The COVID-19 pandemic is running its apocalyptic course around the world. One thing is certain in the midst of total uncertainty and utter chaos: SARS-CoV-2 is a very daunting enemy and we remain totally subjugated by these viral overlords. It is easy, therefore, for we humans to lose patience and become careless as well as to decrease capacity for resilience. We need to regain our composure and to be defiant in order to for us to prevail this human vs virus struggle.
A major part of this resilience and defiance is continue our work in adoption and deployment of artificial intelligence concepts and projects in clinical medicine. Here are key takeaways from today’s AIMed Healthcare Executives webinar in collaboration with the Society of Physician Entrepreneurs (SoPE), Healthcare Executives of Southern California, and American College of Healthcare Trustees (ACHT) as well as Medical Intelligence and Innovation Institute (MI3) at Children’s Hospital of Orange County (CHOC):
My top takeaways:
1. Artificial intelligence in the form of natural language processing (NLP) and machine and deep learning will make major impact in healthcare.
2. The COVID-19 pandemic has emphasized the urgency of telemedicine and digital health delivery and most healthcare organizations were not well prepared (but adapted).
3. We need to focus on the issues or problems in healthcare and not the technology of digital healthcare or artificial intelligence first.
4. The major challenge to deal with the COVID-19 pandemic remains issues with data: consistency, sharing, accuracy, etc. while the pandemic is complex and even chaotic.
5. Provider engagement for workflow alterations for telemedicine is key for success of the new paradigm of healthcare delivery.
6. Everyone has different expectations in healthcare: the challenge is to find the sweet spot to accommodate everyone as much as possible in digital medicine and artificial intelligence.
7. The pandemic has magnified strength and weaknesses in data and IT infrastructrure that need to be the strong foundational layers for intelligence based medicine.
8. The pandemic will hopefully promulgate (and sustain) a more modern healthcare to improve outcome and adopt modern technologies such as artificial intelligence and digital medicine.
9. There is very little connectivity in healthcare and this creates inefficiency and inaccuracy in healthcare that leads to suboptimal outcome.
10. Robotic process automation augments existing human workers so that the worker can reconfigure his/her job.
11. We can reconfigure our thinking to increase return on investment of our human workers and not only calculate ROI for technology.
12. What is good about automated intelligence is that it is pandemic-proof and keeps the work force viable when there are issues with human workers.
13. Administrative tasks such as prior authorization, professional fee billing and accounts receivable can be augmented with artificial intelligence.
14. There can be financial incentives to adoption of AI-enabled practices that safe money so this can serve as a model.
15. Using data science to model data to decrease rate of readmissions is helpful to delineate parameters that human clinicians may not configure into a scoring tool.
16. Using a model for readmission for actionable interventions can enable the clinical team to have a plan to decrease readmissions (and good to include others on the team).
17. Bias and inequities need to be neutralized in the algorithms in healthcare so that these elements are not automated.
18. All aspects of AI in healthcare need to be vigilant about data, algorithm, and output as all three areas are vulnerable to bias.
19. Perhaps machine intelligence is needed to find bias and inequities in algorithms as this may be difficult for humans alone.
20. Human to machine synergy is critical just as human to human bonding and machine to machine vigilance.
21. Health economics show that resource utilization is not an accurate proxy for health or disease but more access.
22. The relationship about the clinician-patient dyad may change into one that involves the algorithm someday when it becomes standard of care in some situations.
23. The legal framework for artificial intelligence in medicine needs to keep up with the exponential advances of this paradigm.
24. Our linear trajectory for ethics, law, and regulation in artificial intelligence in medicine needs to keep up with the exponential one for AI.
25. Artificial intelligence adoption in healthcare systems is more about a cultural change in mindset rather than about technology.
Thank you faculty and attendees for your knowledge and expertise as we all learned a great deal today at AIMed Healthcare Executives !
Anthony Chang, MD, MBA, MPH, MS
Chief Intelligence and Innovation Officer
Medical Director, The Sharon Disney Lund
Medical Intelligence and Innovation Institute (mi3)
Children’s Hospital of Orange County