When I was reading this Effect Measure post, I was reminded of the on-going effort to make all federally funded research available to the public.
I certainly believe that research funded by the public should be considered property of the public. In fact, I think this should also apply to patents. If public funds are used to discover a novel therapeutic or diagnostic, then I don't think that product should be subject to considerable mark-up to recover research costs since the public has already paid for the initial investment and has had to pay the costs for all the biomedical research that did not bring new products to the market. I think a similar logic should apply to discoveries that are made using funds from non-profit organizations (such as March of Dimes, etc.).
Of course, there is going to be a messy gray area where part of the discovery was made using public funds (perhaps the discovery of a genetic association, for example) while some of the funding was provided through private means (a potential example might be the costs of clinical trails). I should also make clear that I'm not against the use of patents - I just think that we need to make sure that the public is not being double charged for research costs. Perhaps this could be implemented by setting a cap at the percentage of price that is allowed to go towards profits for any product directly resulting from research that was conducted using public or non-profit funds. I think this is a moral obligation of scientists, and I think this could help limit the escalating costs of health care.
Thursday, April 22, 2010
Tuesday, April 13, 2010
Do patients report symptoms better than physicians?
There is a very interesting article in the New York Times today that discusses how doctors need to pay more attention to patient complaints. For example, the author opens the article discussing how she stopped taking Bextra after she started to develop a large red blister on her tongue. Her physician said the symptom was most likely a coincidence, but the drug was taken off the market shortly thereafter because it caused dangerous side effects (including mouth blisters). Although it is impossible to say what caused the mouth blister in this case, the author does a good job of demonstrating that the doctor should have taken her complaint more seriously.
I found this article to be especially exciting because it emphasizes that patients can directly provide powerful information for evaluating medical treatments and diagnostics. This reminds me of the success of medical databases based upon information provided directly from patients, such as PatientsLikeMe. Interestingly, the article also describes a side effect database from the FDA called MedWatch, which allows doctors and patients to report negative systems experienced with various treatments. I was very excited to see that even the FDA appreciated the value of information reported directly from patients. I think a system similar to MedWatch can significantly improve the process of conducting clinical trails.
Of course, I should emphasize that the article is not trying to say that medical information should only be based upon information from patients. Physicians definitely need to play a role in assessing medical treatments. However, I think databases of patient feedback can be a powerful tool that can help reshape health care and drug development.
I found this article to be especially exciting because it emphasizes that patients can directly provide powerful information for evaluating medical treatments and diagnostics. This reminds me of the success of medical databases based upon information provided directly from patients, such as PatientsLikeMe. Interestingly, the article also describes a side effect database from the FDA called MedWatch, which allows doctors and patients to report negative systems experienced with various treatments. I was very excited to see that even the FDA appreciated the value of information reported directly from patients. I think a system similar to MedWatch can significantly improve the process of conducting clinical trails.
Of course, I should emphasize that the article is not trying to say that medical information should only be based upon information from patients. Physicians definitely need to play a role in assessing medical treatments. However, I think databases of patient feedback can be a powerful tool that can help reshape health care and drug development.
Wednesday, April 7, 2010
Book Review of “The Decision Tree”
“The Decision Tree” is an excellent book about personalized medicine that inspires readers to take a more active role in their health care. “The Decision Tree” is written by Thomas Goetz, who is a popular science writer and executive editor of Wired. He also has an MPH from UC Berkeley.
I’ve also found lots of videos with Thomas talking about personalized medicine (including a TED talk and a FORA.tv talk). If you can’t get a hold of his book, I would recommend at least checking out some of these videos.
There is too much information in this book to relay in a single blog post. Therefore, I will only summarize the most interesting take-home messages. I’ll also provide a list of useful web-based tools that I learned about from this book.
Selected Take-Home Messages:
1) “Control over destiny” is important for your health - The Whitehall II study showed social status was strongest risk factor for heart disease. Even after adjusting for known risk factors for heart disease, people with low social status still had more than twice the risk of dying of heart disease. Thomas says that lack of control by itself can cause stress sufficient to lead to chronic illness. The need for people to take an active role in maintaining their health is emphasized throughout the book.
2) Tracking your own health statistics is valuable form of preventative medicine – Thomas discusses how self-medicating with constant number crunching can work like a self-imposed Hawthorne Effect. Some success stories include Weight Watchers and Nike+.
3) Development of a drug fact box can help patients decide which medications they wish to take – Similar to the nutrition facts label currently on food, this would allow individuals to quickly access the effectiveness and associated side-effects of a specific drug. I think this could significantly improve consumer knowledge about drug treatments, and I am glad to hear that FDA is considering a recommendation to require drug facts box on pharmaceutical labels.
4) Poor market incentives and CT scans– Although there was a relatively recent study that showed 85% of nodules discovered via CT scan were stage I lung cancer and 92% of the 375 patients with these tumors removed were still alive 10 years later, there has also been a follow-up study showed that mortality rate for those receiving CT scans does not significantly differ from those without CT scans because CT scans are primarily discovering slow growing, non-lethal tumors. This is especially important because the surgery associated with removing lung cancer has a 2-5 percent mortality rate by itself (so, you don’t want to undergo surgery unless it is really necessary). Thomas also discusses how CT scans are an anomaly in that they are a technology that does not follow Moore’s law, meaning that CT scans have actually become more expensive over time. He attributes this market failure to lack of price transparency (can’t shop around and different companies in the same area may pay significantly different prices for CT scanners), ability to pass cost to patients and insurers (no incentives to avoid giving needless tests), and lack of automation (can’t run CT scan without trained radiologist). I think these examples all speak to the need to have a health care system that does not encourage needless testing and can correct at least some of these market failures that are driving up healthcare prices.
Health Care on the Web:
1) PatientsLikeMe – as mentioned in my previous post, PatientsLikeMe contains a database of information provided directly from patients and serves as a community for individuals to discuss symptoms and treatments associated with various diseases.
2) CureTogether – similar to PatientsLikeMe, but without "expert advice" to guide data analysis. Thomas describes advantages and disadvantages to this system.
3) Adjuvant! – also similar to PatientsLikeMe, but this is a database where physicians share information that cannot directly be accessed by patients. This database is specifically intended to help design combination treatments for cancer patients. I think this could potentially be a useful model for assessing the effectiveness of diagnostics and treatments when more formal restrictions are required (such as FDA approval).
4) NIH Gene Test Website – provides a list of currently available genetic tests.
5) UT-San Antonio Prostate Risk Calculator - 75% of men over 80 have some form of prostate cancer, but less 5% of those will actually die from prostate cancer. Therefore, PSA test results should be weighed with other risk factors before deciding to follow an intensive treatment for prostate cancer.
I’ve also found lots of videos with Thomas talking about personalized medicine (including a TED talk and a FORA.tv talk). If you can’t get a hold of his book, I would recommend at least checking out some of these videos.
There is too much information in this book to relay in a single blog post. Therefore, I will only summarize the most interesting take-home messages. I’ll also provide a list of useful web-based tools that I learned about from this book.
Selected Take-Home Messages:
1) “Control over destiny” is important for your health - The Whitehall II study showed social status was strongest risk factor for heart disease. Even after adjusting for known risk factors for heart disease, people with low social status still had more than twice the risk of dying of heart disease. Thomas says that lack of control by itself can cause stress sufficient to lead to chronic illness. The need for people to take an active role in maintaining their health is emphasized throughout the book.
2) Tracking your own health statistics is valuable form of preventative medicine – Thomas discusses how self-medicating with constant number crunching can work like a self-imposed Hawthorne Effect. Some success stories include Weight Watchers and Nike+.
3) Development of a drug fact box can help patients decide which medications they wish to take – Similar to the nutrition facts label currently on food, this would allow individuals to quickly access the effectiveness and associated side-effects of a specific drug. I think this could significantly improve consumer knowledge about drug treatments, and I am glad to hear that FDA is considering a recommendation to require drug facts box on pharmaceutical labels.
4) Poor market incentives and CT scans– Although there was a relatively recent study that showed 85% of nodules discovered via CT scan were stage I lung cancer and 92% of the 375 patients with these tumors removed were still alive 10 years later, there has also been a follow-up study showed that mortality rate for those receiving CT scans does not significantly differ from those without CT scans because CT scans are primarily discovering slow growing, non-lethal tumors. This is especially important because the surgery associated with removing lung cancer has a 2-5 percent mortality rate by itself (so, you don’t want to undergo surgery unless it is really necessary). Thomas also discusses how CT scans are an anomaly in that they are a technology that does not follow Moore’s law, meaning that CT scans have actually become more expensive over time. He attributes this market failure to lack of price transparency (can’t shop around and different companies in the same area may pay significantly different prices for CT scanners), ability to pass cost to patients and insurers (no incentives to avoid giving needless tests), and lack of automation (can’t run CT scan without trained radiologist). I think these examples all speak to the need to have a health care system that does not encourage needless testing and can correct at least some of these market failures that are driving up healthcare prices.
Health Care on the Web:
1) PatientsLikeMe – as mentioned in my previous post, PatientsLikeMe contains a database of information provided directly from patients and serves as a community for individuals to discuss symptoms and treatments associated with various diseases.
2) CureTogether – similar to PatientsLikeMe, but without "expert advice" to guide data analysis. Thomas describes advantages and disadvantages to this system.
3) Adjuvant! – also similar to PatientsLikeMe, but this is a database where physicians share information that cannot directly be accessed by patients. This database is specifically intended to help design combination treatments for cancer patients. I think this could potentially be a useful model for assessing the effectiveness of diagnostics and treatments when more formal restrictions are required (such as FDA approval).
4) NIH Gene Test Website – provides a list of currently available genetic tests.
5) UT-San Antonio Prostate Risk Calculator - 75% of men over 80 have some form of prostate cancer, but less 5% of those will actually die from prostate cancer. Therefore, PSA test results should be weighed with other risk factors before deciding to follow an intensive treatment for prostate cancer.
Friday, April 2, 2010
Why Do Genomic Cancer Diagnostics Cost So Much?
After reading the introduction to this PLoS ONE article, I started to wonder why there are there several published microarray expression profiles for cancer progression yet relatively few microarray-based diagnostics used in a clinical setting. Although this PLoS ONE paper focuses on analysis of ovarian cancer (and mentions the lack of a clinical microarray diagnostic for ovarian cancer), the paper also cites the current use of a breast cancer diagnostic called MammaPrint.
After reading the wikipedia entry on MammaPrint, I was surprised to learn that it took 5 years for the diagnostic to reach the market following the initial publication showing that the expression profiles for a set of 70 genes could successfully predict the cancer progression. This information is important because more aggressive treatments early in cancer progression may be able to help cancer patients who would otherwise have a high mortality rate (as predicted by their gene expression profile). I was also surprised to learn the high price of both MammaPrint and its competitor Oncotype DX. Although I do not think I can provide a complete answer to why these prices are so high, I would like to take a moment to first demonstrate that the price of these tests far exceeds the cost to conduct the test and then discuss how I think these costs can be offset by decreasing the amount of time and effort that it takes to bring a medical diagnostic tool to the market.
Based upon their wikipedia entries, the MammaPrint diagnostic costs $4,200 and the Oncotype DX test costs $3,978. To give you an idea about how much it actually costs to carry out this test, it costs $350 for a full service microarray analysis (including labor and data analysis) of an Agilent Whole Human Genome Microarray for on-campus customers at the UT-Southwestern Micoarray facility. This is comparable to the cost of most of the microarray facilites that I have worked with, and Agilent produces high quality microarrays. Now, most laboratory kits have a warning that they are “intended for research purposes only,” and this is probably true for the human Agilent array. However, I think this warning is mostly to avoid litigation and not due to a severe lack of technical accuracy, and I expect the actual cost for a clinical microarray test to be in the hundreds (not thousands) of dollars. I’m sure that this high cost is the product of a combination of factors, such as research costs, legal costs, patent law, and the US healthcare system. However, I’m going to focus on ways to potentially cut research costs because that is the area that I know most about.
Now, I want to make clear that the initial publication of a potential diagnostic test is not sufficient to prove the widespread effectiveness of that test. For example, the microarray test for ovarian cancer in the PLoS ONE article had substantially better predictive power on the training dataset than when applied to a new dataset. Therefore, I want to make clear that I do think follow-up studies were necessary to prove the effectiveness of MammaPrint. However, I still don’t think it should have taken 5 years to test the effectiveness of this diagnostic and I think effectiveness can be determined without as much government regulation.
Before MammaPrint could be put into widespread use, it had to gain FDA approval. This required multiple verification studies, and this is the crucial event that defines the 5 year gap between initial publication and availability on the free market. First off, I don’t think FDA approval should be necessary for diagnostics. I do think physicians need some way to quickly access the effectiveness of a medical diagnostic and/or therapeutic, but I think there are more better ways to determine the effectiveness of a given treatment. For example, a relatively recently posted TED talk by Jamie Heywood discusses how his start-up Patients Like Me, developed by three MIT engineers, can diagnose medical treatments more quickly and effectively than clinical trails. This website analyses a database of information provided by patients, and therapeutic effectiveness can be assessed immediately based upon currently available data. In the very least, I think this company could be an excellent model for a more formal system using data from physicians that does not carry all the restrictions of a clinical trail. These changes should decrease the cost of medical care because companies claim that these price markups are necessary to recoup the costs of research and development, and a more streamlined process for accessing the effectiveness of treatments will decrease research costs.
After reading the wikipedia entry on MammaPrint, I was surprised to learn that it took 5 years for the diagnostic to reach the market following the initial publication showing that the expression profiles for a set of 70 genes could successfully predict the cancer progression. This information is important because more aggressive treatments early in cancer progression may be able to help cancer patients who would otherwise have a high mortality rate (as predicted by their gene expression profile). I was also surprised to learn the high price of both MammaPrint and its competitor Oncotype DX. Although I do not think I can provide a complete answer to why these prices are so high, I would like to take a moment to first demonstrate that the price of these tests far exceeds the cost to conduct the test and then discuss how I think these costs can be offset by decreasing the amount of time and effort that it takes to bring a medical diagnostic tool to the market.
Based upon their wikipedia entries, the MammaPrint diagnostic costs $4,200 and the Oncotype DX test costs $3,978. To give you an idea about how much it actually costs to carry out this test, it costs $350 for a full service microarray analysis (including labor and data analysis) of an Agilent Whole Human Genome Microarray for on-campus customers at the UT-Southwestern Micoarray facility. This is comparable to the cost of most of the microarray facilites that I have worked with, and Agilent produces high quality microarrays. Now, most laboratory kits have a warning that they are “intended for research purposes only,” and this is probably true for the human Agilent array. However, I think this warning is mostly to avoid litigation and not due to a severe lack of technical accuracy, and I expect the actual cost for a clinical microarray test to be in the hundreds (not thousands) of dollars. I’m sure that this high cost is the product of a combination of factors, such as research costs, legal costs, patent law, and the US healthcare system. However, I’m going to focus on ways to potentially cut research costs because that is the area that I know most about.
Now, I want to make clear that the initial publication of a potential diagnostic test is not sufficient to prove the widespread effectiveness of that test. For example, the microarray test for ovarian cancer in the PLoS ONE article had substantially better predictive power on the training dataset than when applied to a new dataset. Therefore, I want to make clear that I do think follow-up studies were necessary to prove the effectiveness of MammaPrint. However, I still don’t think it should have taken 5 years to test the effectiveness of this diagnostic and I think effectiveness can be determined without as much government regulation.
Before MammaPrint could be put into widespread use, it had to gain FDA approval. This required multiple verification studies, and this is the crucial event that defines the 5 year gap between initial publication and availability on the free market. First off, I don’t think FDA approval should be necessary for diagnostics. I do think physicians need some way to quickly access the effectiveness of a medical diagnostic and/or therapeutic, but I think there are more better ways to determine the effectiveness of a given treatment. For example, a relatively recently posted TED talk by Jamie Heywood discusses how his start-up Patients Like Me, developed by three MIT engineers, can diagnose medical treatments more quickly and effectively than clinical trails. This website analyses a database of information provided by patients, and therapeutic effectiveness can be assessed immediately based upon currently available data. In the very least, I think this company could be an excellent model for a more formal system using data from physicians that does not carry all the restrictions of a clinical trail. These changes should decrease the cost of medical care because companies claim that these price markups are necessary to recoup the costs of research and development, and a more streamlined process for accessing the effectiveness of treatments will decrease research costs.
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