Fujitsu has announced a new AI technology that automates the process of unstructured (free text) medical notes into an electronic health record.
The new medical coding and AI tech mining solution extracts the annotations from text in less than one minute by combining semantic knowledge and Natural Language Processing (NLP) with Deep Learning in order to analyse medical notes and extract valuable data.
The company has worked closely with Madrid’s leading San Carlos Clinical Hospital. Dr Julio Mayol, Chief Medical Officer “We are constantly looking for new ways of improving clinical decision-making, and our work with Fujitsu Laboratories of Europe is helping us to realise important advances to improve efficiency. Most of the EHR systems available today do not fulfil the requirements of the doctor/patient relationship. In fact, the use of EHR has been directly associated to clinician burn-out, as demonstrated by a number of studies. With new technologies such as Fujitsu’s latest AI text mining technology, we can address these challenges directly, and achieve tangible improvements to the clinical decision-making process.”
Fujitsu Laboratories of Europe’s Chief Executive Officer Dr Adel Rouz said “Our co-creation strategy with partners such as the San Carlos Clinical Hospital has provided us with an important insight into the challenges faced by the healthcare sector, particularly in terms of supporting clinical decision-making. We have succeeded in creating a number of important innovations that are already making a difference to medical professionals’ workflow. This latest advance is another step, helping to improve the accuracy of clinical data and automate its digitalisation for hospitals, medical insurance companies and government agencies. We believe that our technology has wider applications and can easily be adapted to solve similar challenges in other domains, such as insurance, legal and compliance.”
The company said the solution will provide more time back for clinicians to spend with patients rather than looking at a computer. The solution automatically extracts the structured information required by the EHR system from clinicians’ free narrative text. Using deep learning, the solution can be retrained to match a clinician’s individual needs. The result is a high degree of accuracy, matched by the ability to extract a wider cross-section of relevant terms than just International Statistical Classification of Diseases and Related Health Problems (ICD) codes, relating to treatment adherence or social background data.
The AI technology has been evaluated across two English language datasets, involving 200 private anonymised clinical notes, and 5000 discharge summaries extracted from MIMIC-III resource.
