Experiences and challenges of acute coronary syndrome patients in care provision: a qualitative systematic review.

BACKGROUND: Coronary artery disease including acute coronary syndrome (ACS) constitutes the most common cause of death in people with cardiovascular disease. Prompt diagnosis and early initiation of treatment significantly impact on patient outcomes. Positive patient experience with their initial care is linked to positive clinical outcomes. OBJECTIVE: This qualitative review aimed to investigate patients' experience of care provision and the challenges faced by them during their different stages of care following an ACS. METHODS: Searches of four databases - MEDLINE, Embase, CINAHL and PsychINFO - were conducted from inception until July 13, 2022, and were limited to English-language publications. Assessment of methodological quality of studies was performed using the Jonna Briggs Institute (JBI) qualitative assessment and review instrument. Data were extracted using the standardised data extraction tool from JBI. Data synthesis following the JBI approach of meta-aggregation was performed. The level of confidence for each synthesised finding was established based on ConQual. RESULTS: Overall, from 578 records, 10 studies were included with 39 findings extracted from the included studies. The main synthesised findings were the need to provide tailored information and appropriate management at different stages of care, and that timely management and trust in health care workers are associated with greater patient satisfaction and more positive experiences. CONCLUSION: Patients with an ACS experience many challenges during different stages of their care. Clinicians should be aware of the challenges they face and provide tailored information to patients that is appropriate for their different stages of management in order to best optimise patient experience and improve patient outcomes.

Automation tools to support undertaking scoping reviews.

OBJECTIVE: This paper describes several automation tools and software that can be considered during evidence synthesis projects and provides guidance for their integration in the conduct of scoping reviews. STUDY DESIGN AND SETTING: The guidance presented in this work is adapted from the results of a scoping review and consultations with the JBI Scoping Review Methodology group. RESULTS: This paper describes several reliable, validated automation tools and software that can be used to enhance the conduct of scoping reviews. Developments in the automation of systematic reviews, and more recently scoping reviews, are continuously evolving. We detail several helpful tools in order of the key steps recommended by the JBI's methodological guidance for undertaking scoping reviews including team establishment, protocol development, searching, de-duplication, screening titles and abstracts, data extraction, data charting, and report writing. While we include several reliable tools and software that can be used for the automation of scoping reviews, there are some limitations to the tools mentioned. For example, some are available in English only and their lack of integration with other tools results in limited interoperability. CONCLUSION: This paper highlighted several useful automation tools and software programs to use in undertaking each step of a scoping review. This guidance has the potential to inform collaborative efforts aiming at the development of evidence informed, integrated automation tools and software packages for enhancing the conduct of high-quality scoping reviews.

Tools to support the automation of systematic reviews: a scoping review.

OBJECTIVE: The objectives of this scoping review are to identify the reliability and validity of the available tools, their limitations and any recommendations to further improve the use of these tools. STUDY DESIGN: A scoping review methodology was followed to map the literature published on the challenges and solutions of conducting evidence synthesis using the JBI scoping review methodology. RESULTS: A total of 47 publications were included in the review. The current scoping review identified that LitSuggest, Rayyan, Abstractr, BIBOT, R software, RobotAnalyst, DistillerSR, ExaCT and NetMetaXL have potential to be used for the automation of systematic reviews. However, they are not without limitations. The review also identified other studies that employed algorithms that have not yet been developed into user friendly tools. Some of these algorithms showed high validity and reliability but their use is conditional on user knowledge of computer science and algorithms. CONCLUSION: Abstract screening has reached maturity; data extraction is still an active area. Developing methods to semi-automate different steps of evidence synthesis via machine learning remains an important research direction. Also, it is important to move from the research prototypes currently available to professionally maintained platforms.