On the heels of my last blog on the Auditor General’s report on AI systems in Ontario, I was asked “how then can AI help in health care?” Certainly policy makers often talk a LOT about how AI can help. Better diagnoses! Faster assessments! Better prediction of which patient is more likely to “crash”! Reduced admin time with the use of AI Scribes! Etc.
These are all valid uses for AI technology. I use an AI scribe myself (following the principle of “trust but verify”in signing off on the notes). I access some evidence based AI software to help me with challenging cases. I always have the final word on what to do next of course, but I would be lying if I said that the tools didn’t help me look after my patients.
However, in a health care system as byzantine as the one in Ontario, there is one area where AI can help almost immediately that is not talked about nearly enough. Given the topic, I get why the many government health care planners/bureaucrats/managers don’t mention this. I’m talking of course, about reducing the number of bureaucrats in health care in Ontario.
I’ve talked about Ontario’s health care system being over bureaucratized many times in the past. But there’s never been a better opportunity to meaningfully cut the bloat. It would be impossible for me to search the entire Ontario government data base to find out how many bureaucrats we have. So………I used an AI search on ChatGPT and Claude AI to review how many managers/bureaucrats we have across all government funded health care agencies in Ontario. (I will put the prompt at the end of the blog for those interested).
Both searches suggested the total size of the health care workforce in Ontario was about 500,000 people. Of that, astounding 90,000-130,000 were non-clinical employees (mostly administrative/support staff). The actual management/bureaucratic layer varied between 25,000-45,000. A precise number was difficult to define, because, in the words of ChatGPT:
“……Ontario’s healthcare system is fragmented across hundreds of entities with inconsistent titles and reporting structures.”
However, given all of that, I think Claude’s estimate of having 85,000 admin/management personnel across all Ontario Health care agencies is defensible. Heck, it’s lower than ChatGPTs 90,000 – 130,000. Claude AI further broke this down and suggested 52,000 of these were in Ontario’s 154 hospitals.
Can AI replace some of these jobs? Replace is probably not the right phrase. There can certainly be a consolidation of the actual tasks required from different jobs, and AI can do those tasks much more efficiently and accurately.
For example, AI can, as of today, help with information movement, repetitive analysis, scheduling, policy retrieval, document generation, compliance monitoring, coordination, coding, and referrals to name but a few examples. All of these tasks are currently being performed by bureaucrats, and it’s virtually certain that there is tremendous duplication in the work being done. There is plenty of software than can do these tasks right now (LeanTaas, Qventus, Nuance DAX to name a few). Yes they are mostly American, but surely can be modified to meet Canadian needs.
The cost savings from reducing the number of bureaucrats can be significant immediately, and frankly enormous as AI continues to evolve over the next five years.
For a case study, let’s look at the University Health Network (I’m not picking on them for any other reason then they are huge!). They have approximately 24,500 employees of which an estimated 4,200 are Admin/management of some sort. Many of these positions are people on Ontario’s Sunshine List (i.e. they make over $100,000 a year). Reducing the number of these positions by 10% should be easily do-able if you have the right AI software.
Then the hospital would save the money right? Especially since Ontario’s hospitals are facing massive deficits? I would say no to that. I would instead say if UHN could cut their admin staff by 420 (which should easily be done), then maybe they could hire 210 clinical staff in return (nurses, physio, rehab, RT, Xray techs etc). Instead they just fired nurses. They would still have 210 fewer positions (so some money saved) but they would have 210 more people who would actually, you know – look at a patient. People who could provide compassionate, front line care and assessments to patients and be an invaluable part of the health care team.
Looking forward five years as AI software continues to evolve, I genuinely believe UHN should set its goal for reducing Admin/Management staff by half (at a minimum). This would allow them hire over a thousand (if not more) nurses to provide that front line care that is so essential to patients well being.
From a system wide perspective, the numbers would be even more dramatic. Currently, Ontario has 38% less inpatient staffing than the Canadian average. In order to just meet the average, about 34,000 more nurses need to be hired. The money for that has to come from somewhere, and I can think of no better place than reducing the admin staffing to find those funds.
I get why the bureaucrats have not talked about these uses for AI. Bureaucracy by its very nature is self perpetuating. But we are facing a serious fiscal calamity in health care with our aging population. While it’s nice to have tools that can help physicians like myself make better diagnoses and provide safer care, the blunt reality is we desperately need more front line staff. No matter how good the tool, it will never be a substitute for the compassion or a real human being providing care. The emotional wellness we experience from having real people look after us at the bedside cannot be understated. We need to adopt bureaucracy replacing AI tools now, and put the money saved in front of patients.
For those interested, this is the AI Prompt I used to get this data: “Review the number of bureaucrats/managers in the health care system in Ontario, Canada. Include ALL health care agencies that are government funded like hospitals, Ontario Health at Home, hospitals, community health centres and more – all government funded health care agencies. Get an approximate number of bureaucrats. Then show where AI can result in cuts to management/bureaucrat jobs right now, and in five years. Use the University Health Network in Toronto Canada as an example to show how many bureaucrat/management jobs could be trimmed, allowing them to funnel resources to hiring front line clinical personnel like nurses.”
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