TY - JOUR T1 - Lending a hand: could machine learning help hospital staff make better use of patient feedback? JF - BMJ Quality & Safety JO - BMJ Qual Saf SP - 93 LP - 95 DO - 10.1136/bmjqs-2017-007151 VL - 27 IS - 2 AU - Chris Gibbons AU - Felix Greaves Y1 - 2018/02/01 UR - http://qualitysafety.bmj.com/content/27/2/93.abstract N2 - In this issue of BMJ Quality and Safety, two articles consider how patients’ opinions of care can be collected, analysed and used to inform healthcare delivery. In the first of the two studies, Lee and colleagues examine how written patient experience comments feedback is used in the National Health Service (NHS).1 Uniquely, the authors focus their investigation on the way in which Boards of Directors use patient experience information to monitor and improve care.The second study, conducted by Griffiths and Leaver, illustrates how computational tools could automate the collection and analysis of patient experience data. The authors’ system scrapes comments from social media websites and machine learning algorithms convert this unstructured information (ie, free text comments) into a zero-to-five ‘star’ rating, which they suggest could help prioritise hospital inspections.2 Lee and colleagues focused their investigation on two NHS Foundation Trusts with experience in collecting patient feedback information. The team interviewed managers, observed Board meetings and interrogated relevant hospital documents to understand how executives in acute hospitals use information about patient experience.Through their careful analysis, Lee et al demonstrate that enthusiasm for collecting patient experience data does not guarantee that these data will be used to monitor improvements and assure the quality of care. In the absence of a clearly defined process for using these data, the eagerness for collecting it dissipates into confusion as busy staff struggle to transform reams of patient comments into useful information. The inevitable result is that, despite the best efforts of staff, information which patients share … ER -