Effect of Telehealth Interventions on Hospitalization Indicators: A Systematic Review

BACKGROUND: Telehealth has been defined as the remote delivery of healthcare services using information and communication technology. Where resource-limited health systems face challenges caused by the increasing burden of chronic diseases and an aging global population, telehealth has been advocated as a solution for changing and improving the paradigm of healthcare delivery to cope with these issues. The aim of this systematic review is to investigate the effect of telehealth interventions on two indicators: hospitalization rate and length of stay. MATERIALS AND METHODS: The reviewers searched the PubMed, ScienceDirect, and Springer electronic databases from January 2005 to November 2013. A search strategy was developed using a combination of the following search keywords: impact, effect, telehealth, telemedicine, telecare, hospitalization, length of stay, and resource utilization. Both randomized controlled trials and observational studies were included in the review. To be included in the review, articles had to be written in English. The results of study were compiled, reviewed, and analyzed on the basis of the review aims. RESULTS: This systematic review examined 22 existing studies with a total population of 19,086 patients. The effect of telehealth on all-cause hospitalization was statistically significant in 40 percent of the related studies, whereas it was not statistically significant in 60 percent. Similarly, the effect of telehealth on the all-cause length of stay was statistically significant in 36 percent of the studies and nonsignificant in 64 percent. CONCLUSION: Considering the fact that hospitalization rate and length of stay can be confounded by factors other than telehealth intervention, studies examining the effect of the intervention on these indicators must take into account all other factors influencing them. Otherwise any judgment on the effect of telehealth on these indicators cannot be valid

Developing a prioritisation framework for patients in need of coronary artery angiography

Background: Effective waiting list management and comprehensive prioritisation can provide timely delivery of appropriate services to ensure that the patient needs are met and increase equity in the provision of health services. We developed a prioritisation framework for patients in need of coronary artery angiography (CAA). Methods: We used a multi-methods approach to elicit effective factors that affect CAA patient prioritisation. Qualitative data wase collected using semi-structured interviews with 15 experts. The final set of factors was selected using experts’ consensus through modifed Delphi technique. The framework was finalised during expert panel meetings. Results: 212 effective factors were identified based on the literature review, interviews, and expert panel discussion of them, 37 factors were selected for modifed Delphi study. Following two rounds of Delphi discussions, seven final factors were selected and weighed by ten experts using pair-wise comparisons. The following weights were given: the severity of pain and symptoms (0.22), stress testing (0.18), background diseases (0.15), number of myocardial infarctions (0.15), waiting time (0.10), reduction of economic and social performance (0.12), and special conditions (0.08). Conclusion: Clinical effective factors were important for CAA prioritisation framework. Using this framework can potentially lead to improved accountability and justice in the health system

The effect of computerized physician order entry and decision support system on medication errors in the neonatal ward: Experiences from an Iranian teaching hospital

Medication dosing errors are frequent in neonatal wards. In an Iranian neonatal ward, a 7.5 months study was designed in three periods to compare the effect of Computerized Physician Order Entry (CPOE) without and with decision support functionalities in reducing non-intercepted medication dosing errors in antibiotics and anticonvulsants. Before intervention (Period 1), error rate was 53, which did not significantly change after the implementation of CPOE without decision support (Period 2). However, errors were significantly reduced to 34 after that the decision support was added to the CPOE (Period 3; P<0.001). Dose errors were more often intercepted than frequency errors. Over-dose was the most frequent type of medication errors and curtailed-interval was the least. Transcription errors did not reduce after the CPOE implementation. Physicians ignored alerts when they could not understand why they appeared. A suggestion is to add explanations about these reasons to increase physicians' compliance with the system's recommendations. © 2009 Springer Science+Business Media, LLC

The role of natural variability and anthropogenic climate change in the 2017/18 Tasman Sea marine heatwave

Two GCM ensembles indicate that the record sea surface temperatures during the 2017/18 Tasman Sea marine heatwave were virtually impossible without anthropogenic influence. However, natural variability was important in the atmospheric initiation of the event