Summary of the 2021 Healthcare AI Industry Survey

The 2021 Healthcare AI Survey reveals the industry's focus on NLP, data integration, and BI. Accuracy, privacy, and model tunability are key for AI system evaluation.

Each of us only gets to experience a tiny part of the world – touching a different part of the elephant. Therefore, it’s essential to come together occasionally and assess industry-wide trends.

The new 2021 Healthcare AI Survey from Gradient Flow, sponsored by my company, aims to do just that: unearth these areas to provide a better overview of where we actually stand when it comes to AI in healthcare.

Key Technologies: Data Integration, BI, and NLP

One of the most telling findings here is the shift in AI technologies that organizations are currently using or plan to implement in 2021. Respondents to the survey said they wanted to have natural language processing (NLP) (36%), data integration (45%), and business intelligence (BI) (33%) as the three most widely applied technologies in their businesses by the close of 2021.

These aren’t just statements of intent — they’re backed by money. The 2020 NLP Industry Survey, published by the same group in the Fall of 2020, reported that more than half of technology leaders — the people overseeing AI investment — have increased the budget allocated to NLP between 2019 to 2020.

Paired with data integration and BI, it’s clear that healthcare systems are getting more serious about the value of unlocking their data — structured and unstructured. NLP, BI, and data integration solve some of the biggest problems the healthcare industry faces, from serving as connective tissue between siloed data sources (in electronic health records, free text, imaging, and more) to safeguarding personally identifiable information (PII) and making sure it stays private, for highly regulated industries, such as healthcare and pharma, AI-powered technologies like the aforementioned will be critical to operations and safety. 

Evaluating AI Systems: Accuracy, No Data Sharing, and Model Tuning

Another encouraging finding is the criteria most important to healthcare users when evaluating which AI technologies to explore further. The top three criteria for technical leaders when evaluating such technologies and tools were providing extreme accuracy (48%), ensuring no data is shared with their software providers and vendors whatsoever (44%), and having the ability to train and tune the models to match their own datasets and use cases. Privacy, trainability, and accuracy are essential for any AI solution, especially when dealing with medical information that can impact care delivery. Access to data and ownership of specialized models are also primary sources of intellectual property that AI organizations build.

Accuracy, in particular, is a big topic of interest in clinical applications. Here’s an example of why this is so important: According to a report from the Journal of General Internal Medicine, "Collection of data on race, ethnicity, and language preference is required as part of the 'meaningful use' of electronic health records (EHRs). These data serve as a foundation for interventions to reduce health disparities." The paper found important inaccuracies in what was recorded in EHRs and what patients reported. For example, "30% of whites self-reported identification with at least one other racial or ethnic group than was reflected in the EHR, as did 37% of Hispanics, and 41% of African Americans." This is a problem when you consider patients from certain backgrounds and ethnicities may have a greater risk of developing certain comorbidities or lack access to appropriate care. This isn’t necessarily an AI problem but a data problem — and data needs to be accurate for AI to work its magic.

Evaluating Vendors: Healthcare-Specific & Production-Ready AI

This emphasis on accuracy also feeds into what technology leaders seek when evaluating software libraries or SaaS solutions to fuel their AI initiatives. Per the 2021 Healthcare AI Survey, healthcare-specific models and algorithms (42%) and a production-ready codebase (40%) topped the list when considering a solution. Healthcare-specific models are familiar with the nuances of medical data, from clinical jargon and language to billing codes and other data from nontext entities, such as X-rays. Additionally, production-grade products empower users, from data scientists to clinicians, to integrate AI technologies into their daily workflows with a reduced risk of problems or inaccuracies. After all, they’ve already been tested and proven and are being updated over time. 

As AI begins to trickle down to use by patients with the advent of chatbots, automated appointment scheduling, or obtaining access to their medical records, it’s essential to be aware of both the value and challenges this technology can bring. A chatbot not being able to connect a person to the correct department may not seem like a big deal — unless the patient is experiencing an acute medical event that needs immediate care. The varying levels of severity in medical settings make it obvious why factors like accuracy, healthcare-specific models, and production-ready code bases could be the difference not just between a successful AI deployment and a failed one but, in some cases, between life and death.

With the global AI in healthcare market size expected to grow from just under $5 billion in 2020 to $45.2 billion by 2026, the investments and recent use cases for this technology are proof that AI is here to stay. But with many of these cutting-edge technologies still in their infancy and many challenges ahead, the jury is still out on what the next few years hold for AI adoption, key players, and clinical advances for the healthcare industry. Thankfully, with more research at our fingertips, we’re a bit closer to getting there.