Data has long been acknowledged as being a key enabler in transforming healthcare, including the NHS. Over the past decade, healthcare organisations have invested heavily in digital systems to better manage the patient journey and experience in order to drive better outcomes. So where are we now?
Data: the current state of play
With this proliferation of systems, software and databases, we’ve seen digital and data silos being created. Yet there’s an increasing need to share data across multiple organisations and stakeholders to provide the integrated care essential for the NHS and other healthcare providers to drive improvements in quality of care for patients.
These digital systems hold a vast amount of data generated and collected by a multitude of stakeholders. If we want to provide the best possible care for patients, we must go beyond the traditional data held in the electronic patient records and consider data generated by medical devices, wearables, search engines and social media, as well as patient-generated healthcare data (PGHD) from smartphones, medical imaging, clinical trial data, and genomics.
In addition, social care data, housing data, financial data, and organisational operational data can be fused with patient data to provide insights that could potentially revolutionise health and social care.
What can health systems do with the data?
The key is to use the vast data assets that we continue to collect to provide valuable data insights to drive better care.
Healthcare systems can use such data insights to facilitate retrospective analyses to ensure continuous improvement and support informed planning activities; real-time analytics can improve decisions at the point of administering clinical care; and predictive analytics helps to better understand patients prone to chronic disease based on insights gained through artificial intelligence (AI) for example.
Is this ‘big data’?
Big data is data that contains greater variety, arriving in increasing volumes and with more velocity - often known as the ‘three Vs’ of big data. Put simply, big data is larger, more complex data sets, especially from new data sources. In reality, big data usage in the healthcare sector is still in its infancy, we are yet to see the true convergence of data, as described above, but its time is coming.
Much of the ‘big data’ that exists in healthcare is unstructured and cannot be processed because it remains unorganised.
We believe there are three key enablers to effective data in healthcare:
Firstly, organisations need to ensure that they have effective data capture, curation, management, storage, and interoperability to create a common data set.
Data liquidity is a key part of this —the ability to access, ingest, and manipulate standardised data sets is required to serve as the foundation for all insights and decisions made. This data liquidity enables value creation and removes silos by allowing users from different organisations, within or outside an ICS, to interact with the same data with increased coordination.
Increased data liquidity will enable health and care organisations to access a complete longitudinal patient record, consisting of patient-generated data, provider-generated data, health and wellness data, financial data, and social data. As standards are established and cloud services continue to proliferate, this data will be easier to access, consume, and integrate.
Secondly, these data elements need to be converted to consumable and actionable insights and, critically, linked to patient centric outcomes.
Successfully converting data into insights requires advanced analytics. Advanced analytics—including machine learning, natural language processing, artificial intelligence, and big data analytics—is critical to gain actionable insights to guide data-users appropriately. Data liquidity will enable advanced analytics, leading to more robust patient risk identification, clinical pathway development, and - something being demanded more and more - personalised and precision medicine with predictive insights to manage population health more effectively.
Thirdly, this intelligent data needs to be brought to life – lifted out of an analytics platform and the insights used to drive the delivery of optimal healthcare for patients and their relatives.
Analysis and reporting processes need to be automated and brought to life through a suite of engaging reports and dashboards which help clinicians in a practical way and without making their lives harder.
The importance of a data strategy
Healthcare professionals rely on data more than ever before, and its importance has been outlined by the UK Government in its recently announced ‘Data Saves Lives’ policy paper. The paper sets out ambitious reforms for the health and care sector by transforming how data is used to drive breakthroughs and efficiencies and create a health and care system fit for the future.
A data strategy for the modern healthcare organisation must be developed beyond the confines of the IT department because the strategy must be about more than just data – it needs to understand the functional goals of the organisation and how the organisation is going to use the insights gained from the data to improve the administrative and clinical efficiency of the organisation to drive improved patient outcomes.
What next?
Healthcare system leaders need to think differently. Leaders need to make the data work for them and their teams by adopting a comprehensive data strategy, building a high-performance analytics infrastructure and using data for health innovation to create more value for their patients and their populations.
Healthcare organisations need to become data-led - using the insights data provides to take decisions, not to use data to justify the decision that has already been taken. Data must be a driver for action not a supporter of it.
Partnering to deliver for your communities
Our experience of data is built on handling sensitivity and complexity at scale. Our teams work across health, local government, as well as welfare to understand citizens and how services deliver every-day to meet their needs. We’re uniquely placed to be able to see a full spectrum view of the individual across whole life journeys. We can link data to actionable automation, whether intelligent processing or communications, to reduce the ‘action’ overhead that traditional data ‘dashboarding’ models typically drive.
Working style is key. Understanding your current situation and working collaboratively to design the data and AI solutions required to meet your objectives organisationally, and for the patient, will deliver better insights, improved planning and responsiveness and streamlined action.
