Sector: health

Why We Make Free, Public Information More Accessible

Curated on Posted on by Stefaan Verhulst

Gabi Fitz and Lisa Brooks in Philantopic: “One of the key roles the nonprofit sector plays in civil society is providing evidence about social problems and their solutions. Given recent changes to policies regarding the sharing of knowledge and evidence by federal agencies, that function is more critical than ever.

Nonprofits deliver more than direct services such as running food banks or providing shelter to people who are homeless. They also collect and share data, evidence, and lessons learned so as to help all of us understand complex and difficult problems.

Those efforts not only serve to illuminate and benchmark our most pressing social problems, they also inform the actions we take, whether at the individual, organizational, community, or policy level. Often, they provide the evidence in “evidence-based” decision making, not to mention the knowledge that social sector organizations and policy makers rely on when shaping their programs and services and individual citizens turn to inform their own engagement.

In January 2017, several U.S. government agencies, including the Environmental Protection Agency and the Departments of Health and Human Services and Agriculture, were ordered by officials of the incoming Trump administration not to share anything that could be construed as controversial through official communication channels such as websites and social media channels. (See “Federal Agencies Told to Halt External Communications.”) Against that backdrop, the nonprofit sector’s interest in generating and sharing evidence has become more urgent than ever…..

Providing access to evidence and lessons learned is always important, but in light of recent events, we believe it’s more necessary than ever. That’s why we are asking for your help in providing — and preserving — access to this critical knowledge base.

Over the next few months, we will be updating and maintaining special collections of non-academic research on the following topics and need lead curators with issue expertise to lend us a hand. IssueLab special collections are an effort to contextualize important segments of the growing evidence base we curate, and are one of the ways we  help visitors to the platform learn about nonprofit organizations and resources that may be useful to their work and knowledge-gathering efforts.

Possible special collection topics to be updated or curated:

→ Access to reproductive services (new)
→ Next steps for ACA
→ Race and policing
→ Immigrant detention and deportation
→ Climate change and extractive mining (new)
→ Veterans affairs
→ Gun violence

If you are a researcher, knowledge broker, or service provider in any of these fields of practice, please consider volunteering as a lead curator. …(More)”

Corporate Social Responsibility for a Data Age

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst in the Stanford Social Innovation Review: “Proprietary data can help improve and save lives, but fully harnessing its potential will require a cultural transformation in the way companies, governments, and other organizations treat and act on data….

We live, as it is now common to point out, in an era of big data. The proliferation of apps, social media, and e-commerce platforms, as well as sensor-rich consumer devices like mobile phones, wearable devices, commercial cameras, and even cars generate zettabytes of data about the environment and about us.

Yet much of the most valuable data resides with the private sector—for example, in the form of click histories, online purchases, sensor data, and call data records. This limits its potential to benefit the public and to turn data into a social asset. Consider how data held by business could help improve policy interventions (such as better urban planning) or resiliency at a time of climate change, or help design better public services to increase food security.

Data responsibility suggests steps that organizations can take to break down these private barriers and foster so-called data collaboratives, or ways to share their proprietary data for the public good. For the private sector, data responsibility represents a new type of corporate social responsibility for the 21st century.

While Nepal’s Ncell belongs to a relatively small group of corporations that have shared their data, there are a few encouraging signs that the practice is gaining momentum. In Jakarta, for example, Twitter exchanged some of its data with researchers who used it to gather and display real-time information about massive floods. The resulting website, PetaJakarta.org, enabled better flood assessment and management processes. And in Senegal, the Data for Development project has brought together leading cellular operators to share anonymous data to identify patterns that could help improve health, agriculture, urban planning, energy, and national statistics.

Examples like this suggest that proprietary data can help improve and save lives. But to fully harness the potential of data, data holders need to fulfill at least three conditions. I call these the “the three pillars of data responsibility.”…

The difficulty of translating insights into results points to some of the larger social, political, and institutional shifts required to achieve the vision of data responsibility in the 21st century. The move from data shielding to data sharing will require that we make a cultural transformation in the way companies, governments, and other organizations treat and act on data. We must incorporate new levels of pro-activeness, and make often-unfamiliar commitments to transparency and accountability.

By way of conclusion, here are four immediate steps—essential but not exhaustive—we can take to move forward:

  1. Data holders should issue a public commitment to data responsibility so that it becomes the default—an expected, standard behavior within organizations.
  2. Organizations should hire data stewards to determine what and when to share, and how to protect and act on data.
  3. We must develop a data responsibility decision tree to assess the value and risk of corporate data along the data lifecycle.
  4. Above all, we need a data responsibility movement; it is time to demand data responsibility to ensure data improves and safeguards people’s lives…(More)”

Beyond prediction: Using big data for policy problems

Curated on Posted on by Stefaan Verhulst

Susan Athey at Science: “Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making…(More)”

Making the Case for Open Contracting in Healthcare Procurement

Curated on Posted on by Stefaan Verhulst

Transparency International “…new report “Making the Case for Open Contracting in Healthcare Procurement”   examines the utility of open contracting in healthcare procurement. The process relies on governments to disclose procurement information to businesses and civil society improves stakeholders’ understanding of procurement processes increasing the integrity, fairness and efficiency of public contracting.

In several countries, including Honduras, Ukraine and Nigeria, corruption was significantly reduced throughout the healthcare procurement process following the implementation of open contracting, according to the report. Click here to download the report”

Crowdsourcing to Be the Future for Medical Research

Curated on Posted on by Stefaan Verhulst
PCORI: “Crowdsourcing isn’t just a quick way to get things done on the Internet. When used right, it can accelerate medical research and improve global cardiovascular health, according to a new best-practices “playbook” released by the American Heart Association (AHA) and the Patient-Centered Outcomes Research Institute (PCORI).

“The benefits of crowdsourcing are substantial,” said Rose Marie Robertson, MD, Chief Science Officer of the AHA, who took part in writing the guide. “You can get information from new perspectives and highly innovative ideas that might well not have occurred to you.”

Crowdsourcing Medical Research Priorities: A Guide for Funding Agencies is the work of Precision Medicine Advances using Nationally Crowdsourced Comparative Effectiveness Research (PRANCCER), a joint initiative launched in 2015 by the AHA and PCORI.

“Acknowledging the power of open, multidisciplinary research to drive medical progress, AHA and PCORI turned to the rapidly evolving methodology of crowdsourcing to find out what patients, clinicians, and researchers consider the most urgent priorities in cardiovascular medicine and to shape the direction and design of research targeting those priorities,” according to the guide.

“Engaging patients and other healthcare decision makers in identifying research needs and guiding studies is a hallmark of our patient-centered approach to research, and crowdsourcing offers great potential to catalyze such engagement,” said PCORI Executive Director Joe V. Selby, MD. “We hope the input we’ve received will help us develop new research funding opportunities that will lead to improved care for people with cardiovascular conditions.”

The playbook offers more than a dozen recommendations on the ins and outs of medical crowdsourcing. It stresses the need to have crystal clear objectives and questions, whether you’re dealing with patients, researchers, or clinicians. … (More)”

What Does Big Data Mean For Sustainability?

Curated on Posted on by Stefaan Verhulst

Saurabh Tyagi at Sustainable Brands: “Everything around us is impacted by big data today. The phenomenon took shape earlier in this decade and there are now a growing number of compelling ways in which big data analytics is being applied to solve real-world problems….Out of the many promises of big data, environment sustainability is one of the most important ones to implement and maintain. Why so?

Climate change has moved to the top of the list of global risks, affecting every country and disrupting economies. While a major part of this damage is irreversible, it is still possible with use of a wide range of technological measures to control the global increase in temperature. Big data can generate useful insights that can be as relevant towards fostering environment sustainability as they have been to other sectors such as healthcare.

Understanding operations

Big data’s usefulness is in its ability to help businesses understand and act on the environmental impacts of their operations. Some of these are within their boundaries while others are outside their direct control. Previously, this information was dispersed across different formats, locations and sites. However, now businesses are trying to make out the end-to-end impact of their operations throughout the value chain. This includes things that are outside of their direct control, including raw material sourcing, employee travels, product disposal, and the like.

Assessing environmental risks

Big data is also useful in assessing environmental risks. For example, Aqueduct is an interactive water-risk mapping tool from the World Resources Institute that monitors and calculates water risk anywhere in the world based on various parameters related to the water’s quantity, quality and other changing regulatory issue in that area. With this free online, users can choose the factors on which they want to focus and also zoom in at a particular location.

Big data is also enabling environmental sustainability by helping us to understand the demand for energy and food as the world population increases and climate change reduces these resources by every passing year.

Optimizing resource usage

Another big contribution of big data to the corporate world is its ability to help them optimize usage of resources. At the Initiative for Global Environment Leadership (IGEL) conference in 2014, David Parker, VP of Big Data for SAP, discussed how Italian tire company Pirelli uses SAP’s big data management system, HANA, to optimize its inventory. The company uses data generated by sensors in its tires globally to reduce waste, increase profits and reduce the number of defective tires going to landfills, thus doing its bit for environment. Similarly, Dutch energy company Alliander uses HANA to maintain the grid’s peak efficiency, which in turn increases profits and reduces environmental impact. While at one time it used to take 10 weeks for the company to optimize the grid, now it takes only three days to accomplish the same; a task which Alliander used to do once in a year now can be accomplished once every month….

Big data helps better regulation

Big data can also be integrated into government policies to ensure better environmental regulation. Governments can now implement the latest sensor technology and adopt real-time reporting of environmental quality data. This data can be used monitor the emissions of large utility facilities and if required put some regulatory framework in place to regularize the emissions. The firms are given complete freedom to experiment and chose the best possible mean of achieving the required result….(More)”

Using data and design to support people to stay in work

Curated on Posted on by Stefaan Verhulst

 at Civil Service Quarterly: “…Data and digital are fairly understandable concepts in policy-making. But design? Why is it one of the three Ds?

Policy Lab believes that design approaches are particularly suited to complex issues that have multiple causes and for which there is no one, simple answer. Design encourages people to think about the user’s needs (not just the organisation’s needs), brings in different perspectives to innovate new ideas, and then prototypes (mocks them up and tries them out) to iteratively improve ideas until they find one that can be scaled up.

Composite graph and segmentation analysis collection
Segmentation analysis of those who reported being on health-related benefits in the Understanding Society survey

Policy Lab also recognises that data alone cannot solve policy problems, and has been experimenting with how to combine numerical and more human practices. Data can explain what is happening, while design research methods – such as ethnography, observing people’s behaviours – can explain why things are happening. Data can be used to automate and tailor public services; while design means frontline delivery staff and citizens will actually know about and use them. Data-rich evidence is highly valued by policy-makers; and design can make it understandable and accessible to a wider group of people, opening up policy-making in the process.

The Lab is also experimenting with new data methods.

Data science can be used to look at complex, unstructured data (social media data, for example), in real time. Digital data, such as social media data or internet searches, can reveal how people behave (rather than how they say they behave). It can also look at huge amounts of data far quicker than humans, and find unexpected patterns hidden in the data. Powerful computers can identify trends from historical data and use these to predict what might happen in the future.

Supporting people in work project

The project took a DDD approach to generating insight and then creating ideas. The team (including the data science organisation Mastodon C and design agency Uscreates) used data science techniques together with ethnography to create a rich picture about what was happening. Then it used design methods to create ideas for digital services with the user in mind, and these were prototyped and tested with users.

The data science confirmed many of the known risk factors, but also revealed some new insights. It told us what was happening at scale, and the ethnography explained why.

  • The data science showed that people were more likely to go onto sickness benefits if they had been in the job a shorter time. The ethnography explained that the relationship with the line manager and a sense of loyalty were key factors in whether someone stayed in work or went onto benefits.
  • The data science showed that women with clinical depression were less likely to go onto sickness benefits than men with the same condition. The ethnography revealed how this played out in real life:
    • For example, Ella [not her real name], a teacher from London who had been battling with depression at work for a long time but felt unable to go to her boss about it. She said she was “relieved” when she got cancer, because she could talk to her boss about a physical condition and got time off to deal with both illnesses.
  • The data science also allowed the segmentation of groups of people who said they were on health-related benefits. Firstly, the clustering revealed that two groups had average health ratings, indicating that other non-health-related issues might be driving this. Secondly, it showed that these two groups were very different (one older group of men with previously high pay and working hours; the other of much younger men with previously low pay and working hours). The conclusion was that their motivations and needs to stay in work – and policy interventions – would be different.
  • The ethnography highlighted other issues that were not captured in the data but would be important in designing solutions, such as: a lack of shared information across the system; the need of the general practitioner (GP) to refer patients to other non-health services as well as providing a fit note; and the importance of coaching, confidence-building and planning….(More)”

GSK and MIT Flumoji app tracks influenza outbreaks with crowdsourcing

Curated on Posted on by Stefaan Verhulst

Beth Snyder Bulik at FiercePharma: “It’s like Waze for the flu. A new GlaxoSmithKline-sponsored app called Flumoji uses crowdsourced data to track influenza movement in real time.

Developed with MIT’s Connection Science, the Flumoji app gathers data passively and identifies fluctuations in users’ activity and social interactions to try to identify when a person gets the flu. The activity data is combined with traditional flu tracking data from the Centers for Disease Control to help determine outbreaks. The Flumoji study runs through April, when it will be taken down from the Android app store and no more data will be collected from users.

To make the app more engaging for users, Flumoji uses emojis to help users identify how they’re feeling. If it’s a flu day, symptom faces with thermometers, runny noses and coughs can be chosen, while on other days, users can show how they’re feeling with more traditional mood emojis.

The app has been installed on 500-1,000 Android phones, according to Google Play data.

“Mobile phones are a widely available and efficient way to monitor patient health. GSK has been using them in its studies to monitor activity and vital signs in study patients, and collect patient feedback to improve decision making in the development of new medicines. Tracking the flu is just the latest test of this technology,” Mary Anne Rhyne, a GSK director of external communications for R&D in the U.S., told FiercePharma in an email interview…(More)”

Billboard coughs when it detects cigarette smoke

Curated on Posted on by Stefaan Verhulst

Springwise: “The World Health Organization reports that tobacco use kills approximately six million people each year. And despite having one of the lowest smoking rates in Europe, Sweden’s Apotek Hjartat pharmacy is running a quit smoking campaign to help smokers make good on New Year resolutions. Located in Stockholm’s busy Odenplan square, the campaign billboard features a black and white image of a man.

When the integrated smoke detector identifies smoke, the man in the billboard image comes to life, emitting a sharp, hacking cough. So far, reactions from smokers have been mixed, with non-smokers and smokers alike appreciating the novelty and surprise of the billboard.

Apotek Hjartat is not new to Springwise, having been featured last year with its virtual reality pain relief app. Pharmacies appear to be taking their role of providing a positive public service seriously, with one in New York charging a man tax to highlight the persistent gender wage gap….(More)”

The science of society: From credible social science to better social policies

Curated on Posted on by Stefaan Verhulst

Nancy Cartwright and Julian Reiss at LSE Blog: “Society invests a great deal of money in social science research. Surely the expectation is that some of it will be useful not only for understanding ourselves and the societies we live in but also for changing them? This is certainly the hope of the very active evidence-based policy and practice movement, which is heavily endorsed in the UK both by the last Labour Government and by the current Coalition Government. But we still do not know how to use the results of social science in order to improve society. This has to change, and soon.

Last year the UK launched an extensive – and expensive – new What Works Network that, as the Government press release describes, consists of “two existing centres of excellence – the National Institute for Health and Clinical Excellence (NICE) and the Educational Endowment Foundation – plus four new independent institutions responsible for gathering, assessing and sharing the most robust evidence to inform policy and service delivery in tackling crime, promoting active and independent ageing, effective early intervention, and fostering local economic growth”.

This is an exciting and promising initiative. But it faces a series challenge: we remain unable to build real social policies based on the results of social science or to predict reliably what the outcomes of these policies will actually be. This contrasts with our understanding of how to establish the results in the first place.There we have a handle on the problem. We have a reasonable understanding of what kinds of methods are good for establishing what kinds of results and with what (at least rough) degrees of certainty.

There are methods – well thought through – that social scientists learn in the course of their training for constructing a questionnaire, running a randomised controlled trial, conducting an ethnographic study, looking for patterns in large data sets. There is nothing comparably explicit and well thought through about how to use social science knowledge to help predict what will happen when we implement a proposed policy in real, complex situations. Nor is there anything to help us estimate and balance the effectiveness, the evidence, the chances of success, the costs, the benefits, the winners and losers, and the social, moral, political and cultural acceptability of the policy.

To see why this is so difficult think of an analogy: not building social policies but building material technologies. We do not just read off instructions for building a laser – which may ultimately be used to operate on your eyes – from knowledge of basic science. Rather, we piece together a detailed model using heterogeneous knowledge from a mix of physics theories, from various branches of engineering, from experience of how specific materials behave, from the results of trial-and-error, etc. By analogy, building a successful social policy equally requires a mix of heterogeneous kinds of knowledge from radically different sources. Sometimes we are successful at doing this and some experts are very good at it in their own specific areas of expertise. But in both cases – both for material technology and for social technology – there is no well thought through, defensible guidance on how to do it: what are better and worse ways to proceed, what tools and information might be needed, and how to go about getting these. This is true whether we look for general advice that might be helpful across subject areas or advice geared to specific areas or specific kinds of problems. Though we indulge in social technology – indeed we can hardly avoid it – and are convinced that better social science will make for better policies, we do not know how to turn that conviction into a reality.

This presents a real challenge to the hopes for evidence-based policy….(More)”

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