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A Virtual Ward to Reduce Readmissions After Hospital Discharge

The recruitment status of this study is unknown. The completion date has passed and the status has not been verified in more than two years.
Verified December 2013 by Irfan Dhalla, St. Michael's Hospital, Toronto.
Recruitment status was:  Active, not recruiting
Sponsor:
ClinicalTrials.gov Identifier:
NCT01108172
First Posted: April 21, 2010
Last Update Posted: October 12, 2017
The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
Collaborators:
Canadian Institutes of Health Research (CIHR)
Toronto Central Local Health Integration Network
Ontario Ministry of Health and Long Term Care
Toronto Central Community Care Access Centre
Women's College Hospital
University Health Network, Toronto
Sunnybrook Health Sciences Centre
Information provided by (Responsible Party):
Irfan Dhalla, St. Michael's Hospital, Toronto
  Purpose
The purpose of this study is to see whether a Virtual Ward reduces readmissions after hospital discharge.

Condition Intervention
Acute Disease Other: Virtual Ward Other: Usual care

Study Type: Interventional
Study Design: Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single (Outcomes Assessor)
Primary Purpose: Health Services Research
Official Title: A Virtual Ward to Reduce Readmissions After Hospital Discharge

Resource links provided by NLM:


Further study details as provided by Irfan Dhalla, St. Michael's Hospital, Toronto:

Primary Outcome Measures:
  • Composite of readmission to hospital or death. [ Time Frame: 30 days after hospital discharge ]
    A binary outcome variable for each patient, representing either readmission to hospital or death within 30 days of hospital discharge. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.


Secondary Outcome Measures:
  • Composite of readmission or death [ Time Frame: 90 days after discharge ]
    A binary outcome variable for each patient, representing either readmission to hospital or death within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Composite of readmission or death [ Time Frame: 6 months after discharge ]
    A binary outcome variable for each patient, representing readmission to hospital within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Composite of readmission or death [ Time Frame: One year after discharge ]
    A binary outcome variable for each patient, representing either readmission to hospital or death within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Emergency department visits [ Time Frame: 30 days after discharge ]
    A binary outcome variable for each patient, representing an emergency department visit within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Emergency department visits [ Time Frame: 90 days after discharge ]
    A binary outcome variable for each patient, representing an emergency department visit within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Emergency department visits [ Time Frame: 6 months after discharge ]
    A binary outcome variable for each patient, representing an emergency department visit within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Emergency department visits [ Time Frame: One year after discharge ]
    A binary outcome variable for each patient, representing an emergency department visit within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Long-term care admission [ Time Frame: 30 days after discharge ]
    A binary outcome variable for each patient, representing a long-term care admission within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Long-term care admission [ Time Frame: 90 days after discharge ]
    A binary outcome variable for each patient, representing a long-term care admission within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Long-term care admission [ Time Frame: 6 months after discahrge ]
    A binary outcome variable for each patient, representing a long-term care admission within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Long-term care admission [ Time Frame: One year after discharge ]
    A binary outcome variable for each patient, representing a long-term care admission within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Death [ Time Frame: 90 days after discharge ]
    A binary outcome variable for each patient, representing death within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences

  • Death [ Time Frame: 6 months after discharge ]
    A binary outcome variable for each patient, representing death within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences

  • Death [ Time Frame: One year after discharge ]
    A binary outcome variable for each patient, representing death within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences

  • Death [ Time Frame: 30 days after discharge ]
    A binary outcome variable for each patient, representing death within the time periods noted above. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences

  • Composite of readmission to hospital or death. [ Time Frame: One year after discharge ]
    Time to the composite outcome of either readmission to hospital or death. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Data will be censored on the date of the last follow up (i.e., 30 days, 90 days or 6 months). Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Readmission [ Time Frame: One year after discharge ]
    Time to readmission to hospital. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Data will be censored on the date of the last follow up (i.e., 30 days, 90 days or 6 months), or on death. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Emergency department visits [ Time Frame: One year after discharge ]
    Time to emergency department visit. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Data will be censored on the date of the last follow up (i.e., 30 days, 90 days or 6 months), or on readmission or death. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Long-term care admission [ Time Frame: One year after discharge ]
    Time to long-term care admission. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Data will be censored on the date of the last follow up (i.e., 30 days, 90 days or 6 months), or on death. Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.

  • Death [ Time Frame: One year after discharge ]
    Time to death. A research assistant blinded to the assignment will ascertain this information via telephone using a standardized script. Data will be censored on the date of the last follow up (i.e., 30 days, 90 days or 6 months). Second, we may also ascertain this information by linking the data we collect to administrative databases housed at the Institute for Clinical Evaluative Sciences.


Estimated Enrollment: 1928
Study Start Date: June 2010
Estimated Study Completion Date: June 2014
Primary Completion Date: June 2013 (Final data collection date for primary outcome measure)
Arms Assigned Interventions
Active Comparator: Usual Care Other: Usual care
The usual care provided to patients after discharge from hospital
Experimental: Virtual Ward Other: Virtual Ward
A multidisciplinary team to optimize medical and social care for patients residing in their own homes

Detailed Description:
We will conduct a pragmatic, randomized controlled trial to evaluate a new model of care for high-risk medical patients after discharge from hospital. This new model of care has two key elements. First, we will use the LACE index (see citation below for details) to identify patients who are at high risk of readmission or death after hospital discharge. These patients will be randomized to either the Virtual Ward or usual care on the day of discharge. Although patients being cared for in the Virtual Ward will reside at home, they will benefit from a hospital-like interdisciplinary team, a shared set of notes, a single point of contact, round-the-clock physician availability and increased co-ordination of specialist, primary and home-based community care for several weeks after hospital discharge.
  Eligibility

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Ages Eligible for Study:   18 Years and older   (Adult, Senior)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Discharge from medical service
  • LACE score greater than or equal to 10
  • Age greater than or equal or 18
  • Resident in Toronto Central Local Health Integration Network catchment area
  • Patient or designate able to speak English well enough for follow up telephone calls

Exclusion Criteria:

  • Previously enrolled in study
  • Discharged to a rehabilitation or complex continuing care facility
  Contacts and Locations
Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01108172


Locations
Canada, Ontario
St. Michael's Hospital
Toronto, Ontario, Canada, M5B 1W8
Sunnybrook Health Sciences Centre
Toronto, Ontario, Canada
Toronto Central Community Care Access Centre
Toronto, Ontario, Canada
University Health Network
Toronto, Ontario, Canada
Women's College Hospital
Toronto, Ontario, Canada
Sponsors and Collaborators
St. Michael's Hospital, Toronto
Canadian Institutes of Health Research (CIHR)
Toronto Central Local Health Integration Network
Ontario Ministry of Health and Long Term Care
Toronto Central Community Care Access Centre
Women's College Hospital
University Health Network, Toronto
Sunnybrook Health Sciences Centre
Investigators
Principal Investigator: Irfan Dhalla, MD, MSc St. Michael's Hospital/University of Toronto
  More Information

Publications:
Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):
Responsible Party: Irfan Dhalla, Staff Physician & Scientist, St. Michael's Hospital, Toronto
ClinicalTrials.gov Identifier: NCT01108172     History of Changes
Other Study ID Numbers: 216852-PHE-CEAJ-25173
First Submitted: April 13, 2010
First Posted: April 21, 2010
Last Update Posted: October 12, 2017
Last Verified: December 2013

Additional relevant MeSH terms:
Acute Disease
Disease Attributes
Pathologic Processes