Comparison of epidemiologic surveillance and Google Trends data on asthma and allergic rhinitis in England

Peer reviewe

In-pandemic development of an application ontology for COVID-19 surveillance in a primary care sentinel network

Background: Creating an ontology for coronavirus disease 2019 (COVID-19) surveillance should help ensure transparency and consistency. Ontologies formalise conceptualisations at either domain or application level. Application ontologies cross domains and are specified through testable use cases. Our use case was extension of the role of the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) to monitor the current pandemic and become an in-pandemic research platform. Objective: To develop an application ontology for COVID-19 which can be deployed across the various use case domains of the Oxford- RCGP RSC research and surveillance activities. Methods: We described our domain-specific use case. The actor was the RCGP RSC sentinel network; the system the course of the COVID-19 pandemic; the outcomes the spread and effect of mitigation measures. We used our established three-step method to develop the ontology, separating ontological concept development from code mapping and data extract validation. We developed a coding system–independent COVID-19 case identification algorithm. As there were no gold standard pandemic surveillance ontologies, we conducted a rapid Delphi consensus exercise through the International Medical Informatics Association (IMIA) Primary Health Care Informatics working group and extended networks. Results: Our use case domains included primary care, public health, virology, clinical research and clinical informatics. Our ontology supported: (1) Case identification, microbiological sampling and health outcomes at both an individual practice and national level; (2) Feedback through a dashboard; (3) A national observatory, (4) Regular updates for Public Health England, and (5) Transformation of the sentinel network to be a trial platform. We have identified a total of 8,627 people with a definite COVID-19 status, 4,240 with probable, and 59,147 people with possible COVID-19, within the RCGP RSC network (N=5,056,075). Conclusions: The underpinning structure of our ontological approach has coped with multiple clinical coding challenges. At a time when there is uncertainty about international comparisons, clarity about the basis on which case definitions and outcomes are made from routine data is essential

Automated differentiation of incident and prevalent cases in primary care computerised medical records (CMR)

Identifying incident (first or new) episodes of illness is critical in sentinel networks to inform about the seasonal onset of diseases and to give early warning of epidemics, as well as differentiating change in health service utilization from change in pattern of disease. The most reliable way of differentiating incident from prevalent cases is through the clinician assigning episode type to the patient's computerized medical record (CMR). However, episode type assignment is often made inconsistently. The objective of this collaborative study between the Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), University of Surrey and the National Physical Laboratory (NPL) is to develop a methodology to reconstruct missing or miscoded episode types. The data, gathered from the RCGP RSC network of over 230 practices, are analyzed and poor episode typing reconstructed by disease type. The methodology is tested in practices with good episode type data quality. This method could be used to improve prediction of epidemics, and to improve the quality of historical rates retrospectively

Automated differentiation of incident and prevalent cases in primary care computerised medical records (CMR)

Identifying incident (first or new) episodes of illness is critical in sentinel networks to inform about the seasonal onset of diseases and to give early warning of epidemics, as well as differentiating change in health service utilization from change in pattern of disease. The most reliable way of differentiating incident from prevalent cases is through the clinician assigning episode type to the patient's computerized medical record (CMR). However, episode type assignment is often made inconsistently. The objective of this collaborative study between the Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), University of Surrey and the National Physical Laboratory (NPL) is to develop a methodology to reconstruct missing or miscoded episode types. The data, gathered from the RCGP RSC network of over 230 practices, are analyzed and poor episode typing reconstructed by disease type. The methodology is tested in practices with good episode type data quality. This method could be used to improve prediction of epidemics, and to improve the quality of historical rates retrospectively

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe