Harnessing the Benefits of Telehealth in Long COVID Service Provision
Bibliographic record
Abstract
Re:Luo S, Zheng Z, Bird SR, Plebanski M, Figueiredo B, Jessup R, Stelmach W, Robinson JA, Xenos S, Olasoji M, Wan DWL, Sheahan J and Itsiopoulos C (2023) An Overview of Long COVID Support Services in Australia and International Clinical Guidelines, With a Proposed Care Model in a Global Context. Public Health Rev 44: 1606084.We commend Luo et al. for their in-depth analysis of the current available services in Australia to support people with Long COVID1. This devastating illness affects over 10% of those after acute COVID-19 infection and is projected to affect over 200 million people in the next decade worldwide2. Many with Long COVID are severely impacted by physical symptoms to the extent that simple activities of daily living are extremely fatiguing, and the demands of travel to a healthcare appointment can trigger episodes of severe post-exertional malaise which has been found to affect over 80% of those with Long COVID3. Provision of healthcare should be available in a format that does not worsen symptoms nor impact upon them financially.As Luo et al. describe, options for Long COVID specialist care in Australia are limited, particularly for people in rural or remote locations1. We note, however, that their summary did not review the option of telehealth as a model of care for Long COVID. Telehealth encompasses provision of medical assessment, diagnosis, treatment, and education through the use of technology, including video and telephone-based consultation4. Our Australian-based clinic, which was not included in Luo et al.’s review, uses a telehealth model of care and to date has provided care to over 500 people with Long COVID (including children) from all states and territories, including the Northern Territory, which has no other dedicated Long COVID services1. Of our cohort, 22% live outside of major metropolitan centres as measured by the Modified Monash model5 (Figure 1).Figure 1. Geographical spread of cohort by Modified Monash Model5 classification (unpublished data)(Footnote 1).This model of care provides an option for patients with physical6 or other disability and geographical limitations7 to equitably access healthcare without physical detriment or disproportionate financial penalty due to travel costs. The need, strengths, safety and limitations of telehealth services to provide rapid and accessible care has been highlighted throughout the COVID-19 pandemic. Systemic changes within the Australian health system provided funding of a wide-scale shift in the modality of care delivery4, and which have been trialled elsewhere including Canada8.The use of telehealth, where service is otherwise limited, provides a real option for many patients to receive care they would not otherwise be able to access6, and the inability to undertake a physical examination can often be mitigated through close collaboration with the person’s primary care provider. This approach has been successfully demonstrated in several settings including with rehabilitation9, an important facet of long COVID care. Furthermore, formal and informal consumer feedback from our clinic indicates that this model of care is desired by many people with Long COVID, in keeping with published literature10. Luo et al. highlight the importance of consumer engagement and empowerment, and including consumers in discussion about models of care is of paramount importance to be able to provide optimal quality care.Provision of care for Long COVID must be equitable, should not exacerbate symptoms, and should be designed with consumer needs and opinions at its heart. The benefits of telehealth are numerous for those with Long COVID and should be embedded within systemic strategies to enhance care.Yours sincerely,[Authors]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".