The healthcare industry is fast realizing the importance of data, collecting information from EHRs, sensors, and other sources. However, the struggle to make sense of the data collected in the process might rage on for years. Since the healthcare system has started adopting cutting-edge technologies, there is a vast amount of data collected in silos. Healthcare organizations want to digitize processes, but not unnecessarily disrupt established clinical workflows. Therefore, we now have as much as 80 percent of data unstructured and of poor quality. This brings us to a pertinent challenge of data extraction and utilization in the healthcare space through NLP in Healthcare. This data as it is today, and given the amount of time and effort it would need for humans to read and reformat it, is unusable. Thus, we cannot yet make effective decisions in healthcare through analytics because of the form our data is in. Therefore, there is a higher need to leverage this unstructured data as we shift from fee-for-service healthcare model to value-based care. This is where Natural Language Processing, a subcategory of Artificial Intelligence can come in. NLP based chatbots already possess the capabilities of well and truly mimicking human behavior and executing a myriad of tasks. When it comes to implementing the same on a much larger use case, like a hospital – it can be used to parse information and extract critical strings of data, thereby offering an opportunity for us to leverage unstructured data. NLP has found applications in healthcare ranging from the most cutting-edge solutions in precision #medicine applications to the simple job of #coding a claim for reimbursement or billing. According to @MarketsandMarkets, the global NLP in healthcare market size will grow from USD 1.5 billion in 2020 to USD 3.7 billion by 2025, at a CAGR of 20.5% during the forecast period. With NLP getting more & more traction in healthcare, providers are focusing on developing solutions that can understand, analyze, & generate languages that humans can understand There is a further need for voice recognition systems that can automatically respond to queries from patients & healthcare users. With conversational #AI already being a success within the healthcare space, a key use-case of implementing NLP is the ability to help patients understand their symptoms & gain more knowledge about their conditions. Link to the complete article in the comments below ⬇️ #NLP #healthcare #HealthTech #digitalhealth

1. 6 Driving
Factors Behind
NLP in
Healthcare
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2. marutitech.com
NLP, a branch of Al, aims at
primarily reducing the distance
between the capabilities of a
human & a machine.
With NLP getting more & more
traction in healthcare, providers
are focusing on developing
solutions that can understand,
analyze, & generate languages
that humans can understand.

3. marutitech.com
According to
MarketsandMarkets,
the global NLP in
healthcare market size
to grow from USD 1.5
billion in 2020 to USD
3.7 billion by 2025, at a
CAGR of 20.5% during
the forecast period.
“

4. marutitech.com
2 MAJOR USE CASES
OF NLP
Understanding human speech &
extracting meaning/intent.
Unlocking unstructured data by
abstracting relevant values & making
this information available for decision
support along with analytics.
1.
2.

5. DRIVING FACTORS
BEHIND NLP IN
HEALTHCARE
marutitech.com
Handle the Surge in Clinical Data
Support Value-Based Care & PHM
Applications
Improve Patient-Provider Interactions
with EHR
Empower Patients with Health Literacy
Address the Need for Higher Quality of
Healthcare
Identify Patients who Need Improved
Care

6. marutitech.com
WHAT WILL THE
FUTURE OF NLP IN
HEALTHCARE LOOK
LIKE?
According to Allied Market Research, the
cognitive computing market size is
projected to reach $87.39 billion by 2026,
growing at a CAGR of 31.6% from 2019 to
2026.

7. marutitech.com
Eventually, natural
language processing
tools might be able to
bridge the gap between
the insurmountable
volume of data in
healthcare generated
every day & the limited
cognitive capacity of the
human brain.

8. marutitech.com
The key to the success of NLP in
Healthcare will be to develop
algorithms that are accurate,
intelligent, &
healthcare-specific while
creating UI that can showcase
decision support data in the
desired format.
Once the industry meets the two
major goals of extraction &
presentation, there is no telling
what big data doors could be
open in the future.

9. INSIGHTFUL,
RIGHT?
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