Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic


Paper by Kaylin Bolt et al: “The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy….

Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity…(More)”.

Why Everyone Hates The Electronic Medical Record


Article by Dharushana Muthulingam: “Patient R was in a hurry. I signed into my computer—or tried to. Recently, IT had us update to a new 14-digit password. Once in, I signed (different password) into the electronic medical record. I had already ordered routine lab tests, but R had new info. I pulled up a menu to add on an additional HIV viral load to capture early infection, which the standard antibody test might miss. R went to the lab to get his blood drawn

My last order did not print to the onsite laboratory. An observant nurse had seen the order and no tube. The patient had left without the viral load being drawn. I called the patient: could he come back? 

 Healthcare workers do not like the electronic health record (EHR), where they spend more time than with patients. Doctors hate it, as do nurse practitionersnursespharmacists, and physical therapists. The National Academies of Science, Engineering and Medicine reports the EHR is a major contributor to clinician burnout. Patient experience is mixed, though the public is still concerned about privacy, errors, interoperability and access to their own records.

The EHR promised a lot: better accuracy, streamlined care, and patient-accessible records. In February 2009, the Obama administration passed the HITECH Act on this promise, investing $36 billion to scale up health information technology. No more deciphering bad handwriting for critical info. Efficiency and cost-savings could get more people into care. We imagined cancer and rare disease registries to research treatments. We wanted portable records accessible in an emergency. We wanted to rapidly identify the spread of highly contagious respiratory illnesses and other public health crises.

Why had the lofty ambition of health information, backed by enormous resources, failed so spectacularly?…(More)”.

Citizen Engagement in Evidence-informed Policy-making: A Guide to Mini-publics


Report by WHO: “This guide focuses on a specific form of citizen engagement, namely mini-publics, and their potential to be adapted to a variety of contexts. Mini-publics are forums that include a cross-section of the population selected through civic lottery to participate in evidence-informed deliberation to inform policy and action. The term refers to a diverse set of democratic innovations to engage citizens in policy-making. This guide provides an overview of how to organize mini-publics in the health sector. It is a practical companion to the 2022 Overview report, Implementing citizen engagement within evidence-informed policy-making. Both documents examine and encourage contributions that citizens can make to advance WHO’s mission to achieve universal health coverage…(More)””

How Mental Health Apps Are Handling Personal Information


Article by Erika Solis: “…Before diving into the privacy policies of mental health apps, it’s necessary to distinguish between “personal information” and “sensitive information,” which are both collected by such apps. Personal information can be defined as information that is “used to distinguish or trace an individual’s identity.” Sensitive information, however, can be any data that, if lost, misused, or illegally modified, may negatively affect an individual’s privacy rights. While health information not under HIPAA has previously been treated as general personal information, states like Washington are implementing strong legislation that will cover a wide range of health data as sensitive, and have attendant stricter guidelines.

Legislation addressing the treatment of personal information and sensitive information varies around the world. Regulations like the General Data Protection Regulation (GDPR) in the EU, for example, require all types of personal information to be treated as being of equal importance, with certain special categories, including health data having slightly elevated levels of protection. Meanwhile, U.S. federal laws are limited in addressing applicable protections of information provided to a third party, so mental health app companies based in the United States can approach personal information in all sorts of ways. For instance, Mindspa, an app with chatbots that are only intended to be used when a user is experiencing an emergency, and Elomia, a mental health app that’s meant to be used at any time, don’t make distinctions between these contexts in their privacy policies. They also don’t distinguish between the potentially different levels of sensitivity associated with ordinary and crisis use.

Wysa, on the other hand, clearly indicates how it protects personal information. Making a distinction between personal and sensitive data, its privacy policy notes that all health-based information receives additional protection. Similarly, Limbic labels everything as personal information but notes that data, including health, genetic, and biometric, fall within a “special category” that requires more explicit consent than other personal information collected to be used…(More)”.

Are Evidence-Based Medicine and Public Health Incompatible?


Essay by Michael Schulson: “It’s a familiar pandemic story: In September 2020, Angela McLean and John Edmunds found themselves sitting in the same Zoom meeting, listening to a discussion they didn’t like.

At some point during the meeting, McLean — professor of mathematical biology at the Oxford University, dame commander of the Order of the British Empire, fellow of the Royal Society of London, and then-chief scientific adviser to the United Kingdom’s Ministry of Defense — sent Edmunds a message on WhatsApp.

“Who is this fuckwitt?” she asked.

The message was evidently referring to Carl Heneghan, director of the Center for Evidence-Based Medicine at Oxford. He was on Zoom that day, along with McLean and Edmunds and two other experts, to advise the British prime minister on the Covid-19 pandemic.

Their disagreement — recently made public as part of a British government inquiry into the Covid-19 response — is one small chapter in a long-running clash between two schools of thought within the world of health care.

McLean and Edmunds are experts in infectious disease modeling; they build elaborate simulations of pandemics, which they use to predict how infections will spread and how best to slow them down. Often, during the Covid-19 pandemic, such models were used alongside other forms of evidence to urge more restrictions to slow the spread of the disease. Heneghan, meanwhile, is a prominent figure in the world of evidence-based medicine, or EBM. The movement aims to help doctors draw on the best available evidence when making decisions and advising patients. Over the past 30 years, EBM has transformed the practice of medicine worldwide.

Whether it can transform the practice of public health — which focuses not on individuals, but on keeping the broader community healthy — is a thornier question…(More)”.

Community views on the secondary use of general practice data: Findings from a mixed-methods study


Paper by Annette J. Braunack-Mayer et al: “General practice data, particularly when combined with hospital and other health service data through data linkage, are increasingly being used for quality assurance, evaluation, health service planning and research.Using general practice data is particularly important in countries where general practitioners (GPs) are the first and principal source of health care for most people.

Although there is broad public support for the secondary use of health data, there are good reasons to question whether this support extends to general practice settings. GP–patient relationships may be very personal and longstanding and the general practice health record can capture a large amount of information about patients. There is also the potential for multiple angles on patients’ lives: GPs often care for, or at least record information about, more than one generation of a family. These factors combine to amplify patients’ and GPs’ concerns about sharing patient data….

Adams et al. have developed a model of social licence, specifically in the context of sharing administrative data for health research, based on an analysis of the social licence literature and founded on two principal elements: trust and legitimacy.In this model, trust is founded on research enterprises being perceived as reliable and responsive, including in relation to privacy and security of information, and having regard to the community’s interests and well-being.

Transparency and accountability measures may be used to demonstrate trustworthiness and, as a consequence, to generate trust. Transparency involves a level of openness about the way data are handled and used as well as about the nature and outcomes of the research. Adams et al. note that lack of transparency can undermine trust. They also note that the quality of public engagement is important and that simply providing information is not sufficient. While this is one element of transparency, other elements such as accountability and collaboration are also part of the trusting, reflexive relationship necessary to establish and support social licence.

The second principal element, legitimacy, is founded on research enterprises conforming to the legal, cultural and social norms of society and, again, acting in the best interests of the community. In diverse communities with a range of views and interests, it is necessary to develop a broad consensus on what amounts to the common good through deliberative and collaborative processes.

Social licence cannot be assumed. It must be built through public discussion and engagement to avoid undermining the relationship of trust with health care providers and confidence in the confidentiality of health information…(More)”

How Health Data Integrity Can Earn Trust and Advance Health


Article by Jochen Lennerz, Nick Schneider and Karl Lauterbach: “Efforts to share health data across borders snag on legal and regulatory barriers. Before detangling the fine print, let’s agree on overarching principles.

Imagine a scenario in which Mary, an individual with a rare disease, has agreed to share her medical records for a research project aimed at finding better treatments for genetic disorders. Mary’s consent is grounded in trust that her data will be handled with the utmost care, protected from unauthorized access, and used according to her wishes. 

It may sound simple, but meeting these standards comes with myriad complications. Whose job is it to weigh the risk that Mary might be reidentified, even if her information is de-identified and stored securely? How should that assessment be done? How can data from Mary’s records be aggregated with patients from health systems in other countries, each with their own requirements for data protection and formats for record keeping? How can Mary’s wishes be respected, both in terms of what research is conducted and in returning relevant results to her?

From electronic medical records to genomic sequencing, health care providers and researchers now have an unprecedented wealth of information that could help tailor treatments to individual needs, revolutionize understanding of disease, and enhance the overall quality of health care. Data protection, privacy safeguards, and cybersecurity are all paramount for safeguarding sensitive medical information, but much of the potential that lies in this abundance of data is being lost because well-intentioned regulations have not been set up to allow for data sharing and collaboration. This stymies efforts to study rare diseases, map disease patterns, improve public health surveillance, and advance evidence-based policymaking (for instance, by comparing effectiveness of interventions across regions and demographics). Projects that could excel with enough data get bogged down in bureaucracy and uncertainty. For example, Germany now has strict data protection laws—with heavy punishment for violations—that should allow de-identified health insurance claims to be used for research within secure processing environments, but the legality of such use has been challenged…(More)”.

Data and density: Two tools to boost health equity in cities


Article by Ann Aerts and Diana Rodríguez Franco: “Improving health and health equity for vulnerable populations requires addressing the social determinants of health. In the US, it is estimated that medical care only accounts for 10-20% of health outcomes while social determinants like education and income account for the remaining 80-90%.

Place-based interventions, however, are showing promise for improving health outcomes despite persistent inequalities. Research and practice increasingly point to the role of cities in promoting health equity — or reversing health inequities — as 56% of the global population lives in cities, and several social determinants of health are directly tied to urban factors like opportunity, environmental health, neighbourhoods and physical environments, access to food and more.

Thus, it is critical to identify both true drivers of good health and poor health outcomes so that underserved populations can be better served.

Place-based strategies can address health inequities and lead to meaningful improvements for vulnerable populations…

Initial data analysis revealed a strong correlation between cardiovascular disease risk in city residents and social determinants such as higher education, commuting time, access to Medicaid, rental costs and internet access.

Understanding which data points are correlated with health risks is key to effectively tailoring interventions.

Determined to reverse this trend, city authorities have launched a “HealthyNYC” campaign and are working with the Novartis Foundation to uncover the behavioural and social determinants behind non-communicable diseases (NCDs) (e.g. diabetes and cardiovascular disease), which cause 87% of all deaths in New York City…(More)”

AI cannot be used to deny health care coverage, feds clarify to insurers


Article by Beth Mole: “Health insurance companies cannot use algorithms or artificial intelligence to determine care or deny coverage to members on Medicare Advantage plans, the Centers for Medicare & Medicaid Services (CMS) clarified in a memo sent to all Medicare Advantage insurers.

The memo—formatted like an FAQ on Medicare Advantage (MA) plan rules—comes just months after patients filed lawsuits claiming that UnitedHealth and Humana have been using a deeply flawed AI-powered tool to deny care to elderly patients on MA plans. The lawsuits, which seek class-action status, center on the same AI tool, called nH Predict, used by both insurers and developed by NaviHealth, a UnitedHealth subsidiary.

According to the lawsuits, nH Predict produces draconian estimates for how long a patient will need post-acute care in facilities like skilled nursing homes and rehabilitation centers after an acute injury, illness, or event, like a fall or a stroke. And NaviHealth employees face discipline for deviating from the estimates, even though they often don’t match prescribing physicians’ recommendations or Medicare coverage rules. For instance, while MA plans typically provide up to 100 days of covered care in a nursing home after a three-day hospital stay, using nH Predict, patients on UnitedHealth’s MA plan rarely stay in nursing homes for more than 14 days before receiving payment denials, the lawsuits allege…(More)”

We urgently need data for equitable personalized medicine


Article by Manuel Corpas: “…As a bioinformatician, I am now focusing my attention on gathering the statistics to show just how biased medical research data are. There are problems across the board, ranging from which research questions get asked in the first place, to who participates in clinical trials, to who gets their genomes sequenced. The world is moving toward “precision medicine,” where any individual can have their DNA analyzed and that information can be used to help prescribe the right drugs in the right dosages. But this won’t work if a person’s genetic variants have never been identified or studied in the first place.

It’s astonishing how powerful our genetics can be in mediating medicines. Take the gene CYP2D6, which is known to play a vital role in how fast humans metabolize 25 percent of all the pharmaceuticals on the market. If you have a genetic variant of CYP2D6 that makes you metabolize drugs more quickly, or less quickly, it can have a huge impact on how well those drugs work and the dangers you face from taking them. Codeine was banned from all of Ethiopia in 2015, for example, because a high proportion of people in the country (perhaps 30 percent) have a genetic variant of CYP2D6 that makes them quickly metabolize that drug into morphine, making it more likely to cause respiratory distress and even death…(More)”