Development and acceptability of a digital tool for promoting syphilis testing in Australian general practice: qualitative study using the Theoretical Framework of Acceptability.

Background In Australia, syphilis notifications increased 2.5-fold during 2013-2022 and 83 congenital syphilis cases were reported. Timely diagnosis and management are crucial. We developed a tool to promote syphilis testing into our existing 'Future Health Today' (FHT) software and explored its acceptability in general practice. Methods Our tool (FHT-syphilis) scans electronic medical record data to identify and prompt testing for pregnant women, and, people recently tested for sexually transmissible infection (STI) or HIV, but not syphilis. It links to relevant guidelines and patient resources. We implemented FHT-syphilis in 52 general practices using FHT for other conditions and interviewed practice clinicians (n =9) to explore it's acceptability. Data were analysed deductively guided by the Theoretical Framework of Acceptability. Results Interviewees considered syphilis an important infection to focus on and broadly viewed FHT-syphilis as acceptable for identifying patients and giving clinicians authority to discuss syphilis testing. Time constraints and unrelated reasons for a patient's visit were barriers to initiating syphilis testing discussions. Australian STI guidelines were considered appropriate to link to. Some interviewees considered prompts should be based on sexual behaviour, however this is not well captured in the electonic medical record. Two interviewees were alerted to updated Australian STI guidelines via their interaction with FHT-syphilis and expanded their syphilis testing practices. Expertise to initiate discussions about syphilis and risk was deemed important. Conclusions A digital tool for prompting syphilis testing was acceptable to clinicians already using FHT. Linkage to STI guidelines alerted some end-users to updated guidelines, informing STI testing practices.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting.

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.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

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.