Industry executives and experts share their predictions for 2021. Read them in this 13th annual VMblog.com series exclusive.
Meeting the Hype in 2021: Machine Learning In Healthcare
By Don Woodlock, VP Healthcare of
Solutions at InterSystems
Since the onset of the COVID-19 pandemic,
machine learning (ML) has been thrust into the spotlight. From streamlining
operations to driving R&D amidst a sometimes volatile and uncertain work
environment, organizations have turned to ML to remain competitive and gain an
advantage. In the healthcare industry, ML has enabled hospitals and health
systems to cope with a number of unique challenges, including how to manage a 65 percent drop in elective surgeries at the
onset of the crisis, and an unprecedented surge in contact tracing and
syndromic surveillance efforts.
According to a recent IDC study, 50 percent of hospitals
indicated that they already have an artificial intelligence (AI) framework in
place to support their organization with the remaining respondents indicating
they will adopt one within 24 months. Despite
the rising rate of adoption of ML in the healthcare setting, experts continue
to speculate whether we have reached the full potential of ML in healthcare or
if it's simply overhyped. Part of the challenge is that many initially assumed
that ML would serve as a magical panacea for a host of problems. After the
pandemic emerged, this issue was compounded as ML made progress in some areas,
but didn't quite meet expectations in others.
While many have traditionally believed ML to
be a "black box", that serviced different organizational needs without much
understanding of how it worked, the technology has become increasingly
explainable, leading to greater credibility in directly helping patients.
Ultimately, the year ahead will see ML continue to make advancements across
different areas of healthcare, including two key pillars of the healthcare
setting: triage and administration.
ML-Based
Triage
In the year to come, we expect to see the use
of ML tools in triage practices increase. Currently, health systems use simple
risk scoring systems with a few variables as a way to identify patients that
need immediate attention or that require higher acuity resources and pathways.
ML will increasingly be used in different aspects of the patient's chart to
make smarter decisions. For example, Northwell Health's ML system identifies
patients who need to be woken up to take their vitals versus patients who are stable
enough to just sleep through the night. Since the onset of the pandemic,
smarter decision-making and personalized care have jumped to the forefront of
healthcare organizations' priorities. At the beginning of the pandemic, Epic developed an ML model to scan health
records, identify patient deterioration, and alert doctors automatically before
patients need an ICU admission or other care intervention in order to better
serve patients with life threatening problems. The use of ML tools in triage
can more accurately deliver quality care to patients by helping providers work
smarter.
ML-Based
Administration
As much of the working world was dispersed
practically overnight following the onset of COVID-19, healthcare organizations
had to adapt quickly to a remote environment. This change caused a 37 percent surge in telehealth visits at its peak in May.
In addition, organizations were forced to push forward other digital
transformation efforts - often haphazardly, creating additional opportunities.
ML algorithms can be used to help address administrative and payment
challenges, such as identifying patients who will not show up for their
appointments, pay their bills or adhere to their medications. For example, physicians from Binghamton University in New York
used ML tools to analyze 1.6 million online medical consultations to predict
patient payment behaviors and identify traits of high-value online
physician-patient interactions. As an excellent pattern recognition tool, ML
functions will be put to use to make healthcare administrative tasks smarter as
we continue on past the pandemic with new practices in place and lessons learned.
Data:
The Gas That Makes ML Go
Every ML project suffers from the same problem
- not enough quality data. In the year to come, it will be essential to
prioritize healthcare data interoperability and data-cleansing so that it's
truly useful and usable in ML projects. The healthcare industry is already
making progress towards expanding interoperability with new rules from CMS in January 2021. With the use of FHIR-based
APIs (Fast Healthcare Interoperability Resources), data sharing will be even
easier for payers and developers, allowing for greater speed of innovation and
more actionable insights from data sets. Furthermore, many hospitals have
already put the frameworks in place to ensure that AI is being used properly.
In fact, 58 percent of hospitals indicated that they have AI-specific data
governance and management policies and procedures in place, according to a
recent IDC study. One of the ways that ML can
successfully defeat the hype train in healthcare is to make data healthy, so
that ML algorithms can run without a hitch and deliver high quality results.
We have seen expectations for ML grow over the
years and in 2021, we should expect to see the gap between expectations and
reality shrink. From ML-based triage to administration, clean data is helping
organizations meet their goals for innovation.
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About the Author
Don Woodlock has had a 30-year history in the healthcare
software industry. As Vice President of HealthShare, he is responsible for
strategy, product development, commercialization, implementation, and overall
customer success in this segment. The HealthShare solutions empower care
providers and connect care communities around clearly presented, comprehensive,
and actionable health information.
Prior to InterSystems, Don has held many senior leadership
positions including VP and GM of GE Healthcare's Enterprise Imaging business
and earlier their Chief Technology Officer. Prior to GE Healthcare, Don worked
at IDX Systems Corporation, where he led the development of many of IDX's
flagship products including the successful Managed Care product and EDI Clearinghouse
offering.
Woodlock holds a BS degree in Electrical Engineering from MIT.