- Patients need to be able to access real-world data from other patients so they can determine, with their doctors, the best treatment options.
- Barriers to global data sharing, including privacy and malpractice concerns, and institutional competition, inhibit people from obtaining these potentially life-changing insights.
- Providers and researchers should capture and share treatment decision logic, while advocacy groups should continue to work with data scientists to accelerate the data-sharing breakthrough.
In July 2018 I was diagnosed with follicular lymphoma, a slow-growing cancer, and I went through several months of a standard chemotherapy. I tolerated the treatment well, and I am lucky that today I show no evidence of disease.
For my disease, there is a high likelihood of recurrence, so I need to be prepared with my next line of therapy. Fortunately, there are many treatment options, including other chemotherapy and newer personalized immunotherapies (such as CAR T-cell and bispecifics) just emerging from labs.
I need to know what is working and what is not for other patients like me. We know each person is unique and that many factors influence patient outcomes, but we do not understand much of it. Some standard drugs kill a small percentage of people who take them. (I do not want to be one of them.) Some drugs that kill most people are miracle cures for others. (I want to be one of them.)
We need to start with what is or is not working for individual patients (“N of 1”), then generalize from their real-world experience.
The Problem: A Breakdown in Sharing Data Globally
At a medical conference in January – before we were aware of COVID-19 – I heard a dozen health institutions describe improvements in decision-making for complex cancer patient situations (“molecular tumor boards”).
Yet, when I spoke to a few of the presenters, I was told they don’t share their decisions or results across institutions. And not only is there no sharing across molecular tumor boards, there also is no learning. Molecular tumor boards make difficult treatment decisions, but generally have no idea what the patients they advised did, much less how well it worked. Some people are working on data standards and data sharing systems, but I am concerned they are not capturing data on the decision logic.
It would be better if patients like me could learn from all patients globally, and especially from the more complicated decisions made by the molecular tumor boards. I want to know which decisions worked out well, and which did not, so I can determine what would be best for me.
Each patient’s treatment is an experiment – albeit a complicated, personalized one. We need access to global data on the logic and results. A physician or board knows why they made a recommendation and may track the results, but they do not have much insight into the decisions of other physicians or other boards and the associated outcomes.
Getting consistently defined, reliable and accurate data on the inputs, decision logic and outcomes of treatment decisions is a complex challenge. The data sources, such as electronic health records and insurance systems, were not designed to consistently capture the needed information on inputs and outcomes (such as response to treatment, disease-free survival and progression-free survival). Furthermore, few systems capture the diagnosis and treatment decision logic.
We need a global learning system to access each treatment decision and the associated outcomes. Why do health institutions not actively address the treatment data challenge and share their decisions and the associated patient outcomes?
Have you read?
3 Barriers to Global Access to Patient Data
Researchers and providers are constrained by three systemic disincentives.
1. Privacy Concerns
Researchers and providers are concerned that sharing a wide range of data about each patient will make it easy to identify patients. These privacy concerns have been written into laws, such as the Health Information and Portability Accountability Act (HIPAA) in the United States, and similar regulations (e.g., General Data Protection Regulation, or GDPR, in Europe) for the use and disclosure of protected health information.
However, many patients (like me) are more concerned about helping researchers develop therapies to address the disease rather than privacy. People with rare diseases and pediatric diseases band together and share data and funding to find a cure. They prioritize better treatment options over privacy concerns. For example, I have donated my data to All of Us for research.
2. Malpractice Concerns
I suspect providers are concerned that if they were to publicize their decisions, especially for the most difficult cases, they would be liable for review and possible malpractice suits by patients and their families. Clinicians are not very good at admitting what they do not know or may be unsure of, especially in the presence of other physicians and patients. There is a lack of real transparency as well as an environment in which things change frequently.
Do we need to indemnify clinicians to free up this data?
3. Institutional and Interpersonal Competition
Medical research has historically focused on finding new therapies. Academic research and pharmaceutical companies’ business models have been geared towards the “blockbuster drug”, i.e., a drug that can be taken by as many people as possible, such as penicillin. The challenges in the drug discovery process are to prove a new therapy works and is safe. Researchers who discover a new therapy yield well-deserved accolades and money. However, they are motivated to protect their intellectual property from competing researchers. They do not want to share data until it has been published, and even then, only the data that supports the approval of their therapy.
And the competition is not just at the institutional level, but also at the personal level – career advancement, tenure, grants and patents are all on the line. It takes hard work to gather records and structure the data so it can be useful for research. Researchers are protective of it.
Regulators and insurers are influential when it comes to lowering barriers to data sharing and setting the terms of competition. The U.S. Food and Drug Administration is promoting the capture and sharing of real-world data for real-world evidence as part of the drug discovery process to complement data from clinical trials. And insurers, through their control of what gets reimbursed, also influence improvement and innovation in healthcare. It would be great if insurers funded patients and providers to provide their data to research, but insurers want proof in advance of paying.
Patients value the stream of new therapies, and care equally about which therapy is relevant personally. Sometimes the information on my best treatment option may not be in the standard of care, nor even in a clinical trial. It may be in a small number of patients who participated in a clinical trial some time ago and had a miraculous recovery, but the drug was abandoned because most patients did not respond. Can I find out about those patients? Probably not.
How is the World Economic Forum bringing data-driven healthcare to life?
The application of “precision medicine” to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseases—and disease in general.
But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.
The World Economic Forum’s Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access – across borders – to sensitive health data for the treatment and diagnosis of rare diseases.
The data will be shared via a “federated data system”: a decentralized approach that allows different institutions to access each other’s data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.
The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).
A Call to Action: Patients + Data = Learning
We need to break down the barriers between health institutions to allow patients and their trusted clinicians to access global data about individual patient treatment decisions and outcomes. Currently, health institutions have more power than patients, and their objectives do not always line up perfectly with ours. The entire healthcare industry finds this acceptable, but we should refuse to accept it. Patients need to fight to open access to relevant data from other patients.
Cancer Commons offers a model for how this can work. Patients get treatment advice from this non-profit, as they might get a “second opinion” from a specialist at an academic cancer research center. Cancer Commons follows similar steps: it gathers medical data, scrubs the data and provides advice.
In contrast to most molecular tumor boards, Cancer Commons captures all treatment options discussed, as well as the rationale for why an option was recommended or contra-indicated for a given patient. They then monitor patient progress using both patient-reported outcomes and outcomes inferred from records, which are accessed under HIPAA with patients’ permission. To encourage sharing and learning, they work with academics within ongoing pilot programs for brain and pancreatic cancers, and hopefully soon in ovarian and pediatric AML cancers.
Patient advocacy groups, especially for rare diseases and pediatric cancers, are bringing together patient data in the search for treatments. Research organizations are offering data gathering services for specific diseases. And patient data services companies are allying with patient advocacy groups to search for better treatment options. Here are some examples:
- The Broad Institute’s “Count Me In” is gathering data for patients with metastatic breast cancer, metastatic prostate cancer, angiosarcoma, gastroesophageal cancer and brain cancer. Each patient can engage directly with scientists who are unlocking new insights and new opportunities for cancer treatment and care. The researchers share data openly and as soon as possible with the larger research community to raise the chances of shared success — all while carefully protecting the privacy and trust of patients.
- The Cholangiocarcinoma Foundation is partnering with a personalized health data repository company (Ciitizen) to give cholangiocarcinoma patients a way to collect and organize their complete health history, free of charge. They launched “MapItForward” and the “Real World Genomics Study” to analyze health records to see what sort of genomic testing cholangiocarcinoma patients are getting.
- The Pediatric Neuro-Oncology Consortium is sharing data about patients across 18 institutions and 100+ clinicians and researchers to bring treatments to patients as quickly as possible.
- Patients with multiple myeloma can contribute their health information, including a free at-home genomic blood test, to a database (“CureCloud” at the Multiple Myeloma Research Foundation) that will help doctors provide them with optimal care and aid researchers in developing new treatments based on the 70 genetic aberrations associated with myeloma.
Patients need to join together to share our data – especially treatment decision logic and outcomes – with other patients with our disease, providers and researchers. Meanwhile, physicians and researchers need to apply emerging global data standards by partnering with patients in small groups. These focused experiments must capture the logic behind treatment decisions, monitor the progress of individual patients and enable sharing of real-world evidence globally for continuous learning.