The NIHR Innovation Observatory is a world leading health and care innovation scanning centre, providing data-driven insights to foster innovation and equitable access to high-quality care.
Like all successful organisations the Innovation Observatory is built on the collective effort of a multidisciplinary team. Each member of our internal team plays an important and necessary role in supporting the delivery of our shared vision.
We aim to transform health systems and improve population health by providing advanced data-driven insights that foster innovation and equitable access to high-quality care.
Our core values are the foundation of the work we do, guiding our research and how we work with our collaborators and stakeholders.
A world leading Horizon Scanning Facility
The NIHR Innovation Observatory is a world leading health and care innovation scanning centre, providing data-driven insights to foster innovation and equitable access to high-quality care.
When approaching the near horizon we see healthcare technologies that have already been launched or are undergoing regulatory and technology appraisal processes.
Engaging with a wide range of different audiences
We provide a gateway to collaboration, intelligence, and growth opportunities in healthcare and life sciences.
Industry engagement is a key strand of NIHR Innovation Observatory's work. Our research, publications, data and insights are crucial for both large corporations and small businesses.
Public involvement in research is a valuable way of making sure that the views of people from different backgrounds and with varied experiences help shape the work we do, even if you’ve had no interest in science research before.
We invest in people and support them in realising their full potential. Through nurturing and encouraging career development, we grow future national and international leaders across quantitative and qualitative methods.
Nurturing relationships within the Health & Life Sciences arena
We have a vast network across the sector both nationally and internationally and like to collaborate wherever possible, whether this is with new stakeholders, partner organisations or sharing our knowledge and expertise.
Ensuring that health care innovation of value can realise its full potential is not a task that we undertake alone. We work closely with a range of national stakeholders from the government, regulators, industry, patients, citizens, and the NHS.
The Innovation Observatory is the gateway to the NICE Technology Assessment (TA) Programme for industry, preventing delays in bringing new and repurposed medicines to the UK market through the early identification and tracking of new and repurposed medicines and health technologies.
We build national and international relationships with partners across the Health & Life Sciences Sector. We engage through speaking and attending high profile events and making valuable connections and introductions.
Data extraction methods for systematic review (semi)automation: Update of a living systematic review
This living systematic review examines published approaches for automatic data extraction from reports of clinical studies. To date it includes 76 papers that describe rule-based methods, machine-learning, and deep learning applied to abstracts and full text to automatically identify PICOs and study characteristics.
Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the dblp computer science bibliography. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This living review update includes publications up to December 2022 and OpenAlex content up to March 2023. Results: 76 publications are included in this review. Of these, 64 (84%) of the publications addressed extraction of data from abstracts, while 19 (25%) used full texts. A total of 71 (93%) publications developed classifiers for randomised controlled trials. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. Data are available from 25 (33%), and code from 30 (39%) publications. Six (8%) implemented publicly available tools Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of literature review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. Between review updates, trends for sharing data and code increased strongly: in the base-review, data and code were available for 13 and 19% respectively, these numbers increased to 78 and 87% within the 23 new publications. Compared with the base-review, we observed another research trend, away from straightforward data extraction and towards additionally extracting relations between entities or automatic text summarisation. With this living review we aim to review the literature continually.
