Big Data, Algorithms and Health Data

Curated on Posted on by Stefaan Verhulst

Paper by Julia M. Puaschunder: “The most recent decade featured a data revolution in the healthcare sector in screening, monitoring and coordination of aid. Big data analytics have revolutionarized the medical profession. The health sector relys on Artificial Intelligence (AI) and robotics as never before. The opportunities of unprecedented access to healthcare, rational precision and human resemblance but also targeted aid in decentralized aid grids are obvious innovations that will lead to most sophisticated neutral healthcare in the future. Yet big data driven medical care also bears risks of privacy infringements and ethical concerns of social stratification and discrimination. Today’s genetic human screening, constant big data information amalgamation as well as social credit scores pegged to access to healthcare also create the most pressing legal and ethical challenges of our time.Julia M. PuaschunderThe most recent decade featured a data revolution in the healthcare sector in screening, monitoring and coordination of aid. Big data analytics have revolutionarized the medical profession. The health sector relys on Artificial Intelligence (AI) and robotics as never before. The opportunities of unprecedented access to healthcare, rational precision and human resemblance but also targeted aid in decentralized aid grids are obvious innovations that will lead to most sophisticated neutral healthcare in the future. Yet big data driven medical care also bears risks of privacy infringements and ethical concerns of social stratification and discrimination. Today’s genetic human screening, constant big data information amalgamation as well as social credit scores pegged to access to healthcare also create the most pressing legal and ethical challenges of our time.

The call for developing a legal, policy and ethical framework for using AI, big data, robotics and algorithms in healthcare has therefore reached unprecedented momentum. Problematic appear compatibility glitches in the AI-human interaction as well as a natural AI preponderance outperforming humans. Only if the benefits of AI are reaped in a master-slave-like legal frame, the risks associated with these novel superior technologies can be curbed. Liability control but also big data privacy protection appear important to secure the rights of vulnerable patient populations. Big data mapping and social credit scoring must be met with clear anti-discrimination and anti-social stratification ethics. Lastly, the value of genuine human care must be stressed and precious humanness in the artifical age conserved alongside coupling the benefits of AI, robotics and big data with global common goals of sustainability and inclusive growth.

The report aims at helping a broad spectrum of stakeholders understand the impact of AI, big data, algorithms and health data based on information about key opportunities and risks but also future market challenges and policy developments for orchestrating the concerted pursuit of improving healthcare excellence. Stateshuman and diplomates are invited to consider three trends in the wake of the AI (r)evolution:

Artificial Intelligence recently gained citizenship in robots becoming citizens: With attributing quasi-human rights to AI, ethical questions arise of a stratified citizenship. Robots and algorithms may only be citizens for their protection and upholding social norms towards human-like creatures that should be considered slave-like for economic and liability purposes without gaining civil privileges such as voting, property rights and holding public offices.

Big data and computational power imply unprecedented opportunities for: crowd understanding, trends prediction and healthcare control. Risks include data breaches, privacy infringements, stigmatization and discrimination. Big data protection should be enacted through technological advancement, self-determined privacy attention fostered by e-education as well as discrimination alleviation by only releasing targeted information and regulated individual data mining capacities.

The European Union should consider establishing a fifth trade freedom of data by law and economic incentives: in order to bundle AI and big data gains large scale. Europe holds the unique potential of offering data supremacy in state-controlled universal healthcare big data wealth that is less fractionate than the US health landscape and more Western-focused than Asian healthcare. Europe could therefore lead the world on big data derived healthcare insights but should also step up to imbuing humane societal imperatives on these most cutting-edge innovations of our time….(More)”.

Algorithmic futures: The life and death of Google Flu Trends

Curated on Posted on by Stefaan Verhulst

Vincent Duclos in Medicine Anthropology Theory: “In the last few years, tracking systems that harvest web data to identify trends, calculate predictions, and warn about potential epidemic outbreaks have proliferated. These systems integrate crowdsourced data and digital traces, collecting information from a variety of online sources, and they promise to change the way governments, institutions, and individuals understand and respond to health concerns. This article examines some of the conceptual and practical challenges raised by the online algorithmic tracking of disease by focusing on the case of Google Flu Trends (GFT). Launched in 2008, GFT was Google’s flagship syndromic surveillance system, specializing in ‘real-time’ tracking of outbreaks of influenza. GFT mined massive amounts of data about online search behavior to extract patterns and anticipate the future of viral activity. But it did a poor job, and Google shut the system down in 2015. This paper focuses on GFT’s shortcomings, which were particularly severe during flu epidemics, when GFT struggled to make sense of the unexpected surges in the number of search queries. I suggest two reasons for GFT’s difficulties. First, it failed to keep track of the dynamics of contagion, at once biological and digital, as it affected what I call here the ‘googling crowds’. Search behavior during epidemics in part stems from a sort of viral anxiety not easily amenable to algorithmic anticipation, to the extent that the algorithm’s predictive capacity remains dependent on past data and patterns. Second, I suggest that GFT’s troubles were the result of how it collected data and performed what I call ‘epidemic reality’. GFT’s data became severed from the processes Google aimed to track, and the data took on a life of their own: a trackable life, in which there was little flu left. The story of GFT, I suggest, offers insight into contemporary tensions between the indomitable intensity of collective life and stubborn attempts at its algorithmic formalization.Vincent DuclosIn the last few years, tracking systems that harvest web data to identify trends, calculate predictions, and warn about potential epidemic outbreaks have proliferated. These systems integrate crowdsourced data and digital traces, collecting information from a variety of online sources, and they promise to change the way governments, institutions, and individuals understand and respond to health concerns. This article examines some of the conceptual and practical challenges raised by the online algorithmic tracking of disease by focusing on the case of Google Flu Trends (GFT). Launched in 2008, GFT was Google’s flagship syndromic surveillance system, specializing in ‘real-time’ tracking of outbreaks of influenza. GFT mined massive amounts of data about online search behavior to extract patterns and anticipate the future of viral activity. But it did a poor job, and Google shut the system down in 2015. This paper focuses on GFT’s shortcomings, which were particularly severe during flu epidemics, when GFT struggled to make sense of the unexpected surges in the number of search queries. I suggest two reasons for GFT’s difficulties. First, it failed to keep track of the dynamics of contagion, at once biological and digital, as it affected what I call here the ‘googling crowds’. Search behavior during epidemics in part stems from a sort of viral anxiety not easily amenable to algorithmic anticipation, to the extent that the algorithm’s predictive capacity remains dependent on past data and patterns. Second, I suggest that GFT’s troubles were the result of how it collected data and performed what I call ‘epidemic reality’. GFT’s data became severed from the processes Google aimed to track, and the data took on a life of their own: a trackable life, in which there was little flu left. The story of GFT, I suggest, offers insight into contemporary tensions between the indomitable intensity of collective life and stubborn attempts at its algorithmic formalization….(More)”.

Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

Curated on Posted on by Stefaan Verhulst

Paper by Michele Samorani et al: “Machine learning is often employed in appointment scheduling to identify the patients with the greatest no-show risk, so as to schedule them into overbooked slots, and thereby maximize the clinic performance, as measured by a weighted sum of all patients’ waiting time and the provider’s overtime and idle time. However, if the patients with the greatest no-show risk belong to the same demographic group, then that demographic group will be scheduled in overbooked slots disproportionately to the general population. This is problematic because patients scheduled in those slots tend to have a worse service experience than the other patients, as measured by the time they spend in the waiting room. Such negative experience may decrease patient’s engagement and, in turn, further increase no-shows. Motivated by the real-world case of a large specialty clinic whose black patients have a higher no-show probability than non-black patients, we demonstrate that combining machine learning with scheduling optimization causes racial disparity in terms of patient waiting time. Our solution to eliminate this disparity while maintaining the benefits derived from machine learning consists of explicitly including the objective of minimizing racial disparity. We validate our solution method both on simulated data and real-world data, and find that racial disparity can be completely eliminated with no significant increase in scheduling cost when compared to the traditional predictive overbooking framework….(More)”.

Benefits of Open Data in Public Health

Curated on Posted on by Stefaan Verhulst

Paper by P. Huston, VL. Edge and E. Bernier: “Open Data is part of a broad global movement that is not only advancing science and scientific communication but also transforming modern society and how decisions are made. What began with a call for Open Science and the rise of online journals has extended to Open Data, based on the premise that if reports on data are open, then the generated or supporting data should be open as well. There have been a number of advances in Open Data over the last decade, spearheaded largely by governments. A real benefit of Open Data is not simply that single databases can be used more widely; it is that these data can also be leveraged, shared and combined with other data. Open Data facilitates scientific collaboration, enriches research and advances analytical capacity to inform decisions. In the human and environmental health realms, for example, the ability to access and combine diverse data can advance early signal detection, improve analysis and evaluation, inform program and policy development, increase capacity for public participation, enable transparency and improve accountability. However, challenges remain. Enormous resources are needed to make the technological shift to open and interoperable databases accessible with common protocols and terminology. Amongst data generators and users, this shift also involves a cultural change: from regarding databases as restricted intellectual property, to considering data as a common good. There is a need to address legal and ethical considerations in making this shift. Finally, along with efforts to modify infrastructure and address the cultural, legal and ethical issues, it is important to share the information equitably and effectively. While there is great potential of the open, timely, equitable and straightforward sharing of data, fully realizing the myriad of benefits of Open Data will depend on how effectively these challenges are addressed….(More)”.

Dissecting racial bias in an algorithm used to manage the health of populations

Curated on Posted on by Stefaan Verhulst

Paper by Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan in Science: “Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts….(More)”.

The Next Step for Human-Centered Design in Global Public Health

Curated on Posted on by Stefaan Verhulst

Tracy Johnson, Jaspal S. Sandhu & Nikki Tyler at SSIR : “How do we select the right design partner?” “Where can I find evidence that design really works?” “Can design have any impact beyond products?” These are real questions that we’ve been asked by our public health colleagues who have been exposed to human-centered design. This deeper curiosity indicates a shift in the conversation around human-centered design, compared with common perceptions as recently as five years ago.

The past decade has seen a rapid increase in organizations that use human-centered design for innovation and improvement in health care. However, there have been challenges in determining how to best integrate design into current ways of working. Unfortunately, these challenges have been met with an all-or-nothing response.

In reality, anyone thinking of applying design concepts must first decide how deeply they want design to be integrated into a project. The DesignforHealth community—launched by the Bill & Melinda Gates Foundation and Center for Innovation and Impact at USAID—defines three types of design integration: spark, ingredient, or end-to-end.

As a spark, design can be the catalyst for teams to work creatively and unlock innovation.

Design can be an ingredient that helps improve an existing product. Using design end-to-end in the development process can address a complex concept such as social vulnerability.

As the field of design in health matures, the next phase will require support for “design consumers.” These are non-designers who take part in a design approach, whether as an inspiring spark, a key ingredient in an established process, or an end-to-end approach.

Here are three important considerations that will help design consumers make the critical decisions that are needed before embarking on their next design journey….(More)”.

Big Data Analytics in Healthcare

Curated on Posted on by Stefaan Verhulst

Book edited by Anand J. Kulkarni, Patrick Siarry, Pramod Kumar Singh, Ajith Abraham, Mengjie Zhang, Albert Zomaya and Fazle Baki: “This book includes state-of-the-art discussions on various issues and aspects of the implementation, testing, validation, and application of big data in the context of healthcare. The concept of big data is revolutionary, both from a technological and societal well-being standpoint. This book provides a comprehensive reference guide for engineers, scientists, and students studying/involved in the development of big data tools in the areas of healthcare and medicine. It also features a multifaceted and state-of-the-art literature review on healthcare data, its modalities, complexities, and methodologies, along with mathematical formulations.

The book is divided into two main sections, the first of which discusses the challenges and opportunities associated with the implementation of big data in the healthcare sector. In turn, the second addresses the mathematical modeling of healthcare problems, as well as current and potential future big data applications and platforms…(More)”.

Citizens’ voices for better health and social policies

Curated on Posted on by Stefaan Verhulst

Olivia Biermann et al at PLOS Blog Speaking of Medicine: “Citizen engagement is important to make health and social policies more inclusive and equitable, and to contribute to learning and responsive health and social systems. It is also valuable in understanding the complexities of the social structure and how to adequately respond to them with policies. By engaging citizens, we ensure that their tacit knowledge feeds into the policy-making process. What citizens know can be valuable in identifying feasible policy options, understanding contextual factors, and putting policies into practice. In addition, the benefit of citizen engagement extends much beyond improving health policy-making processes by making them more participatory and inclusive; being engaged in policy-making processes can build patients’ capacity and empower them to speak up for their own and their families’ health and social needs, and to hold policy-makers accountable. Moreover, apart from being involved in their own care, citizen-patients can contribute to quality improvement, research and education.

Most studies on citizen engagement to date originate from high-income countries. The engagement methods used are not necessarily applicable in low- and middle-income countries, and even the political support, the culture of engagement and established citizen engagement processes might be different. Still, published processes of engaging citizens can be helpful in identifying key components across different settings, e.g. in terms of levels of engagement, communication channels and methods of recruitment. Contextualizing the modes of engagement between and within countries is a must.

Examples of citizen engagement

There are many examples of ad hoc citizen engagement initiatives at local, national and international levels. Participedia, a repository of public participation initiatives around the globe, showcases that the field of citizen engagement is extremely vibrant.  In the United Kingdom, the Citizens’ Council of the National Institute for Health and Clinical Excellence (NICE) provides NICE with a public perspective on overarching moral and ethical issues that NICE has to take into account when producing guidance. In the United States of America, the National Issues Forum supports the implementation of deliberative forums on pressing national policy issues. Yet, there are few examples that have long-standing programs of engagement and that engage citizens in evidence-informed policymaking.

A pioneer in engaging citizens in health policy-making processes is the McMaster Health Forum in Hamilton, Canada. The citizens who are invited to engage in a “citizen panel” first receive a pre-circulated, plain-language briefing document to spark deliberation about a pressing health and social-system issue. During the panels, citizens then discuss the problem and its causes, options to address it and implementation considerations. The values that they believe should underpin action to address the issue are captured in a panel summary which is used to inform a policy dialogue on the same topic, also organized by the McMaster Health Forum….(More)”.

The Promise of Data-Driven Drug Development

Curated on Posted on by Stefaan Verhulst

Report by the Center for Data Innovation: “From screening chemical compounds to optimizing clinical trials to improving post-market surveillance of drugs, the increased use of data and better analytical tools such as artificial intelligence (AI) hold the potential to transform drug development, leading to new treatments, improved patient outcomes, and lower costs. However, achieving the full promise of data-driven drug development will require the U.S. federal government to address a number of obstacles. This should be a priority for policymakers for two main reasons. First, enabling data-driven drug development will accelerate access to more effective and affordable treatments. Second, the competitiveness of the U.S. biopharmaceutical industry is at risk so long as these obstacles exist. As other nations, particularly China, pursue data-driven innovation, especially greater use of AI, foreign life sciences firms could become more competitive at drug development….(More)”.

A fairer way forward for AI in health care

Curated on Posted on by Stefaan Verhulst

Linda Nordling at Nature: “When data scientists in Chicago, Illinois, set out to test whether a machine-learning algorithm could predict how long people would stay in hospital, they thought that they were doing everyone a favour. Keeping people in hospital is expensive, and if managers knew which patients were most likely to be eligible for discharge, they could move them to the top of doctors’ priority lists to avoid unnecessary delays. It would be a win–win situation: the hospital would save money and people could leave as soon as possible.

Starting their work at the end of 2017, the scientists trained their algorithm on patient data from the University of Chicago academic hospital system. Taking data from the previous three years, they crunched the numbers to see what combination of factors best predicted length of stay. At first they only looked at clinical data. But when they expanded their analysis to other patient information, they discovered that one of the best predictors for length of stay was the person’s postal code. This was puzzling. What did the duration of a person’s stay in hospital have to do with where they lived?

As the researchers dug deeper, they became increasingly concerned. The postal codes that correlated to longer hospital stays were in poor and predominantly African American neighbourhoods. People from these areas stayed in hospitals longer than did those from more affluent, predominantly white areas. The reason for this disparity evaded the team. Perhaps people from the poorer areas were admitted with more severe conditions. Or perhaps they were less likely to be prescribed the drugs they needed.

The finding threw up an ethical conundrum. If optimizing hospital resources was the sole aim of their programme, people’s postal codes would clearly be a powerful predictor for length of hospital stay. But using them would, in practice, divert hospital resources away from poor, black people towards wealthy white people, exacerbating existing biases in the system.

“The initial goal was efficiency, which in isolation is a worthy goal,” says Marshall Chin, who studies health-care ethics at University of Chicago Medicine and was one of the scientists who worked on the project. But fairness is also important, he says, and this was not explicitly considered in the algorithm’s design….(More)”.