How to implement AI: best practices

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Adoption of complex technologies such as artificial intelligence requires planning and strategy. Healthcare IT executives and professionals (CTOs, CIOs et al.) need to be aware of best practices for implementing AI systems at their respective organisations. Here's some guidance that they can use for effective rollouts, coming from four AI tech experts.

 
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1. Identifying use-cases

 

Tech and IT decision makers should know and understand AI use-cases and successes from various vendors. Healthcare providers should find out how their peers have successfully adopted AI technology. “This is a good first step, since there are many AI companies in the market that claim they can help healthcare organisations with AI," says Ryan Pretnik, research strategy director at KLAS Research. Identifying vendors with a proven track record of success can help reduce the financial risk related to purchasing an AI system.

 

Before making a purchase, Pretnik notes, make sure to consult clinicians on the use-cases and benefits of an AI tool. By working with clinicians from the start and engaging them on how to use the technology, "the clinicians are more likely to use and adopt the product, which in turn helps drive outcomes,” he adds.

 

2. The expected value

 

CIOs looking to implement AI should focus on the expected value, i.e., how the new system can help solve problems or where considerable value can be extracted. AI as a "transformative technology" has the potential of impacting virtually every process or domain in the hospital, both on the clinical side and the administrative, according to Jean-Claude Saghbini, chief technology officer at Wolters Kluwer Health.

 

Early investments in AI initiatives and systems should be aimed at yielding rapid yet sizeable results while building up internal knowledge in the AI domain. These immediate gains are important especially now that most hospitals are trying to combat revenue and margin compressions while providing increasingly better care for patients, Saghbini points out.

 

3. The operational state

 

Leaders wishing to implement an AI system must have a clear vision of their organisation's operational state and business goals, says Gurjeet Singh, CEO and co-founder of Ayasdi, a vendor of an AI-powered platform and enterprise-grade, intelligent applications. If one begins the project with the intent to have it being operational versus some proof of concept, one will make better, more informed decisions and increase the chances of success, Singh explains.

 

Another consideration should be how the organisation intends to validate the findings of the system. "There has to be a deep, clinician-understandable explanation of what the machine is recommending,” he says. Every lab, drug, order and test: This radical transparency, called justification, is what is required to build trust, Singh notes, adding that trust is what is required to be successful in any system, particularly one as transformational as AI.

 

4. Focus on outcomes

 

When implementing AI technology, the focus should be on outcomes, advised Lois Krotz, research strategy director at KLAS Research. “Set goals and make sure you have ways to benchmark the success of the AI solution – know how long it will take to see an outcome.”

 

Because AI systems can take smaller subsets of data (structured) and/or larger more holistic data sets (structured and unstructured), there tends to be a question of what results one’s data can produce. For instance, IT and physicians can work together to determine a couple use-cases and start with something like 'Decreasing the onset of a heart attack – for patients with congestive heart failure – by 25 percent' or 'Decreasing readmission rates of these patients by 20 percent'.

 

Source: Healthcare IT News
Image credit: Pixabay

Published on : Tue, 5 Mar 2019



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