Telehealth Uptake and Impact on Care Activities in Australian General Practice During the COVID-19 Pandemic.

The COVID-19 pandemic necessitated a shift in the delivery of patient care, with telehealth rapidly scaled to facilitate access to care while reducing risks of COVID-19 transmission. In this paper, we present an overview of key findings regarding telehealth use from a large program of work examining the impact of the pandemic on general practice activity in Australia. Our findings demonstrate the pivotal role telehealth played in enabling patient access to care during the first two years of the pandemic. Importantly, however, we identified several facets of telehealth use including equitable access, workflow and infrastructure, and adequate funding, which require attention to optimise telehealth services in practice.

Classification of Long COVID from General Practitioner Diagnosis Text.

Major challenges exist in identifying Long COVID patients from diagnosis texts recorded by general practitioners. A classification framework is proposed that can be used to identify Long COVID patients given these unstructured diagnostic texts. This framework can be leveraged to provide a general understanding of the risk factors, management strategies, and outcomes associated with Long COVID in Australia.

Primary healthcare needs and service utilisation of people with disability: a data linkage protocol.

INTRODUCTION: General practitioners (GPs) play a crucial role in the early management and treatment of the comorbidities and complications experienced by people with disability. However, GPs experience multiple constraints, including limited time and disability-related expertise. Knowledge gaps around the health needs of people with disability as well as the frequency and extent of their engagement with GPs mean evidence to inform practice is limited. Using a linked dataset, this project aims to enhance the knowledge of the GP workforce by describing the health needs of people with disability. METHODS AND ANALYSIS: This project is a retrospective cohort study using general practice health records from the eastern Melbourne region in Victoria, Australia. The research uses Eastern Melbourne Primary Health Network (EMPHN)-owned de-identified primary care data from Outcome Health's POpulation Level Analysis and Reporting Tool (POLAR). The EMPHN POLAR GP health records have been linked with National Disability Insurance Scheme (NDIS) data. Data analysis will involve comparisons across disability groups and the rest of the population to explore utilisation (eg, frequency of visits), clinical and preventative care (eg, cancer screening, blood pressure readings) and health needs (eg, health conditions, medications). Initial analyses will focus on NDIS participants as a whole and NDIS participants whose condition is either an acquired brain injury, stroke, spinal cord injury, multiple sclerosis or cerebral palsy, as classified by the NDIS. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Eastern Health Human Research Ethics Committee (E20/001/58261), and approval for the general collection, storage and transfer of data was from the Royal Australian College of General Practitioners National Research Ethics and Evaluation Committee (protocol ID: 17-088). Dissemination mechanisms will include the engagement of stakeholders through reference groups and steering committees, as well as the production of research translation resources in parallel with peer-reviewed publications and conference presentations.

Sociodemographic determinants of telehealth utilisation in general practice during the COVID-19 pandemic in Australia.

This analysis assessed the sociodemographic characteristics of telehealth utilisation during the coronavirus disease 2019 (COVID-19) pandemic from March 2020 to August 2021 in Australia. Drawing on 860 general practice providers among 3 161 868 patients, 24 527 274 consultations were recorded. Telehealth accounted for 37.6% of the consultations, with 2.4% through videoconferencing and 35.2% through phone consultations. Our multivariate regression analyses indicated low utilisation of videoconferencing compared with phone consultations among older adults, those living in rural communities and migrants from non-English speaking countries.

A framework for conducting policy-relevant primary care research: a COVID-19 case study in Australia.

The onset of the coronavirus disease 2019 (COVID-19) pandemic, caused by SARS-CoV-2, and the ensuing implementation of response measures directly impacted the delivery of Australian primary care services. Understanding how these measures affected practice activity is important for gauging both their effectiveness and implications for future service planning. During the first 2years of the COVID-19 pandemic, a research project was undertaken to determine the impact of the pandemic on Australian general practice activity as a collaborative undertaking between researchers, general practitioners, data custodians, and five primary health networks from New South Wales and Victoria, Australia. The project methodology was based on an established research approach called action research, which involves participatory involvement from key stakeholders throughout the research process. The strength and success of the project's methodological approach stemmed from the synergistic interrelationship between the four key elements of: collaboration, repeated action research cycles (utilising electronic general practice data), engaged governance, and the production and dissemination of apposite knowledge outcomes. The project approach, knowledge outputs and lessons learned can be adapted to future research undertakings across any primary care setting and highlight the utility of action research and interdisciplinary research collaboration to produce knowledge directly relevant to clinical practice.

Telehealth utilisation in residential aged care facilities during the COVID-19 pandemic: A retrospective cohort study in Australian general practice.

INTRODUCTION: Our earlier analysis during the COVID-19 surges in 2020 showed a reduction in general practitioner (GP) in-person visits to residential aged care facilities (RACFs) and increased use of telehealth. This study assessed how sociodemographic characteristics affected telehealth utilisation. METHODS: This retrospective cohort consists of 27,980 RACF residents aged 65 years and over, identified from general practice electronic health records in Victoria and New South Wales during March 2020-August 2021. Residents' demographic characteristics, including age, sex, region, and pension status, were analysed to estimate the odds ratio (OR) and 95% confidence interval (CI) for the associations with telehealth utilisation (telephone/video vs. in-person consultations) and with video versus telephone consultations, in mixed-effects multiple level regression models. RESULTS: Of 32,330 median monthly GP consultations among 21,987 residents identified in 2020, telehealth visits accounted for 17% of GP consultations, of which 93% were telephone consults. In 2021, of 32,229 median monthly GP consultations among 22,712 residents, telehealth visits accounted for 11% of GP consultations (97% by telephone). Pension holders (OR: 1.14; 95% CI: 1.10, 1.17) and those residing in rural areas (OR: 1.72; 95% CI: 1.57, 1.90) were more likely to use telehealth. However, residents in rural areas were less likely to use video than telephone in GP consultations (OR: 0.41; 95% CI: 0.29, 0.57). Results were similar in separate analyses for each COVID surge. DISCUSSION: Telephone was primarily used in telehealth consultations among pension holders and rural residents in RACFs. Along with the limited use of video in virtual care in rural RACFs, the digital divide may imply potential healthcare disparities in socially disadvantaged patients.

Responding to COVID-19 with real-time general practice data in Australia.

INTRODUCTION: As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies. METHODS: The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures. RESULTS: Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement. CONCLUSIONS: The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response.

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.

BACKGROUND: Health systems around the world have been forced to make choices about how to prioritize care, manage infection control and maintain reserve capacity for future disease outbreaks. Primary healthcare has moved into the front line as COVID-19 testing transitions from hospitals to multiple providers, where tracking testing behaviours can be fragmented and delayed. Pooled general practice data are a valuable resource which can be used to inform population and individual care decision-making. This project aims to examine the feasibility of using near real-time electronic general practice data to promote effective care and best-practice policy. METHODS: The project will utilize a design thinking approach involving all collaborators (primary health networks [PHNs], general practices, consumer groups, researchers, and digital health developers, pathology professionals) to enhance the development of meaningful and translational project outcomes. The project will be based on a series of observational studies utilizing near real-time electronic general practice data from a secure and comprehensive digital health platform [POpulation Level Analysis and Reporting (POLAR) general practice data warehouse]. The study will be carried out over 1.5 years (July 2020-December 2021) using data from over 450 general practices within three Victorian PHNs and Gippsland PHN, Eastern Melbourne PHN and South Eastern Melbourne PHN, supplemented by data from consenting general practices from two PHNs in New South Wales, Central and Eastern Sydney PHN and South Western Sydney PHN. DISCUSSION: The project will be developed using a design thinking approach, leading to the building of a meaningful near real-time COVID-19 geospatial reporting framework and dashboard for decision-makers at community, state and nationwide levels, to identify and monitor emerging trends and the impact of interventions/policy decisions. This will integrate timely evidence about the impact of the COVID-19 pandemic related to its diagnosis and treatment, and its impact across clinical, population and general practice levels.

Influence of serum iron test results on the diagnosis of iron deficiency in children: a retrospective observational study.

BACKGROUND AND OBJECTIVE: Serum iron results are not indicative of iron deficiency yet may be incorrectly used to diagnose iron deficiency instead of serum ferritin results. Our objective was to determine the association between serum iron test results and iron-deficiency diagnosis in children by general practitioners. DESIGN, SETTING, PATIENTS AND MAIN OUTCOME MEASURES: A retrospective observational study of 14 187 children aged 1-18 years with serum ferritin and serum iron test results from 137 general practices in Victoria, Australia, between 2008 and 2018. Generalised estimating equation models calculating ORs were used to determine the association between serum iron test results (main exposure measure) and iron-deficiency diagnosis (outcome measure) in the following two population groups: (1) iron-deplete population, defined as having a serum ferritin <12 µg/L if aged <5 years and <15 µg/L if aged ≥5 years and (2) iron-replete population, defined as having a serum ferritin >30 µg/L. RESULTS: 3484 tests were iron deplete and 15 528 were iron replete. Iron-deplete children were less likely to be diagnosed with iron deficiency if they had normal serum iron levels (adjusted OR (AOR): 0.73; 95% CI 0.57 to 0.96). Iron-replete children had greater odds of an iron-deficiency diagnosis if they had low serum iron results (AOR: 2.59; 95% CI 1.72 to 3.89). Other contributors to an iron-deficiency diagnosis were female sex and having anaemia. CONCLUSION: Serum ferritin alone remains the best means of diagnosing iron deficiency. Reliance on serum iron test results by general practitioners is leading to significant overdiagnosis and underdiagnosis of iron deficiency in children.

Clinical thresholds for diagnosing iron deficiency: comparison of functional assessment of serum ferritin to population based centiles.

Low serum ferritin is diagnostic of iron deficiency, yet its published lower cut-off values are highly variable, particularly for pediatric populations. Lower cut-off values are commonly reported as 2.5th percentiles, and is based on the variation of ferritin values in the population. Our objective was to determine whether a functional approach based on iron deficient erythropoiesis could provide a better alternative. Utilizing 64,443 ferritin test results from pediatric electronic health records, we conducted various statistical techniques to derive 2.5th percentiles, and also derived functional reference limits through the association between ferritin and erythrocyte parameters: hemoglobin, mean corpuscular volume, mean cell hemoglobin concentration, and red cell distribution width. We find that lower limits of reference intervals derived as centiles are too low for clinical interpretation. Functional limits indicate iron deficiency anemia starts to occur when ferritin levels reach 10 µg/L, and are largely similar between genders and age groups. In comparison, centiles (2.5%) presented with lower limits overall, with varying levels depending on age and gender. Functionally-derived limits better reflects the underlying physiology of a patient, and may provide a basis for deriving a threshold related to treatment of iron deficiency and any other biomarker with functional outcomes.

Harnessing the potential of electronic general practice pathology data in Australia: An examination of the quality use of pathology for type 2 diabetes patients.

BACKGROUND: Despite the importance of pathology testing in diagnosis and disease monitoring, there is little in-depth research about pathology test ordering in general practice and how it impacts patient outcomes. This is in part due to the limited availability of high-quality data. With the now-widespread use of electronic software in general practice comes the potential for electronic patient data to be used for research leading to better understanding of general practice activities, including pathology testing. OBJECTIVES: This study aimed to examine the usefulness of electronic general practice pathology data to: (1) identify patients' characteristics, (2) monitor quality of care, (3) evaluate intervention effects, (4) identify variations in patient care, and (5) measure patient outcomes. An exemplar study evaluating kidney function testing in type 2 diabetes mellitus (type 2 diabetes) compared to guidelines was used to demonstrate the value of pathology data. MATERIALS AND METHODS: De-identified electronic data from approximately 200 general practices in Victoria were extracted using Outcome Health's Population Level Analysis & Reporting (POLAR) Aurora research platform. Our study population included patients ≥18 diagnosed with type 2 diabetes before July 2016. Data from July 2016 to June 2018 were used to i) determine frequency of kidney function tests (KFT), and ii) identify whether antihypertensive medications were prescribed for abnormal KFT results. RESULTS: There were 20,514 active patients with type 2 diabetes identified from the data. The age and gender standardised estimate of diabetes prevalence was 4.9%, consistent with Australian estimates (5.2%). Sociodemographic features of prevalence, including higher prevalence in older males, were also consistent with previous Australian estimates. Kidney function testing was performed annually, as recommended by guidelines, in 75.7% of patients, with higher annual testing observed in patients managed under general practice incentive programs (80.1%) than those who were not (72.2%). Antihypertensive medications were prescribed as recommended in 77.4% of patients with suspected microalbuminuria or macroalbuminuria based on KFT results. DISCUSSION: Evaluations using data from diabetes patients in this study illustrate the value of electronic data for identifying patients with the condition of interest (e.g. type 2 diabetes) along with sociodemographic characteristics. This allows for the ability to undertake analyses on pathology testing factors and the identification of variation compared to guidelines, which has a potential to ensure quality of care. Its potential to identify associations with incentive programs further demonstrates the advantages of the data's longitudinal nature. These include the ability to assess temporal order and time interval of tests as a marker of quality of monitoring and evaluate intervention effects on a cohort over time. Finally, analyses on antihypertensive medication prescribing in patients with suspected micro/macroalbuminuria exemplified the electronic data's usefulness in monitoring patient outcomes, such as appropriate prescribing based on pathology test results. CONCLUSIONS: Electronic general practice data is an important resource which can provide valuable insights about the quality use of pathology. There are clear benefits to patients for better monitoring, and consequent better outcomes, and to inform policymakers about the best ways to channel resources to enhance the quality of care.

Coding and classifying GP data: the POLAR project.

BACKGROUND: Data, 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. OBJECTIVE: To 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. METHOD: The 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. RESULTS: Coding 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.

What a Comprehensive, Integrated Data Strategy Looks Like: The Population Level Analysis and Reporting (POLAR) Program.

In Australia, general practice (GP) acts as the gatekeeper to the rest of the healthcare system, and therefore the vast majority of the population have an electronic medical record. It follows that the largest database of the population is therefore on the distributed GP computers. Informed by a comprehensive system-wide data strategy, the Population Level Analysis and Reporting program extracts data from the GP electronic medical records and repurposes it for multiple uses. The program requires the data to be coded and then structured for multiple uses clinical care, clinical governance, research, and policy.

POLAR Diversion: Using General Practice Data to Calculate Risk of Emergency Department Presentation at the Time of Consultation.

OBJECTIVE: This project examined and produced a general practice (GP) based decision support tool (DST), namely POLAR Diversion, to predict a patient's risk of emergency department (ED) presentation. The tool was built using both GP/family practice and ED data, but is designed to operate on GP data alone. METHODS: GP data from 50 practices during a defined time frame were linked with three local EDs. Linked data and data mapping were used to develop a machine learning DST to determine a range of variables that, in combination, led to predictive patient ED presentation risk scores. Thirteen percent of the GP data was kept as a control group and used to validate the tool. RESULTS: The algorithm performed best in predicting the risk of attending ED within the 30-day time category, and also in the no ED attendance tests, suggesting few false positives. At 0 to 30 days the positive predictive value (PPV) was 74%, with a sensitivity/recall of 68%. Non-ED attendance had a PPV of 82% and sensitivity/recall of 96%. CONCLUSION: Findings indicate that the POLAR Diversion algorithm performed better than previously developed tools, particularly in the 0 to 30 day time category. Its utility increases because of it being based on the data within the GP system alone, with the ability to create real-time "in consultation" warnings. The tool will be deployed across GPs in Australia, allowing us to assess the clinical utility, and data quality needs in further iterations.

Compliance with pathology testing guidelines in Australian general practice: protocol for a secondary analysis of electronic health record data.

INTRODUCTION: In Australia, general practitioners usually are the first point of contact for patients with non-urgent medical conditions. Appropriate and efficient utilisation of pathology tests by general practitioners forms a key part of diagnosis and monitoring. However overutilisationand underutilisation of pathology tests have been reported across several tests and conditions, despite evidence-based guidelines outlining best practice in pathology testing. There are a limited number of studies evaluating the impact of these guidelines on pathology testing in general practice. The aim of our quantitative observational study is to define how pathology tests are used in general practice and investigate how test ordering practices align with evidence-based pathology guidelines. METHODS AND ANALYSIS: Access to non-identifiable patient data will be obtained through electronic health records from general practices across three primary health networks in Victoria, Australia. Numbers and characteristics of patients, general practices, encounters, pathology tests and problems managed over time will be described. Overall rates of encounters and tests, alongside more detailed investigation between subcategories (encounter year, patient's age, gender, and location and general practice size), will also be undertaken. To evaluate how general practitioner test ordering coincides with evidence-based guidelines, five key candidate indicators will be investigated: full blood counts for patients on clozapine medication; international normalised ratio measurements for patients on warfarin medication; glycated haemoglobin testing for monitoring patients with diabetes; vitamin D testing; and thyroid function testing. ETHICS AND DISSEMINATION: Ethics clearance to collect data from general practice facilities has been obtained by the data provider from the RACGP National Research and Evaluation Ethics Committee (NREEC 17-008). Approval for the research group to use these data has been obtained from Macquarie University (5201700872). This study is funded by the Australian Government Department of Health Quality Use of Pathology Program (Agreement ID: 4-2QFVW4M). Findings will be reported to the Department of Health and disseminated in peer-reviewed academic journals and presentations (national and international conferences, industry forums).

Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning.

BACKGROUND AND PURPOSE: Trials of restorative therapies after stroke and clinical rehabilitation require relevant and objective efficacy end points; real-world upper extremity (UE) functional use is an attractive candidate. We present a novel, inexpensive, and feasible method for separating UE functional use from nonfunctional movement after stroke using a single wrist-worn accelerometer. METHODS: Ten controls and 10 individuals with stroke performed a series of minimally structured activities while simultaneously being videotaped and wearing a sensor on each wrist that captured the linear acceleration and angular velocity of their UEs. Video data provided ground truth to annotate sensor data as functional or nonfunctional limb use. Using the annotated sensor data, we trained a machine learning tool, a Random Forest model. We then assessed the accuracy of that classification. RESULTS: In intrasubject test trials, our method correctly classified sensor data with an average of 94.80% in controls and 88.38% in stroke subjects. In leave-one-out intersubject testing and training, correct classification averaged 91.53% for controls and 70.18% in stroke subjects. CONCLUSIONS: Our method shows promise for inexpensive and objective quantification of functional UE use in hemiparesis, and for assessing the impact of UE treatments. Training a classifier on raw sensor data is feasible, and determination of whether patients functionally use their UE can thus be done remotely. For the restorative treatment trial setting, an intrasubject test/train approach would be especially accurate. This method presents a potentially precise, cost-effective, and objective measurement of UE use outside the clinical or laboratory environment.

Characteristics of patients presenting to an after-hours clinic: results of a MAGNET analysis.

After-hours access to general practice (GP) is critical to supporting accessibility and reducing emergency department demand. To understand who utilises after-hours GP services, this study examined the characteristics of presentations to an Eastern Melbourne after-hours clinic between 2005 and 2014. Descriptive analyses of patient and presentation characteristics, diagnoses, medications and pathology were conducted. Across the study period, 39.1% of presentations to the clinic (N=64,800) were by patients under 18 years of age. Females were found to attend more often than males, and nearly 79% of patients attended only once. The most common diagnoses were respiratory system diseases (13.4%), gastrointestinal system diseases (12.6%) and eye and ear problems (11.6%). Antibacterial medications accounted for over half (53.0%) of all prescriptions, with 34% of antibiotics prescribed to patients under 18 years of age. Seasonal variation in GP demand was also observed. Presenting patients differed from the wider GP patient population, with more young patients, and a higher proportion of prescriptions for antibacterial medications compared to other predominantly non-after-hours practices. Further research is required to understand the health-seeking, decision-making of patients who utilise after-hours GPs over predominantly non-after-hours primary care services, to inform service promotion and delivery strategies.

Using Patient Flow Information to Determine Risk of Hospital Presentation: Protocol for a Proof-of-Concept Study.

BACKGROUND: Every day, patients are admitted to the hospital with conditions that could have been effectively managed in the primary care sector. These admissions are expensive and in many cases are possible to avoid if early intervention occurs. General practitioners are in the best position to identify those at risk of imminent hospital presentation and admission; however, it is not always possible for all the factors to be considered. A lack of shared information contributes significantly to the challenge of understanding a patient's full medical history. Some health care systems around the world use algorithms to analyze patient data in order to predict events such as emergency presentation; however, those responsible for the design and use of such systems readily admit that the algorithms can only be used to assess the populations used to design the algorithm in the first place. The United Kingdom health care system has contributed data toward algorithm development, which is possible through the unified health care system in place there. The lack of unified patient records in Australia has made building an algorithm for local use a significant challenge. OBJECTIVE: Our objective is to use linked patient records to track patient flow through primary and secondary health care in order to develop a tool that can be applied in real time at the general practice level. This algorithm will allow the generation of reports for general practitioners that indicate the relative risk of patients presenting to an emergency department. METHODS: A previously designed tool was used to deidentify the general practice and hospital records of approximately 100,000 patients. Records were pooled for patients who had attended emergency departments within the Eastern Health Network of hospitals and general practices within the Eastern Health Network catchment. The next phase will involve development of a model using a predictive analytic machine learning algorithm. The model will be developed iteratively, testing the combination of variables that will provide the best predictive model. RESULTS: Records of approximately 97,000 patients who have attended both a general practice and an emergency department have been identified within the database. These records are currently being used to develop the predictive model. CONCLUSIONS: Records from general practice and emergency department visits have been identified and pooled for development of the algorithm. The next phase in the project will see validation and live testing of the algorithm in a practice setting. The algorithm will underpin a clinical decision support tool for general practitioners which will be tested for face validity in this initial study into its efficacy.