9. TURNING THE ELECTRONIC HEALTH RECORD INTO A RELIABLE RISK PREDICTOR
EHRs are a goldmine of patient data, but extracting and analyzing that wealth of information in an accurate, timely, and reliable manner has been a continual challenge for providers and developers.
Data quality and integrity issues, plus a mishmash of data formats, structured and unstructured inputs, and incomplete records have made it very difficult to understand exactly how to engage in meaningful risk stratification, predictive analytics, and clinical decision support.
“Part of the hard work is integrating the data into one place,” observed Ziad Obermeyer, MD, Assistant Professor of Emergency Medicine at BWH and Assistant Professor at HMS. “But another problem is understanding what it is you’re getting when you’re predicting a disease in an EHR.”
“You might hear that an algorithm can predict depression or stroke, but when you scratch the surface, you find what they’re actually predicting is a billing code for stroke. That’s very different from stroke itself.”
Relying on MRI results might appear to offer a more concrete dataset, he continued.
“But now you have to think about who can afford the MRI, and who can’t? So what you end up predicting isn’t what you thought you were predicting. You might be predicting billing for a stroke in people who can pay for a diagnostic rather than some sort of cerebral ischemia.”
EHR analytics have produced many successful risk scoring and stratification tools, especially when researchers employ deep learning techniques to identify novel connections between seemingly unrelated datasets.
But ensuring that those algorithms do not confirm hidden biases in the data is crucial for deploying tools that will truly improve clinical care, Obermeyer maintained.
“The biggest challenge will be making sure exactly what we’re predicting even before we start opening up the black box and looking at how we’re predicting it,” he said.
10. MONITORING HEALTH THROUGH WEARABLES AND PERSONAL DEVICES
Almost all consumers now have access to devices with sensors that can collect valuable data about their health. From smartphones with step trackers to wearables that can track a heartbeat around the clock, a growing proportion of health-related data is generated on the go.
Collecting and analyzing this data – and supplementing it with patient-provided information through apps and other home monitoring devices – can offer a unique perspective into individual and population health.
Artificial intelligence will play a significant role in extracting actionable insights from this large and varied treasure trove of data.
But helping patients get comfortable with sharing data from this intimate, continual monitoring may require a little extra work, says Omar Arnaout, MD, Co-director of the Computation Neuroscience Outcomes Center and an attending neurosurgeon at BWH.
“As a society, we’ve been pretty liberal with our digital data,” he said. But as things come into our collective consciousness like Cambridge Analytica and Facebook, people will become more and more prudent about who they share what kinds of data with.”
However, patients tend to trust their physicians more than they might trust a big company like Facebook, he added, which may help to ease any discomfort with contributing data to large-scale research initiatives.
“There’s a very good chance [wearable data will have a major impact] because our care is very episodic and the data we collect is very coarse,” said Arnaout. “By collecting granular data in a continuous fashion, there’s a greater likelihood that the data will help us take better care of patients.”