Digital Health Data And Information Sharing: A New Frontier For Health Care Competition?


Paper by Lucia Savage, Martin Gaynor and Julie Adler-Milstein: “There are obvious benefits to having patients’ health information flow across health providers. Providers will have more complete information about patients’ health and treatment histories, allowing them to make better treatment recommendations, and avoid unnecessary and duplicative testing or treatment. This should result in better and more efficient treatment, and better health outcomes. Moreover, the federal government has provided substantial incentives for the exchange of health information. Since 2009, the federal government has spent more than $40 billion to ensure that most physicians and hospitals use electronic health records, and to incentivize the use of electronic health information and health information exchange (the enabling statute is the Health Information Technology for Clinical Health Act), and in 2016 authorized substantial fines for failing to share appropriate information.

Yet, in spite of these incentives and the clear benefits to patients, the exchange of health information remains limited. There is evidence that this limited exchange in due in part to providers and platforms attempting to retain, rather than share, information (“information blocking”). In this article we examine legal and business reasons why health information may not be flowing. In particular, we discuss incentives providers and platforms can have for information blocking as a means to maintain or enhance their market position and thwart competition. Finally, we recommend steps to better understand whether the absence of information exchange, is due to information blocking that harms competition and consumers….(More)”

Predictive Big Data Analytics using the UK Biobank Data


Paper by Ivo D Dinov et al: “The UK Biobank is a rich national health resource that provides enormous opportunities for international researchers to examine, model, and analyze census-like multisource healthcare data. The archive presents several challenges related to aggregation and harmonization of complex data elements, feature heterogeneity and salience, and health analytics. Using 7,614 imaging, clinical, and phenotypic features of 9,914 subjects we performed deep computed phenotyping using unsupervised clustering and derived two distinct sub-cohorts. Using parametric and nonparametric tests, we determined the top 20 most salient features contributing to the cluster separation. Our approach generated decision rules to predict the presence and progression of depression or other mental illnesses by jointly representing and modeling the significant clinical and demographic variables along with the derived salient neuroimaging features. We reported consistency and reliability measures of the derived computed phenotypes and the top salient imaging biomarkers that contributed to the unsupervised clustering. This clinical decision support system identified and utilized holistically the most critical biomarkers for predicting mental health, e.g., depression. External validation of this technique on different populations may lead to reducing healthcare expenses and improving the processes of diagnosis, forecasting, and tracking of normal and pathological aging….(More)”.

Responsible Data Governance of Neuroscience Big Data


Paper by B. Tyr Fothergill et al: “Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations.

Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages.

Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance, and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of ‘responsible data governance’, applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by governance of neuroscience big data in the Human Brain Project (HBP)….(More)”.

Responsible data sharing in international health research: a systematic review of principles and norms


Paper by Shona Kalkman, Menno Mostert, Christoph Gerlinger, Johannes J. M. van Delden and Ghislaine J. M. W. van Thiel: ” Large-scale linkage of international clinical datasets could lead to unique insights into disease aetiology and facilitate treatment evaluation and drug development. Hereto, multi-stakeholder consortia are currently designing several disease-specific translational research platforms to enable international health data sharing. Despite the recent adoption of the EU General Data Protection Regulation (GDPR), the procedures for how to govern responsible data sharing in such projects are not at all spelled out yet. In search of a first, basic outline of an ethical governance framework, we set out to explore relevant ethical principles and norms…

We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement. However, at the level of principles and norms we identified substantial variation in the phrasing and level of detail, the number and content of norms considered necessary to protect a principle, and the contextual approaches in which principles and norms are used....

While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to harmonise the identified principles and norms into a coherent governance framework that promotes data sharing while securing public trust….(More)”

Advancing Computational Biology and Bioinformatics Research Through Open Innovation Competitions


HBR Working Paper by Andrea Blasco et al: “Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research where the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research….(More)”.

Nudging the dead: How behavioural psychology inspired Nova Scotia’s organ donation scheme


Joseph Brean at National Post: “Nova Scotia’s decision to presume people’s consent to donating their organs after death is not just a North American first. It is also the latest example of how deeply behavioural psychology has changed policy debates.

That is a rare achievement for science. Governments used to appeal to people’s sense of reason, religion, civic duty, or fear of consequences. Today, when they want to change how their citizens behave, they use psychological tricks to hack their minds.

Nudge politics, as it came to be known, has been an intellectual hit among wonks and technocrats ever since Daniel Kahneman won the Nobel Prize in 2002 for destroying the belief people make decisions based on good information and reasonable expectations. Not so, he showed. Not even close. Human decision-making is an organic process, all but immune to reason, but strangely susceptible to simple environmental cues, just waiting to be exploited by a clever policymaker….

Organ donation is a natural fit. Nova Scotia’s experiment aims to solve a policy problem by getting people to do what they always tend to do about government requests — nothing.

The cleverness is evident in the N.S. government’s own words, which play on the meaning of “opportunity”: “Every Nova Scotian will have the opportunity to be an organ and tissue donor unless they opt out.” The policy applies to kidneys, pancreas, heart, liver, lungs, small bowel, cornea, sclera, skin, bones, tendons and heart valves.

It is so clever it aims to make progress as people ignore it. The default position is a positive for the policy. It assumes poor pickup. You can opt out of organ donation if you want. Nova Scotia is simply taking the informed gamble that you probably won’t. That is the goal, and it will make for a revealing case study.

Organ donation is an important question, and chronically low donation rates can reasonably be called a crisis. But most people make their personal choice “thoughtlessly,” as Kahneman wrote in the 2011 book Thinking, Fast and Slow.

He referred to European statistics which showed vast differences in organ donation rights between neighbouring and culturally similar countries, such as Sweden and Denmark, or Germany and Austria. The key difference, he noted, was what he called “framing effects,” or how the question was asked….(More)”.

Protection of health-related data: new guidelines


Press Release: “The Council of Europe has issued a set of guidelines to its 47 member states urging them to ensure, in law and practice, that the processing of health-related data is done in full respect of human rights, notably the right to privacy and data protection.

With the development of new technological tools in the health sector the volume of health-related data processed has grown exponentially showing the need for guidance for health administrations and professionals.

In a Recommendation, applicable to both the public and private sectors, the Council of Europe´s Committee of Ministers, calls on governments to transmit these guidelines to health-care systems and to actors processing health-related data, in particular health-care professionals and data protection officers.

The recommendation contains a set of principles to protect health-related data incorporating the novelties introduced in the updated Council of Europe data protection convention, known as “Convention 108+”, opened for signature in October 2018.

The Committee of Ministers underlines that health-related data should be protected by appropriate security measures taking into account the latest technological developments, their sensitive nature and the assessment of potential risks. Protection measures should be incorporated by design to any information system which processes health-related data.

The recommendation contains guidance with regard to various issues including the legitimate basis for the data processing of health-care data – notably consent by the data subject -, data concerning unborn children, health-related genetic data, the sharing of health-related data by professionals and the storage of data.

The guidelines list a number of rights of data subjects, crucially the transparency of data processing. They also contain a number of principles that should be respected when data are processed for scientific research, when they are collected by mobile devices or when they are transferred across borders….(More)”.

Play and playfulness for public health and wellbeing


Book edited by Alison Tonkin and Julia Whitaker: “The role of play in human and animal development is well established, and its educational and therapeutic value is widely supported in the literature. This innovative book extends the play debate by assembling and examining the many pieces of the play puzzle from the perspective of public health. It tackles the dual aspects of art and science which inform both play theory and public health policy, and advocates for a ‘playful’ pursuit of public health, through the integration of evidence from parallel scientific and creative endeavors.

Drawing on international research evidence, the book addresses some of the major public health concerns of the 21st century – obesity, inactivity, loneliness and mental health – advocating for creative solutions to social disparities in health and wellbeing. From attachment at the start of life to detachment at life’s ending, in the home and in the workplace, and across virtual and physical environments, play is presented as vital to the creation of a new ‘culture of health’.

This book represents a valuable resource for students, academics, practitioners and policy-makers across a range of fields of interest including play, health, the creative arts and digital and environmental design….(More)”.

Beyond opinion classification: Extracting facts, opinions and experiences from health forums


Paper by Jorge Carrillo-de-Albornoz et al in PLOS-ONE: “Surveys indicate that patients, particularly those suffering from chronic conditions, strongly benefit from the information found in social networks and online forums. One challenge in accessing online health information is to differentiate between factual and more subjective information. In this work, we evaluate the feasibility of exploiting lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-generated contents into three types: “experiences”, “facts” and “opinions”, using machine learning algorithms. In this context, our goal is to develop automatic methods that will make online health information more easily accessible and useful for patients, professionals and researchers….

We work with a set of 3000 posts to online health forums in breast cancer, morbus crohn and different allergies. Each sentence in a post is manually labeled as “experience”, “fact” or “opinion”. Using this data, we train a support vector machine algorithm to perform classification. The results are evaluated in a 10-fold cross validation procedure.

Overall, we find that it is possible to predict the type of information contained in a forum post with a very high accuracy (over 80 percent) using simple text representations such as word embeddings and bags of words. We also analyze more complex features such as those based on the network properties, the polarity of words and the verbal tense of the sentences and show that, when combined with the previous ones, they can boost the results….(More)”.

What you don’t know about your health data will make you sick


Jeanette Beebe at Fast Company: “Every time you shuffle through a line at the pharmacy, every time you try to get comfortable in those awkward doctor’s office chairs, every time you scroll through the web while you’re put on hold with a question about your medical bill, take a second to think about the person ahead of you and behind you.

Chances are, at least one of you is being monitored by a third party like data analytics giant Optum, which is owned by UnitedHealth Group, Inc. Since 1993, it’s captured medical data—lab results, diagnoses, prescriptions, and more—from 150 million Americans. That’s almost half of the U.S. population.

“They’re the ones that are tapping the data. They’re in there. I can’t remove them from my own health insurance contracts. So I’m stuck. It’s just part of the system,” says Joel Winston, an attorney who specializes in privacy and data protection law.

Healthcare providers can legally sell their data to a now-dizzyingly vast spread of companies, who can use it to make decisions, from designing new drugs to pricing your insurance rates to developing highly targeted advertising.

It’s written in the fine print: You don’t own your medical records. Well, except if you live in New Hampshire. It’s the only state that mandates its residents own their medical data. In 21 states, the law explicitly says that healthcare providers own these records, not patients. In the rest of the country, it’s up in the air.

Every time you visit a doctor or a pharmacy, your record grows. The details can be colorful: Using sources like Milliman’s IntelliScript and ExamOne’s ScriptCheck, a fuller picture of you emerges. Your interactions with the health are system, your medical payments, your prescription drug purchase history. And the market for the data is surging.

Its buyers and sharers—pharma giants, insurers, credit reporting agencies, and other data-hungry companies or “fourth parties” (like Facebook)—say that these massive health data sets can improve healthcare delivery and fuel advances in so-called “precision medicine.”

Still, this glut of health data has raised alarms among privacy advocates, who say many consumers are in the dark about how much of their health-related info is being gathered and mined….

Gardner predicted that traditional health data systems—electronic health records and electronic medical records—are less than ideal, given the “rigidity of the vendors and the products” and the way our data is owned and secured. Don’t count on them being around much longer, she said, “beyond the next few years.”

The future, Gardner suggested, is a system that runs on blockchain, which she defined for the committee as “basically a secure, visible, irrefutable ledger of transactions and ownership.” Still, a recent analysis of over 150 white papers revealed most healthcare blockchain projects “fall somewhere between half-baked and overly optimistic.”

As larger companies like IBM sign on, the technology may be edging closer to reality. Last year, Proof Work outlined a HIPAA-compliant system that manages patients’ medical histories over time, from acute care in the hospital to preventative checkups. The goal is to give these records to patients on their phones, and to create a “democratized ecosystem” to solve interoperability between patients, healthcare providers, insurance companies, and researchers. Similar proposals from blockchain-focused startups like Health Bank and Humanity.co would help patients store and share their health information securely—and sell it to researchers, too….(More)”.