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| Name | Class |
|---|---|
| Patient-Centered Outcomes Research Institute | OTHER |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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) |
| |
| 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) |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Exposure to telemedicine, after the onset of the pandemic | Other | The exposure of interest was the switch to primary care telemedicine prompted by the COVID-19 epidemic |
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| Measure | Description | Time Frame |
|---|---|---|
| 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. | Assessed per person per quarter for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 |
| 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. | 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 | 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. | Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 |
| Number of Unplanned Hospital Admissions From the ED | Unplanned hospital admissions from the ED will be obtained from claims data | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Continuity of Care as Assessed by the Bice-Boxerman Continuity of Care Index |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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The study population encompasses patients that are attributed to primary care clinics in one of the four health systems defined above. Patients are included in the study if they are ages 19 or older and received two or more outpatient visits at a participating practice during a one-year period before the COVID-19 pandemic, and 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. For the claims analyses, it will be required that patients are continuously enrolled over the entire study time period.
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| Name | Affiliation | Role |
|---|---|---|
| Jessica Ancker, MPH, PhD | Vanderbilt University Medical Center | Principal Investigator |
| Rainu Kaushal, MD, MPH | Weill Medical College of Cornell University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Florida | Gainesville | New York | 32610 | United States | ||
| Mount Sinai |
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| ID | Title | Description |
|---|---|---|
| FG000 | 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) |
| FG001 | Low Telemedicine |
| Title | Milestones | Reasons Not Completed | |||||
|---|---|---|---|---|---|---|---|
| Overall Study |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Oct 21, 2021 |
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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.
| Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 |
| Number of Unplanned Hospital Admissions From the ED | Unplanned hospital admissions from the ED will be obtained from claims data | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Number of Unplanned Hospital Admissions From the ED | Unplanned hospital admissions from the ED will be obtained from claims data | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Number of Avoidable Emergency Department (ED) Admissions | Avoidable emergency department (ED) admissions will be obtained from claims data | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Number of Avoidable Emergency Department (ED) Admissions | Avoidable emergency department (ED) admissions will be obtained from claims data | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Number of Avoidable Emergency Department (ED) Admissions | Avoidable emergency department (ED) admissions will be obtained from claims data | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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. | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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. | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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. | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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. | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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. | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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. | 12 months 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 | 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 | Continuity of care as assessed by attendance at follow-up appointment. | 60 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 | Continuity of care as assessed by attendance at follow-up appointment. | 6 months 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 | Continuity of care as assessed by attendance at follow-up appointment. | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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%) | 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 | 60 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%) | 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 | 6 months 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%) | 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 | 12 months 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 | 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) | 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 | 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) | 60 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 | 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) | 6 months 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 | 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) | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Days at Home | Days per month not in hospital or institutional setting | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Days at Home | Days per month not in hospital or institutional setting | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Days at Home | Days per month not in hospital or institutional setting | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| Days at Home | Days per month not in hospital or institutional setting | 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) | 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. | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| 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 | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
| New York |
| New York |
| 10029 |
| United States |
| Weill Cornell Medicine | New York | New York | 10065 | United States |
| University of North Carolina | Chapel Hill | North Carolina | 27599 | United States |
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)
| COMPLETED |
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| NOT COMPLETED |
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Upon receiving the data from each study site, we 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, we updated the analytic plan to include two study arms: high- versus low telemedicine practices.
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| ID | Title | Description |
|---|---|---|
| BG000 | 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) |
| BG001 | 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) |
| BG002 | Total | Total of all reporting groups |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean | Standard Deviation | years |
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| Sex: Female, Male | Count of Participants | Participants |
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| Race/Ethnicity, Customized | Number | participants |
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| Rural Urban Destination | Count of Participants | Participants |
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| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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. | 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. | Posted | Mean | Inter-Quartile Range | count of ED admissions per person per q | Assessed per person per quarter for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 |
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| 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. | 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. | Posted | Mean | Inter-Quartile Range | count of admissions per person per q | Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 |
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| 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. | 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. | Posted | Median | Inter-Quartile Range | average score on scale by person by q | Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 |
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| Primary | Number of Unplanned Hospital Admissions From the ED | Unplanned hospital admissions from the ED will be obtained from claims data | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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. | 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. | Posted | Median | Inter-Quartile Range | score on a scale per person per q | Assessed at the quarter level for 3 years, data collected encompasses retrospective data from Q1 2019 to Q4 2021 |
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| Primary | Number of Unplanned Hospital Admissions From the ED | Unplanned hospital admissions from the ED will be obtained from claims data | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Number of Unplanned Hospital Admissions From the ED | Unplanned hospital admissions from the ED will be obtained from claims data | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Number of Avoidable Emergency Department (ED) Admissions | Avoidable emergency department (ED) admissions will be obtained from claims data | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Number of Avoidable Emergency Department (ED) Admissions | Avoidable emergency department (ED) admissions will be obtained from claims data | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Number of Avoidable Emergency Department (ED) Admissions | Avoidable emergency department (ED) admissions will be obtained from claims data | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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. | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
|
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| 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. | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
|
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| 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. | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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. | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
|
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| 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. | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
|
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| 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. | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Continuity of Care as Assessed by Attendance at Follow-up Appointment | Continuity of care as assessed by attendance at follow-up appointment. | Data was not collected for this measure. | Posted | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Continuity of Care as Assessed by Attendance at Follow-up Appointment | Continuity of care as assessed by attendance at follow-up appointment. | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Continuity of Care as Assessed by Attendance at Follow-up Appointment | Continuity of care as assessed by attendance at follow-up appointment. | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Primary | Continuity of Care as Assessed by Attendance at Follow-up Appointment | Continuity of care as assessed by attendance at follow-up appointment. | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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 | Data was not collected for this measure. | Posted | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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 | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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 | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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 | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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) | Data was not collected for this measure. | Posted | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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) | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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) | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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) | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Secondary | Days at Home | Days per month not in hospital or institutional setting | Data was not collected for this measure. | Posted | 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Secondary | Days at Home | Days per month not in hospital or institutional setting | Data was not collected for this measure. | Posted | 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Secondary | Days at Home | Days per month not in hospital or institutional setting | Data was not collected for this measure. | Posted | 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| Secondary | Days at Home | Days per month not in hospital or institutional setting | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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. | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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| 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 | Data was not collected for this measure. | Posted | 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used |
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Adverse event data were not collected.
Adverse event data were not collected.
Not provided
| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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) | 0 | 0 | 0 | 0 | 0 | 0 |
| EG001 | 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) | 0 | 0 | 0 | 0 | 0 | 0 |
Not provided
Not provided
Not provided
Not provided
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Jessica Ancker, MS, PhD | Vanderrbilt University | (615) 322-5000 | jessica.s.ancker@vumc.org |
| Oct 1, 2023 |
| Prot_SAP_000.pdf |
| ID | Term |
|---|---|
| D001249 | Asthma |
| D029424 | Pulmonary Disease, Chronic Obstructive |
| D006333 | Heart Failure |
| D003920 | Diabetes Mellitus |
| D006973 | Hypertension |
| ID | Term |
|---|---|
| D001982 | Bronchial Diseases |
| D012140 | Respiratory Tract Diseases |
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
| D012130 | Respiratory Hypersensitivity |
| D006969 | Hypersensitivity, Immediate |
| D006967 | Hypersensitivity |
| D007154 | Immune System Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
| D014652 | Vascular Diseases |
Not provided
Not provided
| Male |
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| Black |
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| Non-metropolitan |
|
| Regression, Linear | 0.5739 | This is the fully adjusted regression model, which controlled for patient age group, gender, dual status, race, average percent of 65+ patients, risk score category, rural vs urban, and zip-code level characteristics. | Mean Difference (Net) | -0.0786 | 2-Sided | Superiority | Difference-in-differences model |
| Units | Counts |
|---|---|
| Participants |
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| Participants |
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| Participants |
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| Participants |
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