Posted by Erika Greelish on June 23, 2020 | No Comments
The bottom line. Return on investment. Getting the most bang for your buck. However you phrase it, all companies are concerned with profit margins and risk management.
CEOs of major companies have taken a cautious but steady approach to integrating AI technology. According to a 2019 survey done by the KPMG research firm, nearly a third of CEOs reported that they had already implemented AI technology, and over half said they had begun the process of implementation. However, few said that they had already achieved significant return on investment; most said they expected significant ROI in five years or less. This percentage had increased significantly compared to data from 2018, which indicated CEOs expected to see ROI within three years.
These data show that while companies recognize that integration of AI systems is crucial to remaining competitive, they understand that this is a long-term investment. According to the authors of a comprehensive study done by the National Academy of Medicine, despite “the fact that this is one of the strongest growth areas in biomedical research…the cost and burden of implementing of AI tools should be weighed against use case needs.” They add that while AI has the potential to meaningfully “address patient and communal needs” and effect “truly personalized health care,” successful AI implementation will require the engagement of health care stakeholders across the board.
The overhaul of the complicated US healthcare system with regards to AI is well underway. Major insurance companies are already using natural language processing, an AI tool, to streamline organizational processes ranging from directing calls to improving quality control in clinical documentation and coding procedures. They are also applying learning algorithms in the field of predictive analytics. Currently, some of the highest-value applications include predicting trends in healthcare needs and consumption among specific groups of patients. Examples include identifying patients who are likely to need help getting to a follow-up appointment, creating risk scores to determine which individuals are most likely to suffer from post-surgical complications requiring hospital readmission, and even modeling how specific patients are most likely to respond to certain treatments based on the aggregation of relevant data.
As CEOs, CFOs, and quality control managers incorporate data-driven analytics into their decision-making process and see a positive cost-benefit analysis for AI tools and applications, large companies across the board will then begin the next step of AI integration in more areas of health care. Those who move early on the trends may improve not only patient outcomes, but their own bottom line.