One of the sole national issues that the government can agree on is bringing down the astronomical costs of drug prices in the United States. The Senate Finance Committee and House Oversight Committee began to take on drug prices by holding two hearings this year on the issue with leading pharmaceutical companies, and a third planned for early April to examine pharmacy benefit managers’ role in the issue after they were called out as major contributors in previous hearings.
During the February meeting, the biggest players in the industry testified before the committee and it became clear that big pharma and the government are having trouble coming to a solution.
There has been much discussion around this critical issue of drug pricing in America but now a few innovators are shifting the conversation to how precision medicine provides hope for a possible solution. Therapeutics come with the risk of not working effectively for some patients and causing adverse side effects. However, applying precision medicine stratifies patients based on their disease biology and matches that patient group to the drugs that target their specific disease.
The drug prescribed will be more likely to effectively work and the cost of treating the patient group will decrease. While the House Committee on Oversight and Reform’s investigation can eventually lead to legislation to mitigate price hikes, precision medicine technology currently exists to be a key driver in deflating drug spending for patients and payers and, thus, reducing drug prices. Two ways this type of technology can help to lower drug costs are: cutting spending on ineffective treatments and motivating the industry to develop and market personalized treatments.
The healthcare industry currently wastes roughly $2.5 billion in ineffective treatments annually. Take for example, the 1.3 million Americans living with rheumatoid arthritis (RA) – because autoimmune diseases like RA vary significantly from patient to patient, blockbuster therapies, which are “targeted” therapies and therefore only treat one pathway of the disease, are not effective for 66 percent of patients taking them.
This means that patients who see little or no benefit from these treatments are still paying up to $38,000 annually, proving to be a significant financial and emotional burden to patients and their loved ones. As medicine advances, it is becoming more apparent that giving these drugs to everyone diagnosed with the respective diseases no longer works, they need to be prescribed to sub-groups of patients based on their specific disease biology to truly be effective.
Through RNA data analysis, precision medicine can both help patients with autoimmune diseases like RA find targeted treatments that they are more likely to respond to before treatment is prescribed and direct pharmaceutical companies to develop targeted drugs specifically for those who do not respond to currently available therapies.
Artificial intelligence is a topic that should interest us all, as it changes the world with every second. And the healthcare system is one of the areas that AI has already started to revolutionize. These are the main ways in which that is happening.
Precision Medicine
Due to the introduction of personalized diagnosis and precision medicine, now doctors can treat a patient’s condition, by taking into account his/her background, as opposed to merely treating the disease. This is accomplished by using proteomics, which is a type of DNA mapping, as well as advanced AI machine learning.
Killing Occam’s Razor
Occam’s Razor is also known as the Law of Parsimony, and it refers to providing a range of solutions to a given problem. Also, according to this principle, the simplest solution is, most of the time, the correct one. Considering that both machine learning and AI doesn’t have the human assumption element, their capacity of reading and analyzing amounts of data can significantly increase the accuracy of the diagnosis.
Accordingly, this can be really helpful in diagnosing elderly patients, in particular, as they are more likely to suffer from various diseases at the same time.
Google Can Spot Eye Disease
DeepMind is a Google-owned AI company that has come up with a way of diagnosing eye disease. After assessing and attentively analyzing the medical records of a significant number of patients, it has created machine learning technology that should help doctors diagnose eye illness earlier. This merely outlines that, even though AI is innovating almost every field, it still relies on human help.
Automated Cancer Treatment
It appears that AI can also play an important role in treating cancer, which affects more and more people. Accordingly, the CareEdit tool can be utilized by oncologists for crating practice guidelines. To be more specific, the tool analyzes considerable amounts of data such as past treatment regimens, aiming at comprising a clinical decision support system that should help physicians treat each patient. This can significantly enhance the rate of survival, while cutting down the costs associated with the treatments.
Virtual Health Assistant
Interestingly enough, at the time being, there are apps that carry the roles of personal health coaches. This functions the same way as a customer service representative at a call center. What is more, the digital assistant can do as much as take notes, ask questions, even provide specific advice while streaming the information to the healthcare provider. This has the role of simplifying the process.
By Joel Diamond, MD, FAAFP, chief medical officer, 2bPrecise.
Joel Diamond, MD
Patients are becoming more engaged in (and financially responsible for) their own care. As such, they are increasingly interested in information about their health risks and which courses of treatment have the best potential for success. In my practice, I have seen a sharp rise in the number of patients asking about genetic and genomic tests.
Healthcare consumers are drawn to the idea that this information can unlock answers to persistent health problems, or reveal risk for future issues. They want genetic information to lay out a clear path forward for prevention and treatment, perhaps indicating which medications will be most effective for their profile. It’s one of the reasons why direct-to-consumer genetic testing, such as 23andMe, has become so popular.
The precision medicine learning curve
Soon we will move from individual gene tests and panels to exome and full genome testing, some of which is happening today. As the concept of applying genomics and precision medicine gains momentum, physicians are enthusiastic about the potential of personalized care plans to improve patient outcomes.
But are physicians equipped with the right tools to put precision medicine into practice? For example, can we identify which patients might benefit from genetic testing? Do we know what test to order? How do we interpret results? How do we incorporate this information into the patient record? And of course, cost is always an issue: Who pays for these tests?
These are some of the many questions physicians are wrestling with today. If they have a clinical-genomic solution within the electronic health record (EHR) workflow, they can get some of the support they need to meet rising demand for personalized medicine and care plans.
3 trends to watch as consumers drive precision medicine into the mainstream
Consumer interest shows no signs of slowing, which will continue to bring new challenges and opportunities into the physician’s office. Trends include:
Search for genetic destiny.I’m seeing more patients who believe precision medicine will resolve every health issue, especially when diagnosis or treatment is difficult. There is ample reason to hope, but it is up to the physician to educate consumers and set realistic expectations. There are multiple factors that have a bigger impact on health than genetics. Patients are concerned about familial inheritance for diseases, when environment and lifestyle often have a greater influence.
Prescriptive patients. We’re going to see more consumers demand specific courses of treatment, based on the genetic or genomic information they have. For example, someone who finds out she is at risk for cardiovascular disease may request a stress test. Physicians will need new kinds of educational support to assess and stratify risk. They will need to be well informed about which tests will bring the most benefit, so they can educate their patients, too.
Data outpacing science. Genomic knowledge is growing at an exponential rate, at times generating more questions than answers for researchers and physicians. We recognize many variants in DNA codes, but don’t yet know what they all mean. We still have much to learn about the data we are generating. Cloud-based repositories of genomic data, with continual updates and notifications for providers and patients, will be essential.
