September 22, 2021 – Artificial intelligence (AI) is making a dent in healthcare in the US and around the globe, helping leaders in Europe, South America, and Asia improve outcomes and cut costs, dissolving key pain points in medicine.
So said presenters at HL7 International’s recent panel titled: The Use of Artificial Intelligence (AI) in Healthcare, hosted as part of the organization’s 35th Annual Plenary Meeting.
The panel featured four different presentations discussing the different applications of AI in the healthcare realm and was moderated by Walter Suarez, MD, MPH, HL7 International Board Chair.
The first presentation was done by Xihong Lin, PhD, a professor of biostatistics and the coordinating director of the program in quantitative genomics at Harvard University.
In her presentation, AI in Genomics & Population Health, Lin explained that in the last 10 years, the understanding of genome data and whole-genome sequencing data has rapidly expanded with the help of electronic health records. The goal of this data is to improve precision medicine and preventive care strategies.
Promoted by the completion of the Human Genome Project, researchers began putting extensive resources towards whole genome sequencing, emerging with two large whole-genome sequencing programs: TopMed and NHGRI Genome Sequencing Program.
According to Lin, data collected from these genome sequence efforts can be used to address health disparities and advance precision medicine for chronic diseases. However, Lin also highlighted that the collection of genome sequencing information creates large amounts of data for each patient.
With the use of biobanks, genome data is accessible, stored effectively, and can be easily analyzed. Data in the biobank can also be used to diagnose other people with a particular condition and can develop insight into prevention strategies, improving population health.
“We are at the exciting crossroad. It is an exciting time for human genetics and for this research. This requires disciplinary research and science. This is critical for the landscape of house data science and cloud-based infrastructure. Master development is critical for building data sharing, data resources, data analysis tools, and infrastructure,” Lin concluded in her presentation.
The second topic, Overview of AI Lab, Ethics, Skunkworks & Developments in Clinical Care, was presented by Jennifer Hall, AI senior data scientist at NHSX in London, England.
Hall gave an overview of the AI lab and the Skunkworks team’s approach to AI.
NHSX works to transform the National Health Service (NHS) and social care using digital technology. The goal is to provide better access to data, creating an accessible digital space. For NHSX, better access to data means better outcomes for patients.
Additionally, Hall explained that digitalized data content can transform what organizations can do in Population Health Management research, establishing the right standard to exchange data securely. In terms of artificial intelligence, NHSX assists in the regulation process.
“The AI lab part of NHSX has the mission to enable the development and adoption of safe, ethical, and effective AI-driven technologies and the UK healthcare system,” Hall continued.
“The lab looks to connect and speed up the progress of AI-driven technologies, including demonstrating the value of AI healthcare, making it easier for people to develop technologies and offering practical leadership, and how to get up and running.”
According to Hall, NHSX works closely with its partners to ensure a safe, robust, and ethical environment for AI development to create patient-centered technology.
Hall, who is also a member of the Skunkworks team, explained the team’s purpose is to “support ideas from scratch to prove a concept and explore new and innovative ways to use AI support underserved areas of the NHS.”
The team works together to identify problems within the healthcare realm and determine how they can be supported through the application of AI. They then evaluate the solution through risk models, speaking with stakeholders, and assessing if it meets the community’s needs.
The following topic titled, AI in Argentina: Lessons Learned at Hospital Italiano de Buenos Aires, was presented by Sonia Benitez, MD, PhD, internal medicine specialist at Hospital Italiano in Buenos Aires, Argentina.
Benitez explained some of the lessons the hospital has learned when implementing artificial intelligence into its system.
“Our [AI] program was created three years ago with the alliance between our department, health informatics department, and the medical imaging department,” Benitez said.
Last year, program leaders decided to expand its expertise to a data analytics project with the mission of integrating artificial intelligence systems into the healthcare workflow to improve patient safety, quality of care, and efficiency.
According to Benitez, the hospital used a several-step process to ensure that the AI is meeting the hospital’s goals. Additionally, developers are continuing to assess the system, looking for ways to improve upon it.
To develop an effective artificial intelligence system, Benitez said having large amounts of high-quality data is key.
The final presentation, AI: Its Positive Impact on Health Outcomes in Asia Pacific, Japan and Beyond, was done by Julian Sham, MD, the healthcare leader for the Asia Pacific & Japan sectors at Amazon Web Services in Singapore.
Artificial intelligence is critical in data organization, Sham explained. Large data sets can be difficult for an individual to comb through without assistance. According to Sham, the use of artificial intelligence lightens the load for physicians by quickly conducting the analytic process for them.
Artificial intelligence is especially helpful for physicians who are feeling burnout from the past 18 months of the pandemic.
Sham noted that previous presenters talked about data collection and using it to make predictive models and improve patient outcomes. Having the ability to do so allows for better clinical results and practices according to Sham.
“The ability of using AI and ML [machine learning] to help do digital transformation is a key driver to creating those personalized and engaging experiences that people are asking for. It’s also a key driver to help offset the heavy load and enable data-driven operational political decisions, using the information to give you insight as to how to influence and negotiate decisions” Sham continued.
“You’re making a very key point that we see from some of the stakeholders in the Asia Pacific region, because of the different levels of health IT maturity is a key consideration of driving down the cost of care, whilst maintaining or improving care quality.”
With AI, physicians can create personalized and engaging experiences, which assists in the clinical decision-making process, Sham concluded. Additionally, AI can drive down the cost of care and improve research and collaboration efforts.
Source: healthitanalytics.com
