Primary care utilization among telehealth users and non-users at a large urban public healthcare system.

Telehealth services may improve access to care, but there are concerns around whether availability of telehealth may increase care utilization. We assessed whether usage of telehealth was associated with differential primary care utilization at a large, urban public healthcare system. Using electronic health record data from 23 primary care clinics, we categorized patients as telehealth users and non-users. Then, we compared the number of visits per patient between groups using Welch's t-tests while stratifying by comorbidity count. We used multivariable Poisson regression to test for associations between telehealth usage and visit count while controlling for other demographic factors. Compared with telehealth non-users, telehealth users had approximately 1 more primary care visit per patient over the year regardless of comorbidity count or other patient characteristics. Availability of telehealth services may be associated with increased primary care utilization in a safety-net setting, though further research on outcomes, costs of care, and patient and clinician experiences is needed to better inform decisions regarding provision and reimbursement of telehealth services.

Introduction

Telehealth visits (specifically synchronous audio-only and audio-video episodes of care between patients and clinicians) may improve access to care by removing certain barriers such as time and cost related to transportation or logistics for childcare or work coverage [1]. In a primary care setting, these visits can be used to deliver medical evaluation and management for acute, chronic, and preventive care needs—analogous to traditional scheduled in-person visits for initial or follow-up evaluation [2]. However, health systems and payors may hesitate to provide or cover telehealth at the same rate as in-person visits in part due to concerns around potential to increase overall healthcare utilization [3, 4]. Whether telehealth visits can functionally substitute for traditionally in-person visits or whether they, for better or worse, create additional encounters, is unclear. During the coronavirus disease (COVID) pandemic, many regulatory restrictions on telehealth in the United States were paused, allowing more widespread usage of telehealth [3].

In this setting, we sought to assess whether access to telehealth affected primary care utilization at a large, urban public healthcare system.

Methods

We conducted an observational study of electronic health record data for patients with scheduled primary care visits from July 1, 2020 to June 30, 2021 at 23 adult primary care clinics of New York City Health and Hospitals. This period represents when local COVID cases were past initial peak and telehealth visits were available to patients electively instead of preferentially. All adult primary care patients were eligible for participation in telehealth, and we included all adult primary care patients who had primary care encounters during the study interval in our analysis. There were no set exclusion criteria for telehealth participation. Clinicians and patients used their discretion when scheduling primary care appointments to determine whether a telehealth visit was appropriate or preferable for a given concern.

We classified patients as telehealth users and non-users. Telehealth users conducted ≥1 audio-only or video visit in primary care during the sample period. Telehealth non-users exclusively had in-person encounters.

The primary outcome was the average number of completed primary care visits per patient over the one-year timeframe.

We collected patient characteristics (age, sex, race/ethnicity, language, insurance, and number of Elixhauser comorbidities [5]) and compared them between groups using chi-squared tests.

We hypothesized that comorbidity count would be most associated with number of visits, so we stratified patients by quintiles of comorbidity count and compared the average number of completed primary care visits per patient between telehealth users and non-users using two-sided Welch’s t-tests.

Finally, we used multivariable Poisson regression to estimate differences in the primary outcome by telehealth user status while controlling for all measured patient characteristics. Regression assumptions were confirmed, and model fit was tested using a chi-squared goodness-of-fit test (p = 1.00).

We used Stata SE, version 15 (StataCorp), for all analyses. Our threshold for statistical significance was p<0.05. This study was exempt from full review by the Biomedical Research Alliance of New York institutional review board with a waiver of informed consent to access anonymized medical record data.

Results

There were 569,724 visits by 225,147 patients over the one-year study period across all 23 clinic sites. Of these patients, 133,830 (59.4%) were telehealth users. Compared to telehealth non-users, telehealth users were more likely to be older, female, Asian, Medicare beneficiaries, and have more comorbidities; they were less likely to be Black, commercially insured, or uninsured (Table 1; p<0.001).

Table 1

Patient characteristics.

Demographic, N (%)	Telehealth Users N = 133,830	Telehealth Non-users N = 91,317	p
Age, years			<0.001
18–44	36,374 (27.2)	32,584 (35.7)
45–64	60,647 (45.3)	39,036 (42.8)
≥65	36,809 (27.5)	19,697 (21.6)
Female	82,997 (62.0)	52,356 (57.3)	<0.001
Race/Ethnicity			<0.001
White	11,645 (8.7)	8,204 (9.0)
Black	43,134 (32.2)	33,621 (36.8)
Hispanic	46,877 (35.0)	31,034 (34.0)
Asian	11,366 (8.5)	4,736 (5.2)
Other	20,808 (15.6)	13,722 (15.0)
Primary Language			<0.001
English	75,231 (56.2)	52,744 (57.8)
Spanish	47,986 (35.9)	31,883 (34.9)
Other	10,613 (7.9)	6,690 (7.3)
Insurance			<0.001
Commercial	20,931 (15.6)	16,394 (18.0)
Medicaid	63,962 (47.8)	43,270 (47.4)
Medicare	27,943 (20.9)	14,517 (15.9)
Other	990 (0.7)	551 (0.6)
Uninsured	20,004 (15.0)	16,585 (18.2)
Elixhauser Comorbidity Count			<0.001
0	26,858 (20.1)	25,705 (28.2)
1	36,300 (27.1)	26,912 (29.5)
2	33,446 (25.0)	20,050 (22.0)
3	20,515 (15.3)	10,658 (11.7)
≥4	16,711 (12.5)	7,992 (8.8)

The average (standard deviation) number of primary care visits were 2.9 (1.7) for telehealth users and 1.9 (1.3) for non-users. Compared to telehealth non-users, telehealth users had 1 more primary care visit per patient per year regardless of comorbidity count (Table 2; p<0.001). Among telehealth users, the average proportion of visits that were conducted via telehealth was 0.68 (0.28).

Table 2

Number of primary care encounters by telehealth users and non-users stratified by Elixhauser comorbidity count.

Elixhuaser Comorbidity Count	Number of Primary Care Encounters by Telehealth Users, Mean (SD)	Number of Primary Care Encounters by Telehealth Non-users, Mean (SD)	p
0	2.1 (1.3)	1.4 (0.8)	<0.001
1	2.7 (1.5)	1.8 (1.0)	<0.001
2	3.1 (1.6)	2.1 (1.3)	<0.001
3	3.4 (1.7)	2.5 (1.5)	<0.001
≥4	3.9 (2.2)	2.8 (2.0)	<0.001
Total	2.9 (1.7)	1.9 (1.3)	<0.001

While controlling for patient characteristics, the incidence rate ratio for number of primary care visits between telehealth users and non-users was 1.44 [95% confidence interval 1.43, 1.45]. In other words, after accounting for demographic differences, telehealth users had 0.88 [0.87, 0.89] more primary care visits over the year than telehealth non-users (S1 Table).

Discussion

In this study of primary care utilization at a large urban public healthcare system, we found that telehealth users had, on average, approximately 1 more visit per patient over one year than telehealth non-users regardless of comorbidity count and demographics. This suggests that availability of telehealth visits in primary care may be associated with increased individual primary care utilization. Our findings contrast with those of prior studies that included primary care during a contemporary period which showed no increase in utilization [6-8], though our study differs in methodology and population demographics.

We uniquely focus on utilization in primary care only whereas prior literature that included primary care [6-8] aggregated outpatient specialties together. The impact of telehealth on different service lines may be different; for example, compared with historical averages, behavioral health services may have experienced rapid adoption and increased total visit volumes with telehealth during the pandemic [6], whereas surgical specialties may have had slower adoption and depressed visit volumes despite telehealth availability during the pandemic [9]. We also chose not to use a historical comparison for visit volume since total visit volumes during the study period were affected by the pandemic. Another difference is in patient demographics; for example, in prior studies that included primary care, 30–77% of patients were White, 8–40% Hispanic, 6–8% Black, and 4–10% Asian [6-8]. In contrast, 9% of our patients were White, 34% Black, 34% Hispanic, and 7% Asian. One study of a large, integrated healthcare system demonstrated that later in the pandemic, while total visit volumes generally returned towards historical trends, not all age, racial, or income groups had similar trajectories [7].

We are unable to comment on whether the observed increase in primary care utilization is due to misusage or reduced efficacy of telehealth services versus more frequent engagement due to improved access to care. We hypothesize that the observed difference may be because reductions in barriers to primary care, such as transportation, may affect groups within safety-net populations disproportionally [10]. Additionally, since primary care is often the first point of contact for ambulatory healthcare, this increased access may have a specialty-specific effect of increasing utilization.

While payors and policymakers may be concerned that telehealth availability may increase healthcare costs via additive service utilization, increased primary care utilization in the context of telehealth availability may not be a negative consequence if patients can have more health concerns addressed in a safe and effective manner. Changes in reimbursement structure in the United States towards more value- or outcomes-based payment may allow health systems and other entities to provide telehealth services while tempering costs for payors regardless of visit count.

Future studies of telehealth and healthcare utilization may consider using prospective or cohort designs to examine associations more robustly. And, further research will be needed on clinical and experiential outcomes of telehealth encounters to inform longer-term policies around provision and reimbursement of telehealth.

Limitations

Our study examines utilization behavior during a pandemic, which may not be applicable to non-pandemic times. It is possible that some patients who were higher utilizers of the health system elected to change some visits to telehealth due to concern about travelling during a pandemic. We used comorbidity count as a rough proxy for both expected healthcare utilization (more chronic diseases may correlate with more medical service use [11]) and past health system engagement (more encounters with the healthcare system may correlate with more diagnosis codes entered into a patient’s chart) and stratified by comorbidity count to account for these factors. However, telehealth users and non-users may differ in propensity to seek care for reasons unaccounted for by their number of comorbidities or the demographics measured in this study, such as distance to the clinic, education level, or socioeconomic status [12]. Further, we did not account for disease severity, which may also impact the number of encounters. Data were from a single urban healthcare system, though they represent multiple primary care facilities with diverse patients. We used an observational study design, which limits our ability to infer causality.

Conclusions

Availability of telehealth services may be associated with increased primary care utilization in safety-net clinics. Further research on clinical outcomes, costs of care, patient and clinician experiences is essential to better inform policymakers’ and payors’ decisions regarding reimbursement of telehealth services.

Full regression model for number of primary care visits by telehealth user status.

(DOCX)

Click here for additional data file.

20 Apr 2022

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Additional Editor Comments:

In this article, the authors retrospectively compared the number of telehealth visits vs. in-office visits in a large sample in New York. For international readers, it would be very convenient to define the different types of health care provider systems used in the US and included in this article. As an academic editor I completely agree with the comments provided by the first reviewer. I would discuss the limitations more deeply because it would give an idea how to extrapolate the results to other communities. It is very important to understand how the sample was selected (inclusion and exclusion criteria), and to add other confounding variables if they are known (distance to the health care facility). Other important aspect for publishing a research article is the discussion, comparing the authors´results with the previous literature. Surprisingly, there are very few references.

I would suggest the authors to include all the comments provided in this feed back and to send the article again for a second review.

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Reviewer #1: This paper discussed about the association between the use of telehealth and health care utilization. This topic is important especially during crisis (e.g. COVID). The paper is also very concise. My comments are as follows:

1/ to benefit international readers, the authors may want to describe more how telehealth is used in these clinics. Telemedicine ranged from simply transmission of observations (e.g. blood pressure readings) to full consultations. When the authors said telehealth users, what does this include?

Will primary care clinic visits be advised after a telehealth consultation? That will explain the difference of no of clinic visits between the both groups. (e.g. skin lesions that cannot be seen clearly during the telehealth consultation)

Similarly, will telehealth be more likely advised (by healthcare professionals) when the diseases are poorly controlled? that will explain the difference between the two groups

2/ Any inclusion and exclusion criteria of participants? If the "non-users" include simple patients who have good past health and only consult the clinic for episodic problems (e.g. common cold), of course they will have very few clinic visits.

3/ in the tables, I suggest to replace "something else" by "others"

4/ I think the main weakness is that there are many unmeasured parameters to explain the results. The authors may want to discuss possible important parameters and address/discuss this in more details in the discussion; e.g.

- disease severity: people with poorly controlled diseases may want more contact time with the healthcare system. Therefore, they may have more telehealth and also more consultations. No of co-morbidities is not exactly the same as disease severity

- Education level

- level of social support

5/ As this is a cross-sectional study, the causal relationship cannot be established. it is possible that those patients, who had frequent clinical visits, changed some of the visits to telehealth during COVID pandemic

6/ The authors may also want to elaborate more about clinical/research implications of their results. RCTs and cohorts maybe a better choice to answer whether implementation of telehealth systems would increase healthcare utilization

**********

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Reviewer #1: No

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14 Jun 2022

Additional Editor Comments:

In this article, the authors retrospectively compared the number of telehealth visits vs. in-office visits in a large sample in New York.

1. Comment:

For international readers, it would be very convenient to define the different types of health care provider systems used in the US and included in this article.

Response:

As suggested, we have defined what we are referring to when discussing telehealth. Please see our response to Reviewer #1, Comment #1 for full details.

Change:

Please see our response to Reviewer #1, Comment #1 for full details.

2. Comment:

As an academic editor I completely agree with the comments provided by the first reviewer. I would discuss the limitations more deeply because it would give an idea how to extrapolate the results to other communities.

Response:

We appreciate your and the reviewer’s comments on expanding the Limitations discussion. We have added to the Limitations section in response to Reviewer #1, Comments #4-6.

Change:

Please see our response to Reviewer #1, Comments #4-6 for full details.

3. Comment:

It is very important to understand how the sample was selected (inclusion and exclusion criteria), and to add other confounding variables if they are known (distance to the health care facility).

Response:

We have clarified what the inclusion and exclusion criteria were in response to Reviewer #1, Comment #2. Unfortunately, additional confounding variables as suggested in Reviewer #1, Comment #4 were not available, but we did include further discussion of this in the Limitations.

Change:

Please see our response to Reviewer #1, Comments #2 and #4 for full details.

4. Comment:

Other important aspect for publishing a research article is the discussion, comparing the authors´ results with the previous literature. Surprisingly, there are very few references.

I would suggest the authors to include all the comments provided in this feed back and to send the article again for a second review.

Response:

Thank you for this suggestion. We have added more comparisons to existing literature and expanded our discussion with more information about our hypotheses on interpretations of our findings. We have also added more citations throughout the text.

Change:

In Discussion:

Our findings contrast with those of prior studies that included primary care during a contemporary period which showed no increase in utilization [6-8], though our study differs in methodology and population demographics.

We uniquely focus on utilization in primary care only whereas prior literature that included primary care [6-8] aggregated outpatient specialties together. The impact of telehealth on different service lines may be different; for example, compared with historical averages, behavioral health services may have experienced rapid adoption and increased total visit volumes with telehealth during the pandemic [6], whereas surgical specialties may have had slower adoption and depressed visit volumes despite telehealth availability during the pandemic [9]. We also chose not to use a historical comparison for visit volume since total visit volumes during the study period were affected by the pandemic. Another difference is in patient demographics; for example, in prior studies that included primary care, 30-77% of patients were White, 8-40% Hispanic, 6-8% Black, and 4-10% Asian [6-8]. In contrast, 9% of our patients were White, 34% Black, 34% Hispanic, and 7% Asian. One study of a large, integrated healthcare system demonstrated that later in the pandemic, while total visit volumes generally returned towards historical trends, not all age, racial, or income groups had similar trajectories [7].

[…] Additionally, since primary care is often the first point of contact for ambulatory healthcare, this increased access may have a specialty-specific effect of increasing utilization.

In references:

1. Reed ME, Huang J, Parikh R, Millman A, Ballard DW, Barr I, et al. Patient-Provider Video Telemedicine Integrated With Clinical Care: Patient Experiences. Ann Intern Med. 2019 Aug 6;171(3):222-224.

2. Reed M, Huang J, Graetz I, Muelly E, Millman A, Lee C. Treatment and Follow-up Care Associated With Patient-Scheduled Primary Care Telemedicine and In-Person Visits in a Large Integrated Health System. JAMA Netw Open. 2021 Nov 1;4(11):e2132793.

5. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005 Nov;43(11):1130-9.

9. Chao GF, Li KY, Zhu Z, McCullough J, Thompson M, Claflin J, et al. Use of Telehealth by Surgical Specialties During the COVID-19 Pandemic. JAMA Surg. 2021 Jul 1;156(7):620-626.

11. Fortuna D, Berti E, Moro ML. Multimorbidity epidemiology and health care utilization through combined healthcare administrative databases. Epidemiol Prev. 2021 Jan-Apr;45(1-2):62-71.

12. Reed ME, Huang J, Graetz I, Lee C, Muelly E, Kennedy C, et al. Patient Characteristics Associated With Choosing a Telemedicine Visit vs Office Visit With the Same Primary Care Clinicians. JAMA Netw Open. 2020 Jun 1;3(6):e205873.

---

Reviewer #1:

This paper discussed about the association between the use of telehealth and health care utilization. This topic is important especially during crisis (e.g. COVID). The paper is also very concise. My comments are as follows:

1. Comment:

To benefit international readers, the authors may want to describe more how telehealth is used in these clinics. Telemedicine ranged from simply transmission of observations (e.g. blood pressure readings) to full consultations. When the authors said telehealth users, what does this include?

Will primary care clinic visits be advised after a telehealth consultation? That will explain the difference of no of clinic visits between the both groups. (e.g. skin lesions that cannot be seen clearly during the telehealth consultation)

Similarly, will telehealth be more likely advised (by healthcare professionals) when the diseases are poorly controlled? that will explain the difference between the two groups

Response:

Thank you for this comment; we have added a more specific definition in the introduction of what services we are referring to under “telehealth services”. Briefly, we are referring to synchronous audio-only and audio-video care episodes between patients and clinicians for the purpose of medical evaluation and management of acute, chronic, or preventive care needs. To make it clearer that we are discussing such encounters, we have also exchanged the word “services” for “visits” (i.e. telehealth visits instead of telehealth services) to emphasize the analogy to traditionally in-person primary care visits.

Regarding the reviewer’s question of whether clinic visits will be advised after a telehealth visit, that is one of the key questions we are trying to explore. Thus, we have added a sentence in the introduction addressing this. We caveat that more visits are not necessarily bad—it may be that previously, patients were not seeking care often enough due to general barriers to care.

Finally, we have added a few references to expand on the theoretical benefit of telehealth visits and a similar use case to ours.

Change:

In Introduction, paragraph 1:

Telehealth visits (specifically synchronous audio-only and audio-video episodes of care between patients and clinicians) may improve access to care by removing certain barriers such as time and cost related to transportation or logistics for childcare or work coverage [1]. In a primary care setting, these visits can be used to deliver medical evaluation and management for acute, chronic, and preventive care needs—analogous to traditional scheduled in-person visits for initial or follow-up evaluation [2].

…at the same rate as in-person visits in part due to…

In references:

1. Reed ME, Huang J, Parikh R, Millman A, Ballard DW, Barr I, et al. Patient-Provider Video Telemedicine Integrated With Clinical Care: Patient Experiences. Ann Intern Med. 2019 Aug 6;171(3):222-224.

2. Reed M, Huang J, Graetz I, Muelly E, Millman A, Lee C. Treatment and Follow-up Care Associated With Patient-Scheduled Primary Care Telemedicine and In-Person Visits in a Large Integrated Health System. JAMA Netw Open. 2021 Nov 1;4(11):e2132793.

2. Comment:

Any inclusion and exclusion criteria of participants? If the "non-users" include simple patients who have good past health and only consult the clinic for episodic problems (e.g. common cold), of course they will have very few clinic visits.

Response:

We have expanded our Methods section to more explicitly address inclusion and exclusion criteria. All adult primary care patients were eligible for participation in telehealth, and we included all adult primary care patients who had scheduled visits in the study interval in our analysis. There were no set exclusion criteria for telehealth participation. Clinicians and patients used their discretion when scheduling appointments to determine whether a telehealth visit was appropriate or preferable for a given concern.

The issue of whether non-users are generally healthier patients with fewer healthcare needs (for example, coming into clinic only for an in-person “annual physical”) is a notable one. We attempted to account for this by stratifying by comorbidity count, a metric that we acknowledge is flawed for defining multimorbidity, but in the absence of disease severity data, we used this as a rough proxy. The number of comorbidities someone has in their medical record may estimate both expected healthcare utilization (i.e. more comorbidities may mean more medical service needs) and historical utilization (i.e. someone who has more engagement with the health system may accrue more diagnosis codes in their chart). We have added text and a citation in the Limitations to discuss this.

Change:

In Methods, paragraph 1:

All adult primary care patients were eligible for participation in telehealth, and we included all adult primary care patients who had scheduled visits in the study interval in our analysis. There were no set exclusion criteria for telehealth participation. Clinicians and patients used their discretion when scheduling primary care appointments to determine whether a telehealth visit was appropriate or preferable for a given concern.

In Limitations:

We used comorbidity count as a rough proxy for both expected healthcare utilization (more chronic diseases may correlate with more medical service use [11]) and past health system engagement (more encounters with the healthcare system may correlate with more diagnosis codes entered into a patient’s chart) and stratified by comorbidity count to account for these factors.

In references:

11. Fortuna D, Berti E, Moro ML. Multimorbidity epidemiology and health care utilization through combined healthcare administrative databases. Epidemiol Prev. 2021 Jan-Apr;45(1-2):62-71.

3. Comment:

In the tables, I suggest to replace "something else" by "others"

Response:

We have changed “Something Else” in Table 1 to “Other” as suggested.

Change:

In Table 1, column 1: changed “Something Else” to “Other”

4. Comment:

I think the main weakness is that there are many unmeasured parameters to explain the results. The authors may want to discuss possible important parameters and address/discuss this in more details in the discussion; e.g.

- disease severity: people with poorly controlled diseases may want more contact time with the healthcare system. Therefore, they may have more telehealth and also more consultations. No of co-morbidities is not exactly the same as disease severity

- Education level

- level of social support

Response:

Thank you for this comment. We have added additional sentences in the Limitations regarding unmeasured confounders such as disease severity, education level, socioeconomic status, and distance to a clinic. Unfortunately, these data were not available for this study.

Change:

In Limitations:

However, telehealth users and non-users may differ in propensity to seek care for reasons unaccounted for by their number of comorbidities or the demographics measured in this study, such as distance to the clinic, education level, or socioeconomic status [12]. Further, we did not account for disease severity, which may also impact the number of encounters.

In references:

12. Reed ME, Huang J, Graetz I, Lee C, Muelly E, Kennedy C, et al. Patient Characteristics Associated With Choosing a Telemedicine Visit vs Office Visit With the Same Primary Care Clinicians. JAMA Netw Open. 2020 Jun 1;3(6):e205873.

5. Comment:

As this is a cross-sectional study, the causal relationship cannot be established. it is possible that those patients, who had frequent clinical visits, changed some of the visits to telehealth during COVID pandemic

Response:

We have added an acknowledgement of the limitations of an observational study design in inferring causality in the Limitations section. We also added a sentence expanding on the point regarding high utilizers switching what would have been in-person visits to telehealth visits in the context of a pandemic. In our response to Comment #4, we addressed the issue of unmeasured factors that may drive that high utilization. Finally, we replaced language that may have implied causality with more associative language.

Change:

In Abstract:

We assessed whether usage of telehealth was associated with differential primary care utilization at a large, urban public healthcare system.

In Discussion:

This suggests that availability of telehealth visits in primary care may be associated with increased individual primary care utilization.

In Limitations:

Our study examines utilization behavior during a pandemic, which may not be applicable to non-pandemic times. It is possible that some patients who were higher utilizers of the health system elected to change some visits to telehealth due to concern about travelling during a pandemic. … We used an observational study design, which limits our ability to infer causality.

In Conclusions:

Availability of telehealth services may be associated with increased primary care utilization in safety-net clinics.

6. Comment:

The authors may also want to elaborate more about clinical/research implications of their results. RCTs and cohorts maybe a better choice to answer whether implementation of telehealth systems would increase healthcare utilization

Response:

We have added text to elaborate on the implications of our findings and areas for future research. We agree that RCTs and cohorts may be more robust ways to study associations between telehealth availability and healthcare utilization and mention this as well.

Change:

In Discussion:

While payors and policymakers may be concerned that telehealth availability may increase healthcare costs via additive service utilization, increased primary care utilization in the context of telehealth availability may not be a negative consequence if patients can have more health concerns addressed in a safe and effective manner. Changes in reimbursement structure in the United States towards more value- or outcomes-based payment may allow health systems and other entities to provide telehealth services while tempering costs for payors regardless of visit count.

Future studies of telehealth and healthcare utilization may consider using prospective or cohort designs to examine associations more robustly. And, further research will be needed on clinical and experiential outcomes of telehealth encounters to inform longer-term policies around provision and reimbursement of telehealth.

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25 Jul 2022

Primary care utilization among telehealth users and non-users at a large urban public healthcare system

PONE-D-22-05119R1

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28 Jul 2022

PONE-D-22-05119R1

Primary care utilization among telehealth users and non-users at a large urban public healthcare system

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