What a failing US healthcare industry can learn from Europe in using health analytics

Health analytics has the potential to positively manage population health and impact patient lives.

The tricky part is ensuring that data is easily accessible, understood, and actionable. Data may show that the rate of heart failure diagnosis in the US is on the rise, for instance, but what is the next step? How can providers actually use that information to help their patients?

Today, being able to use data in an actionable way typically begins with patient registries that allow providers to work through patient data to find certain criteria, such as the total number of adults over the age of 50 who have heart disease. Then – ideally – providers can use that information to improve patient care, whether that means pushing for more scientific research, instituting clinical changes, or even by encouraging policy updates that could positively impact patients.

But the truth of the matter is, these registries shouldn’t even exist.

The fact that providers have to go through such an arduous process to put data into the registry, then get it back out and make it actionable, is ridiculous. It certainly doesn’t help that the process of working through that information is still primarily driven by people. This happens because the complex technology to link various discrete systems, protocols, resources and processes together in a single orchestrated unit is not yet available at scale.

Not having easy access to real-time data is a huge problem. Dynamic rule sets that are based on risk, and are able to pull live data in real-time, should be available at every provider and health system in the US.

Europe has found success in this approach. For example, the UK’s universal healthcare system sources population data in the form of structured data feeds from every primary care physician in the country. This data is then used to not only design national-level screening and engagement programs that are aimed at prevention, but also to determine which interventions actually work, and most controversially, which interventions should be funded.

In the US, these screening and engagement programs are only recognized when they work, whereas in Europe, such programs are still deemed notable even when they don’t work.

Why is the US not as interested in whether these programs fail? Simply put, it’s because in the US, providers get paid for fixing things, while providers in Europe get paid when there’s nothing to fix.

Technology alone is not enough for improved patient care

Healthcare screening is an important part of preventative care, stopping the development of conditions before they require treatment. This presents a conflict for the US healthcare system though, when treatment is ultimately what drives the payment incentives they depend on through fee-for-service culture. Essentially, for US providers, this means they’re better off when patients get worse. The more treatment activities and interventions they can rack up – rather than putting efforts toward delivering quality, personalized care with good outcomes – the more money they bring in.

The UK health system on the other hand emphasizes screening programs for prevention in value-based care, and ‘national scale’ orchestration that is less about action, and more about flowing funding into specific large-scale interventions that are targeted at specific cohorts. This funding can range from national advertising campaigns to dedicated recall programs and specific funding incentives.

But it is still not perfect, and there is room for improvement where better data analytics can help remedy population health management programs. For example, one of these programs in the UK recently failed to recall patients due for mammograms. A computer algorithm error reportedly led to 450,000 women to not be invited to their final breast screening between 2009 and 2018. It is believed that 270 women died due to that computer error.

Improvements in IT systems, the data they generate, store and analyze, and ways in which health systems can leverage actionable insights from that data can only help improve global population health efforts. When organizations are able to effectively collect patient data and information about the care experience, they can then mine that data to learn from each patient encounter, improve future care, and better manage population health.

But technology improvements alone, aren’t enough. What’s arguably more important is improving care delivery integration, where a meaningful feedback loop can enable population health analytics to examine the wider population on an individual patient level and track interventions against each patient.

For example, let’s say that a 90-year-old male patient had a mini stroke and was sent to the hospital. He was discharged into the care of his daughter, she was given instruction on looking after him, and the patient was prescribed the appropriate prescriptions. However, no follow-up occurs and there is an assumption by the provider that the patient and his caregiver should be able to manage his medications correctly.

These types of scenarios are what population health should identify. The provider should have realized and acted upon the fact that three months after this man was discharged, there has been no follow-up, and that he has not refilled his medication.

Data analytics can help integrated payer systems review patient data and selectively target interventions to each individual in the future. Moreover, they’ll be able to do so with coordination provided by care managers and primary care physicians who are proactively looking after the health of the population, not just the sickness of individuals.

A fully integrated healthcare system can deliver meaningful results and also significantly change the expectations of the population for the better in terms of what they believe they can expect from healthcare systems.

Organizations must develop a roadmap that outlines how they generate, collect, store, process, and report upon their data assets. When healthcare providers prepare for big data analytics, they can ensure that they create a patient-centric environment with timely and accurate care to produce better outcomes for patients.

By Robbie Hughes, Founder and CEO, Lumeon.

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