NEWS2 to assess suspected COVID-19 in the community: a service evaluation of a primary care assessment centre.

BACKGROUND: Primary care has played a central role in the community response to the coronavirus disease-19 (COVID-19) pandemic. The use of the National Early Warning Score 2 (NEWS2) has been advocated as a tool to guide escalation decisions in the community. The performance of this tool applied in this context is unclear. AIM: To evaluate the process of escalation of care to the hospital within a primary care assessment centre (PCAC) designed to assess patients with suspected COVID-19 in the community. DESIGN AND SETTING: A retrospective service evaluation of all adult patients assessed between 30 March and 22 April 2020 within a COVID-19 primary care assessment centre within Sandwell West Birmingham CCG. METHOD: A database of patient demographics, healthcare interactions and physiological observations was constructed. NEWS2 and CRB65 scores were calculated retrospectively. The proportion of patients escalated was within risk groups defined by NHSE guidelines in place during the evaluation period was determined. RESULTS: A total of 150 patients were identified. Following assessment 13.3% (n = 20) patients were deemed to require escalation. The proportion of patients escalated with a NEWS2 greater than or equal to 3 was 46.9% (95% CI 30.8-63.6%). The proportion of patients escalated to secondary care using NHSE defined risk thresholds was 0% in the green group, 22% (n = 4) in the amber group, and 81.3% (n = 13) in the red group. CONCLUSION: Clinical decisions to escalate care to the hospital did not follow initial guidance written for the COVID-19 outbreak but were demonstrated to be safe.

In most cases, coronavirus disease-19 (COVID-19) is a mild illness that resolves on its own. Some patients develop severe disease requiring hospital treatment. Identifying which patients are likely to need hospital treatment is a challenge. Many GP practices have developed specific services designed to assess patients with suspected COVID-19 and establish whether hospital treatment is necessary. We evaluated a service providing this function in Birmingham. We examined the care pathway of 150 patients assessed within the service to established factors associated with the need for hospital assessment. We found a national decision tool designed to aid the process was a poor descriptor of what happened in practice.

NewsMeSH: A new classifier designed to annotate health news with MeSH headings.

MOTIVATION: In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. METHODS: We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. RESULTS: A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. CONCLUSIONS: The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar.