A Scalable Architecture for Incremental Specification and Maintenance of Procedural and Declarative Clinical Decision-Support Knowledge

Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians’ assessment was significantly lower than the assessment of the knowledge engineers

The accuracy of haemoglobin A1c as a screening and diagnostic test for gestational diabetes: a systematic review and meta-analysis of test accuracy studies

PURPOSE OF REVIEW: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy complications. Accurate screening and diagnosis of gestational diabetes are critical to treatment, and in a pandemic scenario like coronavirus disease 2019 needing a simple test that minimises prolonged hospital stay. We undertook a meta-analysis on the screening and diagnostic accuracy of the haemoglobin A1c (HbA1c) test in women with and without risk factors for gestational diabetes. RECENT FINDINGS: Unlike the oral glucose tolerance test, the HbA1c test is simple, quick and more acceptable. There is a growing body of evidence on the accuracy of HbA1c as a screening and diagnostic test for GDM. We searched Medline, Embase and Cochrane Library and selected relevant studies. Accuracy data for different thresholds within the final 23 included studies (16 921 women) were pooled using a multiple thresholds model. Summary accuracy indices were estimated by selecting an optimal threshold that optimises either sensitivity or specificity according to different scenarios. SUMMARY: HbA1c is more useful as a specific test at a cut-off of 5.7% (39 mmol/mol) with a false positive rate of 10%, but should be supplemented by a more sensitive test to detect women with GDM