Big Data Comes to Healthcare: Identifying High-level Trends to Solve Individual Problems

Today, healthcare leaves a trail of data leading up to and following each patient interaction. From check-ups and surgeries to lab work and prescription refills, every moment of treatment yields a piece of data that could be hugely valuable to clinicians developing treatment plans or health systems streamlining their operations.

Unfortunately, most of this data still largely exists on its own. Most patients’ data is disparate, and the healthcare industry largely hasn’t formed processes to connect individual patient data, let alone the ability to show how individual patient data connects at a population level.

MATTER recently hosted NorthShore University HealthSystem’s analytics director, Chad Konchak, and infectious disease physician Nirav Shah, MD, MPH, for a conversation about their analytics work. Watch highlights of the event:

Chad and Nirav are building meaningful prediction modeling software within the NorthShore system. Their tools allow clinicians to make informed predictions about what will happen in the future for many of their patients, allowing them to make better, timelier decisions to improve outcomes.

The tools they are building are focused on high-priority issues for NorthShore – areas where analytics have the potential to improve outcomes and save costs. They highlighted three examples.

We continued the conversation Chad and Nirav began at MATTER last month, asking three questions from the audience that there wasn’t time to answer during the presentation.

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Data collection poses a logistical issue. Doctors often don’t have the time to collect and make sense of data if it doesn’t immediately affect the quality of their care. So how do you get doctors on board with a data collection method that will benefit them in the long run, but only if they use it consistently in the short term?

Takeaway: making data collection non-intrusive and proving value on the spot is key to getting clinicians on board.

What challenges and concerns came up when you were developing this software?

Takeaway: while analytics tools are applied at large scale, and not every recommendation will be accurate, they can improve messaging to patients on the whole and therefore help deliver important care when patients need it.

Any final thoughts to others looking to work in this space?

Takeaway: while healthcare lags behind many other industries in utilizing data, we are at an inflection point where clinicians and health systems can truly embrace data analytics solutions to solve existing problems and increase quality and efficiency of care.