Comparative Effectiveness of Telemedicine in Primary Care

• ClinicalTrials.gov Identifier: NCT04684836
• Recruitment Status: Recruiting
• First Posted: December 28, 2020
• Last Update Posted: January 26, 2023
• Sponsor: Weill Medical College of Cornell University
• Collaborator: Patient-Centered Outcomes Research Institute
• Information provided by (Responsible Party): Weill Medical College of Cornell University

Study Description

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.

Condition or disease	Intervention/treatment
Asthma; Chronic Obstructive Pulmonary Disease (COPD; Congestive Heart Failure; Diabetes; Hypertension	Other: Exposure to telemedicine, after the onset of the pandemic

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.

Study Design

• Study Type: Observational
• Estimated Enrollment: 216000 participants
• Observational Model: Cohort
• Time Perspective: Retrospective
• Official Title: Evaluating the Comparative Effectiveness of Telemedicine in Primary Care: Learning From the COVID-19 Pandemic
• Actual Study Start Date: March 15, 2021
• Estimated Primary Completion Date: May 2023
• Estimated Study Completion Date: December 2024

Groups and Cohorts

Group/Cohort	Intervention/treatment
Synchronous telemedicine alone	Other: Exposure to telemedicine, after the onset of the pandemic; Telemedicine exposure will be defined based on the proportion of all visits at a given clinic that are delivered via telemedicine in each month.
Telemedicine-supplemented in-person care	Other: Exposure to telemedicine, after the onset of the pandemic; Telemedicine exposure will be defined based on the proportion of all visits at a given clinic that are delivered via telemedicine in each month.
In-person care alone	Other: Exposure to telemedicine, after the onset of the pandemic; Telemedicine exposure will be defined based on the proportion of all visits at a given clinic that are delivered via telemedicine in each month.

Outcome Measures

Primary Outcome Measures :

1. Number of avoidable emergency department (ED) admissions [ Time Frame: 30 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
2. Number of avoidable emergency department (ED) admissions [ Time Frame: 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
3. Number of avoidable emergency department (ED) admissions [ Time Frame: 6 months 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
4. Number of avoidable emergency department (ED) admissions [ Time Frame: 12 months 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
5. Number of unplanned hospital admissions from the ED [ Time Frame: 30 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
6. Number of unplanned hospital admissions from the ED [ Time Frame: 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
7. Number of unplanned hospital admissions from the ED [ Time Frame: 6 months 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
8. Number of unplanned hospital admissions from the ED [ Time Frame: 12 months the comparator arms of clinic-level telemedicine used ]
   Unplanned hospital admissions from the ED will be obtained from claims data
9. Continuity of care as assessed by the Bice-Boxerman Continuity of Care Index [ Time Frame: 30 days after the exposure to one of the comparator arms of clinic-level telemedicine used ]
   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.
10. Continuity of care as assessed by the Bice-Boxerman Continuity of Care Index [ Time Frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used ]
    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.
11. Continuity of care as assessed by the Bice-Boxerman Continuity of Care Index [ Time Frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used ]
    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. Continuity of care as assessed by the Bice-Boxerman Continuity of Care Index [ Time Frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used ]
    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.
13. Continuity of care as assessed by the Breslau Usual Provider of Care measure [ Time Frame: 30 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. 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.
14. Continuity of care as assessed by the Breslau Usual Provider of Care measure [ Time Frame: 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. 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.
15. Continuity of care as assessed by the Breslau Usual Provider of Care measure [ Time Frame: 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. 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.
16. Continuity of care as assessed by the Breslau Usual Provider of Care measure [ Time Frame: 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. 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.
17. 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 ]
    Continuity of care as assessed by attendance at follow-up appointment.
18. 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 ]
    Continuity of care as assessed by attendance at follow-up appointment.
19. 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 ]
    Continuity of care as assessed by attendance at follow-up appointment.
20. 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 ]
    Continuity of care as assessed by attendance at follow-up appointment.

Secondary Outcome Measures :

1. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%) [ Time Frame: 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
2. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%) [ Time Frame: 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%), which is the percentage of patients 18 - 75 years of age with diabetes who had hemoglobin A1c > 9.0% during the measurement period
3. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%) [ Time Frame: 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%), which is the percentage of patients 18 - 75 years of age with diabetes who had hemoglobin A1c > 9.0% during the measurement period
4. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0059): Diabetes: Hemoglobin A1c (HbA1c) Poor Control (>9%) [ Time Frame: 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 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
5. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure [ Time Frame: 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)
6. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure [ Time Frame: 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, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (< 140/90 mmHg)
7. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure [ Time Frame: 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, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (< 140/90 mmHg)
8. Evidence of controlled disease as indicated by as indicated by the National Quality Forum (NQF 0018): Controlling High Blood Pressure [ Time Frame: 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, which is the percentage of patients 18 - 85 with hypertension diagnosis and adequate control (< 140/90 mmHg)
9. Days at home [ Time Frame: 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
10. Days at home [ Time Frame: 60 days after the exposure to one of the comparator arms of clinic-level telemedicine used ]
    Days per month not in hospital or institutional setting
11. Days at home [ Time Frame: 6 months after the exposure to one of the comparator arms of clinic-level telemedicine used ]
    Days per month not in hospital or institutional setting
12. Days at home [ Time Frame: 12 months after the exposure to one of the comparator arms of clinic-level telemedicine used ]
    Days per month not in hospital or institutional setting
13. Patient experiences based on the Patient Satisfaction Questionnaire (PSQ-18) [ Time Frame: 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.
14. Ease of use and access to telemedicine based on Telehealth Usability Questionnaire (TUQ) [ Time Frame: 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

Eligibility Criteria

• Ages Eligible for Study: 19 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Sampling Method: Non-Probability Sample

Study Population

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.

Criteria

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

Contacts and Locations

Contacts

Contact: Jessica Ancker, MPH, PhD; 646-248-9281; jessica.s.ancker@vumc.org

Contact: Samuel Carter, MPH; sac7036@med.cornell.edu

Locations

United States, New York

University of Florida; Recruiting

Gainesville, New York, United States, 32610

Contact: Christopher Harle, PhD charle@ufl.edu

Principal Investigator: Christopher Harle, PhD

Mount Sinai; Recruiting

New York, New York, United States, 10029

Contact: Carol Horowitz, MD, MPH carol.horowitz@mountsinai.org

Principal Investigator: Carol Horowitz, MD, MPH

Weill Cornell Medicine; Recruiting

New York, New York, United States, 10065

Contact: Jessica Ancker, MPH, PhD

Contact: Samuel Carter, MPH

Principal Investigator: Jessica Ancker, MPH, PhD

Principal Investigator: Rainu Kaushal, MD, MPH

United States, North Carolina

University of North Carolina; Recruiting

Chapel Hill, North Carolina, United States, 27599

Contact: Saif Khairat, PhD, MPH saif@unc.edu

Principal Investigator: Saif Khairat, PhD, MPH

Sponsors and Collaborators

Weill Medical College of Cornell University

Patient-Centered Outcomes Research Institute

Investigators

• Principal Investigator: Jessica Ancker, MPH, PhD; Weill Medical College of Cornell University
• Principal Investigator: Rainu Kaushal, MD, MPH; Weill Medical College of Cornell University

More Information

• Responsible Party: Weill Medical College of Cornell University
• ClinicalTrials.gov Identifier: NCT04684836
• Other Study ID Numbers: 20-12023014
• First Posted: December 28, 2020
• Last Update Posted: January 26, 2023
• Last Verified: January 2023

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Weill Medical College of Cornell University:

telemedicine; primary care

Additional relevant MeSH terms:

Lung Diseases, Obstructive; Pulmonary Disease, Chronic Obstructive; Heart Failure; Cardiovascular Diseases; Heart Diseases; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes