ArticleBMJ health & care informatics2019
Coding and classifying GP data: the POLAR project.
Article in BMJ health & care informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Supporting alcohol brief interventions and pharmacotherapy provision in Australian First Nations primary care: exploratory analysis of a cluster randomised trial.BMC primary care · 2024Trial
- No improvement in AUDIT-C screening and brief intervention rates among wait-list controls following support of Aboriginal Community Controlled Health Services: evidence from a cluster randomised trial.BMC health services research · 2024Trial
- Impact of Prescription Drug Monitoring Program Implementation on Rates and Characteristics of People Seeing Multiple Prescribers in Primary Care: A Controlled Interrupted Time-Series Analysis.The Medical journal of Australia · 2026Article
- Article
- Understanding the first prescription: clinical and demographic drivers of strong opioid use in primary care.Pain medicine (Malden, Mass.) · 2025Article
- Impact of codeine rescheduling on prescribing of codeine and other opioids: Interrupted time series analyses using Australian general practice data.British journal of clinical pharmacology · 2025Article
- Placing conditions on sharing general practice data for research: Recommendations from two community juries.International journal of population data science · 2025Article
- Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting.BMC medical informatics and decision making · 2024Article
- Community views on the secondary use of general practice data: Findings from a mixed-methods study.Health expectations : an international journal of public participation in health care and health policy · 2024Article
- Automated extraction of standardized antibiotic resistance and prescription data from laboratory information systems and electronic health records: a narrative review.Frontiers in antibiotics · 2024Review
- Changes in opioid agonist treatment initiation among people prescribed opioids for pain following voluntary and mandatory prescription drug monitoring program implementation: A time series analysis.Drug and alcohol review · 2023Article
- Determining the Impact of Opioid Policy on Substance Use and Mental Health-Related Harms: Protocol for a Data Linkage Study.JMIR research protocols · 2023Article
- Integrated Management Systems (IMS) to Support and Sustain Quality One Health Services: International Lessons from the COVID-19 Pandemic by the IMIA Primary Care Working Group.Yearbook of medical informatics · 2023Article
- Article
- Primary healthcare needs and service utilisation of people with disability: a data linkage protocol.BMJ open · 2023Article
- Prevalence and characteristics of musculoskeletal complaints in primary care: an analysis from the population level and analysis reporting (POLAR) database.BMC primary care · 2023Article
- Telehealth use in patients with type 2 diabetes in Australian general practice during the COVID-19 pandemic: a retrospective cohort study.BJGP open · 2022Article
- Responding to COVID-19 with real-time general practice data in Australia.International journal of medical informatics · 2022Article
- Article
- COVID-19: protocol for observational studies utilizing near real-time electronic Australian general practice data to promote effective care and best-practice policy-a design thinking approach.Health research policy and systems · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData, particularly 'big' data are increasingly being used for research in health. Using data from electronic medical records optimally requires coded data, but not all systems produce coded data.
objectiveTo design a suitable, accurate method for converting large volumes of narrative diagnoses from Australian general practice records to codify them into SNOMED-CT-AU. Such codification will make them clinically useful for aggregation for population health and research purposes.
methodThe developed method consisted of using natural language processing to automatically code the texts, followed by a manual process to correct codes and subsequent natural language processing re-computation. These steps were repeated for four iterations until 95% of the records were coded. The coded data were then aggregated into classes considered to be useful for population health analytics.
resultsCoding the data effectively covered 95% of the corpus. Problems with the use of SNOMED CT-AU were identified and protocols for creating consistent coding were created. These protocols can be used to guide further development of SNOMED CT-AU (SCT). The coded values will be immensely useful for the development of population health analytics for Australia, and the lessons learnt applicable elsewhere.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.