We’re talking to FPF senior policy experts about their work on important privacy issues. Today, Dr. Rachele Hendricks-Sturrup, Health Policy Counsel, is sharing her perspective on health data and privacy.
Dr. Hendricks-Sturrup has more than 12 years of experience in healthcare and biomedical research, health journalism, and engagement with digital health companies and startups. She has been a Research Fellow in the Department of Population Medicine at the Harvard Pilgrim Health Care Institute and Harvard Medical School and, as FPF’s health lead, continues to address ethical, legal, and social issues at the forefront of health policy and innovation, including genetic data, wearables, and machine learning with health data. She works with stakeholders to advance opportunities for data to be used for research and as real world evidence to improve patient care and outcomes and support evidence-based medicine.
What first attracted you to working on the privacy implications of health data?
My interest in health data began many years ago. When I finished my undergraduate degree in 2007, the world was turning on its head as far as understanding the utility and value of data. I realized that data is really running the show in academic, healthcare, business, and so many other landscapes. For this reason, I felt compelled to advocate for uses of data in ways that are purposeful, meaningful, and intentional to address specific problems in health care and health science research.
That being said, my appreciation for data and its potential, even if data might be considered a double-edged sword in some ways, has increased and I have studied how data can be used to support health care decision-making in meaningful ways.
You said that “data is running the show.” What does that mean?
Everyone in health care and policy today appreciates the value of evidence – evidence-based medicine, evidence-based policy, precision medicine, precision policy… you name it. Health data drives payment incentive structures in health services and approvals for new drugs and medical devices. Any kind of data point that you can use or leverage – be it quantitative or qualitative – to make an important decision or program is immensely valuable. With that being said, we still have a long way to go as far as figuring out how health data, especially when it’s combined with other data, should be collected and used to create actionable intelligence needed to robustly inform policy and organizational decision-making.
New types and uses of data in the health space mean that it’s really important to get the data right. If whatever you’re doing isn’t based on evidence or reliable data, then you’re likely to see a failed policy due to poor or absent evidence. Data is key to decision making in clinical care, legal settings, policy, and in research; if it’s not there or isn’t collected for intentional reasons or purposes, then how can you back up your claims?
What types and sources of data do you find yourself thinking about a lot these days?
I actually think a lot about behavioral data – mainly how that data is being combined with other types of personal data like geolocation data, genetic data, and consumer health data. Combined data is of strong interest to many, if not all, health companies and other business verticals engaged in health care. For this reason, I think it is important to be forethinking about immediate and downstream uses of such data to safeguard against possible population- or group-level discrimination, especially against populations sharing a certain health or immutable characteristic. Users and generators of that data should foremost consider how that data can be leveraged to improve health behavior in a non-coercive, fair, and transparent way. For example, I think that if I can help guide practitioners and researchers toward thinking more deeply and strategically about why and how they collect and use health data, then there is a greater chance that the data will not be used to stigmatize patients’ or health consumers’ behavior, but instead help those patients and consumers leverage resources within or new to their environments to become better stewards of their own health.
Another example is data collected to demonstrate patient medication adherence. If this sort of data is quantitative (or numbers-driven) and used or interpreted in potentially discriminatory ways to justify increased cost-sharing for a high-risk patient group, such as by stating, “these people are very non-adherent, so if we don’t see the clinical outcome that we’re looking for, then it’s their fault and they should pay instead of their insurer,” then I would argue that there are smarter ways to use that data.
One smart way to use that data is to combine it with qualitative data that can bring context as to why we see poor quantitative medication adherence data. Humans should step in to then ask – do these patients lack access to transportation and needed to refill their prescription? What can we do to eliminate that barrier for those patients? If a patient isn’t opening a smart pill bottle, is it because they forgot? What can we do to remind them or help them remind themselves? Those qualitative data points can supplement quantitative data to ensure all of the data are used to their highest potential to address or solve an actual (versus assumed) problem.
What have you been working on at FPF?
At FPF, I lead the health working group and engage with our working group and advisory board members on and to develop various projects that can inform FPF best practices. I’m currently engaging FPF’s health working group alongside our CEO, Jules Polonetsky, and Policy Fellow Katelyn Ringrose, to create draft guidance around best privacy practices for de-identified data that is shared by and with HIPAA-covered entities. The working group includes many stakeholders from academia and industry and thus attempts to garner consensus about best privacy practices across a wide range of stakeholders that present a range of use cases.
Currently, for FPF’s Privacy and Pandemics project, I help drive new and existing focuses on privacy concerns around the use of tech-driven surveillance measures, like digital contact tracing and thermal imaging, to help contain the spread of the virus and restart economies.
What do you see as the big, rising issues around uses of health data over the next few years?
It’s clear that data is critical to understanding and addressing many health challenges, including the pandemic, but certain uses of data also create a wide range of risks. We will be providing guidance around digital contact tracing and a wide range of additional technologies that are being used or proposed. Some may not be effective, some may create major risks, and others may be useful, if deployed in a proportionate and measured manner with appropriate safeguards.
I also think questions about how we collect or do not collect data to address health disparities exacerbated or exposed by COVID-19 will continue to resonate. The public has become much more exposed to toll of health disparities within the United States and across the world, especially for certain minority or low-income populations, following COVID-19. The biggest challenge to all of this will be how we collect and leverage data to actually address these disparities and help control or prevent them in the future.
Similarly, with regard to issues around social justice, racism, and police brutality that have drawn the attention of prominent health care organizations like the American Medical Association, I see the need to determine how intentional data collection, use, and interpretation fit within that equation to address these issues. Also, and broadly, how can we or are we using data to resolve conflicts or concerns in health care, genetics, and other areas? I foresee this as our greatest challenge – if we’re able to meet it, then we can say that we’ve made a quantum leap.