Community Research and Development Information Service - CORDIS

H2020

OPENCARE Report Summary

Project ID: 688670
Funded under: H2020-EU.2.1.1.

Periodic Reporting for period 1 - OPENCARE (Open Participatory Engagement in Collective Awareness for REdesign of Care Services)

Reporting period: 2016-01-01 to 2016-12-31

Summary of the context and overall objectives of the project

**An extended version of this summary is provided as an annex of Tech report B.**

OPENCARE started in early 2016 with three objectives in mind

First: explore the potential of communities to design and deliver care services. Citizen experts and patient innovators were coming up with exciting new products and services for care. What was going on?

Second: explore the implications of all this for policy. Care is high in the policy agenda. Health and social care services are expensive, and getting more so. What opportunities arise when informal networks of patient innovators step into the arena? What threats?

Third: generalize from the provision of care services to the provision of anything. Care is important. But the engine powering community provision of care is, at the end of the day, collaboration. And collaboration cuts across all domains. In the care domain, it is hard not to see collective intelligence at work. But what is that, exactly? How does it work? Can we detect it, measure it? How?

Here is what we learned so far.

1. Communities have great potential for providing care. We found initiatives achieving incredible results, taking place everywhere, most of them small, addressing the full spectrum of health and social issues.
2. Community provision of care services carries benefits for social welfare. Based on 19 case studies, we identified four such scenarios (see annex for details).
3. We learned four main things about how collective intelligence in action. Collective intelligence has structure, and a network science approach can detect it. There are two main kinds of interaction in opencare. Social interaction – who interacts with whom in the online conversation. Semantic interaction: which key concepts interact with which. From semantic networks we understand how participants see open care as a group

The interface between online and onsite collaboration environments is a single point of failure. We found it difficult to cross the online-onsite barrier. Learning to better connect online and onsite environments will result in stronger collective intelligence dynamics.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

This section lists goals (WSO -- WHAT WE SET OUT TO DO) and actions taken to achieve them (WWD -- WHAT WE DID). (Listing items in a two-column table)

WSO - Convening and curating an online conversation about care (hundreds of participants).
WWD - The conversation is alive and well. At the time of writing it has over 200 participants and 2,000 contributions.

WSO - Performing ethnographic coding
WWD - We have coded about 80% of the conversation, producing over 2,500 ethnographic annotations that use about 600 codes

WSO - Building the semantic social networks (SSN) based on these data
WWD - Processed data in batch mode (September 2016), later built a fully interactive prototype dashboard

WSO - Analysing the SSN and using the result of such analysis as intelligence to inform the action of the community management team
WWD - We ran an event with ethnographers and network scientists to propose appropriate network metrics to analyse ethnographic data

WSO - Iterating, checking at each step that the community management goals where SSN become a policy-relevant tool
WWD - Not done yet. We only completed a first iteration

WSO - Focus on execution and so provide a detailed case study of designing for collaboration
WWD - The large-scale collaboration process has indeed been instantiated and documented

WSO - Attempt to take exploitation out of the participatory design picture
WWD - The approach is to exploit alternative and less massive areas of participatory design, such as specialised communities or niche groups

WSO - Recon extant initiatives of community-driven, open care
WWD - The survey of existing collective intelligence projects shows that a multitude have been used in European healthcare, ranging from health clinics to IT-based systems to compare symptoms to community psychiatric care

WSO - Map finding onto policy recommendations
WWD - Not yet done. Planned by EHFF for Year 2

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

STATE-OF-THE-ART / PROJECTED IMPACTS

Given the short time elapsed since the start of the project, this section voluntarily focuses on our own contributions and projected impacts (as opposed to a review of the state-of-the-art).

This section lists impacts (NOI – NATURE OF IMPACT) and dimensions impacted (DI – DIMENSIONS IMPACTED).

NOI - Emergence of a scalable quali-quantitative research methodology from ethnography and network science
DI - In science: an extended reach of collective intelligence
In society: an increased traction of participatory processes when dealing with open-ended, poorly specified problems like climate change, mounting inequalities or rogue finance

NOI - A (more) reliable set of techniques for sustainable, fair engagement in participatory processes
DI - In society: ability to tap into citizen expertise without depleting social capital. Virtuous cycle, with fair social contracts in participation leading to more participation

NOI - Participant observation of peer-to-peer collaborations initiated as a consequence of involvement in OpenCare
DI - In science: high-quality ethnographic data on open collaboration
In society: potential for deploying policies that make successful peer-to-peer collaboration more likely

Our results on semantic social networks already exceed our expectations. Once coded by ethnographers and translated into network form, the OpenCare conversation displays remarkable convergence. Additionally, the clarity of the data in network form helps non-ethnographers understand the results.

This representation of the data is collective intelligence in a deep sense: it reflects the way the conversation as a whole, not its participants taken one by one. We did not expect this degree of convergence, which is normally not found in digital ethnography literature.

At this stage, we glimpse the possibility of forging such a methodology by reinventing ethnography as a data-driven, collaborative discipline.

The ingredients of our advances are:
• Emphasis on documentation as knowledge stewardship
• Tangible incentives for the best contributors
• Non-monetary rewards for the community as a whole
• Trusting community members as service providers

We are aware of five episodes of collaboration across different initiatives participating in the OpenCare community that appear to be a direct consequence of participation itself. These are:
1. OpenandChange collective funding application.
2. Belgian traumatologist goes on a tour in refugee camps in Greece to offer free treatment to anyone who needs them (with Syrian refugees in mind).
3. Community members at a biohacking lab in Ghent, Belgium connect with other biohackers in the USA and Australia to collaborate on developing an open source method for producing proinsulin.
4. British volunteer in refugee camps takes on artistic residency at an Armenian cultural center, as facilitated by another opencare member in Armenia.
5. Italian community member considers setting up an opencare spinoff.

We decided to follow from up close the development of these collaborations. They are a rare opportunity to document the pathways that lead from online debate (one form of collective intelligence) to collaborative action (another one). This has clear implications for policy makers who desire to encourage community-driven care activities. We have chosen a perspective based on two disciplines: design for social innovation and ethnography.

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