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Background: The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. Methods: We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. Results: Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. Conclusion: The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes. [ABSTRACT FROM AUTHOR]

The use of cannabis for medicinal purposes has increased globally over the past decade since patient access to medicinal cannabis has been legislated across jurisdictions in Europe, the United Kingdom, the United States, Canada, and Australia. Yet, evidence relating to the effect of medical cannabis on the management of symptoms for a suite of conditions is only just emerging. Although there is considerable engagement from many stakeholders to add to the evidence base through randomized controlled trials, many gaps in the literature remain. Data from real-world and patient reported sources can provide opportunities to address this evidence deficit. This real-world data can be captured from a variety of sources such as found in routinely collected health care and health services records that include but are not limited to patient generated data from medical, administrative and claims data, patient reported data from surveys, wearable trackers, patient registries, and social media. In this systematic scoping review, we seek to understand the utility of online user generated text into the use of cannabis as a medicine. In this scoping review, we aimed to systematically search published literature to examine the extent, range, and nature of research that utilises user-generated content to examine to cannabis as a medicine. The objective of this methodological review is to synthesise primary research that uses social media discourse and internet search engine queries to answer the following questions: (i) In what way, is online user-generated text used as a data source in the investigation of cannabis as a medicine? (ii) What are the aims, data sources, methods, and research themes of studies using online user-generated text to discuss the medicinal use of cannabis. We conducted a manual search of primary research studies which used online user-generated text as a data source using the MEDLINE, Embase, Web of Science, and Scopus databases in October 2022. Editorials, letters, commentaries, surveys, protocols, and book chapters were excluded from the review. Forty-two studies were included in this review, twenty-two studies used manually labelled data, four studies used existing meta-data (Google trends/geo-location data), two studies used data that was manually coded using crowdsourcing services, and two used automated coding supplied by a social media analytics company, fifteen used computational methods for annotating data. Our review reflects a growing interest in the use of user-generated content for public health surveillance. It also demonstrates the need for the development of a systematic approach for evaluating the quality of social media studies and highlights the utility of automatic processing and computational methods (machine learning technologies) for large social media datasets. This systematic scoping review has shown that user-generated content as a data source for studying cannabis as a medicine provides another means to understand how cannabis is perceived and used in the community. As such, it provides another potential 'tool' with which to engage in pharmacovigilance of, not only cannabis as a medicine, but also other novel therapeutics as they enter the market. [ABSTRACT FROM AUTHOR]

Amidst growing global acceptance of medicinal cannabinoids as a potential therapeutic interest in cannabidiol (CBD) is increasing. In Australia in 2020, a government inquiry examined the barriers that the public are experiencing in accessing medicinal cannabis. A number of recommendations to improve access were made. In response to these recommendations, the Australian therapeutics regulatory authority down-scheduled CBD from Prescription Only (Schedule 4) to Pharmacist Only (Schedule 3). As a group of early to mid-career researchers of the Australian Centre for Cannabinoid Clinical and Research Excellence (ACRE), we propose some considerations in relation to over-the-counter availability of CBD and opportunities to improve knowledge about its potential therapeutic benefits alongside its increased uptake. [ABSTRACT FROM AUTHOR]

Background: Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. Methods: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. Results: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57–36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices. Conclusion: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes. [ABSTRACT FROM AUTHOR]

Objective: To understand how the COVID‐19 pandemic has impacted sexual and reproductive health (SRH) visits. Design: An ecological study comparing SRH services volume in different countries before and after the onset of the COVID‐19 pandemic. Setting: Seven countries from the INTernational ConsoRtium of Primary Care BIg Data Researchers (INTRePID) across four continents. Population: Over 3.8 million SRH visits to primary care physicians in Australia, China, Canada, Norway, Singapore, Sweden and the USA. Methods: Difference in average SRH monthly visits before and during the pandemic, with negative binomial regression modelling to compare predicted and observed number of visits during the pandemic for SRH visits. Main outcome measures: Monthly number of visits to primary care physicians from 2018 to 2021. Results: During the pandemic, the average volume of monthly SRH visits increased in Canada (15.6%, 99% CI 8.1–23.0%) where virtual care was pronounced. China, Singapore, Sweden and the USA experienced a decline (−56.5%, 99% CI −74.5 to −38.5%; −22.7%, 99% CI −38.8 to −6.5%; −19.4%, 99% CI −28.3 to −10.6%; and −22.7%, 99% CI −38.8 to −6.5%, respectively); while Australia and Norway showed insignificant changes (6.5%, 99% CI −0.7 to –13.8% and 1.7%, 99% CI −6.4 to –9.8%). The countries that maintained (Australia, Norway) or surpassed (Canada) pre‐pandemic visit rates had the greatest use of virtual care. Conclusions: In‐person SRH visits to primary care decreased during the pandemic. Virtual care seemed to counterbalance that decline. Although cervical cancer screening appeared insensitive to virtual care, strategies such as incorporating self‐collected samples for HPV testing may provide a solution in a future pandemic. [ABSTRACT FROM AUTHOR]

Chronic disease identification and management is a significant issue in Australia, with general practice being the primary contact point for those at risk of, or living with, chronic disease. However, there is a well-described gap between guideline recommendations for chronic disease management and translation in the general practice setting. In 2018, a group of researchers, clinicians and software developers collaborated to develop a tool to support the identification and management of chronic disease in general practice, with the aim to create a platform that met the needs of general practice. The co-design process drew together core principles and expectations for the establishment of a technological platform, called Future Health Today (FHT), which would sit alongside the electronic medical record (EMR) management system within general practice. FHT used algorithms applied to EMR data to identify patients with, or at risk of, chronic disease and requiring review. Using chronic kidney disease as a clinical focus, the FHT prototype was piloted in a large, metropolitan general practice, and a large regional general practice. Based on user feedback, the prototype was further developed and improved. This paper provides a report on the key features and functionalities that participants identified and implemented in practice. Future Health Today is a new platform, co-designed by general practice for general practice, which aims to streamline the identification and management of chronic disease to improve health outcomes. This paper describes the development of the technology platform and how it was optimised through an implementation study in general practice. [ABSTRACT FROM AUTHOR]

Background: Patient interest in the use of cannabis-based medicines (CBMs) has increased in Australia. While recent policy and legislative changes have enabled health practitioners to prescribe CBMs for their patients, many patients still struggle to access CBMs. This paper employed a thematic analysis to submissions made to a 2019 Australian government inquiry into current barriers of patient access to medical cannabis. Methods: We identified 121 submissions from patients or family members (n = 63), government bodies (n = 5), non-government organisations (i.e., professional health bodies, charities, consumer organisations or advocacy groups; n = 25), medical cannabis and pharmaceutical industry (n = 16), and individual health professionals, academics, or research centres (n = 12). Data were coded using NVivo 12 software and thematically analysed. The findings were presented narratively using a modified Levesque's patient-centred access to care framework which includes: i) appropriateness; ii) availability and geographic accessibility; iii) acceptability; and iv) affordability. Results: Submissions from government agencies and professional health bodies consistently supported maintaining the current regulatory frameworks and access pathways, whereas an overwhelming majority of patients, advocacy groups and the medical cannabis industry described the current regulatory and access models as 'not fit for purpose'. These differing views seem to arise from divergent persepctives on (i) what and how much evidence is needed for policy and practice, and (ii) how patients should be given access to medical cannabis products amidst empirical uncertainty. Notwithstanding these differences, there were commonalities among some stakeholders regarding the various supply, regulatory, legislative, financial, and dispensing challenges that hindered timely access to CBMs. Conclusions: Progress in addressing the fundamental barriers that determine if and how a patient accesses and uses CBMs needs i) a 'system-level' reform that gives due consideration to the geographic disparity in access to prescribers and medical cannabis, and ii) reframing societal and health professional's views of CBMs by decoupling recreational vs medical cannabis. [ABSTRACT FROM AUTHOR]