data rights
Privacy and Security

Breaking the Mental Model: Individual Data Control Can Deliver Better Research

by Luna

The majority of individuals on Luna want to accelerate research and ensure their data is used as they allow. We considered a recent article in Forbes, and we broke down two recent legal opinion articles on medical data privacy and rights when it comes to your data’s application in research (article links below).

As standard practice in US healthcare, laboratory results, doctor’s conclusions, and any other information collected during your virtual or in-person visit is digitally captured and stored for later reference by the healthcare provider. This information is protected under the Health Insurance Portability and Accountability Act, referred to as HIPAA, to protect your private information from disclosure to parties outside of your care team. There are provisions under HIPAA for the de-identification of health data (which is simply the removal of your name, address, and other information that would clearly link the data back to you) so it can be shared freely for health research purposes – so-called secondary use of health data. Some types of health data, such as DNA information that may be collected to make treatment decisions, are inherently challenging to de-identify, and some argue impossible, despite their significant utility for research.

The balance between research benefit (i.e., the advancement of knowledge to guide improvements in diagnosis and treatment of diseases) and the role that individuals play is evolving. Many of the contemporary data protection and privacy laws around the world such as Europe’s General Data Protection Regulation (GDPR) and California’s Privacy Rights Act (CPRA) are built upon HIPAA and Fair Information Practice Principles (FIPPs) from the 1970s to define a right for individuals to control the use of data that is collected from them. And while this right to have control over the use of one’s data is absolute, the intersection between secondary use of de-identified data and the control granted by privacy legislation needs to find common ground for health data from all peoples to be included for research to have representation from the widest range of backgrounds possible.

As it pertains to the secondary use of health data, a case can be made that shifting the control of data use from institutions to individuals provides a direct pathway to greater study participant engagement and more inclusive participation of individuals in future research studies.

The debate on this intersection of approaches is couched in terms of data ownership and control of data use. Unlike many other tangible assets like real estate or a piece of furniture, data can be used simultaneously by many parties without degrading the value of each party’s use of the data. This difference has shifted thinking to consideration of the control of data use (i.e., rather than data ownership) to be of paramount importance. And moreover, the trend globally and increasingly at the State level in the US is that the control of data use should rest with the individual on whom the data was collected. This argument is most compelling when considering an individual’s DNA data that uniquely characterizes them. As it pertains to the secondary use of health data, a case can be made that shifting the control of data use from institutions to individuals provides a direct pathway to greater study participant engagement and more inclusive participation of individuals in future research studies.

Articles Reviewed for this Blog

“The Future Of Personally Identifiable Information And Health Data”
https://www.forbes.com/sites/forbestechcouncil/2023/07/18/the-future-of-personally-identifiable-information-and-health-data/?sh=694704622468

“Data Unlocked: Why Rights Mean More Than “Ownership” in B2B Data Sharing”
https://gowlingwlg.com/en/insights-resources/articles/2023/data-unlocked-rights-over-data/

“Ensuring Data Privacy in Genomic Medicine: Legal Challenges and Opportunities”
https://www.jdsupra.com/legalnews/ensuring-data-privacy-in-genomic-8975727/

About Luna

Luna’s suite of tools and services connects communities with researchers to accelerate health discoveries. With participation from more than 180 countries and communities advancing causes including disease-specific, public health, environmental, and emerging interests, Luna empowers these collectives to gather a wide range of data—health records, lived experience, disease history, genomics, and more—for research.

Luna gives academia and industry everything they need from engagement with study participants to data analysis across multiple modalities using a common data model. The platform is compliant with clinical regulatory requirements and international consumer data privacy laws.

By providing privacy-protected individuals a way to continually engage, Luna transforms the traditional patient-disconnected database into a dynamic, longitudinal discovery environment where researchers, industry, and community leaders can leverage a range of tools to surface insights and trends, study disease natural history and biomarkers, and enroll in clinical studies and trials.

Community

The Struggles of Our Superheroes Reveal Their Real Strength: Dr. Catalina Lopez-Correa

by Luna

Seeing our superheroes struggle can be difficult. It’s a view behind the ironworks and cladding. It’s intimate. And frightening. After all, if they too stumble and fall, what fate befalls the rest of us? But seeing that struggle is something else as well: It’s revealing. And it’s there, beneath the perfect sheen, the real strength shows.

You couldn’t create a more ideal life sciences superhero than Dr. Catalina Lopez-Correa if you tried. What makes her extraordinary is that she possesses both the “I” courageous attitude AND the “us” inclusive empathy. She advocates for diversity in research AND represents it. She’s led pioneering research efforts AND implemented innovation in practice. She promotes understanding your genetics AND was among the first to explore her entire DNA sequence. She’s worked across academia, pharma, biotech, AND nonprofits. She’s catalyzed partnerships nationally AND internationally in seven countries. Catalina is the real deal, walking the walk with an aura that’s just so incredibly human.

What happens when a superhero – the one harnessing the power of science and technology to protect the rest of us – is diagnosed with breast cancer? 

“People are watching how you are facing this. Do you want to be public about this? Do you want to show that you are Superwoman, that cancer is nothing, that you can just work through it, no big deal,” Catalina sarcastically said, “or do you want to be human and show that you are really taking care of yourself and concentrating on your health?

Image of Catalina Lopez-Correa
Dr. Catalina Lopez-Correa

At the urging of her oncologist, and with deep appreciation for the personal resources and support of her employer, Catalina decided to take a one-year medical leave and focus on herself. True to Catalina’s ambitious core, she also set goals: to use this journey – as a patient – to normalize and reduce the stigma of cancer, and try to find the positive in it.

Catalina’s decision to be public and document her cancer journey took serious contemplation. As a gay, Latina, female in science, she’s experienced her fair share of being treated differently, faced barriers in how people perceive her, AND has achieved remarkably in advancing her career and visibility as a thought leader in life sciences.

“It took me three days of thinking of posting a picture on Twitter where I felt I looked very vulnerable. Sure, people say nice things, but they also say nasty things. Putting yourself out there in a completely different light, being so vulnerable… People are sending love and that’s great, but is hard to see yourself like this. But, I believe there is a need for researchers and industry to hear the voice of the patient in different way.”

The patient journey has much room — and opportunity — for innovation. Despite “patient-centricity” being a phrase nearly every player in medicine regularly uses, Catalina described an experience of anxiety and ambiguity. Life science leaders often talk about a smooth flow from getting a diagnosis to defining the type of cancer through genomics, then treating precisely, successfully. As Catalina is experiencing now, what is shared from the podium at a conference is different than the reality.

“That period between getting diagnosed, testing for your biomarkers, and understanding what’s next is terrible,” she said. “You enter this black box where even doctor’s aren’t answering your questions. There’s a huge information gap filled with anxiety. When there is anxiety, people naturally look to any support they can find. The modern-day crowdsourcing through Facebook and elsewhere on social media can lead to its own problems,” according to Catalina. “There is a vastness of anecdotes, where everyone is just posting, and so much of it is just plain wrong.”

Catalina describes patient-centered care as care that is built with the mindset and goals of the individual as paramount. And it all begins with research.

“A big piece still missing in research is the patient perspective. We [researchers] say it and we keep on trying to incorporate it, but I think we are still doing it as a check box,” Catalina said. “But now I see it for myself – to understand the journeys (and not everyone will have the same journey), to understand and relate to the impact of the side effects, to understand the impact and variability of genomics, you simply need to know the patients much better.”

“Everyone is getting the same three markers – estrogen receptor, progesterone receptor, and HER2 receptor – to determine treatment. Now what?” Catalina said. “Where can the patient find decision-making support to understand how these amazing biomarkers and genomics are guiding treatment? How can treatment be personalized, and what does each treatment mean for prognosis, recurrence, their own goals as a person?”

Catalina embraces her cancer journey, specially the time to put herself first and experience the innovations in cancer care that she has helped pioneer. But, she is acutely aware that many women – without degrees in biosciences, financial resources, medical contacts, a supportive community, and more – will struggle from the myriad flaws in the system. She didn’t use the word but I heard it: injustice. The injustice is weighing on her.

Learn More and Follow Catalina

On X (formerly known as Twitter):
@clopezcorrea

On LinkedIn:
www.tlajfundforcomplexdisease.com

Medium:
Dr. Catalina Lopez-Correa On Her New Book, Barriers For Women In STEM, and The Power Of Mentoring

Click to join the Cancer Innovation community on Luna today.

About Luna

Luna’s suite of tools and services connects communities with researchers to accelerate health discoveries. With participation from more than 180 countries and communities advancing causes including disease-specific, public health, environmental, and emerging interests, Luna empowers these collectives to gather a wide range of data—health records, lived experience, disease history, genomics, and more—for research.

Luna gives academia and industry everything they need from engagement with study participants to data analysis across multiple modalities using a common data model. The platform is compliant with clinical regulatory requirements and international consumer data privacy laws.

By providing privacy-protected individuals a way to continually engage, Luna transforms the traditional patient-disconnected database into a dynamic, longitudinal discovery environment where researchers, industry, and community leaders can leverage a range of tools to surface insights and trends, study disease natural history and biomarkers, and enroll in clinical studies and trials.

data rights
Privacy and Security

De-identified, Pseudonymized, Anonymous Data, Oh My!

by Luna

It seems like everywhere we turn these days some aspect of data privacy is in the news with this or that company sharing your data in some form or fashion. Amongst many of these reports are the use of your de-identified data. What is de-identified data and how is it different from pseudonymized or anonymous data? And how do any of those relate to your personal data/information covered by modern data privacy regulations?

De-identification removes features like your name, address, and date of birth from your data. It is reversible if those accessing your de-identified data have enough other information that can be tied to the remaining details in the de-identified data. Think of this like pixels in an image. With enough pixels, the full image comes together, even if some pixels are missing.

Pseudonymization replaces certain pieces of information in your data set – for example associating your data with a unique ID in place of your name or address. This is also reversible if those with access to your data have enough other information (or have access to the key or decoder that connects your name back with that unique ID).

Anonymization is NOT reversible which means that, in addition to removing your name, address, date of birth, zip code, and so on, other information such as medical diagnoses, job title, and/or geolocation must also be removed.

So, what about DNA data? Everything stated here certainly suggests that DNA information about you that is large enough (e.g., your entire genome sequence) or specific enough (e.g., gene variations that led to a medical diagnosis) could never be considered anonymous. This is why DNA is used in applications ranging from family finder tools to crime scene investigations.

According to many data privacy regulations, de-identified data is likely still considered your personal data/information and you have the right to know how it is being used and prevent it from being used for purposes you don’t agree with, if you choose.

Data privacy regulations vary based on where you live. Some country or state-level data privacy regulations consider your data as personal information unless it has been anonymized. Others only require de-identification or de-identification PLUS defined additional steps (sometimes many such steps!) to help prevent re-identification so it’s no longer considered your personal data.

Yes, this is all a bit confusing and constantly evolving. So, when you see news articles bandying about a company selling access to “de-identified” data that is no longer in the control of you – the individual the data represents – it should set off warning flags. According to GDPR (Global Data Privacy Regulation in Europe) and CCPR (California Privacy Rights Act) and similar US and non-US data privacy regulations, de-identified data is likely still considered your personal data/information and you have the right to know how it is being used and prevent it from being used for purposes you don’t agree with, if you choose.

About Luna

Luna’s suite of tools and services connects communities with researchers to accelerate health discoveries. With participation from more than 180 countries and communities advancing causes including disease-specific, public health, environmental, and emerging interests, Luna empowers these collectives to gather a wide range of data—health records, lived experience, disease history, genomics, and more—for research.

Luna gives academia and industry everything they need from engagement with study participants to data analysis across multiple modalities using a common data model. The platform is compliant with clinical regulatory requirements and international consumer data privacy laws.

By providing privacy-protected individuals a way to continually engage, Luna transforms the traditional patient-disconnected database into a dynamic, longitudinal discovery environment where researchers, industry, and community leaders can leverage a range of tools to surface insights and trends, study disease natural history and biomarkers, and enroll in clinical studies and trials.

Community

Standing on the Precipice — Parents of PANS/PANDAS Patients Shunned by the Medical Establishment Unite

by Luna

Alissa Johnson is a mom and an expert in public health policy. After several years working with state legislators and legislative staff on a variety of issues, Alissa started her own consulting firm, Johnson Policy Consulting, with a special interest in advancing policy solutions to promote the health and wellbeing of children and families in areas like newborn screening, heritable disorders, and insurance discrimination.

In 2019, a complex illness dramatically altered the life of her 11-year-old daughter, Louisa. In that moment, Alissa became the parent for whom she had worked to support for so many years.

“Following my professional experience, it was just astonishing for my child to become ill overnight,” Alissa said. “I had an even greater understanding and empathy for the parents who would testify to legislative committees on the horrific changes that occurred in their children that led to severe disability or death.”

Louisa, affectionately known as “Lulu” by her friends and family, was a happy, healthy, extremely social, straight A student who danced and played guitar. In second grade, Lulu had an abnormally high heart rate (tachycardia). By 6th grade, this condition progressed from being concerning to keeping her from simply walking alone in school at times, let alone running the bases in a softball game. Halfway through 6th grade, Lulu had a different body: an infection impacted her body’s ability to regulate normal functions (dysautonomia). Lulu’s heart rate swung between extremely high and extremely low, she had severe gastrointestinal issues and wasn’t eating, she had no energy, and ultimately her severe pain and difficulty swallowing led to hospitalization. Numerous tests were administered, and everything came back normal. After a week, Lulu returned home. A week later, Lulu was back in the hospital for another week. Following the second hospital stay, Alissa went to her pediatrician to address Lulu’s persistent sore throat. This test came back positive for strep throat. Just a few months later, following a flare-up of neuropsychiatric symptoms, Lulu had a diagnosis of PANDAS.

Understanding PANS/PANDAS

Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS) and Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) are conditions that often affect children, causing a sudden onset of obsessive-compulsive disorder symptoms, severe anxiety, headaches, joint pain, and other neuropsychiatric symptoms. These conditions are thought to be triggered by an immune response to infections, such as strep throat or other environmental factors. PANS/PANDAS can significantly impact a child’s life, causing challenges – often severe – in daily functioning and emotional well-being.

“I really cannot express how extremely isolating and horrific the conditions of PANS and PANDAS are,” Alissa shared. “For many of the parents, the only refuge they have is the community of other families who are going through these unbelievable life circumstances.”

While there are established guidelines for PANS/PANDAS treatment, significant barriers exist, including access to care, clinical diagnosis without a physical biomarker (although, thankfully, researchers are currently working to identify candidates), and physicians’ lack of acceptance of the condition. Without access to proper, holistic care, parents find themselves in an unpredictable cycle of reacting to health changes through hospitalizations and emergency room visits.

“Going to the emergency room with a child experiencing such a complex illness like PANS/PANDAS can be really disappointing, frustrating, and sometimes frightening. All too often, the children and the parents are treated as though they are criminals,” Alissa revealed. “There is a lack of acceptance of PANS/PANDAS which seems to stem from a lack of understanding amongst the medical community. But rather than being curious and asking, ‘How can we help this child who is clearly suffering but we don’t understand it?’ Rather than wanting to dig in and say, ‘How can we improve the life of this child and this family?’ Instead, there is a tendency to blame the child and blame the parent. When physicians resist accepting these realities, it just leads to more pain and suffering beyond the pain and suffering you are already experiencing at home. It’s excruciating.”

Parents often describe teachers, doctors, caregivers, and other authorities blaming the parents or the child for the various neuropsychiatric symptoms.

The Power of Community to Drive Research

Alissa admits that she was fortunate to have resources, domain expertise, and a network of professional connections – even within her own family – that she could leverage to access care. Even then, she faced extremely challenging circumstances.

“Most hospitals will not accept these patients. Even in some highly regarded institutions, when my daughter was in complete crisis, we were told to go home and consult with a care team,” Alissa shared. “With a child that sick, even with the resources we had, it’s very difficult to get in a car and drive two states away. For single parents, people living in rural communities, families with multiple sick kids…the situation is near impossible, and we need to help them.”

“I don’t think there is a way for anyone to truly understand how devastating the circumstances are under which the children and their families are living unless the PANS/PANDAS patient community shares that with others. The most critical people who need to understand are researchers and physicians”.

Fortunately, there are growing opportunities to unite a collective voice of the community and expand research.

“Luna and Genetic Alliance provide a unique opportunity for families to engage with researchers to share their experiences, aid in raising awareness, and advance science so that we can hopefully allow these kids to go on to live healthy lives, “ Alissa said. “The great attention to privacy protection of the information patients share is really important.”

The Road Forward

In July 2021, Lulu died at 14-years-old of complications from PANS/PANDAS. Her parents founded the Louisa Adelynn Johnson Fund for Complex Disease to support the efforts of researchers studying the conditions from which Louisa suffered, including dysautonomia/Postural Orthostatic Tachycardia Syndrome (POTS), neuroimmune disease, and other comorbidities. Subsequently, Alissa joined Amanda Peel Crowley and other parents as co-founders of the National Alliance for PANS/PANDAS Action (NAPPA), a coalition which serves as a lobbying group to increase federal funding for PANS/PANDAS research. On March 23, 2023, Amanda testified on Capitol Hill to Congress about the critical need for federal funding for PANS/PANDAS, and shared some of Lulu’s story.

“What motivates me to continue to be involved in the PANS/PANDAS community is that it’s just unconscionable that this continues to go on, and so many children are affected,” Alissa said. “There is not yet reliable epidemiological data, but we know there are a significant number of families out there and, even if there were only one, we cannot accept them being continually ignored and mistreated.”

Resources and More

Alissa Johnson​, Principal Consultant, Johnson Policy Consulting
www.policyconsult.com

The Louisa Adelynn Johnson Fund for Complex Disease
www.tlajfundforcomplexdisease.com

The National Alliance for PANS/PANDAS Action (NAPPA)
www.facebook.com/NAPPANationalAllianceforPANSPANDASAction

info@panspandasaction.org 

Click to join the PANS/PANDAS community on Luna today.

About Luna

Luna’s suite of tools and services connects communities with researchers to accelerate health discoveries. With participation from more than 180 countries and communities advancing causes including disease-specific, public health, environmental, and emerging interests, Luna empowers these collectives to gather a wide range of data—health records, lived experience, disease history, genomics, and more—for research.

Luna gives academia and industry everything they need from engagement with study participants to data analysis across multiple modalities using a common data model. The platform is compliant with clinical regulatory requirements and international consumer data privacy laws.

By providing privacy-protected individuals a way to continually engage, Luna transforms the traditional patient-disconnected database into a dynamic, longitudinal discovery environment where researchers, industry, and community leaders can leverage a range of tools to surface insights and trends, study disease natural history and biomarkers, and enroll in clinical studies and trials.

Interconnected points
Discovery

Demystifying Artificial Intelligence (AI)

by Luna

By Sanjay John, a software engineer, and Scott Kahn, PhD, the Chief Information and Privacy Officer, at Luna.

With the recent launch of ChatGPT, suddenly every tech company has artificial intelligence (AI) capabilities. News stories everywhere are expounding on the promise and threat of AI and its family of applications including machine learning (ML) and large language models (LLMs). But are these technologies really that new? And what is the truth buried in the confusing technical jargon on which most stories focus? Tune in while we try to demystify AI and related applications.

Large Language Models

The fields of AI and ML are over 70 years old. At their foundation is the mathematics of probability and statistics. LLMs, like ChatGPT, are simply a collection of equations that determine the estimated probability of an answer to a given question. The similarity with known answers can define these probability equations. Consider a model to identify a cat in an image. A model like this “learns” from a large number of cat pictures. However, if the set of cat pictures only shows white cats or only shows wild cats, the model may be faulty and return incorrect answers that do not match the goal of creator. The same model can also be quantitative to represent the likelihood that an answer is present, for example, a model that returns the percent likelihood that there is a cat somewhere in an image. And while there are other models – such as a model that would identify if an image would make a cat-lover happy or sad – and many different types of (machine) learning to train models – these are more technical than we will explore today.

Neural Networks

Now let’s tackle neural networks. As the name implies, neural networks try to emulate the complex reasoning of a human brain. To create a neural network, we must build the instructions and logic that allow for this more complex reasoning to occur. First, we build the instructions using a class of algorithms. Algorithms are specific, unambiguous rules that instruct the model in how to react when presented with external data. Algorithms combine multiple models or “nodes” using a weighting scheme – for example, an answer derived from 50% of one model, 30% of another model, and 20% of a third model to create neural networks.

Neural networks often have many nodes (thousands or even billions) and many combinations of these nodes to present an answer to a question. One version of a neural network, known as a generative adversarial network (GAN), pits a generator network (a network focused on creating fake data) against a discriminator network (a network focused on determining if a piece of data is real). These networks have become famous for their ability to create seemingly realistic images, videos, and text. A more complex version of a neural network is a transformer. Transformers learn context and meaning by tracking relationships within data points, like words in a sentence. For example, the sentence, “The cup was poured into the bowl until it was empty,” compared to, “The cup was poured into the bowl until it was full,” shows how our complete understanding of sentences influences how we consider the meaning of the word “it” in these contexts. Transformers can decipher and apply this context, allowing for better prediction. ML and feedback loops help networks learn and adjust the weights of the various nodes accordingly.

Natural Language Processing

The final piece of the puzzle involves natural language processing, in which a model converts common written or verbal language into the meaning of the phrases. Neural networks typically perform this process, including probability models that encode the similarity of words and phrases, to predict future words and phrases. Combining the processing power of transformer networks, the creative ability of generative networks, and the large available dataset of the internet and/or databases, we arrive at LLMs (large language models). LLMs are at the cutting edge in their understanding of natural language. Unfortunately, the data sets used from the internet and other databases are often unreliable and incomplete, which again, can cause the output to be biased, misleading, and sometimes completely wrong. Meaning AI, ML, and LLMs are still only as good as the attention the creator pays to ensuring the applications learn from valid and representative data sets and that their learning feedback loop incorporates novel data over time and not just a regurgitation of the data they’ve already consumed. The better creators are at monitoring this, the more useful current and future tools using these applications will be.

Unfortunately, the data sets used from the internet and other databases are often unreliable and incomplete, which again, can cause the output to be biased, misleading, and sometimes completely wrong.

Let’s take ChatGPT, for example. It is the marriage of a powerful LLM with predictive neural network models that can learn from user input. However, it has limitations rooted in the information used to create or “train” the underlying models and the user feedback used to reinforce them.  The resulting models will reflect these gaps if the data used to train the models is not comprehensive. For example, if the model used health information strictly from men 21 years or older, you would not be able to use that model to characterize women’s health, or even boys’ health. Further, today’s health data sets typically lack representation of many individuals beyond those of European descent.

The Takeaway

So, while the headlines are provocative, AI, ML, and LLMs are just tools. Like most tools, they work best when the user knows which jobs they are most suitable for, and where the boundaries and risks lie. At Luna, we focus on using AI to assist researchers with the extraction of clinically relevant information from data that our members share in studies they join. The broader the health experiences of our members, the better these tools become in understanding what is important to help drive research faster and with more successful outcomes. At the end of the day, human intelligence and experience still reign supreme, as we decide where and when to apply these technologies, where they fall short, and when to unplug them.

About Luna

Luna’s suite of tools and services connects communities with researchers to accelerate health discoveries. With participation from more than 180 countries and communities advancing causes including disease-specific, public health, environmental, and emerging interests, Luna empowers these collectives to gather a wide range of data—health records, lived experience, disease history, genomics, and more—for research.

Luna gives academia and industry everything they need from engagement with study participants to data analysis across multiple modalities using a common data model. The platform is compliant with clinical regulatory requirements and international consumer data privacy laws.

By providing privacy-protected individuals a way to continually engage, Luna transforms the traditional patient-disconnected database into a dynamic, longitudinal discovery environment where researchers, industry, and community leaders can leverage a range of tools to surface insights and trends, study disease natural history and biomarkers, and enroll in clinical studies and trials.