When she died, I was shocked to learn that very few data points of her sichkness and treatment history would be preserved for later analysis. Doctors work almost entirely on their gut feeling and probably some clinical studies with n being very small. I hate that she died, but it’s even worse to know that all her data died with her and won’t help any other patient. I think this is one of the rare cases where collecting more data would help protect people
One of the problems that really prevents the change is that analysis of patient data is so highly protected that it's virtually impossible to do proper studies. We assume that people with such access are performing them, but I know from second-hand experience that actual time spent on real treatment studies is very small and most of the time is spent dealing with policy and patient privacy issues.
Large insurance and government-based (VA, CMS, etc.) providers have the most data, but either what they're looking for isn't exactly aligned with what patients need, or the organizations are so fubar (e.g. the VA) that real studies are unlikely to happen.
There really needs to be some kind of research exception/feedback loop on massive scale data analytics that allows some group of objective researchers to mine patient data and put in place actionable recommendations that work their way into medicine rapidly -- perhaps with some penalizing stick insurance providers can leverage against practitioners to make sure they adopt newer better practices.
It is very hard to escape from the fact that there are patients who are so difficult to keep alive that the rational choice is probably to let them die. The exact circumstances will depend on your value structure, but once that is settled any scientific approach that optimises for greatest net good or quality of life will involve letting some patients go without really trying to save them.
There is a pretty substantial lobby (no particular affiliation) who just won't accept that attitude being out in public.
I watch the struggles of data science to take root in our medical system with great interest in light of this. Anyone using a statistical argument to justify inaction when confronted with a patient is running a risk of being pilloried. Pictures of ancient grandmas on the front page of a newspaper style stuff. And that is if they get the stats right - get something wrong and then you feel like a loser on top of running that risk.
Between that and the serious privacy concerns, I can believe that the medical world will be mysteriously disorganised and ad-hoc in their decision making no matter what statistics available. I don't see what the real incentive is to look at and use the numbers, without having a statesman like dedication to the greater good, come what may. Such persons combined with leadership and statistical knowhow are rare.
I'm a GP, and I see dozens of patients every year with cases of Foozle disease. I was taught in medical school to give them orange pills, but I follow developments in general medicine and I know the new purple pill has seen good results for Foozle too. Nobody has published even a small study about which is better.
The law says since a responsible doctor wouldn't know one is better I can choose either. I could give all my female Foozle patients the orange pills and prescribe the purple for everybody else, or I could just pick whichever is cheapest, or I could even pick randomly for each patient. All legal.
But, if I pick randomly AND record the results, thus doing science to find out which was best, that's an unauthorised medical experiment and I could lose my job or even go to jail.
Perhaps we can try to cut down the red tape a little but we can't eliminate the idea of informed consent. If you're doing a study on patients, they absolutely have a right to know and a right to refuse to be a part of your study without any repercussions.
I think informed consent is something we can't get rid of even if we do something drastic and eliminate medical licensing altogether.
I am trying to make the survival data of a clinical trial combined with sequencing data of the patients publicly available. Nearly all the patients have died by now, as the patients in this cohort patients have a very poor prognosis (median survival of about 24 months). It is still almost impossible to release clinical data (diagnosis, number and location of metastasis, age of patients, etc) combined with sequencing data without violating privacy laws.
In the meantime, there is almost no career incentive for me to make the data available. I do not have, and am unlikely to get a permanent contract. In my next job interview I will be judged by the number of publications on my CV.
When it's the other way around and someone survives 10 years on a "2 years left to live" diagnosis, we seem quick to credit their strength, determination, spirituality, etc., rather than looking at how inaccurate the original prediction might have been.
However, I believe there is value in her data. The last days she was treated with an experimental drug. If there was an open database on all cases with treatment history I could have looked into that and get a feeling what to expect. These things often come with huge side effects, so you want to do it only if there is a slight chance.
Also, if this data was collected globally, the number of reference cases would be much larger.
https://www.ncbi.nlm.nih.gov/pubmed/21478775
Uses cox regression model which is a survival regression model.
Also the base model aka previous model they're comparing it to is a logistic regression and the link leads to a pdf about how to increase hospital efficiency it seems like. This sounds stupid and heartless.
In statistic we got survival analysis, a whole branch that is focus on patient and their survival rate for the medical field. Google chose to compare to a paper and algorithm that focus on what seems like making hospital more money instead.
I've seen a lot of data science people goes into different field and just telling people they can make money for them. It's great but with healthcare I don't think people should be treated as dollar signs.
If anybody up and coming wants to use data science in bio, I would encourage them to look into statistic and biostatistic. We have tons of stuff already and then branch out to ML later. But at least know what's out there and there are establish organization, nonprofit out there too that all they do is biostat and build model. My friend works at a nonprofit child oncology.
I just want to point out there are people that's building model to help patient with terrible sickness out there to survive. We're not diddling our thumbs trying to make other people richer.
I don't know if you're trying to imply that the authors of this paper didn't know/know of survival analysis, or if it was a general rant. Looking at the names I know on the paper and the affiliations/backgrounds of the others, it's safe to say they are aware of proportional hazards models.
Survival analysis is not called for when predicting the outcome variables of interest in this study, and that seems to be your primary beef - that they chose the wrong outcomes to model in order to "make hospitals money". I would think that being able to predict outcomes help hospitals plan and manage their resources effectively. From your high horse this may appear to be a wasteful endeavor, but controlling costs will do much more to save lives by making healthcare accessible, rather than building survival analysis models for rare diseases that affect some trivially small portion of the population.
The truth is outside of tech, statisticials (or data scientists) are way underpaid relative to the training and specialization demanded of them. This is true for non-profits and academia. Note that administrators in both these fields are not underpaid to the same degree. Instead of money, they are expected to pay their bills with warm fuzzy feelings of doing good for the world, because of attitudes like the ones expressed in your comment.
Also, fun fact: survival analysis was developed for actuarial use to make ugh money, not bio/medical statistics.
You're a bit late to the party, no? Basically mortality calculations and risk transfer is what the whole insurance business is based upon.
Medicine has advanced to a point where some serious illnesses can be kept at bay for decades. People with some formerly mortal diseases can get medicines that cost $100k/year and/or professional help every day, and live well for thirty years.
This means that each of rich societies has to choose:
1. Raise health insurance rates until the budget covers everything that's possible medically. However, in some rich countries, people's after-tax income is only a few doublings above the current health care plus pension costs...
2. Decide that some treatments aren't worthwhile, ie. everyone with Hyperthis or Abnormalthat Syndrome gets cheap palliative treatment and a peaceful, gentle death.
3. Decide on a per-patient basis, often involving numbers such as "cost of treatment" and "years left of productive life".
Option 1 is the humane way, but slightly impossible. Options 2 and 3 involve treating people like dollar signs, one way or another. It's an unpleasant choice, not an avoidable one.
These kinds of models are also great for triage. Healthcare is a limited resource, especially in trauma situations which have been using models to measure survival for decades.
Alarm bells start ringing right about here.
A solid 90% of AI is data munging, so that work isn't going away even a little bit; historically, Google throwing the kitchen sink into healthcare problems with lots of noisy data leads to flu seasons being correlated with softball victories or other wildly spurious correlations.
What's the other 10%?
I'm serious - I've got a better-than-average understanding of statistics, and everything I've seen referred to as "AI" seems to really be just a statical model with a thin layer of code over the top to make decisions based upon it on the fly. Is there more to the state of the art that isn't apparent from the outside?
In fact, the only thing that comes to mind that doesn't fit that description would be genetic algorithms, but I don't hear much about them these days.
For example, here's a paper from the 1980's also predicting when a patient will die, also using a couple of thousands of patients' data: http://europepmc.org/abstract/med/3816253
And, Google's paper wasn't published in Nature, it was in a new open access journal owned by Nature. In academia, that is like the difference between a really fancy porsche, and a really cheap volkswagen (two very different things, both owned by the same company).
"...the novelty of the approach does not lie simply in incremental model performance improvements. Rather, this predictive performance was achieved without hand-selection of variables deemed important by an expert, similar to other applications of deep learning to EHR data. Instead, our model had access to tens of thousands of predictors for each patient, including free-text notes, and identified which data were important for a particular prediction."
So it sounds like the advance here is actually in the following: "a generic data processing pipeline that can take raw EHR data as input, and produce FHIR outputs without manual feature harmonization".
The article doesn't explain this very clearly. Yay!
I think you want to say Skoda or Seat. Both owned by VW and the cheap cars.
Customer A does not receive full medical support because "he's dying anyway"?
Is there a way to ensure that such cruel use of the research is prohibited?
It's better if this becomes an ethical/moral problem about when to care for patients and to what degree, rather than a guessing game problem while anybody's guess is as good as anybody else's.
http://slatestarcodex.com/2013/07/17/who-by-very-slow-decay/
http://www.zocalopublicsquare.org/2011/11/30/how-doctors-die...
Unless the patient consents to stop or reduce care, this would be medical negligence, which is illegal (in the US). Medical providers are required by law to provide medical treatment with all the knowledge and skill they possess.
No, to avoid liability for malpractice, they need to meet the “professional standard of care”, which is assessed by general practice in the medical community, not “all the knowledge and skill” the individual provider possesses.
Anything short of an emergency and you can be denied care due to finances. Even if your problem is cancer or they know it will eventually be an emergency or life-threatening. Money is part of the reason folks will use the emergency room instead of urgent care: Urgent care requires upfront payment, the emergency room does not. Many doctors will refuse to see someone if they do not pay upfront.
Luckily, different agencies tend to step in with end of life care.
The point of the project was to make end of life easier for patients and families. Many patients get transferred between multiple facilities in their last few months, and few end up passing in a place and manner they'd like. This project aimed to ask patients and their families how they'd like to spend their last few months.
Initially the health system resisted this bc they wanted to keep patients in the hospital to make money off them, but then they realized these patients were in the hospital so long they weren't profitable. Then the plan was adopted quickly
at a conference, i once heard that the best single predictor of ICU patient mortality was reduction of doctor attentiveness. in other words, patients in ICUs whom doctors began to visit less, treat less, basically start to ignore -- those patients were the ones for whom death was imminent.
the challenge is that it's hard to untangle cause and effect. was it a self-fulfilling prophecy? etc.
https://www.cbs19.tv/mobile/article/news/north-texas-hospice...
What if the system points out that the particular doctors which appear in the records are the culprit?
IIRC there are a lot of surgeons not taking on cases already because of the risk involved to their career. This might exacerbate it if the data is used blindly.
That said, my wife is an oncology nurse, and I will bet everything that no machine will ever get better at predicting than an experienced, skilled nurse. Humans are built to read humans.
Have you ever asked her if she wants to do the job of predicting when a patient will die? How many hours would she need to spend reading the patient's history to come up with a prediction she feels satisfied to use to decide what service the patient will get? I'm not a medical professional but my gut reaction is I don't want to do anything with predicting or sentencing if some machine can do it almost as well as I can.
I remember this story of a radiologist who told me he thinks we spend too much money for too little during the last about six months of a patient's life. If we had better information on when the last six months starts, maybe we could reduce the cost of healthcare? Apparently, we spend cost to 18% of GDP on healthcare in the US? Iirc, most of Europe is closer to 12?
Just think about the machines/algorithms your wife already (indirectly) uses to predict outcome for patients.
- An MRI scan, which is created from physical laws (Maxwell's law together with a quantum mechanical understanding of hydrogen atoms) and reconstructs a 3D-image of the different tumors. Knowing if and where metastasis are present have an enormous impact on the prognosis.
- The algorithms used to align sequencing reads from biopsies to determine the mutation status, which are critical to determine the prognosis of some tumors.
In fact, I'd wager that if your wife did not have access to these algorithms, she would already perform worse. And, in my opinion, the distinction between machine learning and the algorithms used to reconstruct 3D-images or align sequences is somewhat arbitrary.
http://www.spiegel.de/international/business/short-selling-a...
The problem isn't that insurers are horrible people, it's that insurance is a bad fit for healthcare.
Insurance doesn't solve the problem of a population where the total cost of care is more then they can collectively afford or when an individual's treatment has known costs higher than what they can afford but nobody has a good answer for that.
Insuring everyone from birth can be a solution for the individual but not the population.
My point is - this is completely nonsense. IT world and Real world are two difference worlds.
It's look like Google needs wake up call.
- “Are you sure you want to know?”
- “...”
