| Literature DB >> 34600105 |
Hursuong Vongsachang1, Oded Lagstein2, Michael V Boland3, Michael X Repka1, Courtney L Kraus1, Megan E Collins4.
Abstract
Understanding provider perspectives on telemedicine adoption during the COVID-19 pandemic can help inform best practices for delivering pediatric ophthalmic care safely and remotely. In this online survey distributed to two national pediatric ophthalmology list-servs, respondents in July-August 2020 (n = 104) compared with respondents in March-April 2020 (n = 171) were more likely to report not using and not planning on using telemedicine. The July-August respondents who did not use telemedicine were concerned about the limitations in care provided, challenges with implementation, and perceived negative effects on the doctor-patient relationship. These findings demonstrate a lack of sustained uptake of telemedicine in the first 6 months of the pandemic and concerns that should be addressed to facilitate integration of this approach in pediatric ophthalmic care.Entities:
Mesh:
Year: 2021 PMID: 34600105 PMCID: PMC8479503 DOI: 10.1016/j.jaapos.2021.05.018
Source DB: PubMed Journal: J AAPOS ISSN: 1091-8531 Impact factor: 1.220
Demographics and practice characteristics of March-April 2020 and July-August 2020 respondents
| Characteristic | March respondents (n = 171) | July respondents (n = 104) | |
|---|---|---|---|
| Age years, mean ± SD | 50 ± 11 | 52 ± 10 | 0.12 |
| 30-39, no. (%) | 35 (20) | 14 (13) | 0.06 |
| 40-49, no. (%) | 51 (30) | 21 (20) | |
| 50-59, no. (%) | 44 (26) | 43 (41) | |
| 60-69, no. (%) | 35 (20) | 23 (22) | |
| 70-79, no. (%) | 6 (4) | 3 (3) | |
| Sex, no. (% female) | 94 (55) | 54 (52) | 0.60 |
| Practice region, | 0.24 | ||
| Northeast | 34 (20) | 32 (31) | |
| Midwest | 31 (18) | 14 (13) | |
| South | 70 (41) | 35 (34) | |
| West | 35 (20) | 22 (22) | |
| Other US territories | 1 (1) | 1 (1) | |
| Practice type, no. (%) | 0.08 | ||
| Academic | 67 (39) | 39 (38) | |
| Private | 92 (54) | 56 (54) | |
| Other (including both academic and private) | 12 (7) | 9 (9) | |
| Patient location (%), mean ± SD | |||
| Primary state | 81 ± 28 | 87 ± 18 | 0.04 |
| Adjacent state | 16.5 ± 26 | 12 ± 17 | 0.08 |
| Other state | 2.5 ± 6.5 | 1.4 ± 2.5 | 0.05 |
| Pre-COVID-19 average daily patient volume per ophthalmologist, median (IQR) | 30 (24,35) | 30 (24,36) | 0.88 |
IQR, interquartile range; SD, standard deviation.
States grouped into regions using the US Census categorizations.
Using t-test or Fisher exact test.
Using unequal variance as indicated.
Multinomial logistic regression for telemedicine utilization for all March-April 2020 and July-August 2020 respondents (n = 275)a
| Outcome | Characteristic | Univariable | Multivariable | ||
|---|---|---|---|---|---|
| Ref = Yes | RR ratio (95% CI) | RR ratio (95% CI) | |||
| No, and I do not plan to use telemedicine | Female (vs not) | 0.96 (0.51-1.82) | 0.91 | ||
| Age (years) | |||||
| 30-40 | Ref | Ref | |||
| 40-50 | 1.65 (0.47-5.79) | 0.44 | 0.96 (0.21-4.54) | 0.96 | |
| 50-60 | 3.75 (1.18-11.94) | 0.03 | 2.56 (0.65-10.05) | 0.18 | |
| 60-70 | 2.06 (0.59-7.19) | 0.26 | 1.96 (0.44-8.70) | 0.38 | |
| >70 | 1.35 (0.12-14.73) | 0.81 | 1.16 (0.07-19.98) | 0.92 | |
| Practice region | |||||
| Northeast | Ref | Ref | |||
| Midwest | 0.87 (0.31-2.46) | 0.79 | 1.67 (0.46-6.03) | 0.43 | |
| South | 1.11 (0.51-2.39) | 0.80 | 2.77 (1.03-7.47) | 0.04 | |
| West | 0.26 (0.08-0.85) | 0.03 | 0.32 (0.09-1.21) | 0.09 | |
| Practice type | |||||
| Private | 1.99 (0.97-4.10) | 0.06 | 2.31 (0.87-6.16) | 0.09 | |
| Academic | Ref | Ref | |||
| Other | 1.83 (0.56-6.04) | 0.32 | 3.46 (0.74-16.06) | 0.11 | |
| Patient location | |||||
| % Primary state | 1.02 (1.00-1.04) | 0.08 | 1.08 (0.90-1.29) | 0.43 | |
| % Adjacent state | 0.98 (0.96-1.01) | 0.15 | 1.07 (0.88-1.29) | 0.52 | |
| % Other state | 0.87 (0.76-1.00) | 0.05 | Omitted | ||
| Pre-COVID average daily patient volume | 1.02 (0.99-1.05) | 0.24 | 1.02 (0.98-1.06) | 0.42 | |
| July vs March respondents | 8.66 (3.81-19.70) | <0.001 | 11.59 (4.53-29.65) | <0.001 | |
| No, but I am making plans to use telemedicine | Female (vs not) | 1.09 (0.61-1.95) | 0.77 | ||
| Age (years) | |||||
| 30-40 | Ref | Ref | |||
| 40-50 | 0.73 (0.33-1.63) | 0.45 | 0.79 (0.31-1.96) | 0.61 | |
| 50-60 | 0.50 (0.22-1.15) | 0.10 | 0.70 (0.26-1.86) | 0.48 | |
| 60-70 | 0.46 (0.19-1.13) | 0.09 | 0.50 (0.18-1.40) | 0.19 | |
| >70 | 0.60 (0.10-3.44) | 0.57 | 0.45 (0.06-3.15) | 0.42 | |
| Practice region | |||||
| Northeast | Ref | Ref | |||
| Midwest | 2.63 (1.06-6.53) | 0.04 | 2.20 (0.78-6.19) | 0.14 | |
| South | 1.44 (0.65-3.23) | 0.37 | 0.99 (0.40-2.44) | 0.98 | |
| West | 1.06 (0.43-2.60) | 0.91 | 0.93 (0.34-2.55) | 0.89 | |
| Practice type | |||||
| Private | 0.81 (0.45-1.47) | 0.49 | 0.86 (0.41-1.83) | 0.70 | |
| Academic | Ref | Ref | |||
| Other | 0.46 (0.12-1.74) | 0.25 | 0.47 (0.11-2.01) | 0.31 | |
| Patient location | |||||
| % Primary state | 0.99 (0.98-1.00) | 0.14 | 1.03 (0.96-1.11) | 0.38 | |
| % Adjacent state | 1.01 (1.00-1.02) | 0.10 | 1.04 (0.97-1.13) | 0.28 | |
| % Other state | 0.99 (0.93-1.04) | 0.66 | Omitted | ||
| Pre-COVID average daily patient volume | 0.98 (0.96-1.02) | 0.33 | 0.97 (0.94-1.01) | 0.15 | |
| July vs March respondents | 0.05 (0.01-0.21) | <0.001 | 0.05 (0.01-0.20) | <0.001 | |
CI, confidence interval; RR, relative risk.
Covariates included at level P < 0.25.
“Unsure” excluded due to small cell sizes.
Other territories excluded due to small number (n = 2).