OBJECTIVE: Our objective was to create a framework that can guide future implementation and research on the use of eHealth tools to support patients with growth disorders who require growth hormone therapy.
METHODS: A total of 12 pediatric endocrinologists with experience in eHealth, from a wide geographical distribution, participated in a series of online discussions. We summarized the discussions of 3 workshops, conducted during 2020, on the use of eHealth in the management of growth disorders, which were structured to provide insights on existing challenges, opportunities, and solutions for the implementation of eHealth tools across the patient journey, from referral to the end of pediatric therapy.
RESULTS: A total of 815 responses were collected from 2 questionnaire-based activities covering referral and diagnosis of growth disorders, and subsequent growth hormone therapy stages of the patient pathway, relating to physicians, nurses, and patients, parents, or caregivers. We mapped the feedback from those discussions into a framework that we developed as a guide to integration of eHealth tools across the patient journey. Responses focused on improved clinical management, such as growth monitoring and automation of referral for early detection of growth disorders, which could trigger rapid evaluation and diagnosis. Patient support included the use of eHealth for enhanced patient and caregiver communication, better access to educational opportunities, and enhanced medical and psychological support during growth hormone therapy management. Given the potential availability of patient data from connected devices, artificial intelligence can be used to predict adherence and personalize patient support. Providing evidence to demonstrate the value and utility of eHealth tools will ensure that these tools are widely accepted, trusted, and used in clinical practice, but implementation issues (eg, adaptation to specific clinical settings) must be addressed.
CONCLUSIONS: The use of eHealth in growth hormone therapy has major potential to improve the management of growth disorders along the patient journey. Combining objective clinical information and patient adherence data is vital in supporting decision-making and the development of new eHealth tools. Involvement of clinicians and patients in the process of integrating such technologies into clinical practice is essential for implementation and developing evidence that eHealth tools can provide value across the patient pathway.
METHODS: The study was designed as a randomized controlled trial. After receiving a pre-test, participants were randomly allocated to either an e-learning or non-e-learning group. Only those in the e-learning group gained access to the e-learning system. Two months after the pre-test, both groups received a post-test. The primary endpoint was the difference between the two groups regarding the rate of improvement of their test results.
FINDINGS: 515 endoscopists from 35 countries were assessed for eligibility, and 332 were enrolled in the study, with 166 allocated to each group. Of these, 151 participants in the e-learning group and 144 in the non-e-learning group were included in the analysis. The mean improvement rate (standard deviation) in the e-learning and non-e-learning groups was 1·24 (0·26) and 1·00 (0·16), respectively (P<0·001).
INTERPRETATION: This global study clearly demonstrated the efficacy of an e-learning system to expand knowledge and provide invaluable experience regarding the endoscopic detection of early gastric cancer (R000012039).
DESIGN: Studies on the association between CT values and smear status were included in a descriptive systematic review. Authors of studies including smear, culture and Xpert results were asked for individual-level data, and receiver operating characteristic curves were calculated.
RESULTS: Of 918 citations, 10 were included in the descriptive systematic review. Fifteen data sets from studies potentially relevant for individual-level data meta-analysis provided individual-level data (7511 samples from 4447 patients); 1212 patients had positive Xpert results for at least one respiratory sample (1859 samples overall). ROC analysis revealed an area under the curve (AUC) of 0.85 (95%CI 0.82-0.87). Cut-off CT values of 27.7 and 31.8 yielded sensitivities of 85% (95%CI 83-87) and 95% (95%CI 94-96) and specificities of 67% (95%CI 66-77) and 35% (95%CI 30-41) for smear-positive samples.
CONCLUSION: Xpert CT values and smear status were strongly associated. However, diagnostic accuracy at set cut-off CT values of 27.7 or 31.8 would not replace smear microscopy. How CT values compare with smear microscopy in predicting infectiousness remains to be seen.
RECENT FINDINGS: Definition of EIU and its conceptualization still requires refinement. Recent studies indicate a changing trend towards female predominance of EIU. Women also differ in their internet use compared with men regarding their preference in the internet content and online activities, motives of use and factors related to access to the internet, including the device, sociocultural restrictions, etc. The correlates and sequelae of EIU encompass psychological, physical, biological, family and social domains that could form the basis of identifying individuals at risk and strategizing treatment.
SUMMARY: The findings indicate the need for standardization in definition and measures of EIU for better recognition of EIU and identification of its at-higher-risk females. Effective preventive and treatment measures are still limited by various methodology flaws outlined here.