Clinical service delivery implications of the COVID-19 pandemic on people with Inflammatory bowel disease: a qualitative study.

BACKGROUND: During the COVID-19 pandemic, clinical services were severely disrupted, restricted, or withdrawn across the country. People living with Inflammatory Bowel Disease (IBD) - an auto-immune disorder for which medical treatment often results in immunosuppression, thus requiring regular monitoring-may have struggled to access clinical support. As part of a larger qualitative study, we investigated experiences of access to clinical services during the pandemic, and patient concerns about and preferences for services in the future. METHODS: This exploratory qualitative study used semi-structured interviews to explore participants' experiences of clinical services across the UK during the pandemic. All data were collected remotely (March - May 2021) using online video-calling platforms or by telephone. Audio files were transcribed professionally and anonymised for analysis. Data were analysed using thematic analysis. RESULTS: Of the eight themes found across all data, four related specifically to accessing GP, local (district) hospital, and specialist (tertiary) referral services for IBD: 1) The Risk of Attending Hospital; 2) Missing Routine Monitoring or Treatment; 3) Accessing Care as Needed, and 4) Remote Access and The Future. CONCLUSIONS: Our findings support other studies reporting changes in use of health services, and concerns about future remote access methods. Maintenance of IBD services in some form is essential throughout crisis periods; newly diagnosed patients need additional support; future dependence on IBD services could be reduced through use of treatment / self-management plans. As the NHS digitalises it's future services, the mode of appointment-remote (telephone, video call), or in-person - needs to be flexible and suit the patient.

Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach.

Introduction: The move from a reactive model of care which treats conditions when they arise to a proactive model which intervenes early to prevent adverse healthcare events will benefit from advances in the predictive capabilities of Artificial Intelligence and Machine Learning. This paper investigates the ability of a Deep Learning (DL) approach to predict future disease diagnosis from Electronic Health Records (EHR) for the purposes of Population Health Management. Methods: In this study, embeddings were created using a Word2Vec algorithm from structured vocabulary commonly used in EHRs e.g., Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codes. This study is based on longitudinal medical data from ~50 m patients in the USA. We introduced a novel method of including binned observation values into an embeddings model. We also included novel features associated with wider determinants of health. Patient records comprising these embeddings were then fed to a Bidirectional Gated Recurrent Unit (GRU) model to predict the likelihood of patients developing Type 2 Diabetes Mellitus, Chronic Obstructive Pulmonary Disorder (COPD), Hypertension or experiencing an Acute Myocardial Infarction (MI) in the next 3 years. SHapley Additive exPlanations (SHAP) values were calculated to achieve model explainability. Results: Increasing the data scope to include binned observations and wider determinants of health was found to improve predictive performance. We achieved an area under the Receiver Operating Characteristic curve value of 0.92 for Diabetes prediction, 0.94 for COPD, 0.92 for Hypertension and 0.94 for MI. The SHAP values showed that the models had learned features known to be associated with these outcomes. Discussion: The DL approach outlined in this study can identify clinically-relevant features from large-scale EHR data and use these to predict future disease outcomes. This study highlights the promise of DL solutions for identifying patients at future risk of disease and providing clinicians with the means to understand and evaluate the drivers of those predictions.