The Current State of AI in Healthcare and Where It’s Going in 2023 — [if you’re not investing in AI you’re going to be left in the dust]


Institute4HealthTransformation (i4ht)


Joaquim Cardoso MSc
Founder and Chief Researcher & Editor
Decembr 19, 2022


Executive Summary


What is the currrent situation?


  • Artificial intelligence holds great promise to help medical professionals gain key insights and improve health outcomes.

  • However, AI adoption in healthcare has been sluggish,

What is changing? What are the predictions for next year?


  • Despite the slow uptake of AI in healthcare, health insurer Optum revealed in a December 2021 survey that 85 percent of healthcare executives have an AI strategy, and almost half of executives surveyed now use the technology.

  • “We’re no longer in an infancy stage,” … noting the impact of the COVID-19 pandemic in accelerating digital transformation. That includes AI.

What are the potential applications for AI?


  • AI is playing a role in improving data flow, recognizing and processing both structured and unstructured data. 

  • AI brings new efficiencies in speeding up data analysis. AI identifies patterns, and it’s generating insights that might elude discovery from a physician’s manual efforts

  • Schibell points to new efficiencies in speeding up data analysis. “AI identifies patterns, and it’s generating insights that might elude discovery from a physician’s manual efforts,” she says.

What are the issues?


  • 97 percent of healthcare data goes unused because it’s unstructured. That includes X-rays and medical records attached to slides.

  • Machine learning (ML) allows healthcare professionals to structure and index this information. Amazon HealthLake is one service that enables searching and querying of unstructured data.


AI Healthcare Use Cases in 2023 and Beyond


  • 1.Natural Language Processing and Conversational AI
  • 2.Automated Scheduling
  • 3.Integrating Omics, EHRs and Wearables
  • 4.Government Regulations of AI Will Tighten
  • 5.AI Will Enable Targeted Diagnostics and Personalized Care


“We’re at the point now where if you’re not investing in AI or if you’re on the fence about investing, you’re going to be left in the dust,”


How to Use AI in Healthcare


  • When implementing AI in healthcare in 2023 and beyond, providers should properly incorporate AI solutions into workflows. If the AI technology “complicates clinicians’ workflow and it takes them longer to implement, or if they need to switch to another screen and add steps in their workflow, they’re not going to use it,”

  • Providers should also make the physician a part of the process when developing AI solutions. The physician can be “the best developer of a solution,” …. If you’ve not incorporated the advice and expertise of the physician who’s describing the workflow, your solution is not going to be optimal.”





ORIGINAL PUBLICATION (full version)







The Current State of AI in Healthcare and Where It’s Going in 2023


Artificial intelligence is helping doctors diagnose and manage kidney disease and improving diagnostics and analysis of patient data.


HealthTech
B
rian T. Horowitz
December 16, 2022


Artificial intelligence holds great promise to help medical professionals gain key insights and improve health outcomes. 


However, AI adoption in healthcare has been sluggish, according to a March 9 Brookings Institution report.


Despite the slow uptake of AI in healthcare, health insurer Optum revealed in a December 2021 survey that 85 percent of healthcare executives have an AI strategy, and almost half of executives surveyed now use the technology.


“We’re no longer in an infancy stage,” says Natalie Schibell, vice president and research director for healthcare at Forrester Research, noting the impact of the COVID-19 pandemic in accelerating digital transformation. That includes AI.


“We’re no longer in an infancy stage,” “We’re no longer in an infancy stage,” says Natalie Schibell, … noting the impact of the COVID-19 pandemic in accelerating digital transformation. That includes AI.


Schibell sees a deep need for AI to address healthcare problems such as chronic illness, workforce shortages and hospital readmissions


These factors are leading healthcare organizations, insurance companies and pharma and life sciences organizations to adopt AI, she says.


there is a deep need for AI to address healthcare problems such as chronic illness, workforce shortages and hospital readmissions.


AI is playing a role in improving data flow, recognizing and processing both structured and unstructured data, Schibell says. 


“We’re at the point now where if you’re not investing in AI or if you’re on the fence about investing, you’re going to be left in the dust,” she says.


AI is playing a role in improving data flow, recognizing and processing both structured and unstructured data,… 

“We’re at the point now where if you’re not investing in AI or if you’re on the fence about investing, you’re going to be left in the dust,” she says.

…AI brings new efficiencies in speeding up data analysis. AI identifies patterns, and it’s generating insights that might elude discovery from a physician’s manual efforts 


Schibell points to new efficiencies in speeding up data analysis. “AI identifies patterns, and it’s generating insights that might elude discovery from a physician’s manual efforts,” she says.

Dr. Taha Kass-Hout, vice president of health AI and CMO at Amazon Web Services, notes that 97 percent of healthcare data goes unused because it’s unstructured. 


That includes X-rays and medical records attached to slides. 

Machine learning (ML) allows healthcare professionals to structure and index this information. 

Amazon HealthLake is one service that enables searching and querying of unstructured data.


… 97 percent of healthcare data goes unused because it’s unstructured. That includes X-rays and medical records attached to slides.

Machine learning (ML) allows healthcare professionals to structure and index this information. Amazon HealthLake is one service that enables searching and querying of unstructured data.


In addition, ML and natural language processing (NLP) help healthcare organizations understand the meaning of clinical data, he adds.


In addition, ML and natural language processing (NLP) help healthcare organizations understand the meaning of clinical data, he adds.


For example, the Children’s Hospital of Philadelphia turned to AWS AI services to integrate and facilitate the sharing of genomic, clinical and imaging data to help researchers cross-analyze diseases, develop new hypotheses and make discoveries.


For example, the Children’s Hospital of Philadelphia turned to AWS AI services to integrate and facilitate the sharing of genomic, clinical and imaging data to help researchers cross-analyze diseases, develop new hypotheses and make discoveries.



AI Scours Documentation for Cancer Studies


The Fred Hutchinson Cancer Center in Seattle used NLP in Amazon Comprehend Medical to review mountains of unstructured clinical record data at scale to quickly match patients with clinical cancer studies. 


NLP helped physicians review about 10,000 medical charts per hour to find patients with the right inclusion criteria, removing the “heavy lifting,” Kass-Hout says.


“There are laborious inclusion criteria to go through, where you have to identify a lot of characteristics about the patient to determine whether they meet the criteria to be enrolled in a clinical trial. Often you have to read the entire medical history,” Kass-Hout says.


Less than 5 percent of patients match the recruitment criteria for these types of clinical trials, according to Kass-Hout, partially due to the challenges of identifying the right information among unstructured data.


The Fred Hutchinson Cancer Center in Seattle used NLP in Amazon Comprehend Medical to review mountains of unstructured clinical record data at scale to quickly match patients with clinical cancer studies.

Less than 5 percent of patients match the recruitment criteria for these types of clinical trials, according to Kass-Hout, partially due to the challenges of identifying the right information among unstructured data.



AI Helps Diagnose and Manage Kidney Disease


AI is helping doctors diagnose and manage kidney disease and predict trajectories for kidney patients, …

… says Dr. Peter Kotanko, head of biomedical evidence generation at the Renal Research Institute (RRI) and adjunct professor of medicine for nephrology at the Icahn School of Medicine at Mount Sinai in New York.


Kotanko indicates that nephrologists and other medical disciplines use AI and ML to assess images from radiology or histopathology, as well as images taken by smartphones to diagnose a patient’s condition.


AI is helping doctors diagnose and manage kidney disease and predict trajectories for kidney patients, …

nephrologists and other medical disciplines use AI and ML to assess images from radiology or histopathology, as well as images taken by smartphones to diagnose a patient’s condition.


“AI not only relies on structured lab data or data stored in electronic health records, but also, of course, uses tools like natural language processing to extract insights from the unstructured texts,” he says.


“AI not only relies on structured lab data or data stored in electronic health records, but also, of course, uses tools like natural language processing to extract insights from the unstructured texts,” 


Meanwhile, ML is used to predict patient outcomes, including hospitalization, and to identify which patients may have COVID-19. 


RRI uses deep learning to analyze images from smartphones or tablets to assess a patient’s arterio-venous vascular access, which is used to connect a patient to the dialysis machine.


Meanwhile, ML is used to predict patient outcomes, including hospitalization, and to identify which patients may have COVID-19. 

The Institute uses deep learning to analyze images from smartphones or tablets to assess a patient’s arterio-venous vascular access, which is used to connect a patient to the dialysis machine.


“A convolutional neural network, or CNN, analyzes these kinds of data and sends a respective assessment back to the user within a second or so,” Kotanko says.


 “Images are sent from the tablet or smartphone to the cloud where a CNN receives the data and then provides the respective response.”

This whole aspect of data collection through pervasive sensing devices will grow, and I think AI will help to digest and integrate these high-dimensional inputs.” Dr. Peter Kotanko Head of Biomedical Evidence Generation, Renal Research Institute



AI Healthcare Use Cases in 2023 and Beyond


Here are some trends for AI use in healthcare within the next three years:


  • 1.Natural Language Processing and Conversational AI 
  • 2.Automated Scheduling
  • 3.Integrating Omics, EHRs and Wearables
  • 4.Government Regulations of AI Will Tighten 
  • 5.AI Will Enable Targeted Diagnostics and Personalized Care

1.Natural Language Processing and Conversational AI


NLP and conversational AI have made advances in healthcare, but Schibell expects to see expanded use of virtual assistants in the next one to three years. 

“Symptom checking and triage will be more mainstream, more sophisticated,” she says. 


NLP and conversational AI have made advances in healthcare, and it is expected to see expanded use of virtual assistants in the next one to three years.

“Symptom checking and triage will be more mainstream, more sophisticated,” she says.


AI will help providers weed out which patients have emergency needs versus those that a primary care physician can address.


Healthcare AI use cases that involve employing conversational AI include preparing for an appointment and providing driving directions to a hospital, Schibell says. 

Conversational AI will advise patients on whether to fast before an appointment, what to wear and what they should do before an exam.


2.Automated Scheduling


Look for improvements in automated scheduling in the coming year and beyond. 

“With retail health now shifting to primary care, you will see these companies using automated scheduling the most,” Schibell says. 

“There aren’t a whole lot of traditional healthcare providers using it.”


3.Integrating Omics, EHRs and Wearables


AI will combine omics — biochemical assays such as metabolomics, genomics and transcriptomics — with EHRs and data from wearable devices, according to Kotanko. 

Wearable data combined with omics data could differentiate patient phenotypes, Kotanko says.

“This whole aspect of data collection through pervasive sensing devices will grow, and I think AI will help to digest and integrate these high-dimensional inputs,” Kotanko says.


AI will combine omics — biochemical assays such as metabolomics, genomics and transcriptomics — with EHRs and data from wearable devices, …

Wearable data combined with omics data could differentiate patient phenotypes, 


4.Government Regulations of AI Will Tighten


As the FDA decides which medical devices to recognize, AI regulation will become stricter in the U.S. and in Europe, according to Kotanko.

“Startups in the medical AI space will need to deal with this component,” Kotanko says. 

“I think that will be a significant move from the medical decision support system domain into the medical device domain.”


“I think that will be a significant move from the medical decision support system domain into the medical device domain.”

5.AI Will Enable Targeted Diagnostics and Personalized Care


As healthcare professionals make sense of unstructured data, they’ll be able to develop targeted diagnostics and personalize care, Kass-Hout says. 


As healthcare professionals make sense of unstructured data, they’ll be able to develop targeted diagnostics and personalize care,


“A lot of our customers are trying to index this data so they can structure it. However, it’s an error-prone process,” Kass-Hout says. 

“It’s very challenging operationally and due to cost, with these tools, we are really removing all that heavy lift for a lot of these customers, so they can focus on delivering care for their patients and populations.”


“A lot of our customers are trying to index this data so they can structure it. However, it’s an error-prone process,”…

“It’s very challenging operationally and due to cost, with these tools, we are really removing all that heavy lift for a lot of these customers, so they can focus on delivering care for their patients and populations.”



How to Use AI in Healthcare


When implementing AI in healthcare in 2023 and beyond, providers should properly incorporate AI solutions into workflows, Schibell suggests. 


That way, complications such as latency when analyzing radiology images in the ER can be avoided. 


When implementing AI in healthcare in 2023 and beyond, providers should properly incorporate AI solutions into workflows, Schibell suggests. That way, complications such as latency when analyzing radiology images in the ER can be avoided.


If the AI technology “complicates clinicians’ workflow and it takes them longer to implement, or if they need to switch to another screen and add steps in their workflow, they’re not going to use it,” she says.


If the AI technology “complicates clinicians’ workflow and it takes them longer to implement, or if they need to switch to another screen and add steps in their workflow, they’re not going to use it,” she says.


Providers should also make the physician a part of the process when developing AI solutions.


The physician can be “the best developer of a solution,” Schibell says. 

“If you’ve not incorporated the advice and expertise of the physician who’s describing the workflow, your solution is not going to be optimal.”


Providers should also make the physician a part of the process when developing AI solutions. The physician can be “the best developer of a solution,” …. 

If you’ve not incorporated the advice and expertise of the physician who’s describing the workflow, your solution is not going to be optimal.”


Originally published at https://healthtechmagazine.net on May 10, 2022.



Natalie Schibell, vice president and research director for healthcare at Forrester Research

Dr. Taha Kass-Hout, vice president of health AI and CMO at 
Amazon Web Services

Dr. Peter Kotanko, head of biomedical evidence generation at the Renal Research Institute (RRI) and adjunct professor of medicine for nephrology at the Icahn School of Medicine at Mount Sinai in New York.

The Fred Hutchinson Cancer Center in Seattle used NLP in Amazon Comprehend Medical


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