Asthma, Chronic Obstructive Pulmonary Disease (COPD), Congestive Heart Failure, Diabetes, Hypertension
Conditions
Keywords
telemedicine, primary care
Brief summary
Leveraging a natural experiment approach, the investigators will examine rapidly changing telemedicine and in-person models of care during and after the COVID-19 crisis to determine whether certain patients could safely choose to continue telemedicine or telemedicine-supplemented care, rather than return to in-person care.
Detailed description
During the COVID-19 pandemic, telemedicine has quickly emerged as the primary method of providing outpatient care in many regions with shelter-in-place and social distancing policies. It is critical to understand the impact of this rapid and widespread transition from in-person to remote visits on disparities in access to primary care, especially in chronic disease where ongoing communication between providers and patients is essential. Also, these newly developed or expanded telemedicine programs vary widely, raising important questions about the effect of these differences on uptake of telemedicine among different patient populations and on patient-centered outcomes. Leveraging a natural experiment approach, the investigators will examine rapidly changing telemedicine and in-person models of care during and after the COVID-19 crisis to determine whether certain patients could safely choose to continue telemedicine or telemedicine-supplemented care, rather than return to in-person care. The overarching goals of this study are to describe the features of telemedicine programs in primary care during the COVID-19 pandemic and to use natural experiment methods to provide rigorous evidence on the effects of these programs. PCORI has granted an extension for the final research report to October 1, 2023.
Interventions
OTHERExposure to telemedicine, after the onset of the pandemic
The exposure of interest was the switch to primary care telemedicine prompted by the COVID-19 epidemic
Sponsors
Patient-Centered Outcomes Research Institute
Weill Medical College of Cornell University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
19 Years to No maximum
Inclusion criteria
* patients that are attributed to primary care clinics across four health systems in the INSIGHT (Mount Sinai Health System and Weill Cornell Medicine), OneFlorida (University of Florida Health), and STAR (University of North Carolina Health) CRNs. * Patients received two or more outpatient visits at a participating practice during a one-year period before the COVID-19 pandemic, * Patients had one or more of five chronic illnesses (asthma, chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), diabetes, hypertension) as defined by the Medicare Chronic Conditions Warehouse algorithm
Exclusion criteria
* Patients who tested COVID-positive * Patients from hospice and palliative care practices * Patients from osteopathic medicine practices * Patients from pediatric practices * Patients that did not reside in states where the four health systems were located: the New York-Tri State Area (Connecticut, New York, and New Jersey), Florida, and North Carolina. * Patients that moved out of state (or out of the New York-Tri State Area) or who died during the study period were also excluded. * Patients who were not continuously enrolled over the entire study period (2019-2021).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of Avoidable Emergency Department (ED) Admissions | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used | Avoidable emergency department (ED) admissions will be obtained from claims data |
| Preventable Emergency Department (ED) Admissions | Assessed per person per quarter for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 | Avoidable emergency department (ED) admissions will be obtained from claims data. The Effect of telemedicine on preventable emergency department admissions will be calculated using difference-in-differences methodology. The estimate coefficient of the difference-in-difference model will be reported. |
| Unplanned Hospital Admissions From the ED | Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 | Unplanned hospital admissions from the ED will be obtained from claims data. The effect of telemedicine on unplanned hospital admissions will be calculated using difference-in-difference methodology. The estimate coefficient will be reported. |
| Continuity of Care as Assessed by the Breslau Usual Provider of Care Measure | Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 | Continuity of care as assessed by the Breslau Usual Provider of Care measure. The Breslau Usual Provider of Care index is also an indicator of continuity of care, ranging from 0 to 1, where 1 represents continuity of care. The effect of telemedicine on continuity of care using the Breslau Usual Provider of Care measure will be calculated using difference-in-difference methodology. The estimate coefficient will be reported. |
| Number of Unplanned Hospital Admissions From the ED | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used | Unplanned hospital admissions from the ED will be obtained from claims data |
| Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index | Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 | Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index. The Bice-Boxerman Continuity of Care Index is also an indicator of continuity of care, ranging from 0 to 1, where 1 represents continuity of care. The effect of telemedicine on continuity of care using the Bice-Boxerman Continuity of care index will be calculated using difference-in-difference methodology. The estimate coefficient will be reported. |
| Continuity of Care as Assessed by Attendance at Follow-up Appointment | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used | Continuity of care as assessed by attendance at follow-up appointment. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used | Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (\< 140/90 mmHg) |
| Days at Home | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used | Days per month not in hospital or institutional setting |
| Patient Experiences Based on the Patient Satisfaction Questionnaire (PSQ-18) | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used | Patient experiences based on the Patient Satisfaction Questionnaire (PSQ-18), which is a 5-scale questionnaire including questions on patient satisfaction, communication quality with providers and accessibility/convenience of care. |
| Ease of Use and Access to Telemedicine Based on Telehealth Usability Questionnaire (TUQ) | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used | For individuals who accessed a telemedicine visit, we will ask questions based on the validated Telehealth Usability Questionnaire (TUQ), including the ease of use and access to the telemedicine service, quality of the interaction with the provider, and satisfaction |
| Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%) | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used | Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (\>9%), which is the percentage of patients 18 - 75 years of age with diabetes who had hemoglobin A1c \> 9.0% during the measurement period |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| High Telemedicine Patients in practices that had high telemedicine use, based on the percent of visits the practice delivered via telemedicine from April 2020 to December 2021 (the study post-period) | 17,226 |
| Low Telemedicine Patients in practices that had some telemedicine use, based on the percent of visits the practice delivered via telemedicine from April 2020 to December 2021 (the study post-period) | 15,874 |
| Total | 33,100 |
Baseline characteristics
| Characteristic | High Telemedicine | Low Telemedicine | Total |
|---|---|---|---|
| Age, Continuous | 72.64 years STANDARD_DEVIATION 10.55 | 71.56 years STANDARD_DEVIATION 10.58 | 72.12 years STANDARD_DEVIATION 10.58 |
| Race/Ethnicity, Customized Black | 5627 participants | 3030 participants | 2597 participants |
| Race/Ethnicity, Customized White | 12546 participants | 12489 participants | 25035 participants |
| Rural Urban Destination Metropolitan | 16195 Participants | 14348 Participants | 30543 Participants |
| Rural Urban Destination Non-metropolitan | 1031 Participants | 1526 Participants | 2557 Participants |
| Sex: Female, Male Female | 10448 Participants | 9445 Participants | 19893 Participants |
| Sex: Female, Male Male | 6778 Participants | 6429 Participants | 13207 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 0 | 0 / 0 |
| other Total, other adverse events | 0 / 0 | 0 / 0 |
| serious Total, serious adverse events | 0 / 0 | 0 / 0 |
Outcome results
Primary
Continuity of Care as Assessed by Attendance at Follow-up Appointment
Continuity of care as assessed by attendance at follow-up appointment.
Time frame: 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by Attendance at Follow-up Appointment
Continuity of care as assessed by attendance at follow-up appointment.
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by Attendance at Follow-up Appointment
Continuity of care as assessed by attendance at follow-up appointment.
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by Attendance at Follow-up Appointment
Continuity of care as assessed by attendance at follow-up appointment.
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index
Continuity of care will be measured using the Bice-Boxerman Continuity of Care Index. The Bice-Boxerman continuity of care (COC) index reflects the relative share of all of a patient's visits during the year that are billed by distinct providers and/or practices. The index ranges from 0 to 1, where 0 indicates that each visit involved a different provider than all other visits, and 1 that all visits were billed by a single provider, representing continuity of care.
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index
Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index. The Bice-Boxerman Continuity of Care Index is also an indicator of continuity of care, ranging from 0 to 1, where 1 represents continuity of care. The effect of telemedicine on continuity of care using the Bice-Boxerman Continuity of care index will be calculated using difference-in-difference methodology. The estimate coefficient will be reported.
Time frame: Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021
Population: Upon receiving the data from each study site, the investigators first examined the patterns of telehealth provision among all included practices and the extent to which practices could be categorized into one of these three study arms. Given that the data showed an insufficient number of practices would fall into the three original proposed arms, the investigators updated the analytic plan to include two study arms: high- versus low telemedicine practices.
| Arm | Measure | Value (MEDIAN) |
|---|---|---|
| High Telemedicine | Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index | 0.047 score on a scale per person per q |
| Low Telemedicine | Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index | 0.047 score on a scale per person per q |
Comparison: Difference-in-differencesp-value: 0.8577Regression, Linear
Comparison: Difference-in-differencesp-value: 0.5954Regression, Linear
Primary
Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index
Continuity of care will be measured using the Bice-Boxerman Continuity of Care Index. The Bice-Boxerman continuity of care (COC) index reflects the relative share of all of a patient's visits during the year that are billed by distinct providers and/or practices. The index ranges from 0 to 1, where 0 indicates that each visit involved a different provider than all other visits, and 1 that all visits were billed by a single provider, representing continuity of care.
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index
Continuity of care will be measured using the Bice-Boxerman Continuity of Care Index. The Bice-Boxerman continuity of care (COC) index reflects the relative share of all of a patient's visits during the year that are billed by distinct providers and/or practices. The index ranges from 0 to 1, where 0 indicates that each visit involved a different provider than all other visits, and 1 that all visits were billed by a single provider, representing continuity of care.
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by the Breslau Usual Provider of Care Measure
Continuity of care as assessed by the Breslau Usual Provider of Care measure. The Breslau Usual Provider of Care index is also an indicator of continuity of care, ranging from 0 to 1, where 1 represents continuity of care.
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by the Breslau Usual Provider of Care Measure
Continuity of care as assessed by the Breslau Usual Provider of Care measure. The Breslau Usual Provider of Care index is also an indicator of continuity of care, ranging from 0 to 1, where 1 represents continuity of care. The effect of telemedicine on continuity of care using the Breslau Usual Provider of Care measure will be calculated using difference-in-difference methodology. The estimate coefficient will be reported.
Time frame: Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021
Population: Upon receiving the data from each study site, the investigators first examined the patterns of telehealth provision among all included practices and the extent to which practices could be categorized into one of these three study arms. Given that the data showed an insufficient number of practices would fall into the three original proposed arms, the investigators updated the analytic plan to include two study arms: high- versus low telemedicine practices.
| Arm | Measure | Value (MEDIAN) |
|---|---|---|
| High Telemedicine | Continuity of Care as Assessed by the Breslau Usual Provider of Care Measure | 0.25 average score on scale by person by q |
| Low Telemedicine | Continuity of Care as Assessed by the Breslau Usual Provider of Care Measure | 0.35 average score on scale by person by q |
Comparison: Difference-in-differencesp-value: 0.1101Regression, Linear
Comparison: Difference-in-differencesp-value: 0.4618Regression, Linear
Primary
Continuity of Care as Assessed by the Breslau Usual Provider of Care Measure
Continuity of care as assessed by the Breslau Usual Provider of Care measure. The Breslau Usual Provider of Care index is also an indicator of continuity of care, ranging from 0 to 1, where 1 represents continuity of care.
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Continuity of Care as Assessed by the Breslau Usual Provider of Care Measure
Continuity of care as assessed by the Breslau Usual Provider of Care measure. The Breslau Usual Provider of Care index is also an indicator of continuity of care, ranging from 0 to 1, where 1 represents continuity of care.
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Number of Avoidable Emergency Department (ED) Admissions
Avoidable emergency department (ED) admissions will be obtained from claims data
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Number of Avoidable Emergency Department (ED) Admissions
Avoidable emergency department (ED) admissions will be obtained from claims data
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Number of Avoidable Emergency Department (ED) Admissions
Avoidable emergency department (ED) admissions will be obtained from claims data
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Number of Unplanned Hospital Admissions From the ED
Unplanned hospital admissions from the ED will be obtained from claims data
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Number of Unplanned Hospital Admissions From the ED
Unplanned hospital admissions from the ED will be obtained from claims data
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Number of Unplanned Hospital Admissions From the ED
Unplanned hospital admissions from the ED will be obtained from claims data
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Primary
Preventable Emergency Department (ED) Admissions
Avoidable emergency department (ED) admissions will be obtained from claims data. The Effect of telemedicine on preventable emergency department admissions will be calculated using difference-in-differences methodology. The estimate coefficient of the difference-in-difference model will be reported.
Time frame: Assessed per person per quarter for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021
Population: Upon receiving the data from each study site, the investigators first examined the patterns of telehealth provision among all included practices and the extent to which practices could be categorized into one of these three study arms. Given that the data showed an insufficient number of practices would fall into the three original proposed arms, the investigators updated the analytic plan to include two study arms: high- versus low telemedicine practices.
| Arm | Measure | Value (MEAN) |
|---|---|---|
| High Telemedicine | Preventable Emergency Department (ED) Admissions | 0.038 count of ED admissions per person per q |
| Low Telemedicine | Preventable Emergency Department (ED) Admissions | 0.048 count of ED admissions per person per q |
p-value: 0.0793Regression, Linear
p-value: 0.5739Regression, Linear
Primary
Unplanned Hospital Admissions From the ED
Unplanned hospital admissions from the ED will be obtained from claims data. The effect of telemedicine on unplanned hospital admissions will be calculated using difference-in-difference methodology. The estimate coefficient will be reported.
Time frame: Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021
Population: Upon receiving the data from each study site, the investigators first examined the patterns of telehealth provision among all included practices and the extent to which practices could be categorized into one of these three study arms. Given that the data showed an insufficient number of practices would fall into the three original proposed arms, the investigators updated the analytic plan to include two study arms: high- versus low telemedicine practices.
| Arm | Measure | Value (MEAN) |
|---|---|---|
| High Telemedicine | Unplanned Hospital Admissions From the ED | 0.015 count of admissions per person per q |
| Low Telemedicine | Unplanned Hospital Admissions From the ED | 0.020 count of admissions per person per q |
Comparison: Difference-in-differences modelp-value: <0.01Regression, Linear
Comparison: Difference-in-differences modelp-value: 0.3261Regression, Linear
Secondary
Days at Home
Days per month not in hospital or institutional setting
Time frame: 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Days at Home
Days per month not in hospital or institutional setting
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Days at Home
Days per month not in hospital or institutional setting
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Days at Home
Days per month not in hospital or institutional setting
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Ease of Use and Access to Telemedicine Based on Telehealth Usability Questionnaire (TUQ)
For individuals who accessed a telemedicine visit, we will ask questions based on the validated Telehealth Usability Questionnaire (TUQ), including the ease of use and access to the telemedicine service, quality of the interaction with the provider, and satisfaction
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (\< 140/90 mmHg)
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (\< 140/90 mmHg)
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (\< 140/90 mmHg)
Time frame: 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (\< 140/90 mmHg)
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%)
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (\>9%), which is the percentage of patients 18 - 75 years of age with diabetes who had hemoglobin A1c \> 9.0% during the measurement period
Time frame: 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%)
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (\>9%), which is the percentage of patients 18 - 75 years of age with diabetes who had hemoglobin A1c \> 9.0% during the measurement period
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%)
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (\>9%), which is the percentage of patients 18 - 75 years of age with diabetes who had hemoglobin A1c \> 9.0% during the measurement period
Time frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Evidence of Controlled Disease as Indicated by as Indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%)
Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (\>9%), which is the percentage of patients 18 - 75 years of age with diabetes who had hemoglobin A1c \> 9.0% during the measurement period
Time frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.
Secondary
Patient Experiences Based on the Patient Satisfaction Questionnaire (PSQ-18)
Patient experiences based on the Patient Satisfaction Questionnaire (PSQ-18), which is a 5-scale questionnaire including questions on patient satisfaction, communication quality with providers and accessibility/convenience of care.
Time frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used
Population: Data was not collected for this measure.