With 17 days until the end of the year, Matt Lovell, CTO at Centiq, dives into the world of healthcare data and how this space needs to evolve.
We are public citizens and live on the same planet. Our lives are enriched by sharing of thoughts, ideas, emotions and time. It is this sharing of information which underpins our understanding and some might argue, our evolution.
One of the challenges we face in the globalisation and digitisation of public services is how these transform opinions, policies and beliefs in traditional boundaries.
Many public services were established on a regional basis to support local services and provide a more informed and personal service aligned to our needs. Health, Education, Policing, Social Services, and Public amenities, to name a few, are all crucial services to our daily lives.
Their effectiveness and increased efficiencies will require greater sharing of information to accelerate our understanding. If we rely on policy changes to determine the rate of change, this alone will be too slow.
As citizens, we must work together to ensure the digitisation of services is not constrained by regionally imposed boundaries and that where data is anonymised appropriately and governance surrounding data processing agents are enforced, that we authorise public services to share and use our data to build better services.
Improving Health services
The limited number of projects which have shared unnamed patient data has shown the significant impact on Health services these projects can have.
The UK, like many countries around the world, has embarked on a hugely complex programme of Health System integration across the country with the objective of simplifying the sharing of information and data across Health services.
This is both a hugely complex and ambitious objective but this hinges on the individual’s commitment to the broader objectives and their acceptance and support of these objectives.
With demand for local services increasing beyond forecasts and budgets, there will be inevitable pressure on these objectives over the more immediate day to day pressures to keep services functioning as optimally as possible.
We have much to learn from one another, but if the key objective is shared, which is to deliver the best health care possible, regardless of geography or budgets, then we must accept and permit the use of our data to improve services.
Driving insights with centralised data sets
The success of social media is underpinned by the principle that we share data about ourselves. Wearable technology captures insights and valuable data on our health status and performance.
If we apply the same logic to building better public health services, we need to support and challenge the mindsets to the sharing of anonymised data.
By combining data sources at project levels rather than attempting to centralise all public health services, we can realise the benefits from data analysis faster.
Deploying co-funded modular data processing capabilities such as SAP HANA nodes, into regional health services, can target specific health projects and are governed by updated data management best practices, working across boundaries to accelerate the digitisation of services.
Across the UK, it is estimated that around 4.5 million people are affected by Diabetes. By centralising data sets and analysis on a health focus area, we will more rapidly develop a better understanding surrounding treatment effectiveness, dietary management best practices and digital patient support services.
This approach requires mindsets to change in sharing patient information as well as cross-boundary funding of central services but with the number of Diabetes patients forecast to increase further, we must assess different digital ways to manage and support patients.
Whilst larger objectives are both desirable and imperative to optimise healthcare in a more diverse, digitised and mobile population, smaller projects can and will offer more immediate benefits to Health services, and not just at a regional or national level. But it requires a different thought process at all levels.
Original article from Data Economy.