“Do You Want to Talk to [the Patient]?” Impact of Visit Modality on Shared Decision Making in Pediatric Primary Care

Abstract

Introduction. Telemedicine may improve access to care for children, but whether telemedicine alters decision-making processes is not known. We aimed to describe differences in shared decision making (SDM) in telemedicine and in-person pediatric primary care visits with children with chronic conditions. Methods. We conducted qualitative analyses of primary care visits video recorded across 6 pediatric primary care practices. Telemedicine visits were matched to in-person visits on age, diagnosis, timing of visit, and clinical site. Each recording was summarized by a trained reviewer. The summaries were then coded and analyzed using a framework approach. Codes were organized into a matrix to facilitate thematic analyses both within and across visits and visit types. Results. We analyzed 16 telemedicine and 18 in-person visits with 34 unique patients and 12 unique clinicians. The samples of telemedicine and in-person visits were similar in distribution across patient and parent characteristics. Most of the visits discussed attention-deficit/hyperactivity disorder. Thematic analysis identified 2 major themes. First, employment of SDM processes do not differ by visit modality. Across constructs of team talk, options talk, and decision talk, optimal SDM processes were lacking in both visit modalities. Second, visit modality affects the patient’s decision engagement. Patients were physically present during all in-person visits but only briefly present during most telemedicine visits, limiting their opportunities to engage in decision making. Conclusion. Our study did not identify qualitative differences in SDM across the mode of pediatric primary care delivery: telemedicine versus in person. These data may alleviate concerns about potential negative effects of telemedicine on communication in pediatric primary care visits for chronic conditions. However, there is a need to improve shared decision making across visit types in pediatric primary care.

Highlights

Our study demonstrates that the mode of pediatric primary care delivery does not affect the use of SDM in primary care visits for pediatric chronic conditions.

There are opportunities to improve SDM across both care modalities.

Future work should consider how approaches to increasing SDM and family decision engagement may differ by visit modality.

Keywords

ambulatory pediatrics chronic conditions primary care shared decision making telemedicine

The use of telemedicine in varied practice settings, including pediatric primary care, has significantly expanded over the past decade, but usage initially grew slowly.^1–10 The rapid growth of telemedicine usage was a necessary response to the COVID-19 pandemic,^10–14 and its use has continued now as a routine part of pediatric primary care.^7–10 Because much of the growth was so rapid and urgent, many questions were unanswered at the time of widespread expansion.

One area in need of consideration is how communication between clinicians and families differs in telemedicine compared with in-person care, including the use of shared decision making (SDM) as part of clinical encounters. Ensuring that SDM occurs, regardless of whether care is delivered via telemedicine or in-person visits, is a crucial component of delivering high-quality care. Because telemedicine changes the interaction between patients and health care providers, it may affect SDM.¹⁵ Specifically, in adult care, telemedicine changes both verbal and nonverbal communication¹⁶ as well as relational dynamics between health care providers and adult patients.¹⁷ Compared with in-person visits, telemedicine visits for adults appear more physician centered, with patients appearing to be less engaged,¹⁸ both of which run counter to SDM.

High-quality decision making may be particularly important for children with chronic conditions given their more frequent interactions with the health care system and greater number of treatment decisions. In asthma and attention-deficit/hyperactivity disorder (ADHD), the 2 most common chronic conditions in pediatric primary care,^19,20 there are multiple decisions amenable to SDM. For example, in ADHD, decisions may pertain to whether to start a stimulant medication. In asthma, decisions may involve the potential switch to SMART therapy.²¹ However, a prior quantitative study²² by our team demonstrated low levels of SDM in both telemedicine and in-person primary care visits for children with the 2 most common pediatric primary care conditions Although there was no quantifiable difference between telemedicine and in-person care with respect to SDM, quantitative studies may miss important details that can be seen through a qualitative approach. Therefore, the purpose of this study was to understand, from a qualitative perspective, any differences in how decision making occurs in telemedicine and in-person pediatric primary care of children with chronic conditions.

Methods

Context

This is a prospective cohort study using qualitative analysis of video-recorded primary care visits. Qualitative data, presented here, were collected between February 13, 2023, and November 14, 2024, across 6 pediatric primary care practices. The 6 practices included 3 academic clinics and 3 community practices affiliated with a Midwestern children’s hospital.

Eligibility

All clinicians (physicians and advanced practice providers) providing pediatric primary care via both telemedicine and in-person care at participating clinics were eligible. Any parents and patients aged 5 to 21 y (hereafter referred to as families) attending a telemedicine or in-person visit for asthma or ADHD with a consented clinician were eligible to participate. Families were excluded if they had previously participated in the study or if the visit was conducted in a language other than English (<10% of visits at each site) due to limitations in the institutional telemedicine software, which limited the availability of interpreters during telemedicine visits at the time of the study.

Recruitment

All clinicians who used telemedicine at participating clinics were invited to participate via email and via informational sessions at staff meetings. Families who were seen for asthma or ADHD telemedicine or in-person visits with a consented clinician were contacted in advance of their visit and invited to participate. Families scheduled in-person who could not be reached in advance of their visit were approached in the clinic waiting room. In-person participants were matched to telemedicine participants based on patient age (5 to <9 y, 9 to <13 y, ≥13 y), diagnosis, timing of visit to account for potential seasonality (within 60 d of matched appointment), and clinical site.

Consent

Written informed consent, including a Health Insurance Portability and Accountability Act (HIPAA) authorization, was obtained from all parents, patients >17 y, and clinician participants prior to recording the visit. Written assent was obtained from all patients ages 8 to 17 y.

Recording and Transcription

After obtaining consent and assent, a video camera was set up in the exam room or a recording of the visit was initiated via the telemedicine platform. Recording commenced immediately and continued until the clinician left the exam room or disconnected from the telemedicine visit. One in-person visit experienced technical difficulties and resulted in an audio-only recording.

Data

Patient characteristics

Participants’ self-identified characteristics including age, race, ethnicity, sex, payer, and diagnosis were collected via survey. The characteristics of patients seen by telemedicine or in-person were compared using Fisher’s exact test for categorical variables.

Sample size

Based on sample size calculations for the quantitative portion of the study, we recorded 88 video encounters and report the quantitative evaluation of this full set of encounters elsewhere.²² To complement this quantitative analysis, we a priori planned a qualitative analysis of a portion of the recorded visits. We included a subset of ADHD visits for both telemedicine (n = 10) and in-person care (n = 10) and all the recorded asthma visits (n = 14), for a total initial sample of 34. We planned for qualitative analysis of additional summaries if needed to reach informational saturation.

Qualitative coding and analysis

Each video recording was reviewed by a trained reviewer who wrote a summary of the content, including topics of conversation; interactions between patient, parent, and clinical provider; and nonverbal information. The summaries were then analyzed using a framework approach that consists of 5 stages of qualitative data analysis, starting with familiarization and ending with mapping and interpretation. A preliminary deductive codebook was constructed based on the 3-talk model of SDM.²³ This approach was chosen because of the model’s pragmatism, applicability to primary care, and prior use in qualitative analysis of decision making in pediatric chronic conditions.^24,25 The initial codebook included codes for the 3 components of the model (team talk, option talk, decision talk) as well as behaviors associated with each and is included in the Appendix.

The coding team included a general pediatrician with expertise in SDM, a general pediatrician with expertise in health care communication, and a pediatric research assistant; all 3 have expertise in qualitative methods. The first 12 summaries were independently coded by 2 of 3 trained coders (KS, EAL, RHB), and discrepancy in coding was discussed and resolved by consensus. Once coders reached consistency in their coding approach, 1 of the 3 coders applied codes to each remaining summary with intermittent discussion by all coders to ensure continued agreement. Consistent with a framework approach, we organized the codes into a matrix, which allowed for analyses both within and across visit types. One study team member (K.S.) created an initial group of themes by qualitatively reviewing the coding matrix across visit types. These themes were then modified through group discussion. All summaries and codes were stored in Dedoose (Los Angeles, CA, USAS), a secure, online application that supports qualitative data coding.

Results

We analyzed 16 telemedicine and 18 in-person visits. For both telemedicine and in-person visits, most patients were male (73%, 72%), publicly insured (80%, 78%), discussed ADHD (63%, 56%), and included a female parent (100%, 94%; Table 1). There were no significant demographic differences between the children and parents who attended visits in-person versus via telemedicine. There were 12 clinicians included in the study, 11 physicians and 1 nurse practitioner; most clinicians were female (75%), identified as White (75%), and had been in practice for more than 5 y (83%). Thematic analysis identified 2 major themes described in detail below; illustrative quotations are identified by speaker, patient age, diagnosis, and modality of visit.

Table 1

Self-Identified Demographic Characteristics of Parents and Patients

Characteristic	Telehealth Visit (n = 16)^a	In-Person Visit (n = 18)	Overall (n = 34)	P Value^b
Parents
Gender				1.0
Female	15 (100%)	17 (94%)	32 (97%)
Race				0.3
White	6 (40%)	5 (28%)	11 (36%)
Black/African American	7 (46%)	12 (67%)	19 (58%)
Asian		1 (5%)	1 (3%)
Multirace	1 (7%)		1 (3%)
Prefer not to say	1 (7%)		1 (3%)
Ethnicity				0.1
Hispanic or Latino	1 (7%)		1 (3%)
Non-Hispanic or Latino	12 (80%)	17 (100%)	29 (91%)
Prefer not to say	2 (13%)		2 (6%)
Marital status				0.6
Single	8 (53%)	12 (67%)	20 (61%)
Single, living with partner	3 (20%)	1 (6%)	4 (12%)
Married	2 (13%)	4 (22%)	6 (18%)
Separated	1 (7%)		1 (3%)
Divorced	1 (7%)	1 (6%)	2 (6%)
Highest level of education				0.6
Didn’t graduate high School	1 (7%)	1 (6%)	2 (6%)
High school graduate/GED	3 (20%)	7 (39%)	10 (30%)
Some college/associate degree	8 (53%)	6 (33%)	14 (43%)
Bachelor’s/graduate degree	3 (20%)	4 (22%)	7 (21%)
Child (patient)
Age at visit, mean (SD)	9.5 (3.8)	9.5 (3)	9.5 (3.4)	0.8
Gender				1.0
Male	11 (73%)	13 (72%)	24 (73%)
Female	4 (27%)	5 (28%)	9 (27%)
Race				0.4
White	5 (33%)	2 (11%)	7 (21%)
Black/African American	8 (53%)	11 (61%)	19 (58%)
American Indian or Alaska Native		1 (6%)	1 (3%)
Asian		1 (6%)	1 (3%)
Multirace	2 (13%)	3 (17%)	5 (15%)
Ethnicity				0.1
Hispanic or Latino	1 (7%)		1 (3%)
Non-Hispanic or Latino	12 (80%)	18 (100%)	30 (91%)
Prefer not to say	2 (13%)		2 (6%)
Health insurance				0.9
Private	3 (20%)	2 (11%)	5 (15%)
Public	12 (80%)	14 (78%)	26 (79%)
Self-pay		1 (6%)	1 (3%)
Don’t know		1 (6%)	1 (3%)

Characteristics are missing for 1 telehealth participant; thus, column totals to 15.

Fisher’s exact test for categorical variables.

Theme 1: Use of SDM Processes Does Not Differ Based on Visit Modality

Team talk

Team talk encompasses supporting decision makers through the decision-making process, determining decisional roles, showing support, and eliciting their goals. Most team talk focused on the decision-making process and occurred implicitly through clinicians’ use of “we” to indicate themselves and the clinical team, parents, and patient as one decisional unit, “because I can help us adjust stuff if we need to” (clinician, 15-y-old, ADHD, in-person). Implicit team talk also addressed the ongoing nature of the decision process by signaling that the clinician is available outside of the visit context as needed, “if the school year gets going . . . and you’re like ‘well maybe this 54 [mg dose] is not so perfect’ just give us a call. Don’t wait for 3 months to come back” (clinician, 10-y-old, ADHD, telemedicine).

Another component of team talk, determining decisional roles, occurred in only 1 telemedicine visit and 1 in-person visit. In each instance, a parent deferred decisional authority to the clinician: “I’m ok with the decision, I’m not a professional” (parent, 8-y-old, ADHD, in-person). We did not observe any instances of the clinician initiating discussion of decisional roles.

Clinicians in our study did consistently support parents and patients through communication strategies such as naming emotions and repeating what the family said, with these strategies showing up in both visit types. Clinicians expressed understanding with phrases such as “sounds like in general things are going really well, which makes me happy” (clinician, 6-y-old, ADHD, in-person). Clinicians also verbalized ongoing support such as “if anything changes, we are here” (clinician, 11-y-old, asthma, in-person). We did not observe any instances of the clinicians eliciting the decision makers’ goals in either visit modality.

Option talk

Option talk, which is discussion of specific options, was used in a limited manner. In both visit modalities, this came up most often used when discussing medication options:

Clinician:

What do you guys think? Do you like this one? We could switch to something similar that’s a little bit more long acting?

Patient:

I like this one.

Clinician:

Ok. Alright. What about going up a little bit to see if it lasts? We can do it one of two ways, and I’ll give you the option.

(15-y-old, ADHD, in-person)

When options were presented, they were sometimes accompanied by a basic discussion of the pros and cons of each option, which often skewed toward the cons: “I am tempted to put him on an everyday inhaler . . . the annoying thing is that you kind of have to do it every day morning and night” (clinician, 6-y-old, asthma, in-person). Option talk was often accompanied by clinicians sharing information about disease etiology or their preliminary plan, often in longer, uninterrupted turns of speech, which limited engagement or questions at the point of option talk. This occurred in both types of visits. As in team talk, some option talk was implied rather than being explicit, especially with regard to titration of medication doses. For example, “well, we can try going up on it” (clinician, 8-y-old, ADHD, in-person) was presented as an option with an implied second option of not going up on the dose. Sometimes, an option was verbalized but not presented as a viable choice: “Well, we can increase the dose today to 30. The alternative is—and we can consider this later because I don’t want to make too many changes at one time—we can give a small dose in the afternoon” (clinician, 6-y-old, ADHD, telemedicine). When the clinician seemed to perceive things were going well, there was a scarcity of option talk in both modalities.

Another component of option talk is a discussion about outcomes, specifically the uncertainty surrounding a range of potential outcomes. Uncertainty about the outcome of the intervention was commonly discussed during both types of visits, predominantly in the discussions surrounding medication initiation or changes. Most of the uncertainty discussed was implied. For example, one clinician noted that “I’ll send more of that inhaler that he can take morning and night. And we’ll see how that works” (clinician, 8-y-old, asthma, in-person), implying, but not explicitly stating, that this may not be the definitive treatment plan nor defining the goal of treatment. Occasionally clinicians explicitly acknowledged treatment failure was a possible outcome: “Hopefully this works, and if not, there’s lots of options, so this isn’t the end of the road, ok?” (clinician, 5-y-old, ADHD, telemedicine).

Decision talk

Decision talk includes decision context, preference discussion, goals, and clinician recommendations. We found that only some decision talk components occurred consistently across both settings. Most of the decision context shared by parents was regarding family perspectives such as, “Dad is still on the fence about medicine” (parent, 6-y-old, ADHD, in-person) or “part of the reason we chose Vyvanse was because [sibling] is on it” (parent, 17-y-old, ADHD, telemedicine). Parents also shared logistical considerations for medication dosing (e.g., practice runs until 8:00 pm) or prior experiences with medications. In both modalities, sharing of contextual information was observed, generally offered by the parent without elicitation from the clinician.

In addition to decisional context, sharing of patient and parent preferences is an essential component of SDM. Without prompting in both visit types, parents often shared information that was important to them, especially when there were concerns. For example, “I prefer if there is not reliance, addiction. Because during summer break, I might want him to have a break” (parent, 8-y-old, ADHD, in-person) or “I want him to feel like he’s normal, but also not for teachers to be like ‘did you take your medicine today’ or singling him out” (parent, 12-y-old, ADHD, telemedicine). In other visits, preferences were elicited explicitly by the clinician, such as “are you open to medication management (clinician, 8-y-old, ADHD, in-person)?” In no visits were preferences shared by or elicited directly from the patient. These patterns were similar across visit modality.

Similarly, goals of treatment were infrequently discussed. Clinicians did, however, consistently invite questions at the conclusion of the visit, prompting, “Any other questions for me?” (clinician, 6-y-old, asthma, in-person) or wondering “anything else you need from me?” (clinician, 9-y-old, asthma, telemedicine). These invitations for questions were similar in both sets of visits. There were few invitations for questions at any other points.

When clinicians gave a recommendation in both visit types, it was often done authoritatively, such as “I definitely want you to stay on that controller inhaler because things were not in a great spot last time” (clinician, 13-y-old, asthma, telemedicine) or “first things first, though, I would feel uncomfortable with him skipping 2nd grade” (clinician, 8-y-old, ADHD, in-person). In a few examples, the clinician’s recommendation was couched more clearly as a decision: “Do you guys want to, which I would recommend, switch the albuterol [and] Flovent to one inhaler?” (clinician, 13-y-old, asthma, in-person). When choices were presented without a clear clinician recommendation, there was more discussion about the best way to move forward in both visit types.

Theme 2: Visit Modality Affects Individuals’ Visit Engagement

SDM requires active engagement of the people involved in decision making. We identified 2 ways in which engagement differed by visit modality: there was less patient engagement during telemedicine visits and the use of technology served as a facilitator of engagement during telemedicine and as a distraction from engagement during in-person visits.

For all visits, regardless of modality, the parent(s) were present throughout the visit and engaged in discussion with the clinician. However, the presence of the patient during visits differed by modality: patients were physically present during all in-person visits but only briefly present during most telemedicine visits. When the patient was present, regardless of visit modality, clinicians engaged the patient through inviting questions from the patient and asking questions of the patient: “How do your teachers say you’re doing? Do they call on you a lot or tell you to settle down?” (clinician, 16-y-old, ADHD, in-person). During the in-person visits, this occurred intermittently throughout the visit as questions came up, and patients would spontaneously offer insights to the conversation, which was otherwise occurring primarily between the clinician and parents. However, consistently across telemedicine visits, the majority of the visit time patients were not seen or heard, leading to fewer opportunities to engage the patient in the visit. During some telemedicine visits, the clinicians would request to view the patient to engage them in the conversation:

Clinician:

Can I say hi to [name]?

Mom turns the camera to show patient, who is sitting on the bed next to her.

Clinician:

It sounds like your asthma is doing really well. Do you agree?

Patient:

Yes.

(12-y-old, asthma, telemedicine)

Parents often facilitated interaction between the clinician and the patient during telemedicine visits such as by changing the angle of the camera or prompting the patient to participate, generally at the request of the clinician, whereas during in-person visits, the clinicians would engage the patient directly.

The role that technology played during visits differed by modality. In telemedicine visits, technology was used in ways to actively facilitate patient engagement. Indeed, parents often used their phone intentionally to include the patient in the conversation and decision-making process. For example, parents would ask, “Do you want to talk to [name]?” (parent, 15-y-old, ADHD, telemedicine) and then pass the phone to the patient. In contrast, during in-person visits, technology facilitated disengagement. During in-person visits, there was frequent technology use by all parties. For clinicians, the primary use of technology was interaction with the computer throughout the visit. Parents and patients were often distracted by various forms of technology present in the exam room, including phones, headphones, and video game players. For in-person visits, technology facilitated disengagement of both the family and clinician, which extended to disengagement in the decisional components of the visit.

Discussion

In this study, we investigated whether SDM varied qualitatively by mode of pediatric primary care delivery (telemedicine or in-person) for children with chronic conditions. Our results identified similar gaps in SDM discussion content domains of team talk, option talk, and decision talk across visit modalities. We also identified 2 ways in which engagement differed by visit modality: patient engagement in the visit was lower during telemedicine visits, and technology facilitated family decision engagement during telemedicine visits but not during in-person visits.

Overall, our study showed there are opportunities to improve all aspects of SDM in primary care pediatric chronic disease management. Some aspects of SDM, such as showing support, discussing pros and cons, providing decisional context, and inviting questions, were prevalent throughout both groups of visits. However, most of these decisional aspects were approached implicitly rather than explicitly. Previous research on implicit behaviors and decision making has focused on implicit bias²⁶ or decisions,^24,27 but our findings suggest that key SDM processes of team talk, option talk, and decision talk may also be implicit. On the other hand, aspects of SDM such as asking about goals and preferences, establishing decisional roles, and decisional context were largely absent from both an implicit and explicit perspective. Given that children and adolescents typically are more concrete than adults, being explicit about their role in decision making and eliciting their goals and preferences would likely increase their decision engagement.

It is possible that some aspects of SDM were missing because, often, once a recommendation was made, further discussion was limited or absent. This phenomenon, known as premature closure, has been shown in other clinical settings^24,27 and was found, in this data, in both in-person and telemedicine visits as evidenced by authoritative recommendations ending the treatment discussion. Future intervention work should consider clinician skill building to avoid premature closure. While multiple prior studies have shown limited use of SDM in clinical practice, including in the care of children and adolescents with chronic conditions,^24,28 this study adds to the literature by suggesting that the shifting of some care to telemedicine does not change SDM. Intervention development aimed at increasing SDM in the care of children with chronic conditions should consider the diverse settings in which such care occurs and be adaptable for both telemedicine and in-person care.

SDM requires active engagement of the people involved in the decision making. In pediatrics, there are developmentally appropriate roles for most patients to engage in decision making within the patient-parent-clinician triad. However, for such engagement to occur, the patient must be present during the decision-making discussion, a condition that was often not met during the telemedicine visits we observed. While the patient’s presence for the duration of the entire visit is likely unnecessary, there are unclear norms guiding these visits, and the onus for requesting the patient’s ongoing presence during decision making likely falls on the clinician. These findings are consistent with a previous study showing that telemedicine visits tended to be more physician driven, with the patient taking a more passive role.¹⁸ Lack of presence during telemedicine visits meant less opportunity for engagement in the SDM processes if they occur. One approach to increasing engagement could be offering only in-person care, but given the spread of telemedicine^29–31 and studies showing family satisfaction with telemedicine,^32,33 a more viable approach is to develop SDM and patient engagement strategies that consider the modality of care such as asking families to leave the phone in a stationary place, rather than being held by the parent, to facilitate both patient and parent being on camera more. In addition, future research aimed at understanding if parent, patient, and clinician norms and expectations of engagement during TM visits are different than expectations during in-person visits is worthwhile.

This study was conducted at 1 academic medical center and its affiliated community clinics; therefore, it may not generalize to all settings but does offer an in-depth comparison of visits that differ in modality within 1 setting. However, notably, the goal of qualitative research is to develop a deep understanding of a phenomenon, such as SDM, and not develop generalizable knowledge. Importantly, given limitations in the institutional telemedicine software at the time, interpreters were not available via telemedicine. Therefore, we did not include participants from telemedicine or in-person care who required interpreter services and are therefore unable to address whether interpreters shift SDM in one modality differently than another. Lastly, recording of clinic visits may be subject to the Hawthorne effect, in which subjects’ behavior is modified in response to the presence of a camera. However, recording of clinic visits is a commonly used research method, and studies show that the presence of a video camera in the room has only a minimal effect on patient and provider behaviors.³⁴

Conclusion

Our study demonstrates that the mode of pediatric primary care delivery does not affect the use of SDM in primary care visits for pediatric chronic conditions. However, it is possible that if there had been more SDM across modalities, we would have found differences, such as in which components of SDM occurred in telehealth compared with in-person care. At the same time, this study highlights the value of mixed-methods approaches by showing differences in patient engagement that were not captured in our quantitative work alone.²² Further mixed-methods studies may be beneficial in identifying aspects of telehealth interactions that facilitate or inhibit SDM. While there are opportunities to improve SDM across care modalities, we observed more commonalities in these opportunities for improvement than differences by modality. Future work should consider approaches to increasing SDM and family decision engagement, which may differ by visit modality.

Supplemental Material

sj-pdf-1-mpp-10.1177_23814683261447292 – Supplemental material for “Do You Want to Talk to [the Patient]?” Impact of Visit Modality on Shared Decision Making in Pediatric Primary Care

Supplemental material, sj-pdf-1-mpp-10.1177_23814683261447292 for “Do You Want to Talk to [the Patient]?” Impact of Visit Modality on Shared Decision Making in Pediatric Primary Care by Kelsey Schweiberger, Sandy Lee, Ruby Hyland-Brown, William B. Brinkman, Maria T. Britto, Andrew F. Beck, Kristin N. Ray, Ken Tegtmeyer and Ellen A. Lipstein in MDM Policy & Practice

Footnotes

Acknowledgements

None.

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: EAL is a member of the Editorial Board of MDM Policy and Practice. The author did not take part in the peer review or decision-making process for this submission and has no further conflicts to declare. The authors have no conflicts of interest relevant to this article to disclose. WBB had common stock holdings in the following publicly traded companies from February 2022 to April 2023: Pfizer, Merck, Abbott Laboratories, Viatris, and Johnson & Johnson. All other authors have no financial relationships relevant to this article to disclose. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Financial support for this study was provided in part by a grant from the National Center for Advancing Translational Sciences (KL2 TR001856; KS) and the Agency for Healthcare Research and Quality (R01HS029152; EAL). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Ethical Considerations

This project was approved by the Cincinnati Children’s Hospital Institutional Review Board (study 2022-0567).

Consent to Participate

Written informed consent, including a HIPAA authorization, was obtained from all parents, patients >17 y old, and clinician participants prior to recording the visit. Written assent was obtained from all patients ages 8 to 17 y.

Patient Consent

Not applicable; no identifying information is included in this article.

Consent for Publication

Not applicable, no identifying information is included in this article.

ORCID iDs

Kelsey Schweiberger

Andrew F. Beck

Kristin N. Ray

Ken Tegtmeyer

Ellen A. Lipstein

Data Availability

Deidentified data are available upon request from the authors. Data use agreements will need to be approved to access the data. The data are not publicly available due to privacy and ethical restrictions.

References

Gund

Sjöqvist

Wigert

Hentz

Lindecrantz

Bry

A randomized controlled study about the use of eHealth in the home health care of premature infants. BMC Med Inform Decis Making. 2013;13:22. DOI:10.1186/1472-6947-13-22

Fleischman

Hourigan

Lyon

, et al. Creating an integrated care model for childhood obesity: a randomized pilot study utilizing telehealth in a community primary care setting. Clin Obes. 2016;6(6):380–8. DOI:10.1111/cob.12166

Coker

Porras-Javier

Zhang

, et al. A telehealth-enhanced referral process in pediatric primary care: a cluster randomized trial. Pediatrics. 2019;143(3):e20182738. DOI:10.1542/peds.2018-2738

Olson

McSwain

Curfman

Chuo

The current pediatric telehealth landscape. Pediatrics. 2018;141(3):e20172334. DOI:10.1542/peds.2017-2334

Halterman

Fagnano

Tajon

, et al. Effect of the School-Based Telemedicine Enhanced Asthma Management (SB-TEAM) program on asthma morbidity: a randomized clinical trial. JAMA Pediatr. 2018;172(3):e174938. DOI:10.1001/jamapediatrics.2017.4938

Lin

Ramsey

Miller

, et al. Telehealth delivery of adherence and medication management system improves outcomes in inner-city children with asthma. Pediatr Pulmonol. 2020;55(4):858–65. DOI:10.1002/ppul.24623

Barney

Buckelew

Mesheriakova

Raymond-Flesch

The COVID-19 pandemic and rapid implementation of adolescent and young adult telemedicine: challenges and opportunities for innovation. J Adolesc Health. 2020;67(2):164–71. DOI:10.1016/j.jadohealth.2020.05.006

Wood

White

Peebles

, et al. Outcomes of a rapid adolescent telehealth scale-up during the COVID-19 pandemic. J Adolesc Health. 2020;67(2):172–8. DOI:10.1016/j.jadohealth.2020.05.025

Patel

Cobb

Wright

, et al. Rapid development of telehealth capabilities within pediatric patient portal infrastructure for COVID-19 care: barriers, solutions, results. J Am Med Inform Assoc. 2020;27(7):1116–20. DOI:10.1093/jamia/ocaa065

10.

Fiks

Jenssen

Ray

KN.

A defining moment for pediatric primary care telehealth. JAMA Pediatr. 2021;175(1):9–10. DOI:10.1001/jamapediatrics.2020.1881

11.

Wosik

Fudim

Cameron

, et al. Telehealth transformation: COVID-19 and the rise of virtual care. J Am Med Inform Assoc. 2020;27(6):957–62. DOI:10.1093/jamia/ocaa067

12.

Associated Press. Fad or Future? Telehealth expanasion eyed beyond pandemic. Available from: https://apnews.com/article/virus-outbreak-vt-state-wire-ap-top-news-technology-politics-cc1a2cab5dfd1168c30ae25682be6f12 [Accessed May 6, 2026].

13.

Center for Connected Health Policy. Creating Opportunities Now for Necessary and Effective Care Technologies (CONNECT) for Health Act of 2021. Available from: https://cchp.nyc3.digitaloceanspaces.com/2021/05/CONNECT-Act-Fact-Sheetfinal.pdf [Accessed August 28, 2025].

14.

Mitchell

What 3,000 healthcare leaders told Philips they’re expecting after the pandemic. Becker’s Hospital Review. Available from: https://www.beckershospitalreview.com/hospital-management-administration/what-3-000-healthcare-leaders-told-philips-they-re-expecting-after-the-pandemic/. [Accessed August 28, 2025].

15.

Herzer

Pronovost

PJ.

Ensuring quality in the era of virtual care. JAMA. 2021;325(5):429–30. DOI:10.1001/jama.2020.24955

16.

Perry

Federico

Huebner

Telemedicine: Ensuring Safe, Equitable, Person-Centered Virtual Care. Institute for Healthcare Improvement White Paper. Available from: https://www.ihi.org/sites/default/files/IHI-Telemedicine-Ensuring-Safe-Equitable-Person-Centered-Virtual-Care-White-Paper.pdf. [Accessed August 28, 2025].

17.

Bulik

RJ.

Human factors in primary care telemedicine encounters. J Telemed Telecare. 2008;14(4):169–72. DOI:10.1258/jtt.2007.007041

18.

Agha

Roter

Schapira

RM.

An evaluation of patient-physician communication style during telemedicine consultations. J Med Internet Res. 2009;11(3):e36. DOI:10.2196/jmir.1193

19.

Visser

Danielson

Bitsko

, et al. Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003-2011. J Am Acad Child Adolesc Psychiatry. 2014;53(1):34–46.e2. DOI:10.1016/j.jaac.2013.09.001

20.

Akinbami

Simon

Rossen

LM.

Changing trends in asthma prevalence among children. Pediatrics. 2016;137(1):1–7. DOI:10.1542/peds.2015-2354

21.

Avery

Perrin

Lang

JE.

Updates to the pediatrics asthma management guidelines. JAMA Pediatr. 2021;175(9):966–7. DOI:10.1001/jamapediatrics.2021.1494

22.

Lipstein

Lee

Anderson

, et al. Shared decision making in telehealth versus in-person pediatric primary care. Telemed J E Health. 2025;31(11):1385–92. DOI:10.1089/tmj.2025.0139

23.

Elwyn

Durand

Song

, et al. A three-talk model for shared decision making: multistage consultation process. BMJ. 2017;359:j4891. DOI:10.1136/bmj.j4891

24.

Lipstein

Dodds

Britto

MT.

Real life clinic visits do not match the ideals of shared decision making. J Pediatr. 2014;165(1):178–83.e1. DOI:10.1016/j.jpeds.2014.03.042

25.

Anderson

Pallotto

Dominguez

Ammon

Carle

Lipstein

EA.

Evolving roles: adolescent perspectives on shared decision making with their parents and health care providers. Acad Pediatr. 2025;25(4):102778. DOI:10.1016/j.acap.2025.102778

26.

Peek

Lopez

Williams

, et al. Development of a conceptual framework for understanding shared decision making among African-American LGBT patients and their clinicians. J Gen Intern Med. 2016;31(6):677–87. DOI:10.1007/s11606-016-3616-3

27.

Tarantini

Fraccaro

Porzionato

, et al. Informed consent and shared decision-making in modern medicine: case-based approach, current gaps and practical proposal. Am J Cardiol. 2025;241:77–83. DOI:10.1016/j.amjcard.2025.01.015

28.

Lipstein

Lindly

Anixt

Britto

Zuckerman

KE.

Shared decision making in the care of children with developmental and behavioral disorders. Matern Child Health J. 2016;20(3):665–73. DOI:10.1007/s10995-015-1866-z

29.

Uscher-Pines

McCullough

Dworsky

, et al. Use of telehealth across pediatric subspecialties before and during the COVID-19 pandemic. JAMA Netw Open. 2022;5(3):e224759. DOI:10.1001/jamanetworkopen.2022.4759

30.

Schweiberger

Patel

Mehrotra

Ray

KN.

Trends in pediatric primary care visits during the COVID-19 pandemic. Acad Pediatr. 2021;21(8):1426–33. DOI:10.1016/j.acap.2021.04.031

31.

Spaulding

Fang

Commodore-Mensah

Himmelfarb

Martin

Coresh

Prevalence and disparities in telehealth use among US adults following the COVID-19 pandemic: national cross-sectional survey. J Med Internet Res. 2024;26:e52124. DOI:10.2196/52124

32.

Razi

Ramot

Wolff Sagy

Arbel

Shani

Menashe

Patient satisfaction with telehealth services in primary care. Telemed J E Health. 2024;30(11):2704–11. DOI:10.1089/tmj.2024.0363

33.

Obregon

Ortiz

Wallis

Morgan

Montoya-Williams

Feasibility, acceptability, and health outcomes associated with telehealth for children in families with limited english proficiency. Acad Pediatr. 2024;24(1):13–22. DOI:10.1016/j.acap.2023.06.025

34.

Parry

Pino

Faull

Feathers

Acceptability and design of video-based research on healthcare communication: evidence and recommendations. Patient Educ Couns. 2016;99(8):1271–84. DOI:10.1016/j.pec.2016.03.013

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