scholarly journals 806Advances in the use of primary care databases for monitoring health outcomes in Australia

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

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.


Author(s):  
Olufunso W Odunukan ◽  
Vwaire Orhurhu ◽  
Macaulay Nwojo ◽  
Ahmed S Rahman ◽  
James M Naessens ◽  
...  

Background: Hypertension (HTN) control rate is a core clinical quality measure. It is presently assessed by manual review of a random sample of patients with HTN billing claims. There is an increasing push by regulators towards the use of electronic medical records (EMR) to assess quality of care. Purpose: To use EMR to obtain a more in-depth assessment of quality of hypertension care. Methods: Cross sectional study involving adults with HTN seen at 2 or more office visits in 2009 by eligible primary or specialist providers at a large medical group practice. We measured the proportion of HTN patients with blood pressure (BP) at goal (BP < 140/90 mm Hg) at their last hypertension visit (LHV). Results: Of a total of 10,401 patients, 5970 (57%) were controlled and 1959 (19%) were uncontrolled at their LHV. Control could not be assessed in 2472 (24%) as BP values were unavailable from the vital signs section of the visit notes in the EMR. Of a random sample of 250 patients with unavailable BP, only 94 (37.6%) had BP values documented in other parts of the visit note. In 29 patients (11.6%) billing for HTN was done without adequate evidence that it was addressed at that visit. Control rates were highest for primary care providers where measurement and documentation of BP was a focus - Family Medicine 77% controlled, 1% unavailable; Primary Care Internal Medicine 71% controlled, 4% unavailable. A considerable proportion of patients in the other four provider groups did not have BP available from their last hypertension visit - Cardiology 59% controlled, 22% unavailable; Nephrology 38% controlled, 47% unavailable; Preventive Medicine 48% controlled, 39% unavailable; General Internal Medicine 41% controlled, 45% unavailable. Control rates were similar across provider groups when patients with unavailable BP were excluded: Family Medicine 78%, Primary Care 74%, Cardiology 75%, Nephrology 73%, Preventive Medicine 79%, and General Medicine 75%. Conclusions: Up to a quarter of HTN patients did not have properly documented BP in the EMR at their LHV. For those with measurements recorded, control rates were similar across primary care and specialty clinics, but measurement rates were higher for primary care practices. Use of the EMR facilitates efficient and granular assessment of the measurement process and control rates for HTN among various provider groups.


Cyber Crime ◽  
2013 ◽  
pp. 891-901
Author(s):  
Jingquan Li ◽  
Michael J. Shaw

The continued growth of healthcare information systems (HCIS) promises to improve quality of care, reduce harmful medical errors, and streamline the entire healthcare system. But the resulting dependence on electronic medical records (EMRs) has kindled patient concern about who has access to sensitive medical records. Healthcare organizations are obliged to protect patient medical records under the Health Insurance Portability and Accountability Act (HIPAA) of 1996 and the economic stimulus bill of 2009. The purpose of this study is to develop a formal privacy policy for safeguarding the privacy of EMRs. This study describes the impact of EMRs and HIPAA on patient privacy. It proposes access control and audit logs policies to protect patient privacy. To illustrate the best practices in the healthcare industry, this chapter presents the case of the University of Texas M. D. Anderson Cancer Center. The case demonstrates that it is critical for a healthcare organization to have a formal privacy policy in place.


Author(s):  
Jingquan Li ◽  
Michael J. Shaw

The continued growth of healthcare information systems (HCIS) promises to improve quality of care, reduce harmful medical errors, and streamline the entire healthcare system. But the resulting dependence on electronic medical records (EMRs) has kindled patient concern about who has access to sensitive medical records. Healthcare organizations are obliged to protect patient medical records under the Health Insurance Portability and Accountability Act (HIPAA) of 1996 and the economic stimulus bill of 2009. The purpose of this study is to develop a formal privacy policy for safeguarding the privacy of EMRs. This study describes the impact of EMRs and HIPAA on patient privacy. It proposes access control and audit logs policies to protect patient privacy. To illustrate the best practices in the healthcare industry, this chapter presents the case of the University of Texas M. D. Anderson Cancer Center. The case demonstrates that it is critical for a healthcare organization to have a formal privacy policy in place.


2021 ◽  
Vol 12 ◽  
Author(s):  
Mary J. Kwasny ◽  
Denise M. Oleske ◽  
Jorge Zamudio ◽  
Robert Diegidio ◽  
Günter U. Höglinger

Background: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disorder that is difficult for primary care physicians to recognize due to its progressive nature and similarities to other neurologic disorders. This case-control study aimed to identify clinical features observed in general practice associated with a subsequent diagnosis of PSP.Methods: We analyzed a de-identified dataset of 152 PSP cases and 3,122 matched controls from electronic medical records of general practices in Germany. We used a random forests algorithm based on machine learning techniques to identify clinical features (medical conditions and treatments received) associated with pre-diagnostic PSP without using an a priori hypothesis. We then assessed the relative effects of the features with the highest importance scores and generated multivariate models using clustered logistic regression analyses to identify a subset of clinical features associated with subsequent PSP diagnosis.Results: Using the random forests approach, we identified 21 clinical features associated with pre-diagnostic PSP (odds ratio ≥2.0 in univariate analyses). From these, we constructed a multivariate model comprising 9 clinical features with ~90% likelihood of identifying a subsequent PSP diagnosis. These features included known PSP symptoms, common misdiagnoses, and 2 novel associations, diabetes mellitus and cerebrovascular disease, which are possible modifiable risk factors for PSP.Conclusion: In this case-control study using data from electronic medical records, we identified 9 clinical features, including 2 previously unknown factors, associated with the pre-diagnostic stage of PSP. These may be used to facilitate recognition of PSP and reduce time to referral by primary care physicians.


Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 908-P
Author(s):  
SOSTENES MISTRO ◽  
THALITA V.O. AGUIAR ◽  
VANESSA V. CERQUEIRA ◽  
KELLE O. SILVA ◽  
JOSÉ A. LOUZADO ◽  
...  

2021 ◽  
Vol 30 (5) ◽  
pp. 1124-1138
Author(s):  
Elisabet Rodriguez Llorian ◽  
Gregory Mason

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