Singapore’s healthcare ecosystem is transforming, in response to shifting cost challenges, an ageing population and advancing medical therapies. The nation is set to transition from a workload-based model to a capitation model that integrates the efforts of three healthcare clusters within a new payment ecosystem, providing a pre-determined fee for every resident living in the region.
Under this capitation approach, healthcare clusters are incentivised to support positive population health, providing a new care paradigm that will focus more on keeping people healthy than simply responding when they are unwell.
In the first article of this series, we explored the challenges and opportunities, and the essential need to align the actions of stakeholders across the landscape to successfully deliver on a capitation model. In this article, we will explore one vital element of this transformation — the role of data and analytics.
Data and analytics to drive change
Data and analytics will be critical for successful pivoting of the Singapore healthcare system towards capitation — this will not only allow us to make informed decisions to steer change, but also help provide the evidence of just exactly what that change has achieved.
Data technologies are now widespread in business, and utilised to solve a huge range of problems. In healthcare, the stakes are arguably much higher, but the tools are just as applicable. Increasingly, large data-sets and powerful analytics can be used to cut through the fog that clouds decision-making, guiding policymakers and physicians towards the best policies and care pathways — ultimately leading to better outcomes for patients and healthcare providers.
Data also offers a clear lens of analysis that can help inform overall steer by the Ministry of Health (MOH). In order to determine how capitation models assigned to clusters are determined, the MOH needs an accurate picture of individual health journeys, the different touch-points with healthcare clusters, and the cost of each point of care.
Broad brushstroke calculations are not sufficient in an effective capitation model, with extremely nuanced calculations often required. This includes differences in individual profiles — demographics, clinical conditions, risk factors, and other key elements which could influence care costs. An individual’s level of health, as well as care activities expected across the patient journey, must also be factored in. Finally, the costs incurred by the individual and the cluster, and the subsidies provided by the government, will all form key data-points to inform the capitation model.
Transparent, trusted data will also be critical to engaging ecosystem buy-in from clusters and the different providers such as general practitioners and ancillary care providers, who will be a part of the network. This will ensure they maintain confidence that the capitation rate received is sufficient, and does not expose them to undue financial risk.
Good data will also enable clusters to adapt to deliver cost-effective care, helping them to identify system inefficiencies such as variation in performance of a given provider or clinical setting, avoidable admissions, and other potential waste. On the other side of this, the right data will also help clusters allocate resources in the most effective way, proactively addressing patient needs before they seek care, then tracking the outcomes to evidence success.
Finally, patients will benefit from a “data at your fingertips” view of care that echoes the digital journeys many of us now take for granted. That data backbone will enable personalised healthcare experiences, and seamless transition across different sites of care for a smoother life-long health journey.
Building data on experience
Most areas of healthcare are in early stages of using big data and advanced analytics. The timing is right for data use in Singapore’s healthcare system, with the roll-out of next-generation electronic medical record systems unlocking new data opportunities. This end-to-end electronic record system will seamlessly connect care across the entire patient journey, reducing unnecessary tests or interventions, while providing clear oversight of treatment history and heath tracking across all healthcare clusters.
Boston Consulting Group’s (BCG) work with governments, providers and payers across the world offers an insight into some of the benefits this future could unlock.
In one project, we supported the Department of Health in Victoria, Australia, where overlapping responsibilities between governments and insurers left a disconnect between care delivered and outcomes. This resulted in payers and providers both unwilling to be held accountable for outcomes, with objective data unavailable for comparison.
BCG introduced a data system that combined data from health needs and population surveys with information about services paid for by each of the stakeholders, and with outcomes data from patient, population and clinical sources to provide an integrated, state-wide picture.
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This analysis highlighted neighbourhoods with particularly poor chronic-disease outcomes despite adequate access to and use of services, suggesting areas for quality improvement. The department also discovered that even a modest reduction in avoidable hospital admissions through better primary care would save healthcare payers an estimated A$60 million ($60.8 million) per year.
In another project, BCG worked with a leading European health insurer to improve care pathways and steer costs through AI-powered identification of opportunities, aligning with a key strategy to improve outcomes for its member base.
The AI Patient Finder solution allowed us to map more than 1,000 end-to-end patient journeys through claims data from more than 1.5 million patients. A multi-disciplinary team combining medical, business, and data and analytics expertise worked in rapid iterations to zoom into five cohorts — orthopaedics, cardiovascular, pregnancy, diabetes and mental health — to identify the areas of opportunity and use cases with the highest promise.
In orthopaedics, it became clear that higher rates of complications for knee implants rather than hip implants was driving sub-optimal outcomes. The use of advanced analytics and machine learning models enabled the prediction of patients with the highest risk of complication post-knee-implant surgery and the subsequent risk of revision.
With a clear identification of risk areas, the insurer was able to develop an intervention programme with specific interventions attributed towards providers and patients, as well as driving system-level changes. Across the 10 interventions, the insurer identified more than 300 million euros ($453 million) of avoidable costs that could be saved through better steerage.
Data to inform decisions in Singapore
As Singapore gears up to its own capitation transformation, it will need to embrace a methodical approach to explore the opportunities enabled by data and analytics.
• Start where there is tangible value to capture. Small steps that combine existing data in new ways offer immediate opportunities to solve existing problems while demonstrating the potential of data-driven solutions.
• Focus on the patient and not the institutions. This provides a holistic data-capture opportunity that covers the end-to-end journey, and focuses outcomes on the party that matters most.
• Ensure trust in the system with transparent communication and robust data security, clearly and confidently evidenced to patients.
• Finally, develop analytic capabilities to improve costs, value and care coordination. Providers must integrate the whole ecosystem of IT experience, bringing together a range of skills to incorporate the personal and population-wide data sources to inform an effective healthcare model.
Leveraging data in a smart, informed way provides the opportunity to build a smart, informed roadmap towards capitation in Singapore. Big data can provide critical oversight for improved decision-making, optimising care for patients, better integrating the work of healthcare clusters, and informing the vital structure of the capitation payment model itself — a point we shall explore in greater detail in the third, and final, article in this series.
Prasanna Santhanam is managing director and partner at Boston Consulting Group (BCG). Manav Saxena is project leader at BCG