Understanding and Optimising Antimicrobial Use in Children using Electronic Health Records Data: A Machine Learning and Causal Inference Approach

Antimicrobial resistance is a growing global health crisis, threatening the efficacy of treatments essential for modern medical practice, including infections, surgeries, and chemotherapy. This PhD thesis leverages electronic health records (EHRs) from the Clinical Practice Research Datalink (CPRD) and a specialist children’s hospital to improve antimicrobial stewardship in paediatric care. The first study described trends in therapeutic and prophylactic antibiotic prescribing at a tertiary children’s hospital. It showed that therapeutic prescribing was higher in neonates than in older children. The second study used interrupted time series models to assess changes in days of antibiotic therapy at a tertiary children’s hospital during the first year of the COVID-19 pandemic. There was an increase in antibiotic prescribing during the COVID-19 pandemic, but this was likely driven by changes in the patient population. The third study, a preliminary analysis, explored the potential of using EHR data to predict antibiotic resistance with the aim to reduce inappropriate prescribing. The predictive models did not show high accuracy and further research is needed. The fourth study used routine data from general practice to evaluate the effect of remote consultations on antibiotic prescribing for acute respiratory infections, finding no significant difference in prescribing rates for children but a 23% increase in the odds of antibiotics being prescribed in remote consultations for adults. Collectively, these studies provide important insights into antimicrobial use in children, highlighting both challenges and opportunities for improving antibiotic stewardship. By identifying data quality issues, evaluating the impact of external factors such as the COVID-19 pandemic, and exploring predictive models, this research contributes to a deeper understanding of how EHR data can be leveraged to optimise antimicrobial use in paediatric care. These findings underscore the potential for using data-driven approaches to refine stewardship strategies and ensure more targeted, appropriate antibiotic prescribing in both paediatric and broader healthcare contexts

Consultation Rate and Mode by Deprivation in English General Practice From 2018 to 2022: Population-Based Study

BACKGROUND: The COVID-19 pandemic has had a significant impact on primary care service delivery with an increased use of remote consultations. With general practice delivering record numbers of appointments and rising concerns around access, funding, and staffing in the UK National Health Service, we assessed contemporary trends in consultation rate and modes (ie, face-to-face versus remote). OBJECTIVE: This paper describes trends in consultation rates in general practice in England for key demographics before and during the COVID-19 pandemic. We explore the use of remote and face-to-face consultations with regard to socioeconomic deprivation to understand the possible effect of changes in consultation modes on health inequalities. METHODS: We did a retrospective analysis of 9,429,919 consultations by general practitioners, nurses, or other health care professionals between March 2018 and February 2022 for patients registered at 397 general practices in England. We used routine electronic health records from Clinical Practice Research Datalink Aurum with linkage to national data sets. Negative binomial models were used to predict consultation rates and modes (ie, remote versus face-to-face) by age, sex, and socioeconomic deprivation over time. RESULTS: Overall consultation rates increased by 15% from 4.92 in 2018-2019 to 5.66 in 2021-2022 with some fluctuation during the start of the COVID-19 pandemic. The breakdown into face-to-face and remote consultations shows that the pandemic precipitated a rapid increase in remote consultations across all groups, but the extent varies by age. Consultation rates increased with increasing levels of deprivation. Socioeconomic differences in consultation rates, adjusted for sex and age, halved during the pandemic (from 0.36 to 0.18, indicating more consultations in the most deprived), effectively narrowing relative differences between deprivation quintiles. This trend remains when stratified by sex, but the difference across deprivation quintiles is smaller for men. The most deprived saw a relatively larger increase in remote and decrease in face-to-face consultation rates compared to the least deprived. CONCLUSIONS: The substantial increases in consultation rates observed in this study imply an increased pressure on general practice. The narrowing of consultation rates between deprivation quintiles is cause for concern, given ample evidence that health needs are greater in more deprived areas

Continuity of care and consultation mode in general practice: a cross-sectional and longitudinal study using patient-level and practice-level data from before and during the COVID-19 pandemic in England

Objectives Investigate trends in continuity of care with a general practitioner (GP) before and during the COVID-19 pandemic. Identify whether continuity of care is associated with consultation mode, controlling for other patient and practice characteristics.Design Retrospective cross-sectional and longitudinal observational studies.Setting Primary care records from 389 general practices participating in Clinical Practice Research Datalink Aurum in England.Participants In the descriptive analysis, 100 000+ patients were included each month between April 2018 and April 2021. Modelling of the association between continuity of care and consultation mode focused on 153 475 and 125 298 patients in index months of February 2020 (before the pandemic) and February 2021 (during the pandemic) respectively, and 76 281 patients in both index months.Primary and secondary outcomes measures The primary outcome measure was the Usual Provider of Care index. Secondary outcomes included the Bice-Boxerman index and count of consultations with the most frequently seen GP.Results Continuity of care was gradually declining before the pandemic but stabilised during it. There were consistent demographic, socioeconomic and regional differences in continuity of care. An average of 23% of consultations were delivered remotely in the year to February 2020 compared with 76% in February 2021. We found little evidence consultation mode was associated with continuity at the patient level, controlling for a range of covariates. In contrast, patient characteristics and practice-level supply and demand were associated with continuity.Conclusions We set out to examine the association of consultation mode with continuity of care but found that GP supply and patient demand were much more important. To improve continuity for patients, primary care capacity needs to increase. This requires sufficient, long-term investment in clinicians, staff, facilities and digital infrastructure. General practice also needs to transform ways of working to ensure continuity for those that need it, even in a capacity-constrained environment

Antibiotic prescribing in remote versus face-to-face consultations for acute respiratory infections in English primary care: An observational study using TMLE

Background The COVID-19 pandemic has led to an ongoing increase in the use of remote consultations in general practice in England. Though the evidence is limited, there are concerns that the increase in remote consultations could lead to more antibiotic prescribing. Methods We used patient-level primary care data from the Clinical Practice Research Datalink to estimate the association between consultation mode (remote vs face-to-face) and antibiotic prescribing in England for acute respiratory infections (ARI) between April 2021 - March 2022. We used targeted maximum likelihood estimation, a causal machine learning method with adjustment for patient-, clinician- and practice-level factors. Findings There were 45,997 ARI consultations (34,555 unique patients), of which 28,127 were remote and 17,870 face-to-face. For children, 48% of consultations were remote whereas for adults 66% were remote. For children, 42% of remote and 43% face-to-face consultations led to an antibiotic prescription; the equivalent in adults was 52% of remote and 42% face-to-face. Adults with a remote consultation had 23% (Odds Ratio (OR) 1.23 95% Confidence Interval (CI): 1.18-1.29) higher chance of being prescribed antibiotics compared to if they had been seen face-to-face. We found no significant association between consultation mode and antibiotic prescribing in children (OR 1.04 95% CI 0.98-1.11). Interpretation This study uses rich patient-level data and robust statistical methods and represents an important contribution to the evidence base on antibiotic prescribing in post-COVID primary care. The higher rates of antibiotic prescribing in remote consultations for adults are cause for concern. We see no significant difference in antibiotic prescribing between consultation mode for children. These findings should inform antimicrobial stewardship activities for health care professionals and policy makers. Future research should examine differences in guideline-compliance between remote and face-to-face consultations to understand the factors driving antibiotic prescribing in different consultation modes. Funding No external funding. Keywords general practice; England; antibiotics; remote consultations; telehealth; telemedicine; TMLE; causal inference; machine learning; acute respiratory infections; antimicrobial resistance; covid; ARTI; ARI; antibiotic prescribing; primary care</jats:p

Changes in hospital prescribing activity at a specialist children’s hospital during the COVID-19 pandemic - an observational study

AbstractObjectiveTo compare hospital activity, patient casemix and medication prescribing and administration before and during the COVID-19 emergency.DesignRetrospective observational studySettingA specialist children’s hospital in the UKPatientsInpatients aged 25 and younger treated at a specialist children’s hospital between 29 April 2019 and 6 September 2020ResultsThere were 21,471 day cases and inpatients treated during the 16 month study period. Day cases (no overnight stay) dropped by around 37% per week. Both admissions and discharges for inpatients (at least one overnight stay) decreased leading to a small reduction in hospital bed days but no reduction in hospital bed nights. The effect on hospital activity on different patient groups varied substantially with some groups such as medical oncology day cases increasing by 13%. As a result, the patient case mix in the hospital was very different during the pandemic. Overall weekly medication administrations decreased for day cases and inpatients, but weekly medication administrations per bed day increased by 10% for day cases and 6% for inpatients.ConclusionsDespite not being badly affected by the disease itself, specialist paediatric hospital services have been greatly affected by the pandemic. The average number of medications per inpatient bed day increased, likely reflecting changes to the patient population, with only those children with severe conditions being treated during the pandemic. These data demonstrate the complex pattern of implications on specialist services and provide evidence for planning the impact of future emergencies and resolution strategies.</jats:sec

Abstract W P288: Using “Big Data” Analytics and Visualization for Quality Improvement in Stroke Care

Introduction: The Sentinel Stroke National Audit Programme (SSNAP) is the new national stroke register of England and Wales. It has been designed to harness the power of “Big Data” to produce near real-time data collection, analysis and reporting. Sophisticated data visualization is used to provide customized analytics for clinical teams, administrators, healthcare funders and stroke survivors and carers. Methods: A portfolio of cutting edge data visualisation outputs, including team level slidedecks, performance charts, dashboards , and interactive maps, was produced. Visualisations for patients and the public were co-designed with stroke survivors. Stakeholder feedback regarding accessibility and usefulness of the resources was sought via online polls. Results: Key SSNAP results are made accessible electronically every three months in a range of bespoke graphical formats. Individualised slidedecks and data summaries are produced for every hospital, funding group, and region to enable provider level performance and quality reporting and regional and national benchmarking. Dynamic maps enhance dissemination and use of results. Real time root cause analysis tools help teams identify areas of improvement. Feedback reports unprecedented utility of these resources for clinical teams, funders, regional and national health bodies, patients and the public in identifying areas of good practice and requiring improvements, highlighting variations, and driving change. Conclusion: SSNAP is a potential new model of healthcare quality measurement that uses recent developments in big data analytics and visualization to provide information on stroke care quality that is more useful to stakeholders. Similar approaches could be used in other healthcare settings and populations. </jats:p