When we centre our health and social care system around the person in their context – whānau, families, communities, and environments – we start to think differently about resource allocation and care delivery. We move away from competition for resources towards a collaborative model where the shared purpose (the best possible life outcomes for all) is clear. A whole-system approach centred on people and populations is essential to support them to live their best lives in their own homes and communities.
High-quality, person-centred integrated care is the aspiration of health systems worldwide. However, progress has been inconsistent. To improve our health system, we must shift our focus to a comprehensive, person-centred approach, utilising data to drive improvement. Making data visible to those doing the work allows them to see its impact and innovate without compromising clinical care.
Teaching the health system to see itself as a system is critical, and platforms like signalsfromnoise (sfn) are vital enablers of a ‘whole of system’ approach. Using data to design and plan services for populations, focusing on putting capacity in the right place, and enabling people to live well in their homes and communities is essential. By basing the work on an exploratory data analysis platform using time series analytics for prediction, we challenge traditional risk algorithms and leverage routine operational data to make an immediate difference in health and social care delivery.
Data is integral to a robust health system, providing deep insights into patient needs, system inefficiencies, and areas for improvement. Traditionally, data in health systems has been limited to reporting, managing day-to-day operations, and meeting targets. It has been contained in dashboards, and controlled by a small group of people to tell a predetermined story, stifling innovation. To maximise health data, it must be shared openly with frontline healthcare providers, building a culture of transparency and trust. It needs to be allowed to tell its story instead of being forced into conventional themes. This aligns with Deming’s focus on reducing variation and optimising processes, which is essential for improving patient experiences and outcomes.
Implementing a data-driven approach
A major step in using data to plan and improve an integrated health and social care system is implementing a data platform that allows for exploratory data analysis. Unlike traditional methods that use data to confirm hypotheses, exploratory data analysis encourages us to play with the data and let it reveal the answers. This approach can lead to unexpected insights and innovative solutions to complex problems. The success of a data-driven, people- and population-centred approach is evident in real-world applications in Wales, England, Ireland, Australia, and New Zealand.
Traditional methodologies often limit healthcare data analytics. sfn’s approach offers flexibility, allowing frontline clinicians to ask pertinent questions, discover new questions, and explore data comprehensively in live situations rather than waiting for analysis to be completed. This approach uses the human capability of temporal cognition (the ability to mentally place oneself in the past or future, as well as in counterfactual or hypothetical situations), allowing proactive planning and fostering collaborative decision-making. As John Tukey, an American statistician and mathematician who developed Exploratory Data Analysis, noted, significant gains often come from unexpected data surprises. sfn’s platform encourages exploration, innovation, and transformation, essential for creating a more effective and efficient health system.
By focusing on the ‘why’ behind system performance, we can transform our healthcare system to better serve our communities, taking care of those with the highest needs and improving overall health outcomes.
The success of our healthcare system relies on using data to tell the patient story. By shifting our focus to person-centred care, supported by transparent and accessible data, we can create an integrated, collaborative health system that is not only more efficient but also more effective and equitable.