Improving Patient Prioritization During Hospital-homecare Transition

NCT04136951 | Completed Results

• 2 other identifiers: AAAS72651R01NR018831-01
• Study Type: interventional
• Target: 1,915
• Locations: 1 country
• Sites: 2

Brief Summary

This research work is focused on building and evaluating one of the first evidence-based clinical decision support tools for homecare in the United States. The results of this study have the potential to standardize and individualize nursing decision making using cutting-edge technology and to improve patient outcomes in the homecare setting.

Conditions

Diabetes Mellitus, Type 2; Congestive Heart Failure; Obstructive Pulmonary Disease; Dyspnea; Renal Failure

Keywords

home health; clinical decisions support; care transitions; patient prioritization; health informatics; home care

Outcome Measures

Primary Outcomes (1)

• Number of Rehospitalizations Within 60 Days After Hospital Discharge

  To learn if using PREVENT tool results in decreased incidence of rehospitalization \[defined as recurrent hospital admission within 60 days from hospital discharge\]

  Up to 60 days after hospital discharge

Other Outcomes (1)

• System Usability Scale (SUS) Score

  30-60 days after clinical decision support tool (PREVENT) implementation

Study Arms (1)

Experimental phase

EXPERIMENTAL

The PREVENT recommendation about patient homecare priority will be shared in homecare referral communication with the homecare intake coordinators. Homecare intake coordinators will be instructed to prioritize high risk patients for care.

Other: PREVENT clinical decision support

Interventions

PREVENT clinical decision support OTHER

PREVENT clinical decision support tool consideres five patient risk factors as significant predictors of patient's priority for the first homecare nursing visit: (a) Presence of wounds (either surgical or pressure ulcers); (b) a documented comorbid condition of depression; (c) need for assistive equipment, assistive person, or both for toileting; (d) number of medications; and (e) number of comorbid conditions. Each risk factor was assigned a specific score based on the logistic regression weights. For instance, for a wound (e.g., pressure ulcer, vascular ulcer), the patient received a score of 15 points. For each additional co-morbid condition, one point was added to the final score. Summing the scores for the factors generated a cumulative score. The optimal cut-off point was established based on the regression model performance statistics, indicating that patients with a score greater than 26 points are a high priority for the first nursing visit.

Experimental phase

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• being a patient of either NewYork-Presbyterian (NYP)/Columbia University Irving Medical Center or NewYork-Presbyterian Allen Hospital;
• being referred to Visiting Nurse Services of New York (VNSNY) homecare services
• years old or older.

You may not qualify if:

• All other patients are going to be excluded.
• Aim 2 :
• working as an admission staff for VNSNY
• years old or older.
• All other staff members are going to be excluded.

Sponsors & Collaborators

• Columbia University: lead
• Visiting Nurse Service of New York: collaborator
• National Institute of Nursing Research (NINR): collaborator

Study Sites (2)

Columbia University School of Nursing

New York, New York, 10032, United States

Location

Visiting Nurse Service of New York

New York, New York, 10033, United States

Location

Related Publications (2)

• Topaz M, Trifilio M, Maloney D, Bar-Bachar O, Bowles KH. Improving patient prioritization during hospital-homecare transition: A pilot study of a clinical decision support tool. Res Nurs Health. 2018 Oct;41(5):440-447. doi: 10.1002/nur.21907. Epub 2018 Sep 11.

  PMID: 30203417 RESULT

• Zolnoori M, McDonald MV, Barron Y, Cato K, Sockolow P, Sridharan S, Onorato N, Bowles K, Topaz M. Improving Patient Prioritization During Hospital-Homecare Transition: Protocol for a Mixed Methods Study of a Clinical Decision Support Tool Implementation. JMIR Res Protoc. 2021 Jan 22;10(1):e20184. doi: 10.2196/20184.

  PMID: 33480855 DERIVED

MeSH Terms

Conditions

Diabetes Mellitus, Type 2; Heart Failure; Lung Diseases, Obstructive; Dyspnea; Renal Insufficiency

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Heart Diseases; Cardiovascular Diseases; Lung Diseases; Respiratory Tract Diseases; Respiration Disorders; Signs and Symptoms, Respiratory; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Results Point of Contact

• Title: Maxim Topaz, PhD, Elizabeth Standish Gill Associate Professor of Nursing
• Organization: Columbia University

mt3315@cumc.columbia.edu

6462760460

Study Officials

• Maxim Topaz, PhD

  Associate Professor of Nursing at CUMC

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Model Details: To examine the study aims, the researchers are using an embedded mixed methods study design. The researchers will conduct pre- and post intervention trial of clinical decision support tool PREVENT's integration into clinical practice using homecare admissions from two New York City urban hospitals serving diverse racial and ethnic population. Quantitative methods, including logistic regression and survival analysis with propensity score matching, will be used to evaluate the effects of the tool on process and patient outcomes. Qualitative methods will be used to match the quantitative questions and provide an in depth insight into homecare admission processes using think-aloud simulations and interviews with homecare admission staff.

Study Record Dates

• First Submitted: October 18, 2019
• First Posted: October 23, 2019
• Study Start: August 1, 2023
• Primary Completion: April 1, 2024
• Study Completion: May 1, 2024
• Last Updated: October 8, 2025
• Results First Posted: October 8, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

US (2)