Paper by Rebecca E. Stewart et al: “In healthcare settings, system and organization leaders often control the selection and design of implementation strategies even though frontline workers may have the most intimate understanding of the care delivery process, and factors that optimize and constrain evidence-based practice implementation within the local system. Innovation tournaments, a structured participatory design strategy to crowdsource ideas, are a promising approach to participatory design that may increase the effectiveness of implementation strategies by involving end users (i.e., clinicians). We utilized a system-wide innovation tournament to garner ideas from clinicians about how to enhance the use of evidence-based practices (EBPs) within a large public behavioral health system…(More)”
From Theory to Practice : Open Government Data, Accountability, and Service Delivery
Report by Michael Christopher Jelenic: “Open data and open government data have recently attracted much attention as a means to innovate, add value, and improve outcomes in a variety of sectors, public and private. Although some of the benefits of open data initiatives have been assessed in the past, particularly their economic and financial returns, it is often more difficult to evaluate their social and political impacts. In the public sector, a murky theory of change has emerged that links the use of open government data with greater government accountability as well as improved service delivery in key sectors, including health and education, among others. In the absence of cross-country empirical research on this topic, this paper asks the following: Based on the evidence available, to what extent and for what reasons is the use of open government data associated with higher levels of accountability and improved service delivery in developing countries?
To answer this question, the paper constructs a unique data set that operationalizes open government data, government accountability, service delivery, as well as other intervening and control variables. Relying on data from 25 countries in Sub-Saharan Africa, the paper finds a number of significant associations between open government data, accountability, and service delivery. However, the findings suggest differentiated effects of open government data across the health and education sectors, as well as with respect to service provision and service delivery outcomes. Although this early research has limitations and does not attempt to establish a purely causal relationship between the variables, it provides initial empirical support for claims about the efficacy of open government data for improving accountability and service delivery….(More)”
The European Lead Factory: Collective intelligence and cooperation to improve patients’ lives
Press Release: “While researchers from small and medium-sized companies and academic institutions often have enormous numbers of ideas, they don’t always have enough time or resources to develop them all. As a result, many ideas get left behind because companies and academics typically have to focus on narrow areas of research. This is known as the “Innovation Gap”. ESCulab (European Screening Centre: unique library for attractive biology) aims to turn this problem into an opportunity by creating a comprehensive library of high-quality compounds. This will serve as a basis for testing potential research targets against a wide variety of compounds.
Any researcher from a European academic institution or a small to medium-sized enterprise within the consortium can apply for a screening of their potential drug target. If a submitted target idea is positively assessed by a committee of experts it will be run through a screening process and the submitting party will receive a dossier of up to 50 potentially relevant substances that can serve as starting points for further drug discovery activities.
ESCulab will build Europe’s largest collaborative drug discovery platform and is equipped with a total budget of € 36.5 million: Half is provided by the European Union’s Innovative Medicines Initiative (IMI) and half comes from in-kind contributions from companies of the European Federation of Pharmaceutical Industries an Associations (EFPIA) and the Medicines for Malaria Venture. It builds on the existing library of the European Lead Factory , which consists of around 200,000 compounds, as well as around 350,000 compounds from EFPIA companies. The European Lead Factory aims to initiate 185 new drug discovery projects through the ESCulab project by screening drug targets against its library.
… The platform has already provided a major boost for drug discovery in Europe and is a strong example of how crowdsourcing, collective intelligence and the cooperation within the IMI framework can create real value for academia, industry, society and patients….(More)”
Opening Data for Global Health
Chapter by Matt Laessig, Bryon Jacob and Carla AbouZahr in The Palgrave Handbook of Global Health Data Methods for Policy and Practice: “…provide best practices for organizations to adopt to disseminate data openly for others to use. They describe development of the open data movement and its rapid adoption by governments, non-governmental organizations, and research groups. The authors provide examples from the health sector—an early adopter—but acknowledge concerns specific to health relating to informed consent, intellectual property, and ownership of personal data. Drawing on their considerable contributions to the open data movement, Laessig and Jacob share their Open Data Progression Model. They describe six stages to make data open: from data collection, documentation of the data, opening the data, engaging the community of users, making the data interoperable, to finally linking the data….(More)”
A Taxonomy of Definitions for the Health Data Ecosystem
Announcement: “Healthcare technologies are rapidly evolving, producing new data sources, data types, and data uses, which precipitate more rapid and complex data sharing. Novel technologies—such as artificial intelligence tools and new internet of things (IOT) devices and services—are providing benefits to patients, doctors, and researchers. Data-driven products and services are deepening patients’ and consumers’ engagement and helping to improve health outcomes. Understanding the evolving health data ecosystem presents new challenges for policymakers and industry. There is an increasing need to better understand and document the stakeholders, the emerging data types and their uses.
The Future of Privacy Forum (FPF) and the Information Accountability Foundation (IAF) partnered to form the FPF-IAF Joint Health Initiative in 2018. Today, the Initiative is releasing A Taxonomy of Definitions for the Health Data Ecosystem; the publication is intended to enable a more nuanced, accurate, and common understanding of the current state of the health data ecosystem. The Taxonomy outlines the established and emerging language of the health data ecosystem. The Taxonomy includes definitions of:
- The stakeholders currently involved in the health data ecosystem and examples of each;
- The common and emerging data types that are being collected, used, and shared across the health data ecosystem;
- The purposes for which data types are used in the health data ecosystem; and
- The types of actions that are now being performed and which we anticipate will be performed on datasets as the ecosystem evolves and expands.
This report is as an educational resource that will enable a deeper understanding of the current landscape of stakeholders and data types….(More)”.
Come to Finland if you want to glimpse the future of health data!
Jukka Vahti at Sitra: “The Finnish tradition of establishing, maintaining and developing data registers goes back to the 1600s, when parish records were first kept.
When this old custom is combined with the opportunities afforded by digitisation, the positive approach Finns have towards research and technology, and the recently updated legislation enabling the data economy, Finland and the Finnish people can lead the way as Europe gradually, or even suddenly, switches to a fair data economy.
The foundations for a fair data economy already exist
The fair data economy is a natural continuation of the former projects promoting e-services that were undertaken in Finland.
For example, the Data Exchange Layer is already speeding up the transfer of data from one system to another in Finland and in Estonia, the country where the system originated, and a system unique to just these two countries.
In May 2019 Finland also saw the entry into force of the Act on the Secondary Use of Health and Social Data, according to which the information on social welfare and healthcare held in registers may be used for purposes of statistics, research, education, knowledge management, control and supervision conducted by authorities, and development and innovation activity.
The new law will make the work of researchers and service developers more effective, as the business of acquiring a permit will take place through a one-stop-shop principle and it will be possible to use data from more than one source more readily than before….(More)”.
What can we learn from billions of food purchases derived from fidelity cards?
Daniele Quercia at Medium: “By combining 1.6B food item purchases with 1.1B medical prescriptions for the entire city of London for one year, we discovered that, to predict health outcomes, socio-economic conditions matter less than what previous research has shown: despite being of lower-income, certain areas are healthy, and that is because of what their residents eat!
This result comes from our latest project “Poor but Healthy”, which was published in the Springer European Physical Journal (EPJ) of Data Science this month, and comes with a @tobi_vierzwo’s stunningly beautiful map of London I invite all of you to explore.
Why are we interested in urban health? In our cities, food is cheap and exercise discretionary, and health takes its toll. Half of European citizens will be obese by 2050, and obesity and its diseases are likely to reach crisis proportions. In this project, we set out to show that fidelity cards of grocery stores represent a treasure trove of health data — they can be used not only to (e)mail discount coupons to customers but also to effectively track a neighbourhood’s health in real-time for an entire city or even an entire country.
In research circles, the impact of eating habits on people’s health has mostly been studied using dietary surveys, which are costly and of limited scale.
To complement these surveys, we have recently resorted to grocery fidelity cards. We analyzed the anonymized records of 1.6B grocery items purchased by 1.6M grocery store customers in London over one whole year, and combined them with 1.1B medical prescriptions.
In so doing, we found that, as one expects, the “trick” to not being associated with chronic diseases is eating less what we instinctively like (e.g., sugar, carbohydrates), balancing all the nutrients, and avoiding the (big) quantities that are readily available. These results come as no surprise yet speak to the validity of using fidelity cards to capture health outcomes…(More)”.
Facebook releases a trio of maps to aid with fighting disease outbreaks
Sarah Perez at Techcrunch: “Facebook… announced a new initiative focused on using its data and technologies to help nonprofit organizations and universities working in public health better map the spread of infectious diseases around the world. Specifically, the company is introducing three new maps: population density maps with demographic estimates, movement maps and network coverage maps. These, says Facebook, will help the health partners to understand where people live, how they’re moving and if they have connectivity — all factors that can aid in determining how to respond to outbreaks, and where supplies should be delivered.
As Facebook explained, health organizations rely on information like this when planning public health campaigns. But much of the information they rely on is outdated, like older census data. In addition, information from more remote communities can be scarce.
By combining the new maps with other public health data, Facebook believes organizations will be better equipped to address epidemics.
The new high-resolution population density maps will estimate the number of people living within 30-meter grid tiles, and provide insights on demographics, including the number of children under five, the number of women of reproductive age, as well as young and elderly populations. These maps aren’t built using Facebook data, but are instead built by using Facebook’s AI capabilities with satellite imagery and census information.
Movement maps, meanwhile, track aggregate data about Facebook users’ movements via their mobile phones (when location services are enabled). At scale, health partners can combine this with other data to predict where other outbreaks may occur next….(More)”.
Crowdsourcing Research Questions? Leveraging the Crowd’s Experiential Knowledge for Problem Finding
Paper by Tiare-Maria Brasseur, Susanne Beck, Henry Sauermann, Marion Poetz: “Recently, both researchers and policy makers have become increasingly interested in involving the general public (i.e., the crowd) in the discovery of new science-based knowledge. There has been a boom of citizen science/crowd science projects (e.g., Foldit or Galaxy Zoo) and global policy aspirations for greater public engagement in science (e.g., Horizon Europe). At the same time, however, there are also criticisms or doubts about this approach. Science is complex and laypeople often do not have the appropriate knowledge base for scientific judgments, so they rely on specialized experts (i.e., scientists) (Scharrer, Rupieper, Stadtler & Bromme, 2017). Given these two perspectives, there is no consensus on what the crowd can do and what only researchers should do in scientific processes yet (Franzoni & Sauermann, 2014). Previous research demonstrates that crowds can be efficiently and effectively used in late stages of the scientific research process (i.e., data collection and analysis). We are interested in finding out what crowds can actually contribute to research processes that goes beyond data collection and analysis. Specifically, this paper aims at providing first empirical insights on how to leverage not only the sheer number of crowd contributors, but also their diversity in experience for early phases of the research process (i.e., problem finding). In an online and field experiment, we develop and test suitable mechanisms for facilitating the transfer of the crowd’s experience into scientific research questions. In doing so, we address the following two research questions: 1. What factors influence crowd contributors’ ability to generate research questions? 2. How do research questions generated by crowd members differ from research questions generated by scientists in terms of quality? There are strong claims about the significant potential of people with experiential knowledge, i.e., sticky problem knowledge derived from one’s own practical experience and practices (Collins & Evans, 2002), to enhance the novelty and relevance of scientific research (e.g., Pols, 2014). Previous evidence that crowds with experiential knowledge (e.g., users in Poetz & Schreier, 2012) or ?outsiders?/nonobvious individuals (Jeppesen & Lakhani, 2010) can outperform experts under certain conditions by having novel perspectives, support the assumption that the participation of non-scientists (i.e., crowd members) in scientific problem-finding might complement scientists’ lack of experiential knowledge. Furthermore, by bringing in exactly these new perspectives, they might help overcome problems of fixation/inflexibility in cognitive-search processes among scientists (Acar & van den Ende, 2016). Thus, crowd members with (higher levels of) experiential knowledge are expected to be superior in identifying very novel and out-of-the-box research problems with high practical relevance, as compared to scientists. However, there are clear reasons to be skeptical: despite their advantage to possess important experiential knowledge, the crowd lacks the scientific knowledge we assume to be required to formulate meaningful research questions. To study exactly how the transfer of crowd members’ experiential knowledge into science can be facilitated, we conducted two experimental studies in context of traumatology (i.e., research on accidental injuries). First, we conducted a large-scale online experiment (N=704) in collaboration with an international crowdsourcing platform to test the effect of two facilitating treatments on crowd members’ ability to formulate real research questions (study 1). We used a 2 (structuring knowledge/no structuring knowledge) x 2 (science knowledge/no science knowledge) between-subject experimental design. Second, we tested the same treatments in the field (study 2), i.e., in a crowdsourcing project in collaboration with LBG Open Innovation in Science Center. We invited patients, care takers and medical professionals (e.g., surgeons, physical therapists or nurses) concerned with accidental injuries to submit research questions using a customized online platform (https://tell-us.online/) to investigate the causal relationship between our treatments and different types and levels of experiential knowledge (N=118). An international jury of experts (i.e., journal editors in the field of traumatology) then assesses the quality of submitted questions (from the online and field experiment) along several quality dimensions (i.e., clarity, novelty, scientific impact, practical impact, feasibility) in an online evaluation process. To assess the net effect of our treatments, we further include a random sample of research questions obtained from early-stage research papers (i.e., conference papers) into the expert evaluation (blind to the source) and compare them with the baseline groups of our experiments. We are currently finalizing the data collection…(More)”.
Humans and Big Data: New Hope? Harnessing the Power of Person-Centred Data Analytics
Paper by Carmel Martin, Keith Stockman and Joachim P. Sturmberg: “Big data provide the hope of major health innovation and improvement. However, there is a risk of precision medicine based on predictive biometrics and service metrics overwhelming anticipatory human centered sense-making, in the fuzzy emergence of personalized (big data) medicine. This is a pressing issue, given the paucity of individual sense-making data approaches. A human-centric model is described to address the gap in personal particulars and experiences in individual health journeys. The Patient Journey Record System (PaJR) was developed to improve human-centric healthcare by harnessing the power of person-centred data analytics using complexity theory, iterative health services and information systems applications over a 10 year period. PaJR is a web-based service supporting usually bi-weekly telephone calls by care guides to individuals at risk of readmissions.
This chapter describes a case study of the timing and context of readmissions using human (biopsychosocial) particular data which is based on individual experiences and perceptions with differing patterns of instability. This Australian study, called MonashWatch, is a service pilot using the PaJR system in the Dandenong Hospital urban catchment area of the Monash Health network. State public hospital big data – the Victorian HealthLinks Chronic Care algorithm provides case finding for high risk of readmission based on disease and service metrics. Monash Watch was actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing) at the time of the study.
Three randomly selected intervention cases describe a dynamic interplay of self-reported change in health and health care, medication, drug and alcohol use, social support structure. While the three cases were at similar predicted risk initially, their cases represented different statistically different time series configurations and admission patterns. Fluctuations in admission were associated with (mal)alignment of bodily health with psychosocial and environmental influences. However human interpretation was required to make sense of the patterns as presented by the multiple levels of data.
A human-centric model and framework for health journey monitoring illustrates the potential for ‘small’ personal experience data to inform clinical care in the era of big data predominantly based on biometrics and medical industrial process. ….(More)”.