We have the data, let's get the context

It’s evident that today's clinicians are analyzing their patients on an entirely different level than a decade ago. As an example, developments in digital technology are greatly transforming the role radiologists play in a health system. Radiology is now viewed less as a department in a hospital and more as an essential part of an integrated healthcare network.

Today, there is a vast amount of radiology data available to work with, providing the opportunity to analyze images and associated clinical data to improve diagnosis. Images are becoming far more sophisticated and accurate, and we’re able to view and quantify the inner depths of the human body.

However, as we look toward tomorrow, we see the opportunity for radiologists to enhance their impact by incorporating richer data sets analyzed by machine intelligence. We are interpreting images through deep learning, while quantifying more data, adding more sources and combining modalities that were once disjointed. We are going deeper and deeper into human physiology. With digital pathology we drill-down and analyze patients at a cellular level. We can even learn how these cells are “wired” by analyzing their DNA. So, why is all of this extra perspective important? The answer lies in the richness of contextual data and how we use it.

Not only are we capturing deeper data, but we also are gaining more contextual data. Oncology provides an excellent example of where the richness of data plays a major role in treatment, and we’ve seen significant progress in how contextual data helps diagnose and support patients to propose the best therapy.

For example, by looking at patients longitudinally, radiologists can detect early signs of health deterioration while understanding how their patients’ conditions change. This leads them to determine how effective specific therapies and treatments are, and if interventions are needed. When a rich data pool from an entire radiology department or, better still, a network of radiologists is available and all other longitudinal patient information is transparent and actionable, clinicians can compare and contrast patients with similar conditions.

This greater transparency and context leads to increased visibility for radiologists, more personalized and effective treatment options for oncologists and – most importantly – better results for patients. Leveraging richer data also is giving them more information about specific types of illnesses, which can then be added to the overall available body of knowledge for caregivers and clinicians around the world.

We are getting near-real-time information at the point of care.

Wearables and electronic medical records (EMRs) also are playing an important role in contextual data, so personal health information – not just clinical – can be factored into a patient’s care routine. In addition, we are getting near-real-time information at the point of care – from mobile ultrasounds to handheld blood test devices – which gives us more medical data.

With this growing pool of data points, we know more, which means we can do more. Radiology is just one example where contextual data can add great value. Across the health continuum care it can help care providers to be more precise, delivering better care and more personalized treatments.

With #HIMSS16 in Las Vegas around the corner I look forward to sharing the latest developments in personal and population health management and clinical IT that enable to collect, combine and deliver quality data for actionable insights to help improve patient outcomes, reduce costs and improve access to quality of care.

 This article was initially published on Philips' Innovation Matters blog.