| Literature DB >> 35064390 |
Dini Harsono1, Yanhong Deng2, Sangyun Chung2, Lydia A Barakat3, Gerald Friedland4,3,5, Jaimie P Meyer4,3,6, Elizabeth Porter7, Merceditas Villanueva4,3, Michael S Wolf8, Jessica E Yager9, E Jennifer Edelman4,7.
Abstract
To characterize perspectives and experiences with telemedicine during the COVID-19 pandemic, we conducted a mixed-methods study in two HIV clinics in the US Northeast. Among surveyed patients with HIV (PWH) who had a telemedicine appointment (n = 205), 42.4% perceived telemedicine visits as useful during the pandemic. PWH and clinical staff identified benefits of telemedicine: (1) ability to engage and re-engage patients in care; (2) perceived patient-centeredness and flexibility; (3) opportunity to engage family and multidisciplinary care team members; and (4) opportunity to enhance telemedicine use proficiency through practice and support. Identified barriers included: (1) technical challenges; (2) privacy concerns; (3) loss of routine clinical experiences and interactions; (4) limited objective patient remote monitoring; and (5) reimbursement concerns. Efforts to optimize telemedicine for HIV care should consider strategies to improve technology support for PWH, flexible options to access care, additional platforms to allow patient remote monitoring, and appropriate billing and reimbursement methods.Entities:
Keywords: COVID-19; HIV; Mixed-methods; Telehealth; Telemedicine
Mesh:
Year: 2022 PMID: 35064390 PMCID: PMC8782707 DOI: 10.1007/s10461-021-03556-7
Source DB: PubMed Journal: AIDS Behav ISSN: 1090-7165
Participant characteristics
| Characteristics | Patients, N = 273* | Clinical staff, N = 23 |
|---|---|---|
| Site, n (%) | 273 (100%) | 23 (100%) |
| New Haven, CT | 173 (63.4%) | 14 (61%) |
| Brooklyn, NY | 100 (36.6%) | 9 (29%) |
| Age, mean (SD) | 52.1 (11.3) | 49.2 (13.2) |
| Current gender, n (%) | ||
| Female | 132 (48.4%) | 20 (86.9%) |
| Male | 141 (51.6%) | 2 (8.7%) |
| Nonbinary, agender, genderqueer | 0 | 1 (4.3%) |
| Race, n (%) | ||
| White | 61 (22.3%) | 10 (43.5%) |
| Black | 178 (65.2%) | 4 (17.4%) |
| Other | 34 (12.5%) | 9 (39.1%) |
| Hispanic, n (%) | 43 (15.8%) | 1 (4.3%) |
| Job role, n (%) | ||
| Behavioral health provider | 4 (17.4%) | |
| Physician | 11 (47.8%) | |
| Advanced practice practitioner | 1 (4.3%) | |
| Clinical pharmacist | 1 (4.3%) | |
| Nurse | 2 (8.7%) | |
| Other (e.g., program director) | 4 (17.4%) | |
| Providing direct services to patients, n (%) | 20 (86.9%) | |
| Able to work from home (telecommute), n (%) | 20 (86.9%) | |
| Using telemedicine to deliver care, n (%) | 18 (78.3%) | |
| If using telemedicine (n = 18), location to deliver telemedicine, n (%) | ||
| Home | 3 (16.7%) | |
| Clinic/hospital | 4 (22.2%) | |
| Both | 11 (61.1%) | |
| Occupation, n (%) | ||
| Employed | 72 (26.4%) | |
| Unemployed/disabled/retired/other† | 201 (73.6%) | |
| Annual household income, n (%) | ||
| $0 to $25,000 | 175 (65.3%) | |
| $25,000 or more | 93 (34.7%) | |
| Housing status impacted by COVID-19, n (%) | ||
| No, lived in the same place | 257 (94.2%) | |
| No, still did not have a regular place to stay | 5 (1.8%) | |
| Yes, had to move but have a place to stay | 9 (3.3%) | |
| Yes, no longer have a place to stay | 2 (0.7%) | |
| Engaged in recent HIV care, n (%) | 241 (88.3%) | |
| Undetectable HIV viral load‡, n (%) | 206 (75.5%) | |
| CD4 cell count, cells/mm3, median (IQR, Q1–Q3) | 603.5 (532.4, 395.2–927.6) | |
| Prescribed ART, n (%) | 266 (97.4%) | |
| Took ART at start of COVID-19 pandemic§, n (%) | 271 (99.3%) | |
| Missed any ART during COVID-19 pandemic, n (%) | 51 (18.8%) | |
| Current tobacco use, n (%) | ||
| Every day | 199 (72.9%) | |
| Some days | 34 (12.5%) | |
| Not at all | 40 (14.7%) | |
| Unhealthy alcohol usea, n (%) | 79 (28.9%) | |
| Depressionb, n (%) | ||
| None to slight/mild | 254 (93%) | |
| Moderate/severe | 19 (7%) | |
| Anxietyc, n (%) | ||
| None to slight/mild | 215 (78.8%) | |
| Moderate/severe | 58 (21.2%) | |
| Number of people in household (including self), n (%) | ||
| 1 | 99 (36.3%) | |
| 2 | 84 (30.8%) | |
| 3 | 44 (16.1%) | |
| 4 or more | 46 (16.8%) | |
| Owned a smartphone, n (%) | 186 (74.4%) | |
| Could use a smartphone for videoconferencing, n (%) | 178 (96.2%) | |
| Could use a smartphone for the internet, n (%) | 181 (97.3%) | |
| Had a telemedicine appointment in past two months, n (%) | 205 (75.1%) | |
aAUDIT-C (Alcohol Use Disorders Identification Test)
bPROMIS Short Form v1.0—Depression 4a
cPROMIS Short Form v1.0—Anxiety 8a
*Numbers do not add up to 100% due to small amounts of missing data
†Two participants who indicated “other” provided additional information that they were students
‡ ≤ 50 copies/L
§Two participants indicated “sometimes” and 269 indicated “yes”
Patient participant experiences and preferences for telemedicine use during the COVID-19 pandemic
| Variable | Overall |
|---|---|
| Had telemedicine visit via… | |
| Telephone | 177 (85.9%) |
| Video | 15 (7.3%) |
| Both | 14 (6.8%) |
| Had telemedicine visit with a…a | |
| HIV provider (MD, PA, APRN) | 182 (66.7%) |
| Specialist/Consultant | 40 (14.7%) |
| Pharmacist | 10 (3.7%) |
| Nutritionist | 4 (1.5%) |
| Nurse | 11 (4%) |
| Behavioral health (social worker, counselor) | 28 (10.3%) |
| Other† | 10 (3.7%) |
| Reason for telemedicine visit | |
| In place of a routine face to face appointment | 192 (93.7%) |
| For possible COVID symptoms | 2 (1%) |
| For other symptoms/urgent issue | 11 (5.4%) |
| Have any of your future medical appointments been rescheduled to telemedicine/telehealth? | |
| Yes | 57 (21.0%) |
| No | 140 (51.5%) |
| Don’t know | 75 (27.6%) |
| How important is it to be able to have routine medical appointments with telemedicine/telehealth during the COVID emergency? | |
| Not at all important/Slightly important | 61 (22.3%) |
| Somewhat important | 39 (14.3%) |
| Important/Very important | 173 (63.4%) |
| How important is it to be able to have urgent medical appointments with telemedicine/telehealth for symptoms (cough, fever, other) during the COVID emergency? | |
| Not at all important/Slightly important | 52 (19.0%) |
| Somewhat important | 35 (12.8%) |
| Important/Very important | 186 (68.1%) |
aParticipants were asked to check all that apply
†Ten participants who indicated “other” had telehealth visits with primary care providers (n = 3), care navigators (n = 2), care coordinator (n = 1), pain medicine (n = 1), home attendant (n = 1), physical therapist (n = 1), and dentist (n = 1)
Fig. 1Perceived quality of telemedicine visit compared to in-person visit by patients participating in telemedicine during the COVID-19 pandemic
Fig. 2Perceived likelihood of recommending telemedicine to someone else by patients participating in telemedicine during the COVID-19 pandemic
Patient participant characteristics associated with telemedicine being useful during the COVID-19 pandemic, N = 205§
| Variable | How useful have your medical appointments with telemedicine/telehealth been during the COVID emergency? | Unadjusted odds ratio (95% CI) | Adjusted odds ratio† (95% CI) | |
|---|---|---|---|---|
| Extremely/very | Moderately/slightly/not at all | |||
| Age§§ | 53.0 (10.1) | 51.5 (11.7) | 1.13 (0.88–1.46) | 1.22 (0.91–1.63) |
| Current gender‡ | ||||
| Female | 45 (51.7%) | 55 (46.6%) | 1.23 (0.71–2.14) | |
| Male | 42 (48.3%) | 63 (53.4%) | Ref | |
| Race | ||||
| White | 21 (24.1%) | 27 (22.9%) | Ref | |
| Black | 57 (65.5%) | 76 (64.4%) | 0.96 (0.50–1.88) | |
| Other | 9 (10.3%) | 15 (12.7%) | 0.77 (0.28–2.11) | |
| Ethnicity | ||||
| Hispanic | 12 (14%) | 18 (15.3%) | 0.90 (0.41–1.99) | |
| Non-Hispanic | 74 (86%) | 100 (84.7%) | Ref | |
| Annual household income | ||||
| $0 to $25,000 | 60 (71.5%) | 72 (61.5%) | Ref | |
| $25,000 or more | 24 (28.5%) | 45 (38.5%) | 0.64 (0.35–1.17) | |
| Employment change due to COVID-19 outbreaka | ||||
| Changed | 16 (18.4%) | 20 (16.9%) | 1.10 (0.54–2.28) | |
| Unchanged | 71 (81.6%) | 98 (83.1%) | Ref | |
| Engaged in recent HIV care | ||||
| No | 14 (16.1%) | 8 (6.8%) | 2.03 (0.74–5.61) | |
| Yes | 73 (83.9%) | 110 (93.2%) | Ref | Ref |
| Undetectable HIV viral load | ||||
| No | 10 (11.5%) | 17 (14.4%) | 0.77 (0.33–1.78) | |
| Yes | 77 (88.5%) | 101 (85.6%) | Ref | |
| Missed any ART during COVID-19 pandemic | ||||
| No | 67 (77.9%) | 102 (86.4%) | Ref | |
| Yes | 19 (22.1%) | 16 (13.6%) | 1.81 (0.87–3.76) | |
| Owned a smartphone | ||||
| No | 10 (12.3%) | 36 (34%) | ||
| Yes | 71 (87.7%) | 70 (66%) | Ref | Ref |
| Site location | ||||
| New Haven, CT | 48 (55.2%) | 84 (71.2%) | Ref | Ref |
| Brooklyn, NY | 39 (44.8%) | 34 (28.8%) | 1.10 (0.55–2.22) | |
Statistically significant values are shown in bold
§One participant was excluded from analysis due to missing responses
§§In 10-year increments
*P value less than 0.05
**P value less than 0.01
***P value less than 0.001
†Adjusted for age, recent engagement in HIV care, smartphone ownership, and location
‡ Four participants indicated that their current gender was different from their assigned gender at birth
aThe survey question was “Has there been any change in your employment status due to coronavirus?”
Patient participant characteristics associated with telemedicine being useful after the COVID-19 pandemic, N = 273
| Variable | How useful do you think it will be to have medical appointments with telemedicine/telehealth after the coronavirus emergency is over? | Unadjusted odds ratio (95% CI) | Adjusted odds ratio† (95% CI) | |
|---|---|---|---|---|
| Extremely/very | Moderately/slightly/not at all | |||
| Age§ | 54.5 (9.7) | 51.3 (11.7) | ||
| Current gender‡ | ||||
| Female | 40 (53.3%) | 91 (46.2%) | 1.33 (0.78–2.27) | |
| Male | 35 (46.7%) | 106 (53.8%) | Ref | |
| Race | ||||
| White | 12 (16%) | 49 (24.9%) | Ref | |
| Black | 58 (77.3%) | 119 (60.4%) | 1.99 (0.98–4.03) | |
| Other | 5 (6.7%) | 29 (14.7%) | 0.70 (0.23–2.20) | |
| Ethnicity | ||||
| Hispanic | 11 (14.7%) | 32 (16.3%) | 0.88 (0.42–1.85) | |
| Non-Hispanic | 64 (85.3%) | 164 (83.7%) | Ref | |
| Annual household income | ||||
| $0 to $25,000 | 50 (68.5%) | 125 (64.4%) | Ref | |
| $25,000 or more | 23 (31.5%) | 69 (35.6%) | 0.83 (0.47–1.48) | |
| Employment change due to COVID-19 outbreaka | ||||
| Changed | 9 (12%) | 36 (18.3%) | 0.61 (0.28–1.34) | |
| Unchanged | 66 (88%) | 161 (81.7%) | Ref | |
| Engaged in recent HIV care | ||||
| No | 10 (13.3%) | 22 (11.2%) | 1.22 (0.55–2.72) | |
| Yes | 65 (86.7%) | 175 (88.8%) | Ref | |
| Undetectable HIV viral load | ||||
| No | 13 (17.3%) | 33 (16.8%) | 1.04 (0.52–2.11) | |
| Yes | 62 (82.7%) | 164 (83.2%) | Ref | |
| Missed any ART during COVID-19 pandemic | ||||
| No | 55 (75.3%) | 164 (83.2%) | Ref | |
| Yes | 18 (24.7%) | 33 (16.8%) | 1.63 (0.85–3.12) | |
| Owned a smartphone | ||||
| No | 14 (20.3%) | 50 (27.6%) | 0.67 (0.34–1.31) | 0.80 (0.39–1.66) |
| Yes | 55 (79.7%) | 131 (72.4%) | Ref | Ref |
| Site location | ||||
| New Haven, CT | 39 (52%) | 134 (68%) | Ref | |
| Brooklyn, NY | 36 (48%) | 63 (32%) | 1.78 (0.97–3.27) | |
Statistically significant values are shown in bold
§In 10-year increments
*P value less than 0.05
†Adjusted for age, smartphone ownership, and location
‡Four participants indicated that their current gender was different from their assigned gender at birth
aThe survey question was “Has there been any change in your employment status due to coronavirus?”