Congestive Heart Failure, Diabetes Mellitus, Type 2, Dyspnea, Obstructive Pulmonary Disease, Renal Failure
Conditions
Keywords
home health, clinical decisions support, care transitions, patient prioritization, health informatics, home care
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.
Detailed description
Each year, more than 5 million patients are admitted to the approximately 12,000 homecare agencies across the United States. About 20% of homecare patients are rehospitalized during the homecare episode, with as many as 68% of these rehospitalizations occurring within the first two weeks of services. A significant portion of these rehospitalizations may be prevented by timely and appropriately targeted allocation of homecare services. The first homecare nursing visit is one of the most critical steps of the homecare episode. This visit includes an examination of the home environment, a discussion regarding whether a caregiver is present, an assessment of the patient's capacity for self-care, and medication reconciliation. A unique care plan is created based on this evaluation of the patient's needs. Hence, appropriate timing of the first visit is crucial, especially for patients with urgent healthcare needs. However, nurses often have very limited and inaccurate information about incoming patients and patient priority decisions vary significantly between nurses. The investigators developed an innovative decision support tool called Priority for the First Nursing Visit Tool (PREVENT) to assist nurses in prioritizing patients in need of immediate first homecare nursing visits. In a recent efficacy pilot study of PREVENT, high-risk patients received their first homecare nursing visit a half day sooner as compared to the control group, and 60-day rehospitalizations decreased by almost half as compared to the control group. The proposed study assembles a strong interdisciplinary team of experts in health informatics, nursing, homecare, and sociotechnical disciplines to evaluate PREVENT in a pre-post intervention effectiveness study. Specifically, the study aims are: Aim 1) Evaluate the effectiveness of the PREVENT tool on process and patient outcomes. Using survival analysis and logistic regression with propensity score matching the researchers will test the following hypotheses: Compared to not using the tool in the pre-intervention phase, when homecare clinicians use the PREVENT tool, high risk patients in the intervention phase will: a) receive more timely first homecare visits and b) have decreased incidence of rehospitalization and have decreased emergency department (ED) use within 60 days. Aim 2) Explore PREVENT's reach and adoption by the homecare admission staff and describe the tool's implementation during homecare admission. Aim 2 will be assessed using mixed methods including homecare admission staff interviews, think-aloud simulations, and analysis of staffing and other relevant data. This innovative study addresses several National Institute of Nursing Research strategic priorities, such as promoting innovation and using technology to improve health. Mixed methods will enable us to gain in-depth understanding of the complex socio-technological aspects of hospital-homecare transition.
Interventions
OTHERPREVENT clinical decision support
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.
Sponsors
Visiting Nurse Service of New York
National Institute of Nursing Research (NINR)
Columbia University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Intervention model description
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Aim 1: Inclusion criteria: 1. being a patient of either NewYork-Presbyterian (NYP)/Columbia University Irving Medical Center or NewYork-Presbyterian Allen Hospital; 2. being referred to Visiting Nurse Services of New York (VNSNY) homecare services 3. 18 years old or older.
Exclusion criteria
All other patients are going to be excluded. Aim 2 : Inclusion criteria: 1. working as an admission staff for VNSNY 2. 18 years old or older.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of Rehospitalizations Within 60 Days After Hospital Discharge | Up to 60 days after hospital discharge | To learn if using PREVENT tool results in decreased incidence of rehospitalization \[defined as recurrent hospital admission within 60 days from hospital discharge\] |
Other
| Measure | Time frame | Description |
|---|---|---|
| System Usability Scale (SUS) Score | 30-60 days after clinical decision support tool (PREVENT) implementation | Nurses' usability perception of the clinical decision support tool (PREVENT) will be assessed by using the System Usability Scale (SUS). The SUS is a 10 item questionnaire, each with a five-point Likert scale, allowing users to rate their agreement or disagreement. The SUS score is calculated by converting the raw scores from the Likert scale and then multiplying the sum by 2.5, resulting in a score between 0 and 100, where a higher score indicates greater perceived usability. |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| PREVENT Data will be calculated from patients referred to the Visiting Nurse Service of New York from the 2 hospitals (NewYork-Presbyterian Hospital/Columbia University Irving Medical Center and NYP Allen Hospital) in a 3 month period during the preintervention phase (scores not shared) and a 3 month intervention phase (scores shared). | 1,915 |
| Total | 1,915 |
Baseline characteristics
| Characteristic | PREVENT |
|---|---|
| Age, Categorical <=18 years | 0 Participants |
| Age, Categorical >=65 years | 1915 Participants |
| Age, Categorical Between 18 and 65 years | 0 Participants |
| Race/Ethnicity, Customized Black | 375 Participants |
| Race/Ethnicity, Customized Hispanic | 923 Participants |
| Race/Ethnicity, Customized Other | 125 Participants |
| Race/Ethnicity, Customized Unknown/Not reported | 0 Participants |
| Race/Ethnicity, Customized White | 492 Participants |
| Region of Enrollment United States | 1915 Participants |
| Sex: Female, Male Female | 1113 Participants |
| Sex: Female, Male Male | 802 Participants |
Adverse events
| Event type | EG000 affected / at risk |
|---|---|
| deaths Total, all-cause mortality | 0 / 1,915 |
| other Total, other adverse events | 0 / 1,915 |
| serious Total, serious adverse events | 0 / 1,915 |
Outcome results
Primary
Number of Rehospitalizations Within 60 Days After Hospital Discharge
To learn if using PREVENT tool results in decreased incidence of rehospitalization \[defined as recurrent hospital admission within 60 days from hospital discharge\]
Time frame: Up to 60 days after hospital discharge
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| PREVENT | Number of Rehospitalizations Within 60 Days After Hospital Discharge | 807 rehospitalizations |
Other Pre-specified
System Usability Scale (SUS) Score
Nurses' usability perception of the clinical decision support tool (PREVENT) will be assessed by using the System Usability Scale (SUS). The SUS is a 10 item questionnaire, each with a five-point Likert scale, allowing users to rate their agreement or disagreement. The SUS score is calculated by converting the raw scores from the Likert scale and then multiplying the sum by 2.5, resulting in a score between 0 and 100, where a higher score indicates greater perceived usability.
Time frame: 30-60 days after clinical decision support tool (PREVENT) implementation
Population: Due to operational constraints during implementation, the SUS survey was not administered to nursing staff.