Improving Patient Prioritization During Hospital-homecare Transition: A Mixed Methods Study of a Clinical Decision Support Tool
Phase: NA
Conditions: Diabetes Mellitus, Type 2, Congestive Heart Failure, Obstructive Pulmonary Disease, Dyspnea, Renal Failure
Sponsor: Columbia University

 
 
 
 
 
 
Improving Patient Prioritization During Hospital-homecare Transition: A 
Mixed Methods Study of a Clinical Decision Support Tool 
 
NCT04136951 
March 13, 2023 
 

Background 
Homecare settings across the United States provide care to more than 5 million 
patients every year. About one in ﬁve homecare patients are rehospitalized during the 
homecare episode, with up to two-thirds of these rehospitalizations occurring within the 
ﬁrst 2 weeks of services. Timely allocation of homecare services might prevent a signiﬁcant 
portion of these rehospitalizations. The ﬁrst homecare nursing visit is one of the most 
critical steps of the homecare episode. This visit includes an assessment of the patient’s 
capacity for self-care, medication reconciliation, an examination of the home environment, 
and a discussion regarding whether a caregiver is present. Hence, appropriate timing of the 
ﬁrst visit is crucial, especially for patients with urgent health care needs. However, nurses 
often have limited and inaccurate information about incoming patients, and patient priority 
decisions vary signiﬁcantly between nurses. 
Our team has developed an innovative clinical decision support system (CDSS) 
called Priority for the First Nursing Visit Tool (PREVENT) to assist nurses in prioritizing 
patients in need of immediate ﬁrst homecare nursing visits [18]. PREVENT was developed 
with rigor, using a strong theoretical foundation (transition theory) [19] and methodology for 
eliciting experts’ decisions to create clinical decision support tools [20]. PREVENT was 
constructed using data mining, regression modeling, and expert homecare nurses’ ratings 
of example patients who were transitioned from hospital to homecare. The goal was to 
identify key patient characteristics that are essential to support early homecare admission 
decision making. Overall, more than 70 patient demographic and clinical characteristics 
(eg, comorbidities, level and availability of social support, and detailed functional status) 
were considered for inclusion in the ﬁnal prediction model from which PREVENT was 
developed. The ﬁnal PREVENT CDSS uses 5 factors (including the number of medications, 
number of comorbid conditions, presence of a wound, presence of a comorbid condition 
of depression, and patient’s functional status) to produce a recommendation on whether a 
speciﬁc homecare patient should be prioritized for the ﬁrst homecare nursing visit. See  
We completed a pilot efficacy study [9] to measure the efficacy of PREVENT, 
conducted at a large urban hospital in Brooklyn, New York. In collaboration with the Visiting 
Nurse Service of New York (VNSNY), we enrolled 176 patients admitted to homecare from 
the hospital during April and May 2016. In the control phase (n=90 patients), we calculated 
the PREVENT priority score but did not share the score with the homecare admission staff 
who inﬂuence visit scheduling. In the experimental phase, the PREVENT score was shared 
with the homecare admission staff (n=86 patients). During this phase, patients identiﬁed as 
high priority received their ﬁrst homecare nursing visit about a half-day sooner as 
compared with the control phase (1.8 days vs 2.2 days; P=.09). Rehospitalizations from 

homecare decreased by almost 50% (9.4% point reduction) when comparing the control 
(21.1%) and experimental phases (11.7%), with a signiﬁcant difference between the 
rehospitalization (survival analysis) curves (log-rank P =.03). We acknowledge that this pilot 
study had a relatively small sample size and potentially insufficient adjustment for 
background variables. However, these results were promising in that high-priority patients 
received their ﬁrst homecare visit sooner and overall rehospitalization rates were lower. 
Methods 
Mixed Method Approach 
We are using an embedded mixed methods design. We will conduct a pre- and 
postintervention trial of PREVENT’s integration into clinical practice using homecare 
admissions from two New York City urban hospitals serving diverse racial and ethnic 
populations. We will use quantitative methods, including logistic regression and survival 
analysis, to evaluate the effects of the tool on process and patient outcomes. We will 
utilize qualitative methods integrated with quantitative methods to gain an in-depth insight 
into technology adoption and implementation. 
Setting 
On the basis of our consultations with New York-Presbyterian (NYP) hospitals’ 
leadership and our goal of exploring the effectiveness of the PREVENT system in different 
settings and among sites serving an ethnically diverse population, we will conduct the 
study at 2 NYP hospitals: (1) NYP Hospital/Columbia University Irving Medical Center (large 
academic medical center), a 745-bed adult academic medical center providing emergency, 
primary, and specialty care in all the major ﬁelds of medicine, and (2) NYP Allen Hospital 
(small community hospital), a 196-bed community hospital serving northern Manhattan, 
Riverdale, and other communities in the Bronx. As a homecare site, we will use VNSNY—
the largest not-for-proﬁt home health agency in the United States serving up to 48,500 
patients and health plan members daily. 
Study Intervention: PREVENT 
The PREVENT tool will be integrated with the hospitals’ electronic health record 
(EHR) via a locally developed system called iNYP, which integrates with the EHR and 
provides advanced data review capabilities of all EHR data. iNYP is a Java-based service-
oriented web app that builds on Columbia University’s 25-year history of clinical 
information system innovation [28,29]. iNYP is available as a custom tab within the 
commercial hospital EHR (supplementing the native results review capabilities) and is also 
accessible from a web browser or a mobile device. iNYP is widely used by most clinicians 
alongside the EHR, including the homecare admission staff. The PREVENT score will be 

calculated automatically from EHR data that populate the patient discharge summary or 
other parts of the EHR. We have cross-mapped the elements (eg, number of medications 
and comorbid conditions) needed for the calculation of the PREVENT score to conﬁrm that 
the required elements are readily available in the EHR system. VNSNY admission staff will 
receive an auto-populated ﬁeld within the homecare referral containing the PREVENT 
recommendation about visit priority, presented as high priority and medium or low priority. 
Before any data collection, we will test the accuracy of the PREVENT score on the ﬁrst 50 
priority calculations and correct the EHR integration if any mistakes are found. 
Standard VNSNY Patient Admission Workﬂow 
During our preliminary work, we determined that the scheduling and assignment 
unit assumes responsibility for patient admission to the VNSNY. The unit comprises several 
admission staff members who are involved in the admission processes, including intake 
coordinators, clinical associate managers, and schedulers. Homecare admission starts 
with standard homecare referral signed by the referring physician. 
The referrals are passed to the intake coordinators (administrative staff) who enter 
the referral information into the VNSNY EHR system. Next, clinical ﬁeld managers give 
patients a welcome call and coordinate the general start of care dates. After that, 
schedulers identify the date of a ﬁrst homecare nursing visit. Each geographic location 
(based on city boroughs and street addresses) is served by several admission staff 
members. 
Study Workﬂow 
The workﬂow of PREVENT implementation consists of 3 phases: preintervention 
phase, intervention phase, and postintervention phase. 
 

Preintervention Phase 
During this phase of the study, 3 research activities will be implemented. First, the 
PREVENT priority score will be automatically calculated for all the patients referred to 
VNSNY from the 2 hospitals (step 1). Second, the PREVENT score (and priority 
recommendation based on the score) will be collected but not shared with homecare 
admission staff over about 3 months (step 2). Third, after the preintervention phase data 
are collected, the study team will conduct several 30-min educational sessions for the 
admission staff about the development and validation of PREVENT and this study (step 3). 
We will work with the VNSNY scheduling and assignment unit management to identify all 
the VNSNY staff eligible (15-20 staff) to be exposed to PREVENT’s recommendations during 
the study. We will ensure that each eligible admission staff member undergoes at least one 
educational session about the study workﬂow. 
Intervention Phase 
To minimize periodical and time effects, the intervention phase will start at both 
hospitals on the same date. The PREVENT recommendation will be shared with the 
homecare intake coordinators for about 3 months (step 4). The intake coordinator will enter 
the PREVENT recommendation into the special recommendations ﬁeld of the VNSNY EHR 
system. This ﬁeld stores information about any special programs or services patients 
should receive in homecare, such as recommendations for frontloading of visits. Next, 
clinical ﬁeld managers and schedulers will incorporate the PREVENT priority 
recommendations in their processes related to visit scheduling and patient prioritization. 
The ﬁeld clinician will then conduct the ﬁrst nursing visit. For cases where patient 
prioritization was not possible, we will ask the admission staff to document why a priority 
visit could not happen (such as the patient refused or short staffing; step 5). 
 
 
Study Instruments 
Qualitative interviews and think-aloud simulations will be guided by two robust 
interview guides we will develop for this study. The guides will incorporate aspects of the 
RE-AIM framework dimensions as questions. The guides are as follows: (1) postintervention 
simulation guide (think-aloud protocol) and (2) postintervention phase interview guide. 
Each interview guide will include semi-structured open-ended questions to be answered 
by the admission staff. The Postintervention phase interview guide will include questions 
about PREVENT’s perceived usability and ease of use, leadership support, workﬂow 
adjustments, adequacy of training sessions, and barriers to implementation such as any 

changes the respondent made to his or her regular workﬂow to use PREVENT’s 
recommendations. 
The End-User Computing Satisfaction Instrument [31-33] will be used to 
quantitatively measure satisfaction. The 12 item instrument measures concepts such as 
accuracy and ease of use and has been used to evaluate many types of applications, 
including decision support. A score of 54 corresponds to the 70th percentile. Any concept 
scoring less than the 70th percentile from either user group will guide future tool revision. 
Sample Size Calculation 
In this study, we will calculate the PREVENT scores for all patients referred to 
VNSNY from the 2 hospitals (see study setting) in a 3 month period during the 
preintervention phase (scores not shared) and a 3 month intervention phase (scores 
shared) for an estimated total of 2094 patients and 1508 high-priority patients, respectively. 
This calculation is based on the pilot study rehospitalization decrease. 
In the pre-experimental phase of the study, we used secondary data extracted from 
EHR to calculate PREVENT score. In the experimental phase, we will retrospectively apply 
the PREVENT algorithm on all patients referred from the two hospitals to homecare 
settings. We will then use descriptive statistics to identify the number of high and 
low/medium priority patients (based on PREVENT score) that were referred to homecare. 
Sample size and justiﬁcation: Administrative data shows that average monthly referrals to 
homecare in the ﬁrst semester of 2018 from New York-Presbyterian Hospital/Columbia 
University Irving Medical Center and New York-Presbyterian Allen Hospital were 279 and 70 
patients, respectively, or a total of 1,396 referrals in a four-month period. Of those, we 
expect that approximately 50% patients will be classiﬁed as high risk by PREVENT. In this 
study, we will be calculating the PREVENT scores for all patients referred to homecare from 
these two hospitals in a four-month period during the pre-intervention phase. This sample 
size is sufficient to estimate the number of high priority patients for the later experimental 
phase of the study. 
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