just the start of where data and system capabilities can take government agencies next, as MOHH continues to learn from the early phases of working with capitation. 

He alluded to the potential of future research such as the studying of trends at the very micro level for strategic use of data cost trends over time, as well as looking at the cost variation between healthcare providers. 

“This is really just the beginning because there are many other analytics, models and templates that can be driven on top of it, to go beyond just the financial data or financial use case of what that data can underpin,” Chen Yi explained. 

You can find fireside chat recording between Koleva and Chen Yi on-demand here.