The impact of telehealth on patient-centered communication during the COVID-19 pandemic

Abstract

Purpose

Patient-centered communication (PCC) is essential for quality healthcare. This study examined how PCC was affected by the shift to telehealth and COVID-19 safety measures.

Methods

An online survey was conducted (Sept–Oct 2020) among U.S. adults who had both in-person and telehealth visits after March 2020. PCC was assessed using the Communication Assessment Tool and analyzed with repeated measures ANOVA and multiple regression.

Results

Among 522 respondents (58.2% female; mean age 46.1), PCC scores were higher for in-person visits than telehealth (45% vs. 42%, P = .003). Lower PCC during telehealth was associated with younger age and caregiver presence. For in-person visits, mask use was linked to lower PCC among older adults. More frequent visits (≥3) correlated with higher PCC in both settings.

Conclusions

PCC was generally stronger during in-person visits. Pandemic-related changes may have negatively impacted communication, particularly among certain groups, highlighting areas for future improvement.

Keywords

communication assessment tool equity pandemic safety procedures patient-centered communication telehealth

Introduction

The COVID-19 pandemic changed in-person clinic visits with efforts to reduce COVID-19 risk. Despite public health benefits, concerns exist over Centers for Disease Control and Prevention safety protocols (face masks, visitor restrictions, social distancing, etc.) regarding depersonalization and non-verbal communication, which may impact clinician-patient communication.^1–3 For example, face masks obscure the lower two thirds of the face making emotional states unintelligible.⁴ Though recent randomized trials have shown benefits to empathy and trust with clear versus cloth masks, cloth masks remain the norm.^5,6 The use of masks and social distancing may exacerbate experiences of suboptimal communication, particularly for patients with age-related hearing loss and those with limited English proficiency.^3,4,7–10 Additionally, limits on the numbers of care partners allowed into clinic facilities is a significant concern for the 53 million informal caregivers on whom patients rely for their health management.¹¹

Rapidly switching from in-person visits to telehealth was a disruptive change observed in healthcare. At the peak of the pandemic, telehealth accounted for more than 32% of all visits across the country, leveling out at around 13-17% of visits by late 2021.¹² There are concerns about widespread use of telehealth and its impact on the quality of patient-centered communication (PCC) due to differences in clinician and patient training, and low health or low technology literacy leading to resistance among clinicians and patients.^13–17 Technical barriers are also a concern, disproportionately affecting more vulnerable populations such as older adults, rural communities, patients of lower educational attainment and patients from racial and ethnic minority communities.^16,18–20 On the other hand, if it can be measured that quality communication is maintained, a shift to telehealth options could exemplify the potential role of digital health in driving the Quadruple Aim.²¹ Our goal was to explore if the shift to virtual consultations and the implementation of pandemic safety protocols impacted the quality of patient-centered communication.

Methods

Study and survey design

Between September 2020-October 2020, we conducted an online cross-sectional survey of patients in the United States who have experienced both a telehealth and in-person healthcare visit since the beginning of the COVID-19 pandemic. We adhered to the Checklist for Reporting Results of Internet E-Surveys, CHERRIES (see Appendix A).²²

We developed a 38-item survey, displayed over 40 webpages (1 question per page), that included both validated and newly generated questions, including: a) patient demographics (age, residency, race, ethnicity, education, preferred language, insurance), b) details of in-person and telehealth visits (reason for visit, number of visits, specialty visited, caregiver presence, instructions, summary, overall experience, and patient centered communication (PCC)), c) experiences of in-person clinic visits (safety protocols) and telehealth (technology used for visit, location, privacy, caregiver presence), and d) COVID-19 status. Question response formats included multiple choice or Likert scale, and one open response question about the experience with their healthcare provider (see Appendix B). Patients assessed the quality of PCC during telehealth and in-person visits using 12 questions from the validated Communication Assessment Tool (CAT).²³ The CAT is a 15-item survey designed to specifically assess patient perceptions of an individual physician’s communication effectiveness in a recent visit, unlike other measures in which communication items are bundled with other patient outcomes such as satisfaction and trust, or patients are expected to report experiences over a longer period of time (e.g. in the past 12 months). With a 5-point Likert scale response (poor to excellent), the CAT is scored using Top Box analysis-the mean percentage of item responses marked as excellent. The mean percent-excellent approach mitigates the ceiling effect commonly observed in positively skewed patient-reported means.²³ Effectiveness is defined by the CAT as a physician’s ability to achieve communication tasks associated with patient-centeredness and, as such, we interpret higher CAT scores as higher PCC. Psychometric testing during the development of the CAT found that results are not affected by physician specialty or patient gender, race, level of education, self-reported medical status, or any previous encounters with the physician. Therefore, from participants reporting their communication experiences during both in-person and telehealth visits, we do not expect CAT results to be inherently affected by the differences in modality after removal of more in-person specific items- “staff treated me with respect,” and “paid attention to me (looked at me, listened carefully)”.

The overall survey was informally piloted with clinicians, research staff and patient representatives in our research group to assess interpretability and online usability and technical functionality. In response to pilot feedback, questions were streamlined, reordered to improve flow, or reworded to increase clarity and be more understandable to a general audience. For example, in Q.24 when we asked about equipment needed to participate in telehealth visits, we changed the term ‘devices’ to ‘phones and computers’ based on patient partner feedback.

To protect against order effects, we randomized in-person and telehealth experiences question blocks so that 50% of respondents answered questions on their telehealth experience first, and 50% started with in-person experience items. Outside of screening questions, respondents could review or change their answers if they wished.

Participant eligibility

Eligible individuals were United States residents, ≥18 years, who completed ≥1 (each) telehealth and in-person visit since COVID-19 pandemic restrictions were put in place. We did not specify dates as different States introduced restrictions at different times, however most states had restrictions in place beginning March 2020. Telehealth visits were defined as “a planned telephone or video call with your healthcare provider that prior to the Coronavirus pandemic would have normally been conducted in person”. In-person clinic visits were defined as “a scheduled appointment with your healthcare provider at their office.” Adaptive questioning was used to capture reasons why a respondent had a telehealth or in person visit. Participants who did not select “for my own health issue or condition” for both the telehealth and in-person visits were excluded from analysis.

Recruitment

Participants were recruited using Qualtrics Panels (Qualtrics LLC, Provo, Utah, USA), an online survey platform which draws a broad demographic of participants. The survey was open to panelists who received an invitation email from Qualtrics containing a link to the survey posted online. We planned to recruit 500 patients, and constructed quotas (n=29 per group)²⁴ to ensure a diverse sample of participants by age (18-34; 35-64; ≥65), educational attainment (no college; ≥college) and race (Asian; Black or African American or Afro-Caribbean, American Indian or Alaskan Native, Native Hawaiian or other Pacific Islander; White), factors known to impact PCC.^15,25–27 A sample of 29 respondents per group allowed us to meaningfully describe PCC by varying patient characteristics (e.g., race, ethnicity, age). All potential respondents completed the demographics and eligibility screen initially; potential respondents were removed if they were not eligible or we had already reached the quota for that demographic profile. Responses were automatically captured and Qualtrics performed quality checks based on investigator predetermined criteria for completeness, straight-lining, speeders, duplicates (based on IP address and panelist login) and gibberish/nonsense responses.

Quantitative analysis

We reported means and standard deviations for continuous data, and proportions and ranges for categorical data. We conducted repeated-measures ANOVA to determine whether there was a statistically significant difference between in-person and telehealth PCC by age, sex, race, and ethnicity. We also conducted unadjusted and adjusted linear regression analyses to identify factors associated with PCC in both in-person visits and telehealth visits. Multiple linear regression models included patient demographics, specialty seen, instructions received by the patient from the clinic, and factors specific to in-person visits (e.g., pandemic safety protocols), and telehealth visits (e.g., video or phone visit). Statistical analyses were conducted using R, v1.1.383 (RStudio, Boston, MA). Our target sample of 500 allows for at least 10 observations per parameter in our models to reduce overfitting models.²⁸ All models were tested for potential collinearity between variables using a variance inflation factor of 5.0.²⁹ A predefined alpha level of 0.05 or less was chosen for statistical significance. To increase the quality of data from the survey, only fully completed questionnaires were analyzed and responses from individuals who completed the survey in less than one-third of the median completion time, 5.1 minutes, were excluded from the final dataset.³⁰

Qualitative analysis

We conducted a summative content analysis of responses to the open-ended question to identify salient themes reflecting the respondents’ attitudes towards their healthcare providers during the COVID-19 pandemic.³¹ A priori themes for these responses were aimed at distinguishing positive, negative or neutral comments about the participant’s clinician or about visit modality (in-person and telehealth). Each comment was independently reviewed and double-coded by two members of the research team trained in qualitative codebook generation. After initial coding, the coders met to review and adjust a priori themes, with disagreements and theme revisions resolved by the research team.

Consent and ethical considerations

To ensure informed consent, within the guidelines of the protocol, the Qualtrics invitation email was sent only to people who have self-selected to receive surveys. The opening screen presented an “informed consent” text block detailing the purpose of the study, risks, and data handling. The final sentence of the survey introduction read: “If you wish to participate in this project, please click the “>>” button below If you do not wish to participate in this project, please close your Internet browser.” (see Appendix B) The decision to proceed beyond the survey introduction was considered consent to participate.

Within the introduction of the survey, potential participants were informed that “Names and other identifying information will not be collected and therefore not used in any presentation or paper written about this project. You may choose not to answer any or all questions” (see Appendix B). To identify duplicate entries, IP addresses were used but not captured for identification or analysis. Only the first survey entered from a duplicate IP address was retained for quality check and analysis. Data was stored on secure servers.

Respondents receive incentives and cash honorarium from Qualtrics for taking part in surveys (e.g., gift cards). Participants self-selected their incentives based on options provided by Qualtrics.

Results

The participant flow diagram can be found in Figure 1. Of 2786 individuals who started the survey, 1935 completed the survey (Recruitment Rate: 69.5%). Of the 1935 completers, 1413 were removed from the final analytic dataset for the following reasons: low quality responses (n=276), ineligible (n=498) and quota were reached (n=629) (see Appendix C for demographic profile of those excluded due to reaching quota limits). This resulted in an analytic sample of n=522 respondents.

Figure 1.

Participant flow diagram.

Participant and visit characteristics

Most of our sample identified as female (n=303, 58.2%) and the average age was 46.1 years (SD 18.9). Participant characteristics can be found in Table 1. Respondents attended a similar number of in-person and telehealth visits. Visit characteristics can be found in Table 2. Over half (53.2% telehealth and 54.9% in-person) reported multiple visits and most respondents reported that telehealth (86.0%) and in-person visits (83.9%) were principally for their own health issue(s). Primary care was the most common visit type for both telehealth and in-person, but accounted for a greater proportion of respondents’ telehealth visits (62.4% vs. 58.7%). The largest relative difference between in-person and telehealth specialty visits were in psychiatry (1.9% in-person vs. 5.2% telehealth) and ophthalmology (11.5% in-person vs. 6.7% telehealth). Observed in-person safety precautions can be found in Figure 2.

Table 1.

Survey participant demographics.

Participant characteristics	Total (N=522)
Gender
Male	217 (41.6%)
Female	303 (58.2%)
Nonbinary	2 (0.3%)
Age
Younger Age (18-34)	179 (34%)
Middle Age (35-64)	186 (36%)
Older Age (65+)	157 (30%)
Ethnicity
Hispanic/Latino(a)/Spanish origin	71 (13.6%)
Not Hispanic/Latino(a)/Spanish origin	451 (86.4%)
Race
American Indian/Alaska Native	21 (4.0%)
Asian	157 (30.0%)
Black/African American/Afro-Caribbean	131 (25.1%)
Native Hawaiian/Pacific Islander	3 (0.6%)
White	175 (33.6%)
Other	35 (6.7%)
Education
No schooling	5 (1.0%)
9^th to 12^th grade	15 (2.9%)
High school diploma/equivalent	94 (18.0%)
Some college, no degree	140 (26.8%)
Associate’s degree	41 (7.9%)
Bachelor’s degree	111 (21.3%)
Master’s degree	91 (17.5%)
Doctorate	13 (2.5%)
Professional degree	12 (2.3%)
Insurance
Employer or union	159 (30.5%)
Medicaid	120 (23.1%)
Medicare	246 (47.1%)
No insurance	7 (1.3%)
Self-insured or private insurance	86 (16.5%)
Tricare or VA insurance	29 (5.6%)
Other	10 (1.9%)
Telehealth Visit Pre-Covid
No	329 (63.0%)
Yes	193 (37.0%)

Table 2.

Visit characteristics.

Visit characteristics	In-person	Telehealth
Number of Visits
1	235 (45.1%)	244 (46.8%)
2	165 (31.6%)	164 (31.4%)
3 or More	122 (23.3%)	114 (21.8%)
Provider seen
Primary Care/Internal Medicine	307 (58.7%)	325 (62.4%)
Pediatrics	40 (7.7%)	47 (9.0%)
Obstetrics/Gynecology	29 (5.6%)	21 (4.0%)
Orthopedic	13 (2.5%)	9 (1.7%)
Psychiatry	10 (1.9%)	27 (5.2%)
Ophthalmology	60 (11.5%)	35 (6.7%)
Dermatology	59 (11.3%)	52 (9.9%)
Otolaryngology	23 (4.5%	26 (5.1%)
Neurology	29 (5.3%)	27 (5.3%)
Cardiology	53 (10.2%)	46 (8.8%)
Urology	4 (0.8%)	6 (1.2%)
Reason for Visit
My Own health issue/condition	438 (83.9%)	449 (86.0%)
As a caregiver/care partner	79 (15.1%)	69 (13.2%)
To provide emotional support	5 (1%)	4 (0.8%)

Figure 2.

Reported in-person safety precautions.

Quantitative analysis

Patient-centered communication during in-person and telehealth visits

When comparing within-group differences between the mean percent-excellent CAT scores reported by respondents for their in-person vs telehealth visits (see Table 3), in-person visits had higher PCC scores (in-person 45% vs. telehealth 42%; P=.003). The lowest scored items in both settings where the healthcare provider “encouraged you to ask questions” followed by “showed interest in your ideas about your health”. Every CAT item was rated lower for telehealth visits, with the biggest difference between telehealth and in-person being “let you talk without interruption” followed by “greeted you in a way that made you feel comfortable”, “understood your main health concerns”, “gave you as much information as you wanted” and “checked to make sure you understood everything”. A repeated measures ANOVA (Figure 3) revealed higher PCC experienced during in-person clinic visits for respondents who identified as: ≥65 years (in-person 55% vs. telehealth 50%, P=.051), 18 – 34 years, (in-person 33% vs. telehealth 30%, P<.001), female (in-person 48% vs. telehealth 43% p=0.002), speaking a language other than English at home (in-person 33% vs. telehealth 28%, P=.01) and Hispanic ethnicity (in-person 32% vs. telehealth 26% P=.012).

Table 3.

Mean and top box analysis of communication assessment tool scores.

Components	Mean score		% Excellent
Components	Telehealth	In-person	Telehealth	In-person
Greeted in a way that made you feel comfortable	3.97	4.01	40%	44%
Showed interest in your ideas about your health	4.00	4.06	39%	42%
Understand your main health concerns	4.03	4.11	41%	45%
Let you talk without interruption	4.02	4.13	40%	45%
Gave you as much information as you wanted	4.01	4.13	41%	45%
Talked in understandable terms	4.10	4.17	44%	46%
Checked to make sure you understood everything	4.03	4.14	42%	46%
Encouraged you to ask questions	3.97	4.06	38%	40%
Involved you in the decisions as much as you wanted	4.00	4.04	41%	42%
Discussed next steps and follow-up plans	4.09	4.12	43%	46%
Showed care and concern	4.11	4.12	46%	47%
Spent the right amount of time with you	4.08	4.15	43%	46%
Overall	4.04	4.10	42%	45%

Figure 3.

Anova of differences between in-person and telehealth communication assessment tool scores.

Factors associated with in-person visit patient-centered communication

In the fully adjusted analysis (see Table 4), higher PCC was reported by respondents who reported ≥3 in-person visits compared to having a single in-person visit (β = 0.1; 95% CI = 0.0, 0.20), being screened on arrival to the clinic (β = 0.15; 95% CI = 0.03, 0.26), and age ≥65 years compared to those aged 35 – 64 years (β = 0.48; 95% CI = 0.10, 0.86). An interaction existed between respondent age and mask use, where older adults who reported wearing masks at all times reported lower PCC (β = -0.46; 95% CI = -0.86, -0.07).

Table 4.

Unadjusted and adjusted linear regression analysis of factors associated with patient centered communication (CAT Scores) in-person clinic visits.

Characteristics	Unadjusted in-person score			Adjusted in-person score
Characteristics	Estimates	CI	P	Estimates	CI	P
(Intercept)	0.4	0.34, 0.45	<.001	0.13	-0.21, 0.48	.446
Gender
Male (referent)
Female	0.08	0.00, 0.15	.039	0.07	-0.01, 0.15	.075
Non-binary	0.10	-0.47, 0.67	.727	0.2	-0.38, 0.78	.497
Age
35-64 (referent)
65+	0.06	-0.03, 0.16	.162	0.48	0.10, 0.86	.015
18-34	-0.14	-0.23, -0.05	.003	0.1	-0.22, 0.42	.542
Ethnicity ^†
Hispanic/Latino(a)/Spanish origin	-0.14	-0.26, -0.04	.005	0.07	-0.07, 0.21	.312
Race
White (referent)
American Indian/Alaskan Native	-0.19	-0.38, -0.01	.04	-0.14	-0.34, 0.06	.18
Asian	-0.07	-0.16, 0.02	.13	-0.03	-0.13, 0.08	.602
Black	0.04	-0.05, 0.14	.352	0.05	-0.05, 0.16	.327
Native Hawaiian/Pacific Islander	-0.11	-0.57, 0.35	.642	0.09	-0.48, 0.66	.753
Other	-0.13	-0.28, 0.01	.075	-0.15	-0.33, 0.03	.105
Language other than English at home	-0.18	-0.25, -0.11	<.001	-0.29	-0.61, 0.02	.07
Education
Bachelor’s Degree (referent)
More than a Bachelor’s degree	0.03	-0.08, 0.13	.622	0.02	-0.10, 0.13	.8
Less than a Bachelor’s degree	0.03	-0.06, 0.12	.504	0.02	-0.08, 0.11	.703
Number of Visits
1 (referent)
2	0.04	-0.04, 0.12	.322	0.04	-0.05, 0.13	.403
3+	0.14	0.05, 0.23	.002	0.1	0.00, 0.20	.048
Visit Precautions
Masked at all times	0.02	-0.11, 0.15	.733	0.06	-0.23, 0.36	.671
Screened at the door	0.23	0.13, 0.33	<.001	0.15	0.03, 0.26	.014
Social Distancing during the visit	0.21	0.09, 0.33	.001	0.08	-0.06, 0.22	.28
COVID-19 results
COVID-19 Negative (referent)
COVID-19 Positive	-0.15	-0.28, -0.02	.022	-0.05	-0.24, 0.13	.56
Caregiver Involvement
No Caregiver Prior To COVID or During (referent)
Prior to COVID AND During COVID	-0.14	-0.22, -0.07	<.001	-0.05	-0.14, 0.03	.238
Prior to COVID but NOT During COVID	0	-0.08, 0.07	.979	-0.02	-0.10, 0.06	.588
Visit Instructions
No Instructions	0.03	-0.02, -0.09	.108	-0.05	-0.19, 0.09	.501
Phone Instruction	0.06	-0.01, 0.14	.099	0.05	-0.04, 0.13	.284
Patient Portal/email Instructions	-0.06	-0.18, 0.07	.368	-0.08	-0.22, 0.05	.231
Specialty
Primary Care	0.08	0.01, 0.16	.019	0.06	-0.03, 0.14	.182
Psychiatry	0	-0.13, 0.13	.983	0.1	-0.04, 0.24	.164
Pediatrics	-0.01	-0.11, 0.10	.888	0.04	-0.08, 0.16	.526
OBGYN	-0.01	-0.11, 0.10	.888	0.02	-0.12, 0.12	.977
Dermatology	-0.03	-0.14, 0.08	.644	0.01	-0.12, 0.14	.92
Ophthalmology	0.14	0.04, 0.24	.005	-0.04	-0.17, 0.08	.491
Otolaryngology	0.03	-0.15, 0.20	.772	0.01	-0.19, 0.21	.894
Cardiology	0.09	-0.02, 0.21	.121	0.06	-0.07, 0.19	.387
Urology	-0.02	-0.17, 0.13	.811	-0.07	-0.23, 0.09	.407
Neurology	0.1	-0.06, 0.25	.21	0.02	-0.15, 0.20	.797

^†Referent group = Not of Hispanic, Latino(a) or Spanish origin, statistically significant values in bold.

Factors associated with telehealth visit patient-centered communication

In the fully adjusted analysis (see Table 5), higher PCC was experienced by respondents reporting ≥3 telehealth visits compared to having a single telehealth visit (β = 0.11; 95% CI = 0.01, 0.20) and respondents having an Obstetrics and Gynecology visit (β = 0.15; 95% CI = 0.03, 0.27). Compared to respondents aged 35 - 64 years, those <35 years reported lower telehealth PCC (β = -0.13; 95% CI = -0.24, -0.01). Respondents who usually had a caregiver join their visits prior to the pandemic, and who were joined by a caregiver during their telehealth visits, reported lower PCC compared to respondents who did not usually have a caregiver attend their visits before the pandemic or their caregiver was not present during the telehealth visit (β = -0.11; 95% CI = -0.19, -0.04).

Table 5.

Unadjusted and adjusted linear regression analysis of factors associated with patient centered communication (CAT Scores) of telehealth visits.

Characteristics	Unadjusted telehealth score			Adjusted telehealth score
Characteristics	Estimates	CI	P	Estimates	CI	P
(Intercept)	0.35	0.30, 0.40	<.001	0.05	-0.25, 0.35	.745
Gender
Male (referent
Female	0.05	-0.02, 0.11	.167	0.03	-0.05, 0.11	.465
Non-Binary	0.11	-0.42, 0.63	.686	0.15	-0.40, 0.70	.595
Age
35-64 (referent)
65+	0.03	-0.04, 0.11	.402	0.01	-0.09, 0.12	.799
18-34	-0.14	-0.22, -0.06	<.001	-0.13	-0.24, -0.01	.031
Ethnicity ^†
Hispanic/Latino(a)/Spanish origin	-0.09	-0.19, -0.00	.048	0.01	-0.11, 0.13	.858
Race
White (referent)
American Indian/Alaskan Native	0.05	-0.22, 0.12	.593	-0.06	-0.24, 0.12	.522
Asian	-0.04	-0.12, 0.04	.34	-0.04	-0.13, 0.05	.443
Black	0.07	-0.02, 0.15	.13	0.06	-0.03, 0.15	.214
Native Hawaiian/Pacific Islander	-0.02	-0.44, 0.41	.939	0.22	-0.32, 0.76	.423
Other	-0.05	-0.18, 0.09	.486	-0.03	-0.19, 0.13	.736
Language other than English at home	-0.11	-0.17, -0.04	.002	-0.02	-0.15, 0.10	.707
Education
Bachelor’s Degree (referent)
More than a Bachelor’s Degree	0.06	-0.04, 0.16	.245	0.03	-0.08, 0.13	.605
Less than a Bachelor’s degree	0.05	-0.03, 0.14	.212	0.04	-0.05, 0.13	.356
Tested Positive for COVID-19	-0.09	-0.21, 0.03	.124	0.01	-0.13, 0.15	.875
Number of Telehealth Visits
1 (referent)
2	0.02	-0.05, 0.10	.545	0.04	-0.04, 0.12	.288
3+	0.12	0.04, 0.21	.004	0.11	0.01, 0.20	.025
Telehealth Visit pre-COVID	-0.09	-0.15, -0.02	.012	0	-0.09, 0.09	.979
Telehealth Visit Location
Home (referent)
Other	-0.11	-0.44, 0.22	.508	-0.08	-0.44, 0.27	.635
School	-0.16	-0.41, 0.08	.192	-0.1	-0.36, 0.15	.43
Commuting	-0.04	-0.22, 0.14	.688	0	-0.20, 0.19	.965
Work	-0.15	-0.27, -0.02	.02	-0.09	-0.22, 0.05	.207
Technology
Video (referent)
Telephone	-0.01	-0.28, 0.25	.917	-0.03	-0.22, 0.19	.778
Pre-Visit Instructions
Via Phone	0	-0.08, 0.07	.922	-0.02	-0.10, 0.06	.584
Via Patient Portal or email	-0.02	-0.13, 0.09	.673	-0.06	-0.17, 0.06	.364
No Instructions	-0.02	-0.13, 0.09	.772	-0.05	-0.17, 0.07	.384
Telehealth Caregiver Involvement
No Caregiver Prior To COVID or During (referent)
Prior to COVID AND During COVID	-0.11	-0.22, 0.09	.003	-0.11	-0.19, -0.04	.003
Prior to COVID but NOT During COVID	0.01	-0.15, 0.17	.889	0.01	-0.15, 0.17	.889
Private Telehealth Visit	0.22	-0.09, 0.41	.291	0.17	-0.07, 0.41	.162
Specialty
Primary Care	-0.01	-0.08, 0.06	.776	0	-0.06, 0.07	.90
Psychiatry	0.05	-0.04, 0.14	.30	0	-0.10, 0.10	.981
Pediatrics	0.01	-0.08, 0.10	.769	0.08	-0.02, 0.18	.108
OBGYN	0.08	-0.03, 0.19	.154	0.15	0.03, 0.27	.011
Dermatology	0.04	-0.03, 0.11	.341	0.01	-0.10, 0.12	.894
Ophthalmology	0.02	-0.01, 0.09	.283	0.02	-0.11, 0.15	.739
Orthopedics	-0.03	-0.08, 0.10	.233	0.05	-0.08, 0.19	.445
Otolaryngology	0.02	-0.04, 0.21	.412	0.06	-0.09, 0.22	.41
Cardiology	0.09	-0.19, 0.22	.522	0	-0.11, 0.12	.945
Urology	0.01	-0.02, 0.11	.395	-0.04	-0.17, 0.08	.506
Neurology	-0.03	-0.16, 0.11	.691	-0.01	-0.16, 0.14	.874

^†Referent group = Not of Hispanic, Latino(a) or Spanish origin, statistically significant values in bold.

Qualitative analysis

Summary of results can be found in Box 1. Of 352 comments received, 94 (26.7%) explicitly stated that they had nothing more to offer and 56 (16%) comments were indecipherable (i.e.,random letters or words). Ultimately, 202 comments were coded. Respondent comments about telehealth were mostly positive, with many highlighting comfort and convenience, though some negative comments about telehealth related to its limitations for physical examination were noted.

Box 1. Qualitative analysis of patient clinic visit experiences during the pandemic

Subtheme	Definition	Example
Positive Comments About Their Telehealth Experience
General Positive	Nonspecific positive comments about telehealth	“It was new, and I really liked it”
Convenience	Finding that telehealth was more convenient than in-person visits	“I hope my doctor continues with telemedicine. It is convenient. I stopped driving years ago and having a telemedicine call, I did not have to rely on my wife to drive me to the doctor each time”
Desire to Continue Using Telehealth	Expressing or indicating that telehealth will likely be a future part of their health care plans	“I initially thought that virtual appointments would feel like it isn’t worth doing it, but I think it was a great experience I could have never been able to do if it weren’t for the pandemic. I felt much more comfortable speaking with my doctor”
Limitations of Telehealth
Preference for In Person Visits	A desire to return to in-person visits	“I prefer in clinic visits with my Dr. Telehealth seems inadequate and impersonal and no privacy. Clinician cannot listen to your heart or lungs or take your vital signs”
Positive Comments About Their Physician
General Positive	Nonspecific positive comments about their clinician	“My doctor is great”
Improved clinician experience	Finding that their experience with clinicians is better during the pandemic	“My physicians are actually more accessible during this pandemic. Because there are few or no other waiting patients, the wait time is very short or none at all. The exams start almost immediately with little, or no interruptions and the physicians are much more relaxed, and the visits are very productive for me”
Improved clinician experience		“I like the current way of handling the in-person visits, no wasted time in waiting room!”
Limitations of COVID-19 Safety Protocols
Preference for In Person Visits	Barriers to effective care during COVID-19	“Though the doctor seems to be comfortable in talking to me with both of us wearing masks, the nurses as the clinic did not take my weight and height and blood pressure the way they were doing before the pandemic. It looks they were too afraid to come close to me.”

Discussion

Principal results

In this cross-sectional, online survey of U.S. adult patients conducted in Fall 2020,within-participant experiences of PCC during the COVID-19 pandemic were moderately but consistently lower for telehealth visits compared to in-person visits. PCC in telehealth compared to in-person visits was significantly lower for respondents from the youngest and oldest age groups, females, those who identify as Hispanic and those who speak a language other than English at home. Experiences of more clinic visits, regardless of visit modality (telehealth or in-person) was associated with improved PCC. Lower PCC during telehealth visits was reported by respondents aged <35 years and those who stated that, prior to and during the pandemic, a caregiver attended their visit.

Limitations

Two core items of the CAT were not included in the survey: “staff treated me with respect,” “paid attention to me (looked at me, listened carefully).” However, Top Box CAT analysis would unlikely be affected by their omission, especially given the high internal consistency between items.²³ Communication quality may be confounded by familiarity with one’s clinician or disease burden, two factors we did not explore directly in this study.^32–34 However, we did capture number of clinic visits, which may indicate patients with greater familiarity and indeed, this variable was observed to be related to higher PCC. Our online survey is a convenience sample of self-selected participants who may be more comfortable with technology. This may alter the effect that the “digital divide” has on older and/or remote populations. Therefore, the perceived gap in PCC between in-person and telehealth visits may be larger than reported.^13,34 Although whole online survey panels carry some selection bias, they have been found to produce reliable findings and can act as a good starting point for further research.^35–37

Comparison with prior work

Research to date suggests that PCC during telehealth is similar to in-person visits.^38–40 Yet in our analysis, where respondents experienced both telehealth and in-person visits, serving as their own controls, we found that PCC during telehealth visits was rated lower across various demographic groups. A prior systematic review found that patients were overall satisfied with telehealth, though similar to our findings, reported a preference for in-person visits.¹ Interestingly, the CAT item “let you talk without interruption” received a higher percentage excellent rating during in-person clinic visits (45%) than in telehealth visits (40%), contrary to the finding by Agha and colleagues that patients perceive clinicians to be more attentive and less likely to interrupt during telehealth encounters.⁴⁰ The patient experience reported by Agha and colleagues⁴⁰ may have been more streamlined as the video telehealth visits were conducted in a clinic facilitated by a nurse, compared to the experience of telehealth during the COVID-19 pandemic which is typically conducted independently by the patient at home. Similar to a recent systematic review, we did not find any differences in PCC when telehealth visits were conducted by phone or video.⁴¹

While younger adult patients may be more accustomed to technology, with higher overall telehealth visit completion rates,⁴² those in this study reported lower levels of telehealth visit PCC compared to older adults, similar to prior research.⁴³ Older adults are likely to have longer more trusting relationships with their clinicians. The association between visit frequency and higher PCC may reflect an ‘existing’ clinician-patient relationship, known to create a stronger therapeutic alliance.^33,34 Additionally, as younger adults are more experienced with technology they may have expectations about the capabilities of telehealth. Yet with the pace and scale of telehealth roll out technical challenges were common⁴⁴ which may have impacted younger adults’ perceptions of telehealth visit quality. Challenges related to privacy and security (crowded homes, taking calls at work and discomfort with conducting sensitive exams may impact PCC) may differentially impact vulnerable populations.^39,45–47

Participants in this study who had a caregiver join telehealth visits reported decreased PCC. Despite the opportunity to enable engagement of caregivers via telehealth,¹⁹ the presence of a caregiver at telehealth visits can result in greater technical complications and unintended interruptions (e.g. arranging three people to join a telehealth visit) and security and privacy concerns (e.g. shared passwords, discussion of sensitive patient issues when a caregiver may normally be in a separate waiting area).⁴⁷ Common tips for cybersecurity in healthcare, such as recommendations for users to have strong passwords that are changed regularly and limiting network and physical access to user devices, are increasingly difficult with the addition of one or more caregivers. For example, patients often share passwords to their accounts with loved ones or share their devices with family members. Per Jayousi and colleagues, concerns about device comfort, data privacy and digital literacy create human-centered and ethical obstacles for telemedicine integration.⁴⁸ The rigidity of regulatory solutions and the need to accommodate dynamic care partner networks and shifts in health status creates ample opportunity to incorporate stakeholder preferences in standardization recommendations.

Our research supports prior work regarding language differences: individuals with limited English proficiency and those who identify as Hispanic experience lower CAT scores for telehealth and in-person clinic visits.^33,49,50 This could be secondary to the slower uptake of telehealth in the Hispanic community,⁴³ lack of captioning and interpreter services,^51,52 or the requirement of a working patient portal for scheduling purposes. From a system perspective, it’s important to consider evidence-based communication protocols to aid providers and removing barriers to telehealth for this population such as: 1) the requirement for a patient portal^13,53 and 2) lack of multilingual support, a key equity consideration in addressing access issues and the digital divide.⁵⁴ At the time of data collection, siloed digital infrastructures limited flexibility of communication protocols resulting in a dearth of solutions to unforeseen challenges, such as the lack of captioning and translator services.⁵⁴ The normalization of telehealth delivery should prompt more up-to-date examination of language support services, paying special attention to involvement of vulnerable populations in designing flexible, scalable solutions to care delivery disparities.⁵⁵

Our stratified analysis of observed mask use and CAT score, by age, revealed that mask use was associated with lower CAT score in older adults. This finding lends support to concerns that mask use could result in greater communication challenges in older adults due to the higher prevalence of age-related vision, hearing and memory loss.^3,8,10,56 The presence of screening at the entry of the clinic was associated with higher perceived PCC. While the mechanisms by which this may impact PCC are unclear, it may be related to what Scheid describes as the “psychological need for competence” in which comfort can be provided by a consistent message that gives the impression that clinicians are “effective, capable and have mastery over our circumstances”—in this case management of COVID-19 pandemic risks.⁵⁷

In our study, each item on the CAT was rated lower for telehealth visits. In theory, some of the communication issues may be mitigated by teaching clinicians to treat communication skills as clinical skills. Social cues of when to talk and listen are more difficult to identify over telephone and video, and disruptions by others present in the patient’s space offer less opportunity for patients to speak privately with their clinician.⁴⁰ A potential factor accounting for the lower reported quality of PCC experienced in telehealth may be the absence of non-verbal cues, essential to building rapport and trust during clinic visits.⁵⁸ It would be useful to study the outcomes of some of the following interventions on PCC: 1) incentivizing clinicians to learn and more frequently use verbal cues that convey warmth and support; 2) clinician training in the teach back method to demonstrate engagement; 3) teaching doctors to use empathetic body language that remains visible on the screen or at a distance.⁵⁹ This includes maintaining eye contact, using gestures and open posture; or 4) patient education about the necessity of safety measures with reassurance that providers are still thinking about their needs and emotions. Feinberg and colleagues report that resident physicians believe they are already using the teach back method but increase their actual use of it after training. Extension of this research into the perception of patients is ongoing.^59,60

Conclusions

Within broader health informatics frameworks, our research contributes to the evaluation of telemedicine communication quality, a critical step in technological assessment aimed at standardization of telehealth delivery.⁴⁸ Following the pandemic, telehealth services have become more prominent, yet lessons learned from the pandemic must be applied. Telehealth can help reduce barriers to care, but caution must be taken in how we address disparities in PCC experienced in telehealth. Of immediate concern is supporting visit communication both in-person and via telehealth for patients with limited English proficiency, young adults, and those who benefit from the presence of a caregiver. Translation services and on-screen captioning of visit communication can help reduce observe disparities in quality of communication experienced by patients with limited English proficiency and with hearing difficulties, including those with mild age related hearing loss.^51,52 This need is pressing: approximately 20% of US adult population speak a language other than English, half of whom report limited English proficiency.⁶¹ The availability or translator services in telehealth settings is critical to meet needs of these patients. While mask use is an effective protective tool in clinic, it can impair PCC in older adults; new strategies are emerging where transparent masks allow the clinicians face to be seen which may mitigate the impact of masks on PCC.⁴ Low levels of perceived PCC have significant implications and inadequate communication can impede self-management of care. These challenges result in longer-term negative health outcomes. This disruption of care delivery and impact on PCC points to the need for more careful co-design of care delivery processes to reduce disparities experienced by these populations.

Supplemental material

Supplemental material - The impact of telehealth on patient-centered communication during the COVID-19 pandemic

Supplemental material for The impact of telehealth on patient-centered communication during the COVID-19 pandemic by W. Moraa Onsando, Paul J. Barr, Reed W. Bratches, Kerri L. Cavanaugh, Jesse A. Schoonmaker, Noah Freundlich, Yasir Salih, Gregory Makoul, and Meredith C. Masel in Health Informatics Journal.

Supplemental material

Supplemental material - The impact of telehealth on patient-centered communication during the COVID-19 pandemic

Supplemental material

Supplemental material - The impact of telehealth on patient-centered communication during the COVID-19 pandemic

Footnotes

Acknowledgements

We would like to acknowledge the review and piloting of our survey by: Linda Wilkinson and the Patient and Family Advisors of Dartmouth-Hitchcock, our patient partners, Sheri Piper and Roger Arend, and all the members of our Open Recordings team-particularly Drs. Renata West Yen and Alejandra Martinez-Pereira.

ORCID iD

W. Moraa Onsando

Ethical considerations

This research was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and the protocol information that follows was deemed exempt from further review by the Trustees of Dartmouth College Committee for the Protection of Human Subjects on June 29, 2020 (CPHS# STUDY00032099) and the University of Texas Medical Branch Institutional Review Board on August 18, 2020 (IRB#20-0206). The requirement for written consent was waived.

Consent to participate

Our survey was distributed online from September to October 2020 through Qualtrics. To ensure informed consent, within the guidelines of the protocol, the Qualtrics invitation email was sent only to people who have self-selected to receive surveys. The opening screen presented an “informed consent” text block detailing the purpose of the study, risks, and data handling. The final sentence of the survey introduction read: “If you wish to participate in this project, please click the “>>” button below If you do not wish to participate in this project, please close your Internet browser.” (see ) The decision to proceed beyond the survey introduction was considered consent to participate.

Author contributions

Authorship CRediT statement has been included as a supplement.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for this project was provided by Oliver Center for Patient Safety and Quality Healthcare at The University of Texas Medical Branch (grant #OC200206).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data that support the findings of this study are available from the corresponding author upon reasonable request.*

Supplemental material

Supplemental material for this article is available online.

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