scholarly journals Erratum to: Missed diagnostic opportunities and English general practice: a study to determine their incidence, confounding and contributing factors and potential impact on patients through retrospective review of electronic medical records

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Sudeh Cheraghi-Sohi ◽  
Hardeep Singh ◽  
David Reeves ◽  
Jill Stocks ◽  
Morris Rebecca ◽  
...  
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

2014 ◽  
Vol 99 (8) ◽  
pp. 2729-2735 ◽  
Author(s):  
Wei-Yih Chiu ◽  
Jung-Yien Chien ◽  
Wei-Shiung Yang ◽  
Jyh-Ming Jimmy Juang ◽  
Jang-Jaer Lee ◽  
...  

Background: This study aimed to explore the possible association between osteonecrosis of the jaws (ONJ) and oral alendronate or raloxifene used for osteoporosis and to estimate its absolute and attributable risks in the Taiwanese population. Methods: Using an electronic medical records system and manual confirmation of ONJ, we identified patients who began taking alendronate or raloxifene for osteoporosis and developed ONJ between January 2000 and April 2012. Results: The incidence of ONJ associated with oral alendronate for the management of osteoporosis began after 1 year of drug exposure and progressively increased with longer durations of therapy, specifically from 0.23% to 0.92% as the duration of treatment went from 2 years to 10 years. The overall frequency of ONJ related to oral alendronate over a 12-year period was 0.55%. The incidence rate of ONJ attributed to alendronate exposure was 283 per 100 000 persons per year. On multivariate Cox proportional analysis, adjusting for the potential confounders, alendronate remains an independent predictor for ONJ occurrence [hazard ratio 7.42 (1.02–54.09)] compared with raloxifene. Advanced age, drug duration, and coexisting diabetes and rheumatoid arthritis are contributing factors to the development of oral alendronate-related ONJ. Conclusion: We provided the evidence to support the association of ONJ with oral alendronate used in the treatment or prevention of osteoporosis.


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


2020 ◽  
Vol 14 (6) ◽  
pp. 605-609
Author(s):  
Carla De Oliveira Bernardo ◽  
David Alejandro González‐Chica ◽  
Monique Chilver ◽  
Nigel Stocks

1996 ◽  
Vol 26 (2) ◽  
pp. 94-96 ◽  
Author(s):  
Tarun Weeramanthri

The author reviews the politics, publicity, methods and findings of the Quality in Australian Health Care Study, which was released to a blaze of media attention in 1995. The study is a significant contribution to the growing literature on the identification and categorisation of preventable adverse events, using expert retrospective review of medical records, and a mix of explicit and implicit criteria. However, its potential impact has been lessened by the way its findings were released.


2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Carla Bernardo ◽  
David Gonzalez ◽  
Nigel Stocks

Abstract Background Influenza is a respiratory infection responsible for 645,000 annual deaths worldwide. Surveillance systems provide valuable data for monitoring influenza in order to detect outbreaks and guide public health responses. This study aimed to investigate the epidemiology of influenza-like illness (ILI) using two Australian general practice databases (MedicineInsight and the Australian Sentinel Practice Research Network (ASPREN)) and compare them with laboratory-confirmed influenza from the National Notifiable Diseases Surveillance System (NNDSS). Methods All patients who had a consultation in MedicineInsight general practices or ASPREN and all laboratory-confirmed influenza reported by the NNDSS between 2015-2017 were included. Weekly ILI rates per 1,000 consultations (MedicineInsight/ASPREN) were compared with influenza notifications (NNDSS). Results Data was consistent among sources, with higher cases in 2017, among women and patients aged 20-49 years. The peak rate in MedicineInsight almost doubled in 2017 compared to 2015, while in ASPREN it was less pronounced. MedicineInsight ILI curves more closely resembled NNDSS patterns (shape, the start of the season, peaks) than ASPREN, although both were highly correlated with NNDSS (r = 0.90 to 0.97 and r = 0.88 to 0.98, respectively). Conclusions MedicineInsight and ASPREN provided consistent ILI results, both resembling confirmed influenza epidemic curves, suggesting the potential use of routinely collected electronic medical records (MedicineInsight) in influenza surveillance. MedicineInsight provides comprehensive medical data, such as underlying conditions, medications prescribed and vaccination status, which could be used to improve accuracy on influenza detection. Key messages Electronic medical records could be used to monitor ILI in combination with ASPREN for effective early detection of outbreaks.


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