Use of data was at the forefront of the World’s fight against a two-year global pandemic. The advances in data analytics using data collected from a large proportion of the population through COVID-19 again showed the potential for data platforms to address long-term challenges vital to the future of health and social care.
The scale of the opportunity (and the challenge) can be seen in the Goldacre Report’s 185 wide-ranging recommendations for the better use of digital technology and data in healthcare settings. This report flags critical data privacy and inclusion issues and although there are huge opportunities for better use of data, there is also an urgent need to be careful and considered in the collection and use of patient data. This must be guided by ethical frameworks for the provision of access to personal information, as well as systems designed to reduce inequality of healthcare access and provision.
The need for acceleration of care delivery is evolving across different care settings beyond that of the hospital, driven by a public demand for access to care in environments most appropriate to individuals and their specific needs.
However, while some care providers, local authorities, and other organisations are trailblazers in the use of data, digital tools and technology, social care lags behind. Improving the quality of data in this new vision of health services and care pathways is of critical importance, as the title of the Department of Health & Social Care strategy states in its 2022 strategy Data Saves Lives.
Underpinning any transition to an intelligence-led health service is the need for the exchange of high-quality data, using information transferred within and between Integrated Care Systems (ICSs) to make vital decisions about what care is delivered and where.
Data matters for making sure the right people get the right care at the right time and in the right place. It’s a key ingredient for organising care and joining up health and social care services around people’s abilities and needs. It’s also a central ingredient for improving population health and care; which is key to tackling healthcare disparities, unequal outcomes, and access; and a catalyst for driving healthcare productivity and value for money.
To ensure a high standard of data quality, the public needs to be more comfortable with sharing and providing access to personal information. This comfort and trust comes from building confidence in the way their sensitive, personal data is shared, stored and used so that it streamlines and accelerates access to the care and treatment they need. If the data is not accurate or ‘decision grade’, then its ability to do this is greatly reduced.
Putting the patient at the centre of the data collection and accuracy challenge is important, but this must be done efficiently. In November 2020, The Department of Health and Social Care published a report on excessive bureaucracy in the health and social care system, which evidenced data collection directly impacting the provision of care. The wrong people, doing the wrong jobs, but for the right reasons. While not disputing the importance of high-quality data, this also serves as a warning that data collection must be efficiently designed into processes so as not to damage the care it seeks to improve.
The mapping of demographics and where people live has led to the ability to geographically plan and place services and care where it is most needed, building models to inform the structure and size of cohorts, such as to provide vaccinations or supply medicines to mitigate localised outbreaks of disease.
With Integrated Care Systems (ICSs) becoming a reality as an essential ingredient of the NHS Long Term Plan, the need to ensure that data and information flows readily and securely between central, regional, and local care settings is vital. The NHS does not lack data, however it does suffer from siloed data from multiple sources.
The global health care sector was already using new technologies and processes to extend care delivery outside the hospital setting when COVID-19 forced providers to transform operations overnight and dramatically adopt virtual consultations, visits, and remote patient monitoring.
This enforced shift in patient engagement and care delivery has brought many opportunities and benefits, not least the potential to deliver physical and virtual care in a meaningful and integrated way that delivers better patient experiences and better clinical outcomes regardless of where patients are.
To make this vision an effective reality, the NHS and the whole supplier community must provide the integration, collaboration, communication, and information sharing needed to serve the needs of the whole population and a growing body of patients that require care in remote settings – receiving care where they need it most, and where their needs can be best met. This care will be delivered by multi-disciplinary teams collaborating remotely to deliver a whole system approach to care throughout communities.
With existing levels of fragmentation and divergence across the NHS, interoperability remains a fundamental building block for new digitally enabled integrated care services. When set against the move towards care provision outside of physical wards, our view is that both data quality and data sharing platforms should not merely be defined by the available technology, but by the needs of patients, clinicians, and the wider health and social care community.
A truly effective interoperability ecosystem will provide a unified infrastructure that uses technical standards, policies, and protocols to enable seamless and secure capture, discovery, exchange, and utilization of health information, with appropriate controls to ensure proper and effective use. Alongside this, the reshaping of legacy systems with platforms that talk to each other and work better together will be used to more effectively access and share data.
This ecosystem must also better meet the challenge of sharing unstructured data, for example by employing Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) techniques to discover and provide relevant information at the point of use. However, to meet this challenge the development of new data science models must come hand-in-hand with an improvement of the quality, availability, and appropriate use of data sets to account for ethnic, gender, and societal diversity and equality.
As a two-time Queens Award innovation winner for our ability to accelerate and de-risk digital business change, we see several keys to the challenge of data quality across the NHS estate including addressing data validation and support for the cleansing, matching and validation of address data. There is a clear potential here to use Machine Learning to improve demographics data quality, leading to better management of data quality streams including the cleansing and auto-correcting of huge swathes of demographics data.
In successfully addressing the quality of information so the health service has access to decision grade data, we also see through our work with NHS England (NHSE) on the national Learn from Patient Safety Events (LFPSE) service, the importance of industry signing up to technical standards such as FHIR, which is already having a significant and positive impact on the efficient sharing of learning and improved care delivery.
And that leads us back to the start of this article – the acceleration of care delivery, which is evolving across different care settings beyond that of the hospital, is being driven by the demand for access to care in environments most appropriate to individuals and their specific needs. Hospitals and social care without walls – for everyone. Quality, decision grade data is the key to making this promise a reality.
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