It is expected that the use of AI in healthcare will continue to
grow, and the number of digital health services will continue to
expand. The COVID-19 pandemic underscored the need for
digital health platforms and services. A study by Mckinsey &
Company showed that after one year, the use of telehealth has
increased 38 times from pre-pandemic levels (Bestsennyy et al.,
2021).
There are many ways that data analytics can be used to
improve healthcare services, outcomes, and workflow. Some of
the areas that are seeing the most growth are medical image
analysis, virtual nursing assistants, administrative workflow
assistants, clinical trial participant identifiers, predictive diagnostic
tools, and fraud detection (Accenture, 2020). According to
Accenture, growth in these areas have the potential to save the
healthcare industry $150 billion annually by 2026.
While there is a lot of potential for the use of analytics in
healthcare, one of the biggest obstacles is managing data
overload (Mylod & Lee, 2022). Not only is there an
overwhelming amount of healthcare data available, but the way
that the data is stored and managed can vary widely across
platforms, organizations and even within medical specialties.
Integrating data from different sources and standardizing the
way that information is recorded still presents a challenge in the
healthcare field. AI tools can help with standardizing medical
data and increase operational efficiency by automating
administrative tasks that would otherwise burden healthcare
professionals.
Sources:
Bestsennyy, O., Gilbert, G., Harris, A. & Rost, J. (2021).
Telehealth: A quarter-trillion-dollar post-COVID-19 reality?
Mckinsey & Company.
https://www.mckinsey.com/industries/healthcare-systems-and-
services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-
19-reality
