Objectives: To compare H index scores for healthcare researchers returned by Google Scholar, Web of Science and Scopus databases, and to assess whether a researcher’s age, country of institutional affiliation and physician status influences calculations. Subjects and Methods: One hundred and ninety-five Nobel laureates in Physiology and Medicine from 1901 to 2009 were considered. Year of first and last publications, total publications and citation counts, and the H index for each laureate were calculated from each database. Cronbach’s alpha statistics was used to measure the reliability of H index scores between the databases. Laureate characteristic influence on the H index was analysed using linear regression. Results: There was no concordance between the databases when considering the number of publications and citations count per laureate. The H index was the most reliably calculated bibliometric across the three databases (Cronbach’s alpha = 0.900). All databases returned significantly higher H index scores for younger laureates (p < 0.0001). Google Scholar and Web of Science returned significantly higher H index for physician laureates (p = 0.025 and p = 0.029, respectively). Country of institutional affiliation did not influence the H index in any database. Conclusion: The H index appeared to be the most consistently calculated bibliometric between the databases for Nobel laureates in Physiology and Medicine. Researcher-specific characteristics constituted an important component of objective research assessment. The findings of this study call to question the choice of current and future academic performance databases.

The UK government had intended to introduce a comprehensive Electronic Health Record (EHR) system in England by 2020. These EHRs would run across primary, secondary, and social care, linking data in a single digital platform. The objectives of this systematic review were to identify studies that compare EHR in terms of direct comparison between systems and to evaluate them using System and Software Quality Requirements and Evaluation (SQuaRE) ISO/IEC 25010. A systematic review was performed by searching Embase and Ovid MEDLINE databases between 1974 and April 2021. All original studies that appraised EHR systems and their providers were included. The main outcome measures were EHR system comparison and the eight characteristics of SQuaRE: functional suitability, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability. A total of 724 studies were identified using the search criteria. After a review of titles and abstracts, this was filtered down to 40 studies as per the exclusion and inclusion criteria set out in our study. Seven studies compared more than one EHR. The following number of studies looked at the various aspects of the SQuaRE, respectively – 19 studies: functional suitability, performance efficiency: 18 studies, compatibility: 12 studies, usability: 25 studies, reliability: 6 studies, security: 2 studies, maintainability: 16 studies, portability: 13 studies. Epic was the most studied EHR system and one of the most implemented systems in the US market and one of the top ten in the UK. It is difficult to assess which is the most advantageous EHR system when they are assessed by SQuaRE’s 8 characteristics for software evaluation.

Artificial intelligence (AI) presents a novel platform for improving disease diagnosis. However, the clinical utility of AI remains limited to discovery studies, with poor translation to clinical practice. Current data suggests that 26% of diminutive pre-malignant lesions and 3.5% of colorectal cancers are missed during colonoscopies. The primary aim of this study was to explore the role of artificial intelligence in real-time histological prediction of colorectal lesions during colonoscopy. A systematic search using MeSH headings relating to “AI”, “machine learning”, “computer-aided”, “colonoscopy”, and “colon/rectum/colorectal” identified 2290 studies. Thirteen studies reporting real-time analysis were included. A total of 2958 patients with 5908 colorectal lesions were included. A meta-analysis of six studies reporting sensitivities (95% CI) demonstrated that endoscopist diagnosis was superior to a computer-assisted detection platform, although no statistical significance was reached (p = 0.43). AI applications have shown encouraging results in differentiating neoplastic and non-neoplastic lesions using narrow-band imaging, white light imaging, and blue light imaging. Other modalities include autofluorescence imaging and elastic scattering microscopy. The current literature demonstrates that despite the promise of new endoscopic AI models, they remain inferior to expert endoscopist diagnosis. There is a need to focus developments on real-time histological predictions prior to clinical translation to demonstrate improved diagnostic capabilities and time efficiency.