January 28, 2022

2022 Predictions for AI and Machine Learning in Healthcare

Original source here.

As we head into 2022, we asked the Healthcare IT Today community to share some predictions for the new year. I always find it interesting to learn what people think is coming down the road. Be sure to check out all our Health IT Predictions.

One of the most exciting topics in healthcare today is the use of AI and machine learning. I love this topic because we can finally start having this conversation. Even 10 years ago we didn’t have the data electronically available to do this. Now we do. As you’ll see in these healthcare AI and machine learning predictions, the big task ahead of us is how do we take all this data and make it useful?

Here’s a look at some of the 2022 healthcare AI and machine learning we received:

Prashant Natarajan, VP, Strategy and Products at H2O.ai
1. COVID will continue to impact health data and AI. Providers’ financial and patient realities will increase modernization and transformation in data, analytics, and AI-ML.

2. In order to manage costs effectively and scale beyond point/custom-built solutions, healthcare organizations will see the rise demand for Responsible AI programs and platforms. Capabilities will focus on flexibility and time to value + interpretability/explainability and fairness.

3. The focus on automation will go from memorization & template-based programs/products to RPA plus AutoML-driven AI – the latter will bring enable insights-driven intelligence & workflows.

4. Clinical AI will require more validation – within and across health systems – and end user clinical validation. Human in the loop will become the new normal as opposed to any goal of 100% automation – that doesn’t involve the HCP or the patient.

5. New value streams will come out AI applications in supply chain, revenue cycle, HR/talent, population health, and operations

6. Value creation in healthcare AI will see the acceleration of multi-organizational partnerships and ecosystems. AI app creators, distributors, contributors, and collaborators will work more closely together in 2023.

Praduman “PJ” Jain, CEO at Vibrent Health
Infectious disease research will use AI: As coronavirus becomes endemic and new pandemics emerge, it will become critical to rely on digital infrastructure and mobile apps to create crucial data sets to understand the severity of diseases and their transmission patterns. Use of machine learning and AI would be needed to analyze massive amounts of collected data to help our citizens to mitigate diseases to remain safe and enable our society to continue to function normally.

Jeff Wessinger, VP, GM, Life Sciences at PointClickCare
We’ve seen big tech enter the healthcare arena, and I believe this investment will continue in the future. We expect pandemic-born collaboration between pharma, tech and life science companies to pave the way and enhance future healthcare innovation, new medicines, and digital health.

By centralizing and coordinating data collection and access across a single shared data network, researchers and partners can eliminate the costly ‘lift and shift’ to quickly and safely connect longitudinal data required in a convenient system. The ability to analyze real-time data and apply predictive analytics to target treatment is the next level of care.

For example, simple diagnostic tools that use artificial intelligence to analyze everyday speech can now be embedded into a caregiver’s daily workflow to routinely identify patients at risk of developing specific neurologic diseases.

Derek Baird, President, North America at Sensyne Health
As the race to develop new therapeutics and diagnostics continues, demand for real-world data (RWD) plus analytical tools will only increase. There’s been a lot of buzz around real-world data, but too many life sciences companies and researchers still don’t have affordable, convenient ways to analyze curated, de-identified, clinical data.

This is a natural stage in market evolution, but is ultimately unsustainable and we can expect to see significant changes in 2022 and beyond. New technologies – combining data and AI tools – are already democratizing access to important insights while protecting patient information. These tools won’t just be for those with the largest coffers; they will soon be within reach of life sciences and medical researchers within all types of organizations across the globe.

Kyle Silvestro, CEO and Founder at SyTrue, Inc.
As the healthcare industry continues to focus on the impact that social determinants of health (SDoH) on overall patient well-being, industry stakeholders will face ongoing challenges in extracting meaningful SDoH information from unstructured data in electronic health records. To overcome barriers posed by dirty data in clinical documentation, payers and providers will increasingly turn to artificial-intelligence-based tools that automate the capture unstructured SDoH data, increasing efficiency and saving time compared with manual chart reviews performed by highly paid clinicians.

JT Finnell, M.D., Chief Medical Officer at VisualDx
As we continue to see increased digital health adoption in 2022, we need to watch out for the pitfalls that come with digital tools. A prime example is information overload that can impair decision-making and have negative consequences on patient care and satisfaction. Now is the time to rely on “augmented” intelligence to filter and group relevant information together for the clinician to diagnose, refer, and treat patients accurately and efficiently.

In the coming year, technology will continue to aid clinicians by providing specialty knowledge and shifting away from reliance on total memorization, false association with multiple presenting systems, and other issues that stem from information overload. Assistive tools will also be used to eliminate fatigue with repetitive administrative duties and documentation – allowing doctors to focus in on the information most critical to patient health.

Josh Gluck, Vice President of Global Healthcare Technology Strategy at Pure Storage
A voracious appetite for faster-time-to-science is here to stay — liquid data is key to satiating the hunger.

The appetite for faster time to science is voracious and will likely continue. The world’s scientific community continues to break records in the fight against COVID-19 – leveraging massive information sharing that is leading to a more accurate picture of COVID-19 and accelerated development and testing of vaccines and therapeutic treatment candidates. We’ve seen what can be done faster than ever imagined.

Health sciences organizations across the board seek to build on this momentum safely and effectively to further accelerate the pace of personalized medicine. Genomics and artificial intelligence (AI) are key to this quest. To realize AI at scale, however, requires liquid data and modern data infrastructure that re-imagines the role of data and how it is used.

Dr. Tamir Wolf, CEO & Co-Founder at Theator
2022 is set to be the year in which routine capture and analysis of intraoperative video becomes the gold standard for all stakeholders in the surgical ecosystem. With sophisticated AI and data strategies now a must-have for any medical institution, the impact of harnessing untapped OR data on quality of care and patient safety is self-evident. Surgeons, healthcare organizations and professional societies have already started to recognize the advantages of routine video capture.

Now, as more and more real-life case studies generate an expanding mass of evidence as to how AI and routine video capture are facilitating this paradigm shift, expect to see this trend make major strides forward over the coming year.

Dr. Bob Lindner, Chief Technology Officer at veda
Machines and humans will start to get along: Using real automation to solve complex tasks with imperfect data (i.e., human-generated data) requires human-in-the-loop AI solutions. The human brain remains the uncompleted champion of problem solving, and the best industry solutions will be those that integrate human creativity, not replace it. This is key in healthcare, where everything ties back to patients.

The data science bubble will burst: Data science is a catch-all phrase that can encompass statistics, scientific methods, AI, data collection, data visualization, cloud architecture, and more. This can lead to confusion about what a data scientist “should” actually be doing inside companies. In 2022, roles will begin to splinter into the tried-and-true professions of traditional scientist, statistician, cloud engineer, ML engineer, and other more specific roles as the value of these practices inside business are made clearer. With its mountain of data, healthcare has a clear need for this talent, who can tightly manage automated solutions and ensure key decisions remain in human hands.

Industries like healthcare that have long lagged behind in terms of technological advancement will start to catch up as they adopt automation: Healthcare will catch up in AI, because the pandemic forced many companies to test drive these solutions, and what they found is that today’s platforms are sophisticated enough to deal with the messy, human-generated data that’s endemic to this industry.

Dr. Ofer Sharon, CEO at OncoHost
As immunotherapy options increase and modalities in tumor types continue being approved, 2022 will put the issue of personalized cancer treatment options in the spotlight. Clinicians are gradually recognizing that new drugs and treatment combinations need to be developed together with effective biomarkers to have the highest chance of success in identifying the best treatment plan for each individual patient.

Machine learning and bioinformatic tools will be required to sift through all the information available and create valuable clinical insights. Diagnostics companies will look to expand into multi-omics-based tests to provide clinicians with a profound understanding of tumor resistance, collaborating with each other to combine expertise for the ultimate outcomes.