Primary Care Post-COVID syndrome Diagnosis and Referral Coding (preprint)

Introduction - Guidelines for diagnosing and managing Post-COVID syndrome have been rapidly developed. Consistency of the application of these guidelines in primary care is unknown. Electronic health records provide an opportunity to review the use of codes relating to Post-COVID syndrome. This paper explores the use of primary care records as a surrogate uptake measure for NICEs rapid guideline managing the long-term effects of COVID-19 by measuring the use of Post-COVID syndrome diagnosis and referral codes in the pathway. Method - With the approval of NHS England we used routine clinical data from the OpenSafely-EMIS/-TPP platforms. Counts of Post-COVID syndrome diagnosis and referral codes were generated from a cohort of all adults, establishing numbers of diagnoses and referrals following diagnosis. The relationship between Post-COVID syndrome diagnosis and referral codes was explored with reference to NICEs rapid guideline. Results - Of over 45 million patients, 69,220 (0.15%) had a Post-COVID syndrome diagnostic code, and 67,741 (0.15%) had a referral code. 78% of referral codes did not have an associated diagnosis code. 79% of diagnosis codes had no subsequent referral code. Only 18,633 (0.04%) had both. There were higher rates of both diagnosis and referral in those who were more deprived, female and some ethnic groups. Discussion - This study demonstrates variation in diagnosis and referral coding rates for Post-COVID syndrome across different patient groups. The results, with limited crossover of referral and diagnostic codes, suggest only one type of code is usually recorded. Recording one code limits the use of routine data for monitoring Post-COVID syndrome diagnosis and management, but suggests several areas for improvement in coding. Post-COVID syndrome coding, particularly diagnosis coding, needs to improve before administrators and researchers can use it to evaluate care pathways.

Managing the evidence infodemic: Automation approaches used for developing NICE COVID-19 living guidelines (preprint)

Background and Objectives The National Institute for Health and Care Excellence (NICE) produces evidence-based guidance and advice for health, public health and social care practitioners in England and Wales. Between March 2020 and March 2022, NICE produced 24 COVID-19 guidelines to support healthcare workers during the COVID-19 pandemic. This article outlines three automation strategies NICE utilised to facilitate faster processing of evidence on COVID-19 and describes the value of those approaches when there is an increasing volume of evidence and demand on resources. Study Design and Setting Text classification using machine learning, and regular expression-based pattern matching were used to automate screening of literature search results. Relevant clinical trials were tracked by automated monitoring of clinical trial databases and Pubmed. Results The strategies discussed here brought considerable efficiencies in the processing time without impacting on quality compared to equivalent manual efforts. Additionally, the paper illustrates how to incorporate automation into established processes of the evidence management pipeline. Conclusions We have demonstrated through testing and use in live guideline development and surveillance that these are effective and low risk approaches at managing high volumes of evidence. Highlights - To illustrate how NICE utilised automation to handle the Covid-19 ‘infodemic’-managing the ‘infodemic’ of evidence surveillance is a shared global issue. - To outline automation strategies to facilitate faster processing of evidence, especially when there is an increasing volume of evidence and demand on resources. - How automation can be included in established processes without disrupting business as usual operations. - Automation can take many forms, and depending on risk appetite, can be supplemented with manual checking. - Automation can be adopted easily with the right tools and techniques.