by Richard Vazquez, MD FACS, July 12, 2022
I am delighted to join the advisory board as a clinical advisor to AISH. I look forward to working with the team to assure the success of AISH by contributing my clinical experience, clinical decision skills, and extensive patient care experience to the collective wisdom of AISH.
My activity in medical device, medical systems improvement, and safety-quality innovation began during my surgicaltraining at RUSH in Chicago, continued during my extensive surgical practice,and the past 5 years as CEO/CMO of SafeStart Medical, Inc. where we create transparent, patient centric surgical planning and safety software. My extensive and varied clinical experience compliments AISH’s goals to make health care delivery be widely available, efficient, affordable, safe, secure, and error free.
These healthcare delivery necessities have only been aspirations and not fully realized over the past halfcentury despite significant technological advances such as those in diagnostic imaging, information technology,information warehousing (EHRs), advances in surgery such as laparoscopic or robotic surgery, cardiac and transplant surgery, and telehealth.
Simply having more information and better medical devices without improving delivery and the quality of medical care diminishes the value of digital warehouses full of information.
Medical process improvement resists change perhaps because it requires humans to change their behavior. Many ofthe shortcomings of healthcare delivery systems addressable by AISH have their origin in deficient system design and medical care delivery processes e.g. communication and handoff errors, information silos that result in diagnostic errors, lapses in patient safety and PHI security.I feel privileged to join the advisory board of AISH as the team seeks innovative solutions to problems with healthcare delivery by invoking the novel approach of bringing companies together that excel in the use of artificial intelligence to buttress human intelligence and to compensate for human factors errors.
