The MedMon project draws on social media and web sources to harness patient insights for the novel and transformative concept of patient-centred drug development. MedMon will use these resources to improve the efficiency and economic impact of pharmaceutical research and development in the company. The project will establish a novel path for producing new medicines for the therapeutic areas by bringing stakeholder voices, and especially the patient’s voice, to the drug development teams. Advanced Information Extraction components will provide efficient and effective means for unfolding this potential.
ProjectMedMon - Monitoring of internet resources for pharmaceutical research and development
LeadSwiss Institute for Information Research (SII) More about the Institute/Centre
Project LeaderWeichselbraun Albert More about Weichselbraun Albert
Involved partiesUniversity of Zurich (Institute for Computer Linguistics)
F. Hoffmann-La Roche Ltd. More about the involved
TeamHörler Sandro More about Hörler Sandro Kuntschik Philipp More about Kuntschik Philipp Süsstrunk Norman More about Süsstrunk Norman
Research fieldsData Analytics More about the research fields
FundingInnosuisse (Commission for Technology and Innovation CTI)
DurationFebruary 2018 – January 2020
Health authorities are increasingly recognising the importance of patient-centred drug research and development as a significant driver of innovation. The Patient Focused Drug Development (PFDD) meetings are an FDA (US Food and Drug Administration) commitment to systematically obtain the patient’s perspective in drug development. The FDA is planning to hold 25 PFDD meetings concerning different conditions until the end of 2017 (FDA Draft PDUFA V Implementation Plan). Pharmaceutical companies that will adapt to this novel path will be best positioned to maintain their competitive edge. Thus, patient centricity has the potential to transform traditional pharmaceutical drug research and development.
Patient organisations can be valuable partners for gathering patients’ insights. A broader and more direct access to the patient’s voice can be obtained through social media content. Patients today report their experiences with their disease and medications on social media. However, analyses have shown that only a minority of companies is using social and digital media for patient engagement, recruitment and retention. Drug sponsors are primarily using social media for commercial purposes and to listen to patient and professional conversations about marketed products, and not to support clinical research. Thus, the pharmaceutical industry is not yet leveraging the full potential of social media.
The MedMon project will develop web intelligence systems for monitoring, integrating and analysing social and news media sources relevant to the domains of healthcare, pharmaceutical and the bioscience domain.
These systems will then be used to guide and support domain experts in decision-making processes relevant to the area of Patient Focused Drug Development (PFDD).
- 7 March 2018 – MedMon opening meeting in Basel
- 15 February 2018 – a first prototype of the MedMon data acquisition pipeline with support for web and social media pages has been activated. Components for the parsing of threaded dialogues within web forums will be developed within the next project phase.
- 25 January 2018 – MedMon teleconference and definition of data sources.
MedMon will develop novel information extraction techniques for factual knowledge extraction, the identification and classification of affective knowledge and for mining relevant facts from social media and the web.
Visual dashboards and analytics will support domain experts in analysing the extracted information, computing aggregated metrics and performance measures to anticipate trends and performing drill-down analyses.
The MedMon project is funded by the Swiss Federal Department of Economic Affairs (FDEA) Commission for Technology and Innovation (CTI). The project partners include the University of Applied Sciences HTW Chur (Institute for Information Research), the University of Zurich (Institute for Computer Linguistics) and F. Hoffmann-La Roche Ltd.