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Clinical Trial Details — Status: Withdrawn

Administrative data

NCT number NCT02815462
Other study ID # SGH_OIC_FAMFACESG/5/2016
Secondary ID
Status Withdrawn
Phase N/A
First received
Last updated
Start date August 2016
Est. completion date August 2017

Study information

Verified date April 2023
Source Singapore General Hospital
Contact n/a
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

In an earlier study using electronic health records (EHR), the investigators have identified nine factors to be significantly associated with FA risk. These nine predictors include Furosemide intravenous 40 milligrams or more; Admissions in the past one year; Medifund status; Frequent emergency department use; Anti-depressants treatment in past one year; Charlson comorbidity index; End Stage Renal Failure on dialysis; Subsidized ward stay and Geriatric patient. The investigators have combined these nine predictors into the FAM-FACE-SG score for FA risk (defined as 3 or more inpatient admissions in the following 12 months). The FAM-FACE-SG risk score has the advantage of being deployed in our hospital's enterprise data repository known as Electronic Health Intelligence System or eHINTs for short, on a real-time or near real-time basis. On a daily basis, data from multiple data sources are extracted, transformed and loaded onto the eHINTS system. The system can be programmed to run every midnight to provide risk scores the following morning for patients admitted the previous day. In this trial, the intervention is to combine the FAM-FACE-SG risk score in addition to a decision making algorithm to guide referrals to various transitional care services based on needs assessment on nursing and function. The primary objective is to evaluate the impact of our intervention in improving healthcare utilization (hospital readmissions, emergency department (ED) attendances, length of stay up to 90 days post-discharge).


Description:

In an earlier study using electronic health records (EHR), The investigators have identified nine factors to be significantly associated with FA risk. These nine predictors include Furosemide intravenous 40 milligrams or more; Admissions in the past one year; Medifund status; Frequent emergency department use; Anti-depressants treatment in past one year; Charlson comorbidity index; End Stage Renal Failure on dialysis; Subsidized ward stay and Geriatric patient. The investigators have combined these nine predictors into the FAM-FACE-SG score for FA risk (defined as 3 or more inpatient admissions in the following 12 months). The FAM-FACE-SG risk score has the advantage of being deployed in our hospital's enterprise data repository known as Electronic Health Intelligence System or eHINTs for short, on a real-time or near real-time basis. On a daily basis, data from multiple data sources are extracted, transformed and loaded onto the eHINTS system. The system can be programmed to run every midnight to provide risk scores the following morning for patients admitted the previous day. In this trial, the intervention is to combine the FAM-FACE-SG risk score in addition to a decision making algorithm to guide referrals to various transitional care services based on needs assessment on nursing and function. The primary objective is to evaluate the impact of our intervention in improving healthcare utilization (hospital readmissions, emergency department (ED) attendances, length of stay up to 90 days post-discharge). The aims of this cluster RCT are to: (1) evaluate the impact of implementing the FAM-FACE-SG risk score in addition to a decision making algorithm to guide Patient Navigator (PN) referrals to various transitional care services based on needs assessment on nursing and function on improving healthcare utilization (hospital readmissions, emergency department (ED) attendances, length of stay up to 90 days post-discharge); (2) measure the implementation of the risk score (Fidelity of the PNs in adhering to the protocol in recruiting patients according the score priority; Referral rate of the PNs to various transitional care services; Qualitative feedback from PNs on the perceived benefits and behavior change after receiving the scores); (3) conduct an economic analysis of the cost-benefit of implementing the risk score.


Recruitment information / eligibility

Status Withdrawn
Enrollment 0
Est. completion date August 2017
Est. primary completion date August 2017
Accepts healthy volunteers No
Gender All
Age group 21 Years and older
Eligibility Inclusion Criteria: - Singapore General Hospital wards with patient navigators - Patients who are frequent admitters (defined as 3 or more hospital admissions in the preceding 12 months) Exclusion Criteria: - Haematology, Oncology, Emergency department, obstetrics and neonatology wards

Study Design


Related Conditions & MeSH terms


Intervention

Other:
FAMFACESG
- PNs will receive the FAM-FACE-SG FA risk scores for frequent admitters admitted to their ward.
Control
- Usual hospital Care
FAMFACESG
- PNs will be instructed to prioritize intervention of frequent admitters for intervention based on the FA risk score.
FAMFACESG
For high and moderate risk patients who do not require transitional home care (THC), PN will do the case management and follow up. For high and moderate risk patients & have complex nursing and / or high functional needs, PNs will refer these patients for THC. For mild risk patients, PNs will refer them to care coordinator.
FAMFACESG
- For low risk patients, PNs will continue usual hospital care.

Locations

Country Name City State
Singapore Singapore General Hospital Singapore

Sponsors (1)

Lead Sponsor Collaborator
Singapore General Hospital

Country where clinical trial is conducted

Singapore, 

References & Publications (5)

Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009 Apr 2;360(14):1418-28. doi: 10.1056/NEJMsa0803563. Erratum In: N Engl J Med. 2011 Apr 21;364(16):1582. — View Citation

Kripalani S, Theobald CN, Anctil B, Vasilevskis EE. Reducing hospital readmission rates: current strategies and future directions. Annu Rev Med. 2014;65:471-85. doi: 10.1146/annurev-med-022613-090415. Epub 2013 Oct 21. — View Citation

Longman JM, I Rolfe M, Passey MD, Heathcote KE, Ewald DP, Dunn T, Barclay LM, Morgan GG. Frequent hospital admission of older people with chronic disease: a cross-sectional survey with telephone follow-up and data linkage. BMC Health Serv Res. 2012 Oct 30 — View Citation

Low LL, Vasanwala FF, Ng LB, Chen C, Lee KH, Tan SY. Effectiveness of a transitional home care program in reducing acute hospital utilization: a quasi-experimental study. BMC Health Serv Res. 2015 Mar 14;15:100. doi: 10.1186/s12913-015-0750-2. — View Citation

Robst J. Developing Models to Predict Persistent High-Cost Cases in Florida Medicaid. Popul Health Manag. 2015 Dec;18(6):467-76. doi: 10.1089/pop.2014.0174. Epub 2015 Jun 23. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary 90-day readmission rate 90 days
Secondary 30-day readmission rate 30 days
Secondary 30-day ED attendance rate 30 days
Secondary 90-day ED attendance rate 90 days
Secondary index hospital admission length of stay 90 days
Secondary cumulative length of stay 90 days after index hospital discharge 90 days
Secondary Fidelity of the PNs in following the protocol in recruiting patients according the score priority 90 days
Secondary Proportion of high and medium risk patients recruited in both intervention and control groups 90 days
Secondary Referral rate of the PNs to various transitional care services 90 days
Secondary Qualitative feedback from PNs on the perceived benefits and behaviour change after receiving the scores Questionnaire survey 1 year
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