Patient experience of telemedicine for osteoporosis care during the COVID‐19 pandemic
Bibliographic record
Abstract
In response to the coronavirus disease 2019 (COVID-19) pandemic, expansion of telemedicine (telephone and video) billing numbers in March 2020 led to the rapid, widespread implementation of telemedicine across Australia, with minimal consumer involvement in service development. Osteoporosis predominantly affects older people, who are also at risk of more severe COVID-19. In response to reports that osteoporosis treatments were delayed during the COVID-19 pandemic, a number of professional societies released statements discouraging delaying certain therapies and underlying the importance of continuing best practice osteoporosis care.1, 2 In March 2020, osteoporosis clinics at our tertiary health service in Melbourne, Australia, moved to a telemedicine model of care. This study evaluated the patient experience of telemedicine for osteoporosis care and the impact on osteoporosis management. We invited patients attending osteoporosis clinics between 1 April 2020 and 28 February 2021, to complete an anonymous online survey, adapted from previous studies, regarding satisfaction and concerns using telemedicine, and changes to their management during the COVID-19 pandemic.3, 4 The clinics manage post-menopausal and secondary osteoporosis and include a paediatric transition service. We excluded patients aged <18 years, unable to consent, no mobile phone, and non-English speaking patients. The hospital's Human Research Ethics Committee approved the study (RES-20-0000-546L). Of 904 patients attending the clinics, 700 patients were eligible and sent text message invitations to the survey, and 129 completed surveys. The mean (SD) age was 61.6 (1.57) years and 77.3% were female. Most consultations were via telephone (89%). Only 15.5% of patients had previously used telemedicine. Although 83% used a smartphone, tablet or computer daily, only 56% rated themselves as confident with computers/technology. Most patients were satisfied with telemedicine, 70% thought it adequately addressed their needs, 72% thought it was convenient, and 83% thought the system was easy to use. However, 30% were concerned about inadequate treatment using telemedicine, and 19% thought the quality of care differed from in-person consultations. This differs from a study of telemedicine for osteoporosis in Canada before the COVID-19 pandemic, where only 5% thought the quality of care differed.4 Reasons given for the lack of satisfaction included communication barriers (missing body language cues, hearing difficulties), difficulty accessing paperwork such as referrals or prescriptions, and uncertainty about how to contact the clinician/clinic if they had further questions. Figure 1 shows patients' preferences for telemedicine or in-person consultations. If offered again, 68% of patients would use telemedicine for osteoporosis, while 22% would not. Using multiple logistic regression analysis, neither age, sex, nor confidence with technology was associated with either the overall preference for telemedicine or in-person consultations, or the likelihood of using telemedicine again. Unlike an international survey of clinicians, where 62% delayed osteoporosis imaging and 43% had difficulty arranging osteoporosis treatment during the COVID-19 pandemic, a minority of our patients had changes to their management.5 Only 11% of patients delayed blood tests or treatment, and 9% delayed imaging, likely reflecting the lower burden of COVID-19 in Australia. Limitations of our study include that it is single-centre and the low response rate. The findings may not be applicable to other cohorts, where the value of remote, out-of-hospital care in the form of telemedicine may differ. This includes countries with a greater COVID-19 burden, rural health services, and areas with poor telephone/internet access. Finally, due to the clinics sampled, our cohort was relatively young, which may influence the experience of telemedicine. Among patients treated at an Australian tertiary health service for osteoporosis, most had a positive experience with telemedicine. Although telemedicine was preferred to in-person consultation for travel and waiting time, only 27% prefer the overall experience of telemedicine. There was no association between age, sex, or confidence with technology, and preferences for telemedicine. Lack of personal interaction and system factors contributed to dissatisfaction with telemedicine; future studies should explore other contributing factors. Coproduction with consumers is needed to optimise telemedicine services for osteoporosis. Alicia R. Jones is the recipient of a National Health and Medical Research Council postgraduate research scholarship (Grant no. 1169192). The authors declare that there are no conflict of interests. Data are available from the corresponding author on reasonable request. Data are available from the corresponding author on reasonable request.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".