The clinical relevance and newsworthiness of NIHR HTA-funded research: a cohort study

ObjectiveTo assess the clinical relevance and newsworthiness of the UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme funded reports.Study designRetrospective cohort study.SettingThe cohort included 311 NIHR HTA Programme funded reports publishing in HTA in the period 1 January 2007–31 December 2012. The McMaster Online Rating of Evidence (MORE) system independently identified the clinical relevance and newsworthiness of NIHR HTA publications and non-NIHR HTA publications. The MORE system involves over 4000 physicians rating publications on a scale of relevance (the extent to which articles are relevant to practice) and a scale of newsworthiness (the extent to which articles contain news or something clinicians are unlikely to know).Main outcome measuresThe proportion of reports published in HTA meeting MORE inclusion criteria and mean average relevance and newsworthiness ratings were calculated and compared with publications from the same studies publishing outside HTA and non-NIHR HTA funded publications.Results286/311 (92.0%) of NIHR HTA reports were assessed by MORE, of which 192 (67.1%) passed MORE criteria. The average clinical relevance rating for NIHR HTA reports was 5.48, statistically higher than the 5.32 rating for non-NIHR HTA publications (mean difference=0.16, 95% CI 0.04 to 0.29, p=0.01). Average newsworthiness ratings were similar between NIHR HTA reports and non-NIHR HTA publications (4.75 and 4.70, respectively; mean difference=0.05, 95% CI ?0.18 to 0.07, p=0.402). NIHR HTA-funded original research reports were statistically higher for newsworthiness than reviews (5.05 compared with 4.64) (mean difference=0.41, 95% CI 0.18 to 0.64, p=0.001).ConclusionsFunding research of clinical relevance is important in maximising the value of research investment. The NIHR HTA Programme is successful in funding projects that generate outputs of clinical relevance

Artificial Intelligence for industrial process supervision

This paper presents some difficulties of complex industrial process supervision and explains why artificial intelligence may help to solve some problems. Qualitative or semi-qualitative trend extraction is mentioned first. Then fault detection and fault supervision are evoked. The necessity for intelligent interfaces is explained next and distributed supervision is finally mentioned