scholarly journals Influenza‐like illness in Australia: A comparison of general practice surveillance system with electronic medical records

2020 ◽  
Vol 14 (6) ◽  
pp. 605-609
Author(s):  
Carla De Oliveira Bernardo ◽  
David Alejandro González‐Chica ◽  
Monique Chilver ◽  
Nigel Stocks
2018 ◽  
Author(s):  
Cheng-Yi Yang ◽  
Ray-Jade Chen ◽  
Wan-Lin Chou ◽  
Yuarn-Jang Lee ◽  
Yu-Sheng Lo

BACKGROUND Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. OBJECTIVE We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. METHODS First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. RESULTS Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. CONCLUSIONS Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations.


2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Michael J van den Berg ◽  
Mieke Cardol ◽  
Frans JM Bongers ◽  
Dinny H de Bakker

2015 ◽  
Vol 65 (634) ◽  
pp. e305-e311 ◽  
Author(s):  
Matthew J Ridd ◽  
Diana L Santos Ferreira ◽  
Alan A Montgomery ◽  
Chris Salisbury ◽  
William Hamilton

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Carla Bernardo ◽  
David Gonzalez-Chica ◽  
Jackie Roseleur ◽  
Luke Grzeskowiak ◽  
Nigel Stocks

Abstract Focus and outcomes for participants Modern technologies offer innovative ways of monitoring health outcomes. Electronic medical records (EMRs) stored in primary care databases provide comprehensive data on infectious and chronic conditions such as diagnosis, medications prescribed, vaccinations, laboratory results, and clinical assessments. Moreover, they allow the possibility of creating a retrospective cohort that can be tracked over time. This rich source of data can be used to generate results that support health policymakers to improve access, reduce health costs, and increase the quality of care. The symposium will discuss the use and future of routinely-collected EMR databases in monitoring health outcomes, using as an example studies based on the MedicineInsight program, a large general practice Australian database including more than 3.5 million patients. This symposium welcomes epidemiologists, researchers and health policymakers who are interested in primary care settings, big data analysis, and artificial intelligence. Rationale for the symposium, including for its inclusion in the Congress EMRs are becoming an important tool for monitoring health outcomes in different high-income countries and settings. However, most countries lack a national primary care database collating EMRs for research purposes. Monitoring of population health conditions is usually performed through surveys, surveillance systems, or census that tend to be expensive or performed over longer time intervals. In contrast, EMR databases are a useful and low-cost method to monitor health outcomes and have shown consistent results compared to other data sources. Although these databases only include individuals attending primary health settings, they tend to resemble the sociodemographic distribution from census data, as in countries such as Australia up to 90% of the population visit these services annually. Results from primary care-based EMRs can be used to inform practices and improve health policies. Analysis from EMRs can be used to identify, for example, those with undiagnosed medical conditions or patients who have not received recommended screenings or immunisations, therefore assessing the impact of government programmes. At a practice-level, healthcare staff can have better access to comprehensive patient histories, improving monitoring of people with certain conditions, such as chronic cardiac, respiratory, metabolic, neurological, or immunological diseases. This information provides feedback to primary care providers about the quality of their care and might help them develop targeted strategies for the most-needed areas or groups. Another benefit of EMRs is the possibility of using statistical modelling and machine learning to improve prediction of health outcomes and medical management, supporting general practitioners with decision making on the best management approach. In Australia, the MedicineInsight program is a large general practice database that since 2011 has been routinely collecting information from over 650 general practices varying in size, billing methods, and type of services offered, and from all Australian states and regions. In the last few years, diverse researchers have used MedicineInsight to investigate infectious and chronic diseases, immunization coverage, prescribed medications, medical management, and temporal trends in primary care. Despite being initially created for monitoring how medicines and medical tests are used, MedicineInsight has overcome some of the legal, ethical, social and resource-related barriers associated with the use of EMRs for research purposes through the involvement of a data governance committee responsible for the ethical, privacy and security aspects of any research using this data, and through applying data quality criteria to their data extraction. This symposium will discuss advances in the use of primary care databases for monitoring health outcomes using as an example the research activities performed based on the Australian MedicineInsight program. These discussions will also cover challenges in the use of this database and possible methodological innovations, such as statistical modelling or machine learning, that could be used to improve monitoring of the epidemiology and management of health conditions. Presentation program The use of large general practice databases for monitoring health outcomes in Australia: infectious and chronic conditions (Professor Nigel Stocks) How routinely collected electronic health records from MedicineInsight can help inform policy, research and health systems to improve health outcomes (Ms Rachel Hayhurst) Influenza-like illness in Australia: how can we improve surveillance systems in Australia using electronic medical records? (Dr Carla Bernardo) Long term use of opioids in Australian general practice (Dr David Gonzalez) Using routinely collected electronic health records to evaluate Quality Use of Medicines for women’s reproductive health (Dr Luke Grzeskowiak) The use of electronic medical records and machine learning to identify hypertensive patients and factors associated with controlled hypertension (Ms Jackie Roseleur) Names of presenters Professor Nigel Stocks, The University of Adelaide Ms Rachel Hayhurst, NPS MedicineWise Dr Carla Bernardo, The University of Adelaide Dr David Gonzalez-Chica, The University of Adelaide Dr Luke Grzeskowiak, The University of Adelaide Ms Jackie Roseleur, The University of Adelaide


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