A Virtual Ward to Reduce Readmissions After Hospital Discharge

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

The purpose of this study is to see whether a Virtual Ward reduces readmissions after hospital discharge.

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.

A multidisciplinary team to optimize medical and social care for patients residing in their own homes

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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

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

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.

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.

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.

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.

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.

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

van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, Austin PC, Forster AJ. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010 Apr 6;182(6):551-7. doi: 10.1503/cmaj.091117. Epub 2010 Mar 1.

Dhalla IA, O'Brien T, Morra D, Thorpe KE, Wong BM, Mehta R, Frost DW, Abrams H, Ko F, Van Rooyen P, Bell CM, Gruneir A, Lewis GH, Daub S, Anderson GM, Hawker GA, Rochon PA, Laupacis A. Effect of a postdischarge virtual ward on readmission or death for high-risk patients: a randomized clinical trial. JAMA. 2014 Oct 1;312(13):1305-12. doi: 10.1001/jama.2014.11492.