Big Data Analytics in Healthcare


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


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


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


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)”.

Examining Civic Engagement Links to Health


Findings from the Literature and Implications for a Culture of Health by the Rand Corporation: “The Robert Wood Johnson Foundation (RWJF) is leading a pioneering effort to advance a culture of health that “enables all in our diverse society to lead healthier lives, now and for generations to come.” The RWJF Culture of Health Action Framework is divided into four Action Areas, and civic engagement (which RWJF defines broadly as participating in activities that advance the public good) is identified as one of the three drivers for the Action Area, Making Health a Shared Value, along with mindset and expectations, and sense of community. Civic engagement can serve as a mechanism for translating changes in a health-related mindset and sense of community into tangible actions that could lead to new health-promoting partnerships, improvements in community health conditions, and the degree of integration among health services and systems for better health outcomes.

The authors of this report seek a closer focus on the causal relationship between civic engagement and health and well-being — that is, whether better health and well-being might promote more civic engagement, whether civic engagement might promote health or well-being, or perhaps both.

In this report, authors conduct a structured review to understand what the scientific literature presents about the empirical relationship between health and civic engagement. The authors specifically examine whether health is a cause of civic engagement, a consequence of it, or both; what causal mechanisms underlie this link; and where there are gaps in knowledge for the field….(More)”

Real-time flu tracking. By monitoring social media, scientists can monitor outbreaks as they happen.


Charles Schmidt at Nature: “Conventional influenza surveillance describes outbreaks of flu that have already happened. It is based on reports from doctors, and produces data that take weeks to process — often leaving the health authorities to chase the virus around, rather than get on top of it.

But every day, thousands of unwell people pour details of their symptoms and, perhaps unknowingly, locations into search engines and social media, creating a trove of real-time flu data. If such data could be used to monitor flu outbreaks as they happen and to make accurate predictions about its spread, that could transform public-health surveillance.

Powerful computational tools such as machine learning and a growing diversity of data streams — not just search queries and social media, but also cloud-based electronic health records and human mobility patterns inferred from census information — are making it increasingly possible to monitor the spread of flu through the population by following its digital signal. Now, models that track flu in real time and forecast flu trends are making inroads into public-health practice.

“We’re becoming much more comfortable with how these models perform,” says Matthew Biggerstaff, an epidemiologist who works on flu preparedness at the US Centers for Disease Control and Prevention (CDC) in Atlanta, Georgia.

In 2013–14, the CDC launched the FluSight Network, a website informed by digital modelling that predicts the timing, peak and short-term intensity of the flu season in ten regions of the United States and across the whole country. According to Biggerstaff, flu forecasting helps responders to plan ahead, so they can be ready with vaccinations and communication strategies to limit the effects of the virus. Encouraged by progress in the field, the CDC announced in January 2019 that it will spend US$17.5 million to create a network of influenza-forecasting centres of excellence, each tasked with improving the accuracy and communication of real-time forecasts.

The CDC is leading the way on digital flu surveillance, but health agencies elsewhere are following suit. “We’ve been working to develop and apply these models with collaborators using a range of data sources,” says Richard Pebody, a consultant epidemiologist at Public Health England in London. The capacity to predict flu trajectories two to three weeks in advance, Pebody says, “will be very valuable for health-service planning.”…(More)”.

The business case for integrating claims and clinical data


Claudia Williams at MedCityNews: “The path to value-based care is arduous. For health plans, their ability to manage care, assess quality, lower costs, and streamline reporting is directly impacted by access to clinical data. For providers, the same can be said due to their lack of access to claims data. 

Providers and health plans are increasingly demanding integrated claims and clinical data to drive and support value-based care programs. These organizations know that clinical and claims information from more than a single organization is the only way to get a true picture of patient care. From avoiding medication errors to enabling an evidence-based approach to treatment or identifying at-risk patients, the value of integrated claims and clinical data is immense — and will have far-reaching influence on both health outcomes and costs of care over time.

On July 30, Medicare announced the Data at the Point of Care pilot to share valuable claims data with Medicare providers in order to “fill in information gaps for clinicians, giving them a more structured and complete patient history with information like previous diagnoses, past procedures, and medication lists.” But that’s not the only example. To transition from fee-for-service to value-based care, providers and health plans have begun to partner with health data networks to access integrated clinical and claims data: 

Health plan adoption of integrated data strategy

A California health plan is partnering with one of the largest nonprofit health data networks in California, to better integrate clinical and claims data. …

Providers leveraging claims data to understand patient medication patterns 

Doctors using advanced health data networks typically see a full list of patients’ medications, derived from claims, when they treat them. With this information available, doctors can avoid dangerous drug to-drug interactions when they prescribe new medications. After a visit, they can also follow up and see if a patient actually filled a prescription and is still taking it….(More)”.

How Should Scientists’ Access To Health Databanks Be Managed?


Richard Harris at NPR: “More than a million Americans have donated genetic information and medical data for research projects. But how that information gets used varies a lot, depending on the philosophy of the organizations that have gathered the data.

Some hold the data close, while others are working to make the data as widely available to as many researchers as possible — figuring science will progress faster that way. But scientific openness can be constrained b y both practical and commercial considerations.

Three major projects in the United States illustrate these differing philosophies.

VA scientists spearhead research on veterans database

The first project involves three-quarters of a million veterans, mostly men over age 60. Every day, 400 to 500 blood samples show up in a modern lab in the basement of the Veterans Affairs hospital in Boston. Luis Selva, the center’s associate director, explains that robots extract DNA from the samples and then the genetic material is sent out for analysis….

Intermountain Healthcare teams with deCODE genetics

Our second example involves what is largely an extended family: descendants of settlers in Utah, primarily from the Church of Jesus Christ of Latter-day Saints. This year, Intermountain Healthcare in Utah announced that it was going to sequence the complete DNA of half a million of its patients, resulting in what the health system says will be the world’s largest collection of complete genomes….

NIH’s All of Us aims to diversify and democratize research

Our third and final example is an effort by the National Institutes of Health to recruit a million Americans for a long-term study of health, behavior and genetics. Its philosophy sharply contrasts with that of Intermountain Health.

“We do have a very strong goal around diversity, in making sure that the participants in the All of Us research program reflect the vast diversity of the United States,” says Stephanie Devaney, the program’s deputy director….(More)”.

Raw data won’t solve our problems — asking the right questions will


Stefaan G. Verhulst in apolitical: “If I had only one hour to save the world, I would spend fifty-five minutes defining the questions, and only five minutes finding the answers,” is a famous aphorism attributed to Albert Einstein.

Behind this quote is an important insight about human nature: Too often, we leap to answers without first pausing to examine our questions. We tout solutions without considering whether we are addressing real or relevant challenges or priorities. We advocate fixes for problems, or for aspects of society, that may not be broken at all.

This misordering of priorities is especially acute — and represents a missed opportunity — in our era of big data. Today’s data has enormous potential to solve important public challenges.

However, policymakers often fail to invest in defining the questions that matter, focusing mainly on the supply side of the data equation (“What data do we have or must have access to?”) rather than the demand side (“What is the core question and what data do we really need to answer it?” or “What data can or should we actually use to solve those problems that matter?”).

As such, data initiatives often provide marginal insights while at the same time generating unnecessary privacy risks by accessing and exploring data that may not in fact be needed at all in order to address the root of our most important societal problems.

A new science of questions

So what are the truly vexing questions that deserve attention and investment today? Toward what end should we strategically seek to leverage data and AI?

The truth is that policymakers and other stakeholders currently don’t have a good way of defining questions or identifying priorities, nor a clear framework to help us leverage the potential of data and data science toward the public good.

This is a situation we seek to remedy at The GovLab, an action research center based at New York University.

Our most recent project, the 100 Questions Initiative, seeks to begin developing a new science and practice of questions — one that identifies the most urgent questions in a participatory manner. Launched last month, the goal of this project is to develop a process that takes advantage of distributed and diverse expertise on a range of given topics or domains so as to identify and prioritize those questions that are high impact, novel and feasible.

Because we live in an age of data and much of our work focuses on the promises and perils of data, we seek to identify the 100 most pressing problems confronting the world that could be addressed by greater use of existing, often inaccessible, datasets through data collaboratives – new forms of cross-disciplinary collaboration beyond public-private partnerships focused on leveraging data for good….(More)”.

How Tulsa is Preserving Privacy and Sharing Data for Social Good


Data across Sectors for Health: “Data sharing between organizations addressing social risk factors has the potential to amplify impact by increasing direct service capacity and efficiency. Unfortunately, the risks of and restrictions on sharing personal data often limit this potential, and adherence to regulations such as HIPAA and FERPA can make data sharing a significant challenge.

DASH CIC-START awardee Restore Hope Ministries worked with Asemio to utilize technology that allows for the analysis of personally identifiable information while preserving clients’ privacy. The collaboration shared their findings in a new white paper that describes the process of using multi-party computation technology to answer questions that can aid service providers in exploring the barriers that underserved populations may be facing. The first question they asked: what is the overlap of populations served by two distinct organizations? The results of the overlap analysis confirmed that a significant opportunity exists to increase access to services for a subset of individuals through better outreach…(More)”