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.

Activating pharmacists to reduce the frequency of medication-related problems (ACTMed): a stepped wedge cluster randomised trial.

BACKGROUND: Medicines are the most frequent health care intervention type; their safe use provides significant benefits, but inappropriate use can cause harm. Systemic primary care approaches can manage serious medication-related problems in a timely manner. OBJECTIVES: ACTMed (ACTivating primary care for MEDicine safety) uses information technology and financial incentives to encourage pharmacists to work more closely with general practitioners to reduce the risk of harm, improve patients' experience of care, streamline workflows, and increase the efficiency of medical care. METHODS AND ANALYSIS: The stepped wedge cluster randomised trial in 42 Queensland primary care practices will assess the effectiveness of the ACTMed intervention. The primary outcome will be the proportion of people at risk of serious medication-related problems - patients with atrial fibrillation, heart failure, cardiovascular disease, type 2 diabetes, or asthma or chronic obstructive pulmonary disease - who experience such problems. We will also estimate the cost per averted serious medication-related problem and the cost per averted potentially preventable medication-related hospitalisation. ETHICS APPROVAL: The University of Queensland Human Research Ethics Committee approved the pilot (2021/HE002189) and trial phases of the ACTMed study (2022/HE002136). Access to Patron data was granted by the Patron Data Governance Committee (PAT052ACTMed). Access to linked hospitalisations and deaths data are subject to Public Health Act approval (pending). DISSEMINATION OF FINDINGS: A comprehensive dissemination plan will be co-developed by the researchers, the ACTMed steering committee and consumer advisory group, project partners, and trial site representatives. Aboriginal and Torres Strait Islander communities will be supported in leading community-level dissemination. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (pilot: ACTRN12622000595718; 21 April 2022; full trial: ACTRN12622000574741; 14 April 2022).

An evaluation of existing text de-identification tools for use with patient progress notes from Australian general practice.

INTRODUCTION: Digitized patient progress notes from general practice represent a significant resource for clinical and public health research but cannot feasibly and ethically be used for these purposes without automated de-identification. Internationally, several open-source natural language processing tools have been developed, however, given wide variations in clinical documentation practices, these cannot be utilized without appropriate review. We evaluated the performance of four de-identification tools and assessed their suitability for customization to Australian general practice progress notes. METHODS: Four tools were selected: three rule-based (HMS Scrubber, MIT De-id, Philter) and one machine learning (MIST). 300 patient progress notes from three general practice clinics were manually annotated with personally identifying information. We conducted a pairwise comparison between the manual annotations and patient identifiers automatically detected by each tool, measuring recall (sensitivity), precision (positive predictive value), f1-score (harmonic mean of precision and recall), and f2-score (weighs recall 2x higher than precision). Error analysis was also conducted to better understand each tool's structure and performance. RESULTS: Manual annotation detected 701 identifiers in seven categories. The rule-based tools detected identifiers in six categories and MIST in three. Philter achieved the highest aggregate recall (67%) and the highest recall for NAME (87%). HMS Scrubber achieved the highest recall for DATE (94%) and all tools performed poorly on LOCATION. MIST achieved the highest precision for NAME and DATE while also achieving similar recall to the rule-based tools for DATE and highest recall for LOCATION. Philter had the lowest aggregate precision (37%), however preliminary adjustments of its rules and dictionaries showed a substantial reduction in false positives. CONCLUSION: Existing off-the-shelf solutions for automated de-identification of clinical text are not immediately suitable for our context without modification. Philter is the most promising candidate due to its high recall and flexibility however will require extensive revising of its pattern matching rules and dictionaries.

Gathering data for decisions: best practice use of primary care electronic records for research.

In Australia, there is limited use of primary health care data for research and for data linkage between health care settings. This puts Australia behind many developed countries. In addition, without use of primary health care data for research, knowledge about patients' journeys through the health care system is limited. There is growing momentum to establish "big data" repositories of primary care clinical data to enable data linkage, primary care and population health research, and quality assurance activities. However, little research has been conducted on the general public's and practitioners' concerns about secondary use of electronic health records in Australia. International studies have identified barriers to use of general practice patient records for research. These include legal, technical, ethical, social and resource-related issues. Examples include concerns about privacy protection, data security, data custodians and the motives for collecting data, as well as a lack of incentives for general practitioners to share data. Addressing barriers may help define good practices for appropriate use of health data for research. Any model for general practice data sharing for research should be underpinned by transparency and a strong legal, ethical, governance and data security framework. Mechanisms to collect electronic medical records in ethical, secure and privacy-controlled ways are available. Before the potential benefits of health-related data research can be realised, Australians should be well informed of the risks and benefits so that the necessary social licence can be generated to support such endeavours.

Monitoring the Control of Sexually Transmissible Infections and Blood-Borne Viruses: Protocol for the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance (ACCESS).

BACKGROUND: New biomedical prevention interventions make the control or elimination of some blood-borne viruses (BBVs) and sexually transmissible infections (STIs) increasingly feasible. In response, the World Health Organization and governments around the world have established elimination targets and associated timelines. To monitor progress toward such targets, enhanced systems of data collection are required. This paper describes the Australian Collaboration for Coordinated Enhanced Sentinel Surveillance (ACCESS). OBJECTIVE: This study aims to establish a national surveillance network designed to monitor public health outcomes and evaluate the impact of strategies aimed at controlling BBVs and STIs. METHODS: ACCESS is a sentinel surveillance system comprising health services (sexual health clinics, general practice clinics, drug and alcohol services, community-led testing services, and hospital outpatient clinics) and pathology laboratories in each of Australia's 8 states and territories. Scoping was undertaken in each jurisdiction to identify sites that provide a significant volume of testing or management of BBVs or STIs or to see populations with particular risks for these infections ("priority populations"). Nationally, we identified 115 health services and 24 pathology laboratories as relevant to BBVs or STIs; purposive sampling was undertaken. As of March 2018, we had recruited 92.0% (104/113) of health services and 71% (17/24) of laboratories among those identified as relevant to ACCESS. ACCESS is based on the regular and automated extraction of deidentified patient data using specialized software called GRHANITE, which creates an anonymous unique identifier from patient details. This identifier allows anonymous linkage between and within participating sites, creating a national cohort to facilitate epidemiological monitoring and the evaluation of clinical and public health interventions. RESULTS: Between 2009 and 2017, 1,171,658 individual patients attended a health service participating in ACCESS network comprising 7,992,241 consultations. Regarding those with unique BBV and STI-related health needs, ACCESS captured data on 366,441 young heterosexuals, 96,985 gay and bisexual men, and 21,598 people living with HIV. CONCLUSIONS: ACCESS is a unique system with the ability to track efforts to control STIs and BBVs-including through the calculation of powerful epidemiological indicators-by identifying response gaps and facilitating the evaluation of programs and interventions. By anonymously linking patients between and within services and over time, ACCESS has exciting potential as a research and evaluation platform. Establishing a national health surveillance system requires close partnerships across the research, government, community, health, and technology sectors. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11028.

Using web technology to support population-based diabetes care.

BACKGROUND: Managed clinical networks have been used to coordinate chronic disease management across geographical regions in the United Kingdom. Our objective was to review how clinical networks and multidisciplinary team-working can be supported by Web-based information technology while clinical requirements continually change. METHODS: A Web-based population information system was developed and implemented in November 2000. The system incorporates local guidelines and shared clinical information based upon a national dataset for multispecialty use. Automated data linkages were developed to link to the master index database, biochemistry, eye screening, and general practice systems and hospital diabetes clinics. Web-based data collection forms were developed where computer systems did not exist. The experience over the first 10 years (to October 2010) was reviewed. RESULTS: The number of people with diabetes in Tayside increased from 9694 (2.5% prevalence) in 2001 to 18,355 (4.6%) in 2010. The user base remained stable (~400 users), showing a high level of clinical utility was maintained. Automated processes support a single point of data entry with 10,350 clinical messages containing 40,463 data items sent to external systems during year 10. The system supported quality improvement of diabetes care; for example, foot risk recording increased from 36% in 2007 to 73.3% in 2010. CONCLUSIONS: Shared-care datasets can improve communication between health care service providers. Web-based technology can support clinical networks in providing comprehensive, seamless care across a geographical region for people with diabetes. While health care requirements evolve, technology can adapt, remain usable, and contribute significantly to quality improvement and working practice.