It should be in everyone’s interest to give patients their medical records
We all know the story: as soon as you visit two or more doctors from different institutions, the coherence of you medical history depends on your brain memory.
We dream of a different reality. An era where medical records became “bearer” assets controlled by the patient.
For one thing, a healthcare record at the hands of the patient would ease communication between the doctor and a patient taking multiple medications.
Conversations like these are still a common reality:
Doctor: “What kind of pills are you taking?”
Patient: “They are round and blue.”
Beyond basics and common sense, digitized records in the hands of patients have broader implications of network effects: encouragement of the development of new business models, predictive health policies, chronic disease management solutions, population health interventions …
We are in the era of gathering large amounts of data accompanied by the development of artificial intelligence. Genomics, epigenetics, and biomics are shining a light on new insights. New biomarkers are recognized as pointers to specific diseases or therapy responses.
Further anticipations are high, and many situations show the urgency for improvements in medical accuracy.
The current problem of precision in medicine
Let’s take an example of depression: More than 350 million people across the world — of all ages and from all communities — suffer from depression. Depression is a long known problem with rising prevalence. Yet, treatment of the disease is far from optimal. The clinical guidelines say “treating any kind of mental illness often requires an element of trial and error.” There are several classes of antidepressants, with each of them including many agents. Months can pass before finding proper treatment.
A patient needs to be lucky for his doctor to guess the right drug in the first attempt.
This shouldn’t be the case in the 21st-century high-tech medicine, where we expect treatments and diagnosis to be precise, based on data-driven decision-making.
While meaningful medical progress in most areas is undeniable, medicine is at the moment still often based on personal experience and opinions of doctors. A study published in 2015 in The American Journal of Medicine showed second opinions resulted in changes in diagnosis 14.8% of the time and changes in treatment 37.4% of the time. An older systematic review has even worse statistics. 10% to 62% of second opinions yield a significant difference in the diagnosis, treatment, or prognosis.
Moreover, 90% of patients with poorly defined conditions remain undiagnosed.
Medical conditions can get incredibly complex with same symptoms present in very different conditions. A repetitive headache can be just a headache, a migraine, meningitis, a stroke or brain tumor.
Cancer illustrates the complexity of decision making problem nicely.
While the accurate diagnosis itself is hard to get to, things can get even more complicated when it comes to the right combination of therapies. At the moment, it is often hard to predict how a specific patient will react and what side effects will he suffer from. So we are left with trial and error.
How can data reduce discrepancies in second opinions and drive precision
New algorithms and predictive analytics companies are arising every day. Soon we will be able to get personalized recommendations based on our genetic and epigenetic blueprint and microbiome.
Just a few months ago Myriad Genetics presented results of their GeneSight test. Its use significantly improved the accuracy of drug efficacy for the treatment-resistant major depressive disorder (MDD). Patients with MDD were 30% more likely to respond to treatment and 50% more likely to achieve remission when their medication selection was guided by the GeneSight (Myriad Genetics) psychotropic genetic test.
If we want to see similar faster progress in other medical areas as well, more accurate algorithms and precision drugs in the future, we need data gathering today, even if certain data points seem useless at the moment.
In this regard, optimistic times lie ahead.
Healthcare data is growing exponentially — from 153 exabytes (one exabyte = one billion gigabytes) produced in 2013 to an estimated 2,314 exabytes to be generated in 2020, which translates to an increase at least 48 percent annually, according to Stanford Medicine.
Give me my data
Looking at the rising markets of IoT, wearable devices and other wellness and health-related data capturing inventions, two things are clear:
a) we will soon be unable to avoid data gathering on every corner,
b) consumers are curious about themselves and interested in their data to improve their health.
It will take awhile before broader interoperability among healthcare systems and databases will be achieved. Until that happens, patients shouldn’t be hostages of the situation. They should own their data firstly because we are talking about sensitive personal information, and secondly because of the broader changes triggered due to the shift in the power dynamic of data ownership.
Doctors emphasize patient empowerment as the crucial path to better disease management.
If healthy life and disease management are an individuals responsibility first, patient data ownership should not be something only legally true while not executed in practice.
Giving patients modern technological solutions for storage and management of their data does not undermine the existing medical practice. On the opposite:
- it encourages people to take better care of their health or have interest for their healthcare data management,
- it opens up new doors for extended collaborations and in the end better health results.
In that scenario, patients would be better incentivized to contribute to new discoveries, which makes giving data ownership to the patient not only individually, but politically and a business-wise reasonable way to go forward.
Note: I am a business developer at Iryo - a Slovenia healthcare IT startup building an open-source healthcare data management protocol, making the patient the main decision maker around his medical data use.
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