By Jon White, Director, Public sector health analytics team
This blog is the third in our series of articles examining the challenges in establishing Integrated Care Systems (ICSs) and explores the critical role of population health analytics. Indeed, there is a growing realisation across the healthcare sector about the potential for data analytics to transform everything from strategic planning to clinical decision making. Today, data technology has already transformed many aspects of our lives with unprecedented access to information, connecting people and ideas in ways no-one would have predicted twenty years ago. We have all seen first hand how analytics has impacted other sectors such as retail and marketing and we are now entering a period of rapid innovation in the healthcare sector, with numerous opportunities to utilise data effectively. In this blog we are taking a pragmatic view of some of the things ICSs can do to work collaboratively and focus on improving population health outcomes.
What new analytics capabilities do we need for integrated care?
Developing an integrated care system is a great opportunity to build a culture of transparent evidence based decision making. But there is no magic solution which you can switch on to become an insight driven organisation overnight, it takes time, dedication and planning. This includes the time it takes to train managers and other healthcare professionals to utilise the benefits of advanced analytics. A good place to start is understanding your baseline position. Deloitte’s population health analytics maturity framework is designed to help systems identify what to prioritise in order to achieve their objectives for analytics.1
Figure 1: The domains within Deloitte’s Population Health IT and Data Analytics Maturity Framework
Aspiring integrated care systems should be ambitious with their plans but realistic about the timeline for developing advanced analytics capabilities. The good news is that there are lots of quick wins that can be introduced to showcase the power of modern analytics.
Population health analytics is a great example which doesn’t require large investments in complex data infrastructure. There is a lot you can do to get started with the standard data technology most health systems already have, for example, introducing forward looking population health demand modelling to help identify long term resource requirements. Traditional demand management schemes remain important in the short term, but long term most health systems need to consider how they can pool resources and invest in reducing the prevalence and cost of managing complex long term conditions. Population health demand models help by quantifying the strategic importance of preventative medicine and are a great way to shape system priorities.
What new data technology do we need for integrated care?
It sometimes seems like there is too much management information in the NHS and the last thing anybody needs it another set of performance measures. Integrated care is an opportunity to simplify system level management information but there is a risk that it adds another layer of complexity. In an ICS, priorities and objectives should be measured in one place with individuals and organisations from across the system using a single set of tools to measure performance. This doesn’t happen naturally, it requires a well thought out plan and the right technology partners. However, ICSs can build on their existing technology, given many health systems have already made significant investments in reporting and data infrastructure, and, with the right expertise, can ensure it works for integrated care.
More advanced integrated care systems may consider investing in specialist analytics technology. There is a lot to choose from, suppliers from all over the globe are continually developing innovative algorithms and software which can inject new levels of insight into health systems. These technologies can help identify and manage high risk patients, they can single out inefficient pathways or gaps in care and they can highlight patients who would benefit from preventative intervention. These innovations have the potential to transform health systems but there is no such thing as plug and play transformation. We recommend using NHS networks and reaching out to systems who have tried specific tools for feedback, it’s also worth regularly scanning the market for new products as this is a rapidly developing field.
How do Business Intelligence and analytics support the development of integrated systems?
Integrated care systems need a common set of shared goals which inspire and engage managers, clinicians and healthcare professionals from across health and social care. Every health system has unique challenges and no population is the same. Analytics can help identify priorities which are tailored to the health needs of the local population to target health inequalities and improve health outcomes. Priorities can also be defined for different population levels within a system, at the place, neighbourhood or whichever sub population level works best. This is a complicated process and analytics can help make sense of it by highlighting the patient cohorts with poor outcomes and high instances of ‘triple fail’ events.
When systems come together to review care pathways from a patient’s perspective there are lots of opportunities to reduce duplication and address gaps in care. Analytics can highlight these opportunities and help systems prioritise them by contextually benchmarking the local health and care system against other high performing systems. This is nothing new, but population health brings a new perspective to the process. Traditionally health systems take disease pathways in isolation and manage them on case by case basis. From a complex patient’s perspective with multiple long term conditions this can often result in inefficient pathways and poorly coordinated care. By placing the patient at the centre of pathway design health systems can build services around the unique health needs of their local population.
How do we design legally compliant information governance frameworks for integrated care?
Lack of clarity about information governance (IG) can become the main barrier to using data to innovate and it can be a daunting task to develop a system level IG framework.2 We recommend setting up a system-wide IG steering group and breaking the process down into work-streams with dedicated leads where possible.
All systems should start with a baseline assessment of existing arrangements. This should become a routine process with regular reviews of policy and documentation. Taking outside legal advice can also help. The legal framework for IG is constantly evolving, and most health systems are working to develop similar frameworks, so perspective on current best practice can offer valuable insight.
There is no perfect solution for futureproofing an IG framework but a good place to start is by clearly outlining your data and analytics strategy on a timeline. Use cases for data sharing agreements need to be specific, so it’s normal to amend them as capabilities and technologies are developed. Our best practise tips are:
- look closely at other systems with similar objectives and seek guidance from regulators
- there is no such thing as too much transparency, engage as much as possible with stakeholders from across the system and the public
- don’t treat developing an IG framework as a one off exercise, establish routine processes to review policy and regularly seek advice on current best practice.
Our clients often tell us that they hear a lot of hype about analytics which doesn’t reconcile to their experience of data in their health system. It can seem like there is a big gap between where most health systems are now and where the best in class are. It is true that there are pockets of innovation where analytics has had an important role in addressing big challenges but health systems are extremely complex and there is no one size fits all solution. The important thing is that every health system should seek to identify the solutions which will help them address their unique challenges and achieve their objectives. This is a cumulative process where the value of the investment in analytics will compound as new capabilities and new technologies are introduced. A solid foundation of good data infrastructure and a well-designed system level IG framework is a good place to start, from there it should all be about analytics adding value.