4 ms·

I'm very much a skeptic, but it just hit me, what about blood work? A scattered history of labs probably provides an opportunity to notice something early, eve

by ip26 4y ago

I'm very much a skeptic, but it just hit me, what about blood work? A scattered history of labs probably provides an opportunity to notice something early, even if you don't know what you are looking for. But humans are categorically bad at detecting complex patterns in tabular numbers. Could routinely feeding people's lab history into a model serve as a viable early warning system for problems no one thought to look for yet?

haldujai 4y ago
My advice to anyone trying to tackle an AI problem in medicine is ask yourself what problem are you solving? We have established and validated reference ranges for bloodwork, there is also inherent lab error and variability in people's bloodwork (hence a reference range). People < 50 should not be having routine bloodwork, and routine blood work on annual check-ups in older patients are very easy to interpret and trend. Early warning systems need to be proven to improve patient outcomes. We have a lot of hard-learned experience in medicine where early diagnosis = bad outcomes for patients or no improved outcomes (lead-time bias). If an algorithm somehow suspected pancreatic cancer based on routine labs, what am I supposed to do with that information? Do I schedule every patient for an endoscopic ultrasound with its associated complication rates? Do I biopsy something? What are the complication rates of those procedures versus how many patients am I helping with this early warning system? In some case (screening mammography, colonoscopy) demonstrably improved patient outcomes but took years to decades to gather this information. In other cases (ovarian ultrasound screening) it led to unnecessary ovary removal and harmed patients. We have to be careful about what outcomes we are measuring and not rely on 'increased diagnosis' as the end goal.
- ip26 4y ago
  I’m just a parent, not a medical professional, whose infant went through a lot of blood work with multiple parameters very out of range. It took five or six primary care physicians, six months, and probably twenty five labs to figure it out. The helpful recommendation in that case would have been something like “given the trend & relationship of these six out of range parameters, these other three specific blood tests could support or reject conditions X, Y, and Z”, e.g. moving beyond the cbc and so forth. Perhaps it’s simple for most patients, but we learned a large number of the markers are really just second order effects. For example, concerning readings on your liver enzymes can mean a million different things, and are only useful when integrated with other data to develop a hypothesis on the root cause.
  - haldujai 4y ago
    I agree with your point, liver enzymes (or all medical tests) don't have relevance without specific pre-test probabilities and diagnoses in mind. But what you're arguing we should do is what physicians are taught to / should do. We also have plenty of great point of care resources (UpToDate being the most popular) that provide current evidence based recommendations for investigation of abnormal bloodwork written by experts that you really shouldn't be doing arbitrary tests. Without knowing the details of your case I can't comment very well, nor is this my area of expertise, but a child with multiple persistent lab values seems out of the scope of most primary care physicians, and why multiple? Are you somewhere where you weren't sent to a paediatrician or don't have access to paediatric hematologists/hepatologists? Some conditions unfortunately involve a lot of investigation. There are obviously also bad doctors. I don't mean to suggest every one of us is good (just like any profession). AI would be a great tool to augment physicians but we just have to be careful about what outcome we are trying to achieve. Diagnosis isn't a linear thing like increasing transistor density it comes with tradeoffs of overdiagnosis and harm.

by ip26 4y ago

haldujai 4y ago

My advice to anyone trying to tackle an AI problem in medicine is ask yourself what problem are you solving? We have established and validated reference ranges for bloodwork, there is also inherent lab error and variability in people's bloodwork (hence a reference range). People < 50 should not be having routine bloodwork, and routine blood work on annual check-ups in older patients are very easy to interpret and trend. Early warning systems need to be proven to improve patient outcomes. We have a lot of hard-learned experience in medicine where early diagnosis = bad outcomes for patients or no improved outcomes (lead-time bias). If an algorithm somehow suspected pancreatic cancer based on routine labs, what am I supposed to do with that information? Do I schedule every patient for an endoscopic ultrasound with its associated complication rates? Do I biopsy something? What are the complication rates of those procedures versus how many patients am I helping with this early warning system? In some case (screening mammography, colonoscopy) demonstrably improved patient outcomes but took years to decades to gather this information. In other cases (ovarian ultrasound screening) it led to unnecessary ovary removal and harmed patients. We have to be careful about what outcomes we are measuring and not rely on 'increased diagnosis' as the end goal.

ip26 4y ago
I’m just a parent, not a medical professional, whose infant went through a lot of blood work with multiple parameters very out of range. It took five or six primary care physicians, six months, and probably twenty five labs to figure it out. The helpful recommendation in that case would have been something like “given the trend & relationship of these six out of range parameters, these other three specific blood tests could support or reject conditions X, Y, and Z”, e.g. moving beyond the cbc and so forth. Perhaps it’s simple for most patients, but we learned a large number of the markers are really just second order effects. For example, concerning readings on your liver enzymes can mean a million different things, and are only useful when integrated with other data to develop a hypothesis on the root cause.
- haldujai 4y ago
  I agree with your point, liver enzymes (or all medical tests) don't have relevance without specific pre-test probabilities and diagnoses in mind. But what you're arguing we should do is what physicians are taught to / should do. We also have plenty of great point of care resources (UpToDate being the most popular) that provide current evidence based recommendations for investigation of abnormal bloodwork written by experts that you really shouldn't be doing arbitrary tests. Without knowing the details of your case I can't comment very well, nor is this my area of expertise, but a child with multiple persistent lab values seems out of the scope of most primary care physicians, and why multiple? Are you somewhere where you weren't sent to a paediatrician or don't have access to paediatric hematologists/hepatologists? Some conditions unfortunately involve a lot of investigation. There are obviously also bad doctors. I don't mean to suggest every one of us is good (just like any profession). AI would be a great tool to augment physicians but we just have to be careful about what outcome we are trying to achieve. Diagnosis isn't a linear thing like increasing transistor density it comes with tradeoffs of overdiagnosis and harm.