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The Data Daily

Q&A: Healthcare, BI, and AI | Transforming Data with Intelligence

Q&A: Healthcare, BI, and AI | Transforming Data with Intelligence

You found that just over a third of participants aren’t even considering AI as a business solution. Why is that?

Applying machine learning, data science, and AI-related techniques in healthcare is a new concept -- less than a decade old. The fact that two-thirds of practitioners have added AI as a priority within a few short years -- in an industry dealing with COVID-19, an opioid epidemic, a mental health epidemic, a physician and nursing shortage, an affordability crisis, EHR deployment, precision medicine, cybersecurity attacks, and ongoing regulatory and policy changes -- shows more than anything how big this industry considers the potential of AI.

Your survey discovered healthcare and life sciences technical leaders say the top technologies they plan to have in place by year end include data integration (46 percent). What are the biggest challenges when integrating health care data?

Bringing together structured and unstructured data is one of the biggest challenges when integrating healthcare data. In addition to data integration, the survey also found natural language processing (NLP) is one of the foundational AI technologies respondents planned to have in place by the end of 2022. NLP enables users to bridge the gap between structured data (claims, electronic medical records) and unstructured data (including clinical notes, pathology and radiology reports, lab results, research papers, clinical trial documents, and social media posts). Serving as the connective tissue, NLP can accurately understand information in unstructured formats and systems to create a clearer, more accurate picture. This enables data scientists or domain experts (in this case clinicians) to make better decisions.

I also thought it interesting that 44 percent of respondents plan to have BI in place by the end of the year. I would have thought BI was already in common use, but the survey indicates otherwise. Would you care to comment?

That is a surprising finding. One possible explanation is that half of AI practitioners have not yet reached the mature stage of integrating the models and systems they’ve built into the clinical and operational workflows where BI systems currently operate.

What survey results were as you expected?

Many results were expected and haven’t changed much from last year, but growing areas such as data annotation (especially the increase in use by domain experts) and the need for healthcare-specific models point to more sophisticated uses of healthcare AI, which is encouraging.

As part of the survey, we also identified over 40 startups that have raised significant funding specifically to build AI solutions to improve healthcare outcomes and reduce overall costs. Between aging populations and skyrocketing healthcare costs, it's good -- but not at all surprising -- that venture capital dollars are going to AI applications for the healthcare, pharmaceutical, and medical sectors.

We mentioned the use of BI being used by under half of respondents. What other survey results surprised you?

The increased importance of data annotation tools, as well as the in-house data validation and model tuning that these tools enable, is a welcome indicator of higher sophistication and maturity by users and seems to be happening faster than we expected. The reduced popularity of cloud services, given their known issues with data privacy and the ability to tune models, is another indicator of industry maturity, and also somewhat surprising given the marketing and sales investments by cloud providers.

Where do you see AI headed for healthcare and life sciences professionals in the next year or two?

As the survey indicates, there will be more individuals outside of the traditional role of data scientist operating AI as part of their daily responsibilities. This will contribute to wider adoption, more sophisticated solutions, and more use cases.

With greater adoption and usage comes a darker side, though. Healthcare organizations have increasingly become the target of cyberattacks, and with AI proliferation and a growing user base, there are more entry points for bad actors. In fact,a recent study found that over half of internet-connected devices used in hospitals have a vulnerability that could put patient safety, confidential data, or the usability of a device at risk. Greater AI proficiency in healthcare is a good thing, but we need to address the real risks and ensure that systems consistently operate in a safe and ethical manner.

[Editor’s note: David Talby, Ph.D., MBA, is CTO at John Snow Labs, helping fast-growing companies apply AI, big data, and data science techniques to solve real-world problems in healthcare, life science, and related fields. You can reach him via email,Twitter, or LinkedIn.]

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