With AI adoption increasing in response to COVID-19, new guidance on ethics and governance

Gabriel Perna | June 30, 2021

After two years of careful analysis, the World Health Organization (WHO) has weighed in on the ethics and governance of artificial intelligence in health care—and it’s not a moment too soon. 

Adoption of AI systems in health care has gone up significantly in the last few years. According to a survey from Deloitte, 73 percent of all health care organizations increased their AI funding in 2020. Moreover, 75 percent of large organizations (annual revenue of more than $10 billion) invested more than $50 million in AI projects or technologies. 

Organizations with higher annual revenue invest more heavily in AI. Note: Total number of respondents, N=120 (US=87, other global regions=33). Source: Deloitte’s State of AI in the Enterprise, 3rd Edition survey




Another report, from KPMG, found that AI adoption accelerated during the pandemic across all sectors, particularly in health care. Both health care and life sciences leaders have bought into its ability to monitor the spread of COVID-19 cases (94 percent and 91 percent of respondents), and help with vaccine development (90 percent and 94 percent) and distribution (90 percent and 88 percent), respectively. 

“Artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm,” said Tedros Adhanom Ghebreyesus, MD, WHO Director-General in a statement.  

WHO’s report is bullish on AI, crediting the technology with the ability to improve the speed and accuracy of diagnosis and screening for diseases, assist with clinical care, strengthen health research and drug development, and support diverse public health interventions. However, WHO also cautions against relying too much on the technology and advises health care organizations to be wary of unethical collection and use of health data and biases encoded in the algorithms.  

WHO shared six principles to guide AI adoption in health care: 

  • Protecting human autonomy: Ensuring that humans remain in control of health care systems and medical decisions and protecting privacy and confidentiality.  
  • Promoting human wellbeing and safety and the public interest. Satisfying regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications.   
  • Transparency. Organizations must publish data on the design or deployment of an AI technology before its launched.   
  • Fostering responsibility and accountability. Only using AI for the appropriate conditions and by appropriately trained people.   
  • Ensuring inclusiveness and equity. Organizations should design AI to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes. 
  • Promoting AI that is responsive and sustainable. Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements.            

AI uses cases during COVID  

Across health care, COVID was a spark for AI adoption in numerous forms. Mark Ziemianski of Children’s Health in Dallas says that the AI implementation within his organization was born out of necessity during the pandemic. The hospital wasn’t sure it could get personal protective equipment (PPE) and COVID tests to its workers in a sufficient time.   

“It forced us to manage our supplies. We learned very quickly we were going to have to do a little bit better than manage the existing supplies, we were going to have to forecast the usage,” says Ziemianski. 

The hospital built a couple data models using AI, which allowed them to look at five years of operating room cases by procedure and identify who was in the room at the time and the equipment they would likely need. “As cases were scheduled, we could forecast the need against the inventory that we had on hand. That allowed us to be able to carefully maintain inventory so we wouldn’t run out or if we were going to run out, we could reach out to vendors,” he says.  

Later in the pandemic, Children’s Health used the AI forecasting tool to manage vaccine supply and demand among the hospital’s 35,000 employees. The health system has since expanded the use of the AI tool into operational purposes, such as managing capacity for how many kids are in the emergency department.  

Parkview Medical Center is a smaller health care organization, as a non-profit, independent community hospital based in Pueblo, Colorado with 260 acute-care beds and approximately 2,700 employees. Like Children’s, Parkview leveraged AI to manage capacity, which is of particular importance during COVID for a hospital that has a limited number of beds across a large service area that stretches into New Mexico. 

Sandeep Vijan, MD, Vice President of Medical Affairs and Quality and CMO of Parkview, says the organization used natural language processing, predictive modeling, machine learning technology to garner insights from the EHR, structured or unstructured, to help providers identify discharge barriers throughout the patient’s stay. Using this tool helped reduce excess length of stay by 88 percent and ensured the small hospital didn’t run out of beds during COVID. Vijan says the complex challenges of today’s health system require these kinds of data-driven solutions.  

Our CEO has been involved in helping us build out our data science program. He’s been very supportive. I don't know that he knew what it looked like or what it was going to look like, but he felt strongly it was something we had to do.

Mark Ziemianski , Children's Health

“Health care is increasing in complexity. Patients’ conditions are becoming more complex and intertwined. There’s more than just standardization that needs to be done. The idea of throwing a process or an FTE at a problem doesn’t work in today’s world. We’re moving into an era where certain types of care delivery models and care coordination needs to be customized and tailored for the patient. And for that you need data and systems that tell you which patients need what care at what time,” Vijan says.  

Novant Health, an integrated health system in North Carolina, uses AI for patient safety, specifically hand hygiene. The technology can track how often providers and employee use hand hygiene products. Chief Medical and Scientific Officer for Novant Health, Eric Eskioglu, MD, says the system actually adopted the technology before COVID in one of its hospitals, but expanded it across the organization during COVID. This investment proved critical during the pandemic. 

Eskioglu says that four things are pushing health care organizations towards AI: The adoption of the cloud, the improvement of bandwidth through 5G networks and satellite transmission, the maturity of EHRs, and the pandemic. On the last one, he says it has forced organizations to think differently.  

“I told my CEO that the way we entered this pandemic is going to be a lot different than the way we come out of it. It’s not going back to business as usual,” says Eskioglu. “Our data knowledge in medicine is expanding. It’s doubling every 72 days. There is no way a physician can keep up with that. Physicians won’t be replaced by AI, but physicians who embrace AI are going to replace physicians who resist AI.”  

Buy in 

Eskioglu’s sentiment speaks to one of the persistent challenges health care organizations continue to face in this area: buy in from clinicians. Garrett Vygantas, MD, Managing Director of Venture Investments for OSF Ventures, the venture arm of OSF Healthcare, has a unique insight into this challenge as both a physician and an entrepreneur and investor of health care technology. He says that there may be various reasons for hesitancy from doctors:  

“Is it fear of learning a new technology? Is it security issues around data sharing? There is also the impending sense of worry on what health care service lines will look like in the future and if there will be redundancies among manual labor. Does the future hold a lesser role for humans? Can care that’s being done by humans be done by machines? Those all could be reasons against adoption,” Vygantas says.  

At Children’s Health, Ziemianski says that the organization had some push back from its physicians on the accuracy of the data model. The algorithm needed to be tailored more specifically towards pediatrics, so they worked with the vendor (Irving, Texas-based Pieces) to create a more precise model.  

“We started to get more buy in when that happened because I think the physician saw how the model could move based on the parameters and the configuration that we set them up on. That gave them a little bit more confidence. By the time we developed our models around sepsis, they were heavily engaged,” Ziemianski says.  

Engaging physicians has led to better results, as the organization has been able to drive decisions by data rather than feel, he adds. Ziemianski also credits his data scientist team, which works hand in hand with clinicians to develop these models. Without that kind of collaboration, it’s impossible to develop the precise data models needed to solve the operational, financial, and clinical challenges of a health care organization. And when that happens, he says, people lose confidence. 

“With all these EHRs, we have a wealth of data. We’re just scratching the surface with it. What’s going to happen, as these tools develop and the sophistication that comes along with it, we’ll start to see more and more AI adoption. But I think it will be a challenge for a lot of organizations to get there,” says Ziemianski, who adds that buy in from the top is just as critical as buy in from clinicians.  

“Our CEO has been involved in helping us build out our data science program. He’s been very supportive. I don’t know that he knew what it looked like or what it was going to look like, but he felt strongly it was something we had to do. And it really helped us out with our COVID management.”  

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About the Author

Gabriel Perna, Senior Manager, Digital Content

Gabriel Perna is the Senior Manager of Digital Content at Health Evolution. He brings 10+ years of experience in covering the intersection of health care and business. Previously, he was at Chief Executive, Physicians Practice and Healthcare Informatics. You can reach him via email at or on Twitter at @GabrielSPerna