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Merino-Barbancho B, Abril Jiménez P, Mallo I, Lombroni I, Cea G, López Nebreda C, Cabrera MF, Fico G, Arredondo MT. Innovation through the Quintuple Helix in living labs: lessons learned for a transformation from lab to ecosystem. Front Public Health 2023; 11:1176598. [PMID: 37601223 PMCID: PMC10436200 DOI: 10.3389/fpubh.2023.1176598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/11/2023] [Indexed: 08/22/2023] Open
Abstract
Introduction In the process of growing societies, and especially in the digital era we live in, there is a need for a strong push for innovation that puts citizens at the center of the process from the beginning to build more resilient, cooperative and flexible communities. Different collaborative design approaches have emerged in recent decades, one of the most interesting being Living Labs, which involves user-centered design and co-creative innovation that bring together different actors and roles. However, although these new methodologies are harnessing creativity, some aspects of this new, more ecosystemic and complex vision are not clearly understood: possible barriers, how to facilitate local and operational solutions, overcoming institutional blockage, integrating new roles, etc. Methods The incorporation of the Quintuple Helix as a driver to ensure greater coordinated participation of local actors has proven its usefulness and impact during the re-adaptation of LifeSpace (previously named Smart House Living Lab), managed by the Polytechnic University of Madrid (Spain), a transformation based on the experiences and lessons learned during the large-scale ACTIVAGE pilot funded by the European Commission, more specifically at the Madrid Deployment Site. It involved more than 350 older adult people and other stakeholders from different areas, including family members, formal and informal caregivers, hospital service managers, third-age associations, and public service providers, forming a sense of community, which was called MAHA. Results The living lab infrastructure evolved from a single multi-purpose environment to incorporate three harmoniously competing environments: (1) THE LAB: Headquarters for planning, demonstration, initial design phases and entry point for newcomers to the process, (2) THE CLUB: Controlled interaction environment where returning users validate solutions, focusing mainly on AHA services (MAHA CLUB), such as exergames, social interaction applications, brain training activities, etc. (3) THE NEIGHBOURHOOD: Real-life environments for free and open interaction between actors and implementation of previously validated and tested solutions. Conclusion The Quintuple Helix model applied in LifeSpace's new vision allows a coordinated involvement of a more diverse set of actors, beyond the end-users and especially those who are not traditionally part of research and innovation processes.
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Affiliation(s)
- Beatriz Merino-Barbancho
- Life Supporting Technologies, Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
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Núñez Reiz A, Martínez Sagasti F, Álvarez González M, Blesa Malpica A, Martín Benítez JC, Nieto Cabrera M, Del Pino Ramírez Á, Gil Perdomo JM, Prada Alonso J, Celi LA, Armengol de la Hoz MÁ, Deliberato R, Paik K, Pollard T, Raffa J, Torres F, Mayol J, Chafer J, González Ferrer A, Rey Á, González Luengo H, Fico G, Lombroni I, Hernandez L, López L, Merino B, Cabrera MF, Arredondo MT, Bodí M, Gómez J, Rodríguez A, Sánchez García M. Big data and machine learning in critical care: Opportunities for collaborative research. Med Intensiva 2018; 43:52-57. [PMID: 30077427 DOI: 10.1016/j.medin.2018.06.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 05/23/2018] [Accepted: 06/09/2018] [Indexed: 01/25/2023]
Abstract
The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems. A multidisciplinary event (Critical Care Datathon) was held in Madrid (Spain) from 1 to 3 December 2017. Under the auspices of the Spanish Critical Care Society (SEMICYUC), the event was organized by the Massachusetts Institute of Technology (MIT) Critical Data Group (Cambridge, MA, USA), the Innovation Unit and Critical Care Department of San Carlos Clinic Hospital, and the Life Supporting Technologies group of Madrid Polytechnic University. After presentations referred to big data in the critical care environment, clinicians, data scientists and other health data science enthusiasts and lawyers worked in collaboration using an anonymized database (MIMIC III). Eight groups were formed to answer different clinical research questions elaborated prior to the meeting. The event produced analyses for the questions posed and outlined several future clinical research opportunities. Foundations were laid to enable future use of ICU databases in Spain, and a timeline was established for future meetings, as an example of how big data analysis tools have tremendous potential in our field.
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Affiliation(s)
- Antonio Núñez Reiz
- Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España.
| | | | | | - Antonio Blesa Malpica
- Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España
| | | | - Mercedes Nieto Cabrera
- Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España
| | | | | | - Jesús Prada Alonso
- Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España
| | - Leo Anthony Celi
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States
| | - Miguel Ángel Armengol de la Hoz
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States; Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Biomedical Engineering and Telemedicine Group, Biomedical Technology Centre CTB, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - Rodrigo Deliberato
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States
| | - Kenneth Paik
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States
| | - Tom Pollard
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States
| | - Jesse Raffa
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States
| | - Felipe Torres
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States
| | - Julio Mayol
- Department of Surgery, Hospital Clinico San Carlos de Madrid, Instituto de Investigación Sanitaria San Carlos, Universidad Complutense de Madrid, Madrid, Spain
| | - Joan Chafer
- Unidad de Innovación, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Arturo González Ferrer
- Unidad de Innovación, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Ángel Rey
- Unidad de Innovación, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Henar González Luengo
- Unidad de Innovación, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
| | - Giuseppe Fico
- Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain
| | - Ivana Lombroni
- Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain
| | - Liss Hernandez
- Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain
| | - Laura López
- Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain
| | - Beatriz Merino
- Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain
| | - María Fernanda Cabrera
- Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain
| | - María Teresa Arredondo
- Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain
| | - María Bodí
- Service of Intensive Care Medicine, Hospital Universitari Joan XXIII, IISPV-URV, Tarragona, Spain
| | - Josep Gómez
- Service of Intensive Care Medicine, Hospital Universitari Joan XXIII, IISPV-URV, Tarragona, Spain; Department of Electronic Engineering, Metabolomics Platform, Rovira i Virgili University, IISPV, Tarragona
| | - Alejandro Rodríguez
- Service of Intensive Care Medicine, Hospital Universitari Joan XXIII, IISPV-URV, Tarragona, Spain
| | - Miguel Sánchez García
- Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España.
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