The majority of concerted efforts in healthcare data science focuses on processing and integration of structured data streams coming from clinical coding, diagnostic tests, sensor measurements, questionnaires, etc. to support timely clinical interventions and facilitate patients' self-management.
Nonetheless, natural language remains the main means of communication within health and social care, with its written accounts in the form of free text becoming increasingly available in an electronic form. Prominent examples include text data embedded within electronic health records (e.g.
referral letters, case notes, pathology reports, hospital discharge summaries, etc.), patient-reported outcome measures (e.g. questionnaires, diaries, etc.) or unsolicited informal feedback shared openly on the Web 2.0 (e.g. social media, Twitter, etc.). The fact that the majority of actionable
information in healthcare is contained within free-text data (some estimates shows as much as 85%) clearly indicates a potential to dramatically transform community health and care by the ability to process and integrate such information with the rest of healthcare data. However, automated
and large-scale "understanding" of diverse healthcare sublanguages is still a largely unsolved research challenge due to their dynamics, idiosyncrasy, ambiguity and variability. Healtex is a healthcare text analytics network that has been established with support from the Engineering and Physical
Sciences Research Council (EPSRC) to bring together experts from academia, the National Health Service (NHS), regulators and industry with an aim to share best practice where free-text data has been successfully used to extract evidence to support research and clinical practice. The network
also focuses on scoping the needs and shaping future research directions; identifying and addressing barriers in processing free-text data; and facilitating engagements with the wider stakeholder community. Given the enormous complexity of what the network is trying to tackle, it follows that
multi-disciplinarity is key to its success. The main outcome of the network is a strong community that works together in particular to encourage early career researchers to develop new methods to unlock the evidence contained in free-text data. The network works towards including clinical
narrative in routinely analysed health data that is then used to facilitate actionable analytics both at the patient level (timely interventions) and to the entire population. Information extracted from clinical narratives can be used to ensure that patient pathways are designed to optimise
quality, patient outcomes and cost effectiveness. The outcomes from the network will also provide benefits to pharmaceutical and healthcare businesses, by exploring how to provide anonymous access to free-text data.
No References for this article.
No Supplementary Data.
No Article Media