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Improving patient prioritization during hospital‐homecare transition: A pilot study of a clinical decision support tool

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Patient admission to homecare is a complex process. Medicare policy requires that all patients receive a first home visit within 48 hr after the referral is received at the homecare agency. For unstable or high risk patients, waiting 48 hr to be seen by homecare nurses may not be safe. In this pilot study we tested an innovative clinical decision support tool (called PREVENT), designed to identify patients who may need to be prioritized for early homecare visits. The study was conducted in 2016 at a large homecare agency in the Northeastern US with 176 patients admitted to homecare from the hospital. In the control phase (n = 90 patients), we calculated the PREVENT priority score (indicative of high or medium/low first nursing visit priority) but did not share the score with the homecare intake nurses who influence visit scheduling. In the experimental phase, the PREVENT score was shared with the homecare intake nurses (n = 86 patients). During the experimental phase, high‐risk patients received their first homecare nursing visit about one‐half a day sooner than in the control phase (1.8 days vs. 2.2 days). Rehospitalizations from homecare decreased by 9.4% between the control (21.1%) and experimental phases (11.7%). This pilot study of patient prioritization showed promising results: high priority patients received their first homecare visit sooner and overall rehospitalization rates were lower.
Content may be subject to copyright.
Received: 23 July 2018
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Accepted: 16 August 2018
DOI: 10.1002/nur.21907
RESEARCH ARTICLE
Improving patient prioritization during hospital-homecare
transition: A pilot study of a clinical decision support tool
Maxim
Q1
Topaz
1,2
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MaryGrace Trifilio
3
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Donna Maloney
3
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Ofrit Bar-Bachar
1
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Kathryn H. Bowles
3,4
1
Faculty of Social Welfare and Health
Sciences, School of Nursing, University of
Haifa, Haifa, Israel
2
Brigham and Women's Health Hospital,
Boston, Massachusetts
3
Visiting Nurse Service of New York, New
York, New York
4
School of Nursing, University of
Pennsylvania, Philadelphia, Pennsylvania
Correspondence
Maxim Topaz, Faculty of Social Welfare and
Health Sciences, School of Nursing, University
of Haifa, Haifa, Israel.
Email: mtopaz80@gmail.com
Funding information
Eugenie and Joseph Doyle Research
Partnership Fund
ABSTRACT
Patient admission to homecare is a complex process. Medicare policy requires that all
patients receive a first home visit within 48 hr after the referral is received at the
homecare agency. For unstable or high risk patients, waiting 48 hr to be seen by
homecare nurses may not be safe. In this pilot study we tested an innovative clinical
decision support tool (called PREVENT), designed to identify patients who may need to
be prioritized for early homecare visits. The study was conducted in 2016 at a large
homecare agency in the Northeastern US with 176 patients admitted to homecare
from the hospital. In the control phase (n= 90 patients), we calculated the PREVENT
priority score (indicative of high or medium/low first nursing visit priority) but did not
share the score with the homecare intake nurses who influence visit scheduling. In the
experimental phase, the PREVENT score was shared with the homecare intake nurses
(n= 86 patients). During the experimental phase, high-risk patients received their first
homecare nursing visit about one-half a day sooner than in the control phase (1.8 days
vs. 2.2 days). Rehospitalizations from homecare decreased by 9.4% between the
control (21.1%) and experimental phases (11.7%). This pilot study of patient
prioritization showed promising results: high priority patients received their first
homecare visit sooner and overall rehospitalization rates were lower.
KEYWORDS
clinical decision support, homecare, home health care, nursing informatics, patient
prioritization, transitional care
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INTRODUCTION
Q2
Each year, homecare agency administrators and nurses working for the
approximately 12,000 homecare agencies across the United States (US)
admit more than 11 millionpatients to homecare services (The National
Association for HomeCare and Hospice, 2010). Registered nurses (RNs)
in administrative and clinical positions make several critical decisions
before and duringhomecare admission, including identifying patients at
risk for poor outcomes who might benefit from early interventions. Yet,
there are no empirically derived standards to assist in making these
important decisions. Nationwideevidence shows that about 1220%of
those admitted to homecare services are rehospitalized during the
homecare episode (up to 60 days during which homecare services are
provided) (Centers for Medicare and Medicaid Services, 2016; MedPac,
2014). There is a growing body of evidence showing that a significant
portion of these rehospitalizations may be prevented by timely and
appropriately targeted allocation of healthcare services (Markley,
Sabharwal, Wang, Bigbee, & Whitmire, 2012;McDonald, King, Moodie,
& Feldman, 2008; Murtaugh et al., 2016; OConnor, 2012). Patient
prioritization for homecare services might be key to better outcomes,
but there is a lack of decision support tools to help nurses prioritize
which patients might benefit from early nursing visits.
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Journal MSP No. Dispatch: August 29, 2018 CE:
NUR 21907 No. of Pages: 8 PE: Jennifer Chinworth
Res Nurs Health. 2018;18. wileyonlinelibrary.com/journal/nur © 2018 Wiley Periodicals, Inc.
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THE PROBLEM: IDENTIFYING PATIENT
PRIORITY BEFORE THE FIRST HOMECARE
NURSING VISIT
The first homecare visit, usually conducted by a RN, is one of the most
important steps of the homecare episode for several reasons. This
start-of-care visit includes an examination of the home environment, a
discussion regarding whether a caregiver is present and able to help,
and an assessment of the patient's capacity for self-care. A unique care
plan is created based on this evaluation of the patient's needs. The RN
reconciles medications to avoid errors and assure adherence to the
treatment plan and reviews the hospital discharge instructions with the
patient and caregivers. These activities are important to prevent
adverse events that may lead to rehospitalization. For some patients,
the sooner this visit occurs, the better.
Currently, the Center for Medicare and Medicaid Services (CMS)
requires that all patients admitted to homecare services receive an
initial assessment within 48 hr of their referral to homecare (Centers
for Medicare & Medicaid Services, 2012). In our extensive literature
search, however, we did not identify empirical evidence to support this
regulation. According to the CMS, homecare agencies fail to see about
11% (SD 10.7%) of their patients within 48 hr of referral to homecare,
and there is a large variation of time to first visit among states/
territories (from 28.2% failure in the Virgin Islands to 5% in North
Dakota) (Centers for Medicare and Medicaid Services, 2016).
Homecare admission is a complex process that often involves
multiple care providers and healthcare organizations. For example,
some homecare agencies have hospital-based liaisons that help
coordinate and streamline homecare admissions, while other agencies
rely on hospital discharge planning staff or referring physicians to
discern the patient's condition prior to the first homecare visit.
Inefficient information flow can complicate the homecare admission
process and may result in delayed care and poorer patient outcomes
(Sockolow, Ms, & Bass, 2016).
Homecare agencies serve patients of varied clinical complexities,
from patients requiring just a few visits for assessment and instruction,
to patients with daily wound care or those who are ventilator-
dependent. The responsibility of patient prioritization and visit-timing
falls on the homecare agency's administrative nurses or their hospital-
based colleagues. They must decide whether patients need an
immediate visit or if the patients can wait. Accurate allocation for
the appropriate timing of the first visit is crucial, especially for patients
with urgent healthcare needs. In this pilot study, we focused on
patients admitted to homecare from hospitals rather than other
settings (e.g., skilled nursing facility).
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WHY PATIENT PRIORITIZATION IS
NECESSARY AND IMPORTANT
As many as 60% of patients admitted to homecare from hospitals are
not ready for hospital discharge for a variety of reasons, including lack
of knowledge on how to deal with their complex medical conditions
(old and new) and poor self-management skills (Barnason, Zimmerman,
Nieveen, Schulz, & Young, 2012; Bobay, Jerofke, Weiss, & Yakusheva,
2010; Coffey & McCarthy, 2012; Foust, Vuckovic, & Henriquez, 2012;
Weiss, Yakusheva, & Bobay, 2010). Moreover, patients often do not
visit their primary care providers for follow-up visits within the first
60 days of hospital discharge, resulting in the homecare nurse
becoming the key clinician for their health needs (Grafft et al., 2010;
Hernandez et al., 2010; Jencks, Williams, & Coleman, 2009; Misky,
Wald, & Coleman, 2010; Sharma, Kuo, Freeman, Zhang, & Goodwin,
2010; Zisberg et al., 2011).
Typical homecare patients have at least four medical conditions
(Caffrey, Sengupta, Moss, Harris-Kojetin, & Valverde, 2011; Leung
et al., 2016) and take, on average, eight to twelve medications
(McDonald et al., 2012; McDonald et al., 2016). The combination of
lack of discharge readiness, poor follow-up with primary care
providers, and high medication complexity makes homecare patients
vulnerable to poorer outcomes. Operating with limited information,
resources, and time (Sockolow et al., 2016), administrative homecare
nurses and those nurses working in the home must decide which
patients to prioritize to prevent rehospitalizations and other negative
outcomes.
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FIRST NURSING VISIT PRIORITIZATION
TOOL
Our team developed an innovative decision support tool called
PREVENT to assist administrative nurses in determining which
patients should be prioritized for the first homecare nursing visit
(Topaz, 2014). Tools similar to PREVENT have been developed
internationally. For example, researchers in Canada have developed a
tool called Method for Assigning Priority Levels (MAPLe)
Q3
(Hirdes
et al., 2008). This tool helps prioritize homecare patients for
community and other post-acute services. Tools like MAPLe, however,
were developed for different purposes and in different homecare
environments, possibly limiting their applicability in the unique US
healthcare system. To our knowledge, there are no other existing US-
specific homecare first-visit prioritization tools.
PREVENT was developed using data mining, regression
modeling, and expert homecare nursesratings 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 (e.g., comorbid-
ities, level and availability of social support, detailed functional
status) were considered for inclusion in the final prediction model
from which PREVENT was developed. The total study sample
included 670 patients. Model development was done using 70% of
the sample for model development and 30% model validation was
done using the other 30% of the sample. The resulting prediction
model achieved an Area under the Curve (AUC) of 75.9 when
validated on the hold-out sample. Models with AUC > .70 are
considered valid (Hosmer et al., 2013).
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TOPAZ ET AL.
In the PRVENT development study, nurses were asked to read
patientsdischarge case summaries and identify key risk factors,
defined as any clinical or demographic factors that can help identify
high priority patients for the first homecare visit. The nurses chose the
following five patient risk factors as significantly associated with
prioritizing the patient's first homecare nursing visit: (a) presence of
wounds (either surgical or pressure ulcers); (b) a documented comorbid
condition of depression; (c) limitation in current toileting status
(requiring use of any assistive equipment, assistive person, or both); (d)
number of medications; and (e) number of comorbid conditions. These
factors, identified as significant predictors of the first homecare
nursing visit priority, were validated statistically (each factor was
statistically significant in the adjusted regression model) and confirmed
in the clinical literature on patient prioritization. For example,
depression, or multiple comorbid conditions are known as major
contributors to rehospitalizations from homecare (Ma, Shang, Miner,
Lennox, & Squires, 2017; OConnor, 2012).
Each of the risk factors was assigned a specific score based on the
logistic regression weights from the original study (Topaz, 2014). For
example, if a wound was present (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. The scores
for the factors were summed, generating a cumulative score ranging
between 0 and 50, with any score >26 suggesting high priority for the
first homecare visit. The cut-off of 26 points was established based on
the PREVENT development study regression model (Topaz, 2014).
More details on the methods are published elsewhere (Bowles et al.,
2016; Topaz, 2013; Topaz, Rao, Masterson Creber, & Bowles, 2013).
In the study reported here, we aimed to prospectively pilot-test
the PREVENT tool during hospital-to-homecare transitions. We tested
the PREVENT tool within a large homecare agency in New York City to
identify: (a) the tool's effect on the timing of the first nursing visit (i.e.,
whether high priority patients were given priority and visited ahead of
others) and (b) the association between PREVENT's use and the rate of
30 and 60 day rehospitalizations.
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METHODS
This quasi-experimental pilot study was conducted in two phases.
First, in the control phase, the PREVENT score was calculated by the
researchers but not shared with the in-hospital homecare intake team.
The rationale for this choice was to establish the baseline comparison
group for the experimental phase. The in-hospital homecare intake
team included intake associates and RN homecare consultantsa
detailed description of the homecare intake team membersresponsi-
bilities is provided in the next section and patient's homecare intake
workflow is described on Figure 1. Second, in the experimental phase,
the PREVENT score was calculated by the in-hospital homecare intake
team and shared with the homecare scheduling and assignment unit to
support patient prioritization. The study was compliant with the Health
Insurance Portability and Accountability Act (HIPPA) regulations on
data privacy. All patient data were stored on secure and password-
protected devices within the research organization. No identifiers
were disclosed to any outside entity. This study was approved by the
homecare organization's Institutional Review Board (IRB).
Before integrating the PREVENT tool into the workflow, we
discussed the potential implementation strategy and study design with
key organizational stakeholders involved with the intake and admis-
sion process. The stakeholders included the Senior Vice President of
Patient Care Services, the Director of Intake Services, a Regional Intake
Manager, an Intake Coordinator, an Interdisciplinary Care Team
Manager, and a Regional Director of Scheduling and Assignments.
These discussions helped us identify the key steps of the admission
process and study workflow.
5.1
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Integrating the PREVENT decision support tool
into clinical workflow
The pilot test of the PREVENT tool was conducted during hospital-to-
homecare transitions in collaboration with an in-hospital homecare
intake team working within a large urban hospital in Brooklyn, New
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FIGURE 1 Patient hospital-to-homecare transition in normal,
Q4
control, and experimental settings. Skilled need: a physician authorized
assessment that confirms the patient requires the skill of a registered nurse or physical therapist to perform and instruct in aspects of care
TOPAZ ET AL.
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York during April and May 2016. Because homecare agencies differ
from one another in organizational structure, geographical location,
and other factors, we will explain the workforce structure of the
agency that participated in this study (see also Figure 1).
The regular course of transition from hospital to homecare started
with a referral from the attending physician based on the patient's
need for skilled care. Skilled care included rehabilitation services and
use of nurses to manage, observe, provide, and evaluate patient care.
Skilled nursing care was given or supervised by RNs. Referral from the
attending physician was received by an in-hospital homecare intake
team. Within the homecare intake team, an agency-employed
administrator, received the physician referrals and passed the
information to the homecare consultant, a RN, who also worked
within the hospital but was employed by the homecare agency. The RN
homecare consultant assessed the patient and affirmed the need for
skilled homecare. In the hospital, RN homecare consultants also had
access to patient charts, which ensured that the most current
information on the patient's health status was collected and
communicated. The patient's information was then passed on to the
homecare regional scheduling and assignment unit managers who
placed the patient into the scheduling system for the first home visit
with a RN (summarized in Figure 1). The time of the first nursing visit,
was considered the moment of admission to homecare.
The original plan for conducting the pilot study was to use the
PREVENT tool on 100 eligible patients in each phase of the studycontrol
and experimental as explained above. We excluded pediatric and post-
partum patients because the PREVENT tool was not designed or validated
for these patient groups. For the control phase, 100 consecutively
referred patients meeting the study criteria were included. We could not
calculate the PREVENT score for 10 patients because of missing or
incomplete clinical data. The control sample, therefore, included 90
patients. For the experimental phase, another 100 consecutively referred
patients fitting the criteria were included. For 14 patients in this group, the
PREVENT score could not be calculated because of missing or incomplete
clinical data, leaving a final sample of 86 patients.
To maintain blinding of the RN homecare consultants and visiting
nurses in the control phase of the study, a research assistant and the
primary investigator applied the PREVENT assessment tool and
calculated the PREVENTpriority scorefor a subset of patientspreviously
referred to homecare from the hospital. These scores were not shared
with the in-hospital homecare intake team. The data for completing the
PREVENT tool were collected from patientsmedical records.
For the experimental phase, the investigators trained the four
hospital-based RN homecare consultants on how to complete the
PREVENT tool and calculate the priority score. The training included a
description of the tool development and validation processes. The
participating RN homecare consultants were also asked to calculate
patientspriorityusing the tool on a seriesof clinical scenariosreflecting a
diverse range of patients. Each of the clinical scenarios was then
discussed until agreement on the priority score was achieved by all the
participants. Any questions about the tool implementation or priority
calculation were discussed during the training session and also
communicated during PREVENT's implementation with the primary
investigator by telephone or electronic mail. The clinicians within the
scheduling and assignment unit responsible forthe timing of the patient's
first homecare visit by a nurse were also educated about the study.
Once all four RN homecare consultants were trained, they applied
the PREVENT tool and computed the score for 100 consecutive
patients referred to homecare during the experimental phase of the
study. Once the priority score was obtained by a RN homecare
consultant, the score was uploaded into the computerized referral
processing system. The priority recommendation (highor medium/
lowpriority for a first homecare nursing visit) was then automatically
sent to staff in the scheduling and assignment unit, who then
forwarded the priority recommendation along with the case assign-
ment to the nurse who made the first homecare visit.
5.2
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Data collection
Patient-related information was collected from the electronic health
records of the homecare agency and included socio-demographic
characteristics (patient's age and gender), clinical characteristics
(primary and comorbid conditions captured as International Classifi-
cation of Diseases version 9 codes [ICD-9]), and episode of care
information (hospital discharge, the timing of the first homecare visit,
and, when applicable, rehospitalization). Additional PREVENT-specific
information also was collected from electronic health records including
the number of medications, the patient's need for toileting assistance,
and the presence of wounds.
The outcome measure of rehospitalization was defined as any
hospital admission that occurred between the patient's first homecare
visit until homecare discharge (up to 60 days). For the study purposes,
any rehospitalization was considered the end of the homecare episode.
Because rehospitalization data were extracted from the homecare
records, any and all rehospitalizations were noted, independent of the
hospital where the patient was readmitted. Time to rehospitalization
was calculated as the period between the patient's hospital discharge
date and the date of rehospitalization.
5.3
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Data analysis
Appropriate statistical tests (bivariate association tests including t-tests
and chi-square tests) were conducted to compare the data from the
control and experimental phases. Weused Kaplan Meir survival analysis
to compare differences in time until rehospitalization (in days) for
patients in the control and experimental phases. Becausethis was a pilot
study with insufficient power to identify group differences in an
adjusted manner, all the comparisonswere unadjusted for other factors.
All statisticalanalyses were conducted in STATA v.11 (StataCorp, 2009).
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RESULTS
6.1
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General sample details
Characteristics of the patient sample for the control and
experimental study phases are reported in Table 1. The overall
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TOPAZ ET AL.
study sample was two-thirds female with a mean age of 69.8 years
(SD = 17.3). On average, participants had 5.8 co-morbid conditions.
About 10% of the sample had a documented history of clinical
depression and 25% had wounds (skin impairment of any etiology).
Over half (59.7%) of the study sample required assistance when
toileting. Based on the PREVENT score, 25% of the study sample
was scored as medium/low priority for the first homecare nursing
visitandtherestwerehighpriority.Therewereafewstatistically
significant differences in patient characteristics between the
control and experimental study phase samples. For instance, the
experimental phase patients had slightly more co-morbid con-
ditions (6.1 vs. 5.4) and higher percentages of lipid metabolism
disorders and anemia. Conversely, control phase patients had
significantly more wounds (33.3% vs. 16.3%). Other differences
between the study samples in control and experimental phases
were not statistically significant.
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Timing of the first nursing visit
During the control phase, there was no difference detected in time to
first nursing visit to homecare for both high and medium/low priority
patients. Both types of patients had a first nursing visit on average 2.2
days after hospital discharge. By contrast, in the experimental phase
high priority patients had a first nursing visit on average one-half a day
sooner than medium/low priority patients (1.8 vs. 2.6 days). These
differences were not statistically different although they have clinical
relevance.
6.3
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Differences in rehospitalization during
homecare
Data on rehospitalizations were available only for the period in which
the patient received homecare services, so it is important to note the
homecare length of stay (LOS) for each phase. In the control phase the
average LOS was 24.7 days (SD = 17.9). In the experimental phase the
average LOS was 32.9 days (SD = 25.5). The LOS differences between
the two phases were not statistically significant. In the control phase,
21.1% (n= 19) of patients were rehospitalized during homecare versus
11.7% (n= 10) of patients in the experimental phase, a 9.4%
rehospitalization rate reduction for patients in the experimental phase.
Survival analysis, depicted in Figure 2, shows that risk of rehospitali-
zation was significantly higher (Log-Rank p= .025) in the control phase
versus the experimental phase.
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DISCUSSION
In this study we examined the application of a clinical decision support
tool called PREVENT for homecare patient visit prioritization. Our
patient population was similar to the general homecare population
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TABLE 1 Patient characteristics in the control and experimental study phase
Characteristics Total N= 176 Control phase n= 90 Experimental phase n=86
Female, n(%) 113 (64.2) 58 (64.4) 55 (62.7)
Age, M (SD) 69.8 (17.3) 69.3 (18.2) 70.4 (15.2)
Comorbid conditions
a
,n(%)
Hypertension 134 (76.1) 66 (76.7) 68 (75.6)
Cardiac morbidity 102 (58) 47 (54.7) 55 (61.1)
Diabetes mellitus 67 (38.1) 34 (39.5) 33 (36.7)
Urologic morbidity 62 (35.2) 30 (34.9) 32 (35.6)
Other nervous system 46 (26.1) 16 (18.6) 21 (23.3)
Lipid metabolism disorders 37 (21) 14 (16.3)* 22 (24.4)*
COPD 36 (20.5) 13 (15.1) 12 (13.3)
Anemia 20 (11.4) 6 (7)* 12 (13.3)*
Delirium, dementia, and other cognitive disorders 18 (10.2) 8 (9.3) 10 (11.1)
High-priority patients based on PREVENT, n(%) 132 (75) 65 (72.2) 67 (77.9)
Length of stay, days M (SD) 28.7 (22.2) 24.7 (17.9) 32.9 (25.5)
PREVENT items
Medications, M (SD) 9.5 (4.2) 9.7 (4.2) 9.5 (4.3)
Comorbid conditions, M (SD) 5.8 (1.7) 5.4 (.9)* 6.1 (2.4)*
Presence of depression
b
,n(%) 16 (9) 7 (7.8) 9 (10)
Presence of wound, n(%) 44 (25) 30 (33.3)* 14 (16.3)*
Limitation of toileting, n(%) 105 (59.7) 50 (55.5) 55 (61.1)
a
10 most common comorbid conditions are presented.
b
Depressive disorder, Not Elsewhere Classified COPD = chronic obstructive pulmonary disease.
*p< .01.
TOPAZ ET AL.
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5
reported across the US with a few minor differences. For example,
patients in our sample had slightly more comorbid conditions, 5.8
versus 4 reported by Caffrey et al. (2011), and were slightly younger
(The National Association for Home Care and Hospice, 2010). With a
16.5% rate of 30-day rehospitalizations, our sample was similar to the
average rehospitalization rates in the US (The National Association for
Home Care and Hospice, 2010).
We also found a few differences between the control and
experimental study phases, the most significant being the number of
patients with wounds, which were 33.3% in the control versus 16.3%
in the experimental phase. We speculate that the difference might be
explained by a relatively lower rate of certain surgical procedures
conducted in the experimental phase. The experimental phase
coincided with the Jewish Passover holiday, during which Jewish
patients, who made up a significant proportion of the sample, are less
likely to schedule an elective surgery. In general, patientsreluctance to
undergo surgical procedures and be unable to leave their homes during
warmer months might account for the differences in wounds between
the control sample (data collected in the winter) and the experimental
sample (data collected in the spring). Otherwise, the differences
between the study samples were only minor.
Three-quarters of the patients in our sample were indicated as
high priority for the first homecare nursing visit based on the PREVENT
score. The PREVENT tool was developed based on data from a similar
healthcare system, however only about half of the sample in the
PREVENT tool development study were rated as high priority (Topaz,
2013). This might suggest differences in the severity of patient
populations between the studies. The large proportion of high-priority
patients in this study might also serve as an indication that additional
factors need to be taken into consideration in the patient priority
calculations by the PREVENT tool to improve its precision. On the
other hand, homecare organizations might need to allocate more
resources to respond effectively to large numbers of high priority
patients. To address these issues, further study with a larger sample is
needed to examine the sensitivity and specificity of the tool with
various populations.
In designing and using the PREVENT tool, we hope to help
homecare clinicians identify patients who are at increased risk of
poor outcomes, namely rehospitalization, so they may prioritize their
first homecare nursing visit during the critical transition period from
hospital to homecare. After application of PREVENT in the
experimental phase, we found that high priority patients were
admitted to homecare about a half day sooner than in the control
phase. In practice, this meant that high risk patients' problems were
potentially addressed earlier in the homecare episode. The
rehospitalization rates during homecare between the study phases
differed significantly, with 9.4% fewer rehospitalizations in the
experimental phase.
Several other studies of early interventions in homecare aimed
at reducing rehospitalizations also showed positive results. For
example, Murtaugh et al. (2016) showed that providing early and
intensive skilled nursing visits (also called frontloading) and early
physician follow-up in the first week of homecare services reduced
30-day rehospitalizations on average up to eight percentage points,
a 40% relative reduction. By contrast, several other studies have
found little or no benefits of early visits, potentially because patients'
priority was defined differently across agencies and implementation
of frontloading varied (OConnor, Bowles et al., 2014; OConnor,
Hanlon, & Bowles, 2014). We believe that better outcomes can be
achieved for homecare patients by combining standardized decision
support toolslike PREVENT- to aid decision making among
homecare nurses with other proven strategies such as ensuring
that patients see their primary care providers for follow-up visits
(Hernandez et al., 2010; Murtaugh et al., 2016).
Our study is not without limitations. This study was designed as a
feasibility pilot-study, and did not allow us to identify significant causal
relationships between the application of the PREVENT tool for
homecare patient prioritization, and patient rehospitalizations. The
quasi-experimental design did not support controlling for pre-post
differences or to do propensity scores or instrumental variable
analyses. Also, we applied the tool in one location, thus generalizability
to other locations and populations is limited.
We also did not conduct an in-depth examination of the inter-rater
reliability for nurses using the PREVENT tool. Further studies can also
explore the homecare admission processes in greater detail with
additional in-depth qualitative interviews with more managers, nurses,
and patients. In addition, data in the control phase were collected from
patients' charts by our study team, while data for the experimental
phase were collected by in-hospital RN homecare consultants, from
patientscharts and sometimes at the patientsbedside. This
distinction in data collection methods might have affected the
differences found between the control and experimental study
phases. Our study mainly focused on the implementation of the
PREVENT tool to streamline homecare admissions based on indica-
tions of severity/urgency of the patients, and rehospitalizations during
the homecare episode. Other factors at play during the homecare
episode like contact with and treatment by the primary care physician,
and content of the homecare treatment, were not taken into account in
this assessment.
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FIGURE 2 Survival plot of rehospitalizations in the control and
experimental study phases
COLOR
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TOPAZ ET AL.
Importantly, we calculated rehospitalizations during the homecare
episode only, which might differ from general rehospitalization rates
outside of homecare. We were only able to follow patients during their
homecare episode, which ended after discharge from homecare. In
future studies, rehospitalization rates should be explored through
patient telephone follow-up, or from other data sources. Finally, we did
not account for differences in the admission times over weekends
given the low number of patients in our sample admitted on weekends.
8
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CONCLUSION
We found promising results in this pilot study of patient prioritization
for the first homecare nursing visit. After applying and sharing the
PREVENT decision support tool with the homecare nurses, high
priority patients were seen sooner for the first homecare nursing visit
and rehospitalizations from homecare were significantly lower. Future
work is necessary to validate these pilot results using a larger sample in
a randomized controlled trial.
ACKNOWLEDGMENTS
This study was funded by the Eugenie and Joseph Doyle Research
Partnership Fund at the Center for Home Care Policy & Research,
Visiting Nurse Service of New York. We also thank the RINAH
reviewers and editorial team for their excellent guidance on these
manuscript revisions.
ORCID
Maxim Topaz http://orcid.org/0000-0002-2358-9837
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How to cite this article: 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;18.
https://doi.org/10.1002/nur.21907
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... Our examination of stages of automation using the automation of human information processing framework showed that the stages of information analysis and decision selection were automated by all DSSs (n ¼ 28, 100%). Examples include DSSs for triage in emergency departments 29,30,32,34,35,37,38 ; risk management such as falls, pressure injuries, and medication errors 18,[21][22][23]25,[40][41][42]44 ; dose calculation 26,27 ; symptom management for pain, diarrhea, and fever 19,31,36,39,41,43 ; and nursing problem identification. 20,24,45 Five DSSs automated information acquisition by collecting information from medical records 41,42,44 or patient input. ...
... Examples include DSSs for triage in emergency departments 29,30,32,34,35,37,38 ; risk management such as falls, pressure injuries, and medication errors 18,[21][22][23]25,[40][41][42]44 ; dose calculation 26,27 ; symptom management for pain, diarrhea, and fever 19,31,36,39,41,43 ; and nursing problem identification. 20,24,45 Five DSSs automated information acquisition by collecting information from medical records 41,42,44 or patient input. 29,38 Only one DSS automated action implementation by documenting nursing interventions directly into patients' records. ...
... Four of these reported reduction in readmission when DSS was used to manage patient care. 22,31,41,42 Two examined patient's length of stay, which was increased 42 or remained unchanged. 31 Functional outcomes of patients with lower back pain also remained unchanged with the use of DSSs. ...
Article
Objective The study sought to summarize research literature on nursing decision support systems (DSSs); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes. Materials and Methods We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed. Results Of 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed. Conclusions Current nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses’ decision making, care delivery, and patient outcomes.
... Facilitating a safe return home following hospitalization should be a collaboration between the patient, their families, and acute and outpatient providers. This involves discharge planning to identify patients who might benefit from post-acute home health care and coordinate the referral of services, as well as patient prioritization by home health agencies and prompt outpatient follow-up [12][13][14][15][16]. This collaborative process relies on numerous people, structures, and processes to create, communicate, and enact the discharge plan. ...
... Racial/ethnic disparities in post-acute referral and utilization of home health care were observed in both studies for non-Hispanic Black, Asian American/Pacific Islander (AAPI), American Indian/Alaska Native (AIAN), and Hispanic patients compared to (non-Hispanic) WHITE patients [17,18]. Efforts are ongoing to standardize institutional processes using clinical decision tools for referral decisions during the discharge planning and to prioritize home health visits at the agency level [12,19]. ...
... Among the patients who received home health care that started promptly (days 0-2 after discharge) or was delayed (days 3-8), 20% were rehospitalized (Table 1). In contrast, 40% of the patients were rehospitalized when services started late (days [8][9][10][11][12][13][14]. Additional descriptive results are presented in Table 1 stratified by timing of home health care initiation. ...
Article
Full-text available
Older adults with diabetes are at elevated risk of complications following hospitalization. Home health care services mitigate the risk of adverse events and facilitate a safe transition home. In the United States, when home health care services are prescribed, federal guidelines require they begin within two days of hospital discharge. This study examined the association between timing of home health care initiation and 30-day rehospitalization outcomes in a cohort of 786,734 Medicare beneficiaries following a diabetes-related index hospitalization admission during 2015. Of these patients, 26.6% were discharged to home health care. To evaluate the association between timing of home health care initiation and 30-day rehospitalizations, multivariate logistic regression models including patient demographics, clinical and geographic variables, and neighborhood socioeconomic variables were used. Inverse probability-weighted propensity scores were incorporated into the analysis to account for potential confounding between the timing of home health care initiation and the outcome in the cohort. Compared to the patients who received home health care within the recommended first two days, the patients who received delayed services (3–7 days after discharge) had higher odds of rehospitalization (OR, 1.28; 95% CI, 1.25–1.32). Among the patients who received late services (8–14 days after discharge), the odds of rehospitalization were four times greater than among the patients receiving services within two days (OR, 4.12; 95% CI, 3.97–4.28). Timely initiation of home health care following diabetes-related hospitalizations is one strategy to improve outcomes.
... There is some evidence that early initiation of home care services after a hospitalization is beneficial; for instance, a recent study indicated that when the most complex patients were prioritized for their start-of-care visit, the rehospitalization rate fell almost 50%. 6 Similarly, for specific patient populations, such as patients with sepsis 10 and heart failure, 8 early initiation of home health care services and outpatient primary care provider follow-up were associated with reduced rates of rehospitalization. For patients with diabetes, however, the impact of early home health care visits was not associated with lower risk of rehospitalizations when comparing those who received a start-of-care home health care visit in the first 2 days, to those whose first visit occurred between days 3 and 7 after hospital discharge. ...
... Our team recently conducted studies exploring reasons for delayed start-of-care home health care nursing visits and characteristics of patients who are likely to receive delayed start-of-care visits. 6,13,14 In these studies, we found that non-Hispanic Black and Hispanic patients, and having Medicaid and managed care insurance, were associated with delayed start-of-care. 13 The most common reasons for delayed start-of-care visits were "no answer at the door or phone," "patient/family request to postpone or refuse some services," and "administrative or scheduling issues." ...
Article
Objectives This study explored the association between the timing of the first home health care nursing visits (start-of-care visit) and 30-day rehospitalization or emergency department (ED) visits among patients discharged from hospitals. Design Our cross-sectional study used data from 1 large, urban home health care agency in the northeastern United States. Setting/Participants We analyzed data for 49,141 home health care episodes pertaining to 45,390 unique patients who were admitted to the agency following hospital discharge during 2019. Methods We conducted multivariate logistic regression analyses to examine the association between start-of-care delays and 30-day hospitalizations and ED visits, adjusting for patients’ age, race/ethnicity, gender, insurance type, and clinical and functional status. We defined delays in start-of-care as a first nursing home health care visit that occurred more than 2 full days after the hospital discharge date. Results During the study period, we identified 16,251 start-of-care delays (34% of home health care episodes), with 14% of episodes resulting in 30-day rehospitalization and ED visits. Delayed episodes had 12% higher odds of rehospitalization or ED visit (OR 1.12; 95% CI: 1.06–1.18) compared with episodes with timely care. Conclusions and Implications The findings suggest that timely start-of-care home health care nursing visit is associated with reduced rehospitalization and ED use among patients discharged from hospitals. With more than 6 million patients who receive home health care services across the United States, there are significant opportunities to improve timely care delivery to patients and improve clinical outcomes.
... For older adults with multiple chronic conditions, post-acute care transitions are a particularly high-risk period for adverse events and rehospitalization [6,7]. Home health care visits are one important aid to effective transition from hospital to home and may reduce adverse events among high-risk patients [8][9][10]. For patients with diabetes, home health visits can be used to individualize teaching and support of patients with management of medications and dietary guidelines study was to explore racial/ethnic differences in post-acute home health care referral and utilization following diabetes-related hospitalizations among a cohort of Medicare beneficiaries. ...
... Are there structural barriers in place that prevent patients from accessing equitable discharge planning and transitional care? Since discharge planning is often subjective in nature, clinical decision support programs [8,18] may assist in identifying patients who could benefit from home health care services. Patients who did not have recent use of home health care should be given additional attention at hospital admission and in discharge planning. ...
Article
Full-text available
Racial and ethnic disparities exist in diabetes prevalence, health services utilization, and outcomes including disabling and life-threatening complications among patients with diabetes. Home health care may especially benefit older adults with diabetes through individualized education, advocacy, care coordination, and psychosocial support for patients and their caregivers. The purpose of this study was to examine the association between race/ethnicity and hospital discharge to home health care and subsequent utilization of home health care among a cohort of adults (age 50 and older) who experienced a diabetes-related hospitalization. The study was limited to patients who were continuously enrolled in Medicare for at least 12 months and in the United States. The cohort (n = 786,758) was followed for 14 days after their diabetes-related index hospitalization, using linked Medicare administrative, claims, and assessment data (2014–2016). Multivariate logistic regression models included patient demographics, comorbidities, hospital length of stay, geographic region, neighborhood deprivation, and rural/urban setting. In fully adjusted models, hospital discharge to home health care was significantly less likely among Hispanic (OR 0.8, 95% CI 0.8–0.8) and American Indian (OR 0.8, CI 0.8–0.8) patients compared to White patients. Among those discharged to home health care, all non-white racial/ethnic minority patients were less likely to receive services within 14-days. Future efforts to reduce racial/ethnic disparities in post-acute care outcomes among patients with a diabetes-related hospitalization should include policies and practice guidelines that address structural racism and systemic barriers to accessing home health care services.
... 32 Recently, CDSS have emerged for discharge planning in specific diseases and settings including chronic obstructive pulmonary disease, 33 pediatric gastroenteritis, 34 chest pain in the emergency department, 35 and transition to homecare. 36 As a result, newer systematic reviews of CDSS in specific clinical domains have emerged, [37][38][39] but there is little synthesis in CDSS research for PAC prediction models as a whole. Paper 1 of this study addresses this gap. ...
Article
Statement of the Problem: As healthcare data becomes increasingly prolific and older adult patient needs become more complex, there is opportunity for evidence-based technology such as clinical decision support systems (CDSS) to improve decision making at the point of care. Although CDSS for discharge planning is available, few published tools have been translated to new settings. Existing studies have not explored discordance between recommended and actual discharge disposition. Understanding the reasons why patients do not receive optimal post-acute care referrals is critical to improving the discharge planning process for older adults and their families. Methods: Three-paper dissertation examining CDSS. Paper 1 is a systematic review of studies with prediction models for post-acute care (PAC) destination. Paper 2 is a retrospective simulation of a discharge planning CDSS on electronic health record (EHR) data from two hospitals to examine differences in patient characteristics and 30-day readmission rates based on a CDSS recommendation among patients discharged home to self-care. Paper 3 is a natural language processing (NLP) study including retrospective analysis of narrative clinical notes to identify barriers to PAC among hospitalized older adults and create an NLP system to identify sentences containing negative patient preferences. Results: Most prediction models in the literature were developed for specific surgical populations using retrospective structured EHR data. Most models demonstrated high risk of bias and few published follow-up studies. In the simulation study, surgical patients identified by the CDSS as needing PAC but discharged home to self-care experienced adjusted 51.8% higher odds of 30-day readmission compared to those not identified. In the NLP study, the top three barriers were patient has a caregiver, negative preferences, and case management clinical reasoning. Most patients experienced multiple barriers. The negative preferences NLP system achieved an F1-Score of 0.916 using a deep learning model after internal validation. Conclusions: Future prediction modeling studies should follow TRIPOD guidelines to ensure rigorous reporting. Findings from the simulation and NLP studies suggest transportability of the CDSS to large urban academic health systems, especially among surgical patients. Incorporating natural language processing variables into CDSS tools may aid the identification of barriers to PAC.
... Twenty-eight papers, each evaluating a unique DSS, met the inclusion criteria. Eighteen studies were quasiexperimental [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], six were randomized controlled trial [26][27][28][29][30][31], three were descriptive [32][33][34], and there was one observational study [35]. ...
Chapter
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The use of computerized decision support systems (DSS) in nursing practice is increasing. However, research about who uses DSS, where are they implemented, and how they are linked with standards of nursing is limited. This paper presents evidence on users and settings of DSS implementation, along with specific nursing standards of practice that are facilitated by such DSS. We searched six bibliographic databases using relevant terms and identified 28 studies, each evaluating a unique DSS. Of these, 24 were used by registered nurses and 19 were implemented in short-term care units. Most of the DSS were found to facilitate nursing standards of assessment and intervention, however, outcome identification and evaluation were the least included standards. These findings not only highlight gaps in systems but also offer opportunities for further research development in this area.
... Despite national and local efforts for quality improvement, approximately 1 in 5 HHC patients are hospitalized or visit the emergency department during their HHC episode [7,10]. Up to two-thirds of these hospitalizations occur within the first 2 weeks of HHC services [11][12][13][14][15]. A significant portion of hospitalizations and emergency department visits from HHC may be prevented by timely and appropriately targeted home care services [13,[15][16][17]. ...
Article
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Background: Delayed start-of-care nursing visits in home health care (HHC) can result in negative outcomes, such as hospitalization. No previous studies have investigated why start-of-care HHC nursing visits are delayed, in part because most reasons for delayed visits are documented in free-text HHC nursing notes. Objective: The aims of this study were to (1) develop and test a natural language processing (NLP) algorithm that automatically identifies reasons for delayed visits in HHC free-text clinical notes and (2) describe reasons for delayed visits in a large patient sample. Methods: This study was conducted at the Visiting Nurse Service of New York (VNSNY). We examined data available at the VNSNY on all new episodes of care started in 2019 (N=48,497). An NLP algorithm was developed and tested to automatically identify and classify reasons for delayed visits. Results: The performance of the NLP algorithm was 0.8, 0.75, and 0.77 for precision, recall, and F-score, respectively. A total of one-third of HHC episodes (n=16,244) had delayed start-of-care HHC nursing visits. The most prevalent identified category of reasons for delayed start-of-care nursing visits was no answer at the door or phone (3728/8051, 46.3%), followed by patient/family request to postpone or refuse some HHC services (n=2858, 35.5%), and administrative or scheduling issues (n=1465, 18.2%). In 40% (n=16,244) of HHC episodes, 2 or more reasons were documented. Conclusions: To avoid critical delays in start-of-care nursing visits, HHC organizations might examine and improve ways to effectively address the reasons for delayed visits, using effective interventions, such as educating patients or caregivers on the importance of a timely nursing visit and improving patients' intake procedures.
... A recent study demonstrated that prioritizing clinically complex patients for the start-of-care visit can reduce rehospitalizations by 50%. 15 Other studies have also demonstrated that the timing of home health care services, such as frontloading of visits after admission, are associated with significantly lower rehospitalization rates. 10,14,17,18 The Centers for Medicare & Medicaid Services (CMS) encourages timely visits through a requirement (described in the CMS Conditions of Participation) that states that "the initial assessment visit must be held either within 48 hours of referral, or within 48 hours of the patient's return home, or on the physician-ordered start-of-care date." ...
Article
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Objectives Home health care patients have critical needs requiring timely care following hospital discharge. Although Medicare requires timely start-of-care nursing visits, a significant portion of home health care patients wait longer than 2 days for the first visit. No previous studies investigated the pattern of start-of-care visits or factors associated with their timing. This study's purpose was to examine variation in timing of start-of-care visits and characterize patients with visits later than 2 days postdischarge. Design Retrospective cohort study. Setting/participants Patients admitted to a large, Northeastern US, urban home health care organization during 2019. The study included 48,497 home care episodes for 45,390 individual patients. Measurement We calculated time to start of care from hospital discharge for 2 patient groups: those seen within 2 days vs those seen >2 days postdischarge. We examined patient factors, hospital discharge factors, and timing of start of care using multivariate logistic regression. Results Of 48,497 episodes, 16,251 (33.5%) had a start-of-care nursing visit >2 days after discharge. Increased odds of this time frame were associated with being black or Hispanic and having solely Medicaid insurance. Odds were highest for patients discharged on Fridays, Saturdays, and Mondays. Factors associated with visits within 2 days included surgical wound presence, urinary catheter, pain, 5 or more medications, and intravenous or infusion therapies at home. Conclusions and Implications Findings provide the first publication of clinical and demographic characteristics associated with home health care start-of-care timing and its variation. Further examination is needed, and adjustments to staff scheduling and improved information transfer are 2 suggested interventions to decrease variation.
... Topaz et al suggest that patient prioritization with CDS has the potential to significantly reduce rehospitalizations. 36,37 For example, CDS tools can help nurses identify patients who are deteriorating during the HHC episode and should be prioritized for timely nursing interventions. In addition, CDS tools can help identify patients who experience lack of social support and connect them to social services, such as meals, housing, or financial support. ...
... Coupled with decision support algorithms, agencies would be in a better position to schedule the first visit as well as the rest of the episode. 22,23 Frequency of scheduling could be facilitated with HIT supported HHC updates on clinician appointments external to the agency (eg, physician). Electronic updates would supplant asking the patient or caregiver. ...
Article
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During home health care (HHC) admissions, nurses provide input into decisions regarding the skilled nursing visit frequency and episode duration. This important clinical decision can impact patient outcomes including hospitalization. Episode duration has recently gained greater importance due to the Centers for Medicare and Medicaid Services (CMS) decrease in reimbursable episode length from 60 to 30 days. We examined admissions nurses’ visit pattern decision-making and whether it is influenced by documentation available before and during the first home visit, agency standards, other disciplines being scheduled, and electronic health record (EHR) use. This observational mixed-methods study included admission document analysis, structured interviews, and a think-aloud protocol with 18 nurses from 3 diverse HHC agencies (6 at each) admitting 2 patients each (36 patients). Findings show that prior to entering the home, nurses had an information deficit; they either did not predict the patient’s visit frequency and episode duration or stated them based on experience with similar patients. Following patient interaction in the home, nurses were able to make this decision. Completion of documentation using the EHR did not appear to influence visit pattern decisions. Patient condition and insurance restrictions were influential on both frequency and duration. Given the information deficit at admission, and the delay in visit pattern decision making, we offer health information technology recommendations on electronic communication of structured information, and EHR documentation and decision support.
Article
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and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to identify high-risk patients in home health. This dissertation study aimed to (1) identify factors associated with priority for the first home health nursing visit and (2) to construct and validate a decision support tool for patient prioritization. I recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care coordination to identify factors supporting home health care prioritization. Methods: This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included sociodemographics, diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability, self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the final model. Results: The final model identified five factors associated with first home health visit priority. A cutpoint for decisions on low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid condition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04). Discussion: This dissertation study developed one of the first clinical decision support tools for home health, the "PREVENT"- Priority for Home Health Visit Tool. Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes.
Article
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Abstract Frontloading of skilled nursing visits is one way home health providers have attempted to reduce hospital readmissions among skilled home health patients. Upon review of the frontloading evidence, visit intensity emerged as being closely related. This state of the science presents a critique and synthesis of the published empirical evidence related to frontloading and visit intensity. OVID/Medline, PubMed and Scopus were searched. Seven studies were eligible for inclusion. Further research is required to define frontloading and visit intensity, identify patients most likely to benefit, and to provide a better understanding of how home health agencies can best implement these strategies.
Preventing hospital readmissions is one of the top priorities of the U.S. health care system. This systematic review examined the current evidence about hospital readmissions from home health care (HHC). Literature was searched in PubMed, CINAHL, PsycINFO, Scopus, and Web of Science. Eligible studies were reviewed and evaluated using a validated tool. Eighteen articles were reviewed. Reported readmission rates and risk factors varied dramatically between studies. Reasons for readmissions were understudied. Findings of reviewed studies were limited by small sample sizes, single data source, and methodological flaws. Future studies should use multiple national data sources across patients’ care spectrum and advanced statistical models to identify who among HHC patients are most likely to be readmitted to hospital and for what reason.
Article
Objective: To compare the effectiveness of two "treatments"-early, intensive home health nursing and physician follow-up within a week-versus less intense and later postacute care in reducing readmissions among heart failure (HF) patients discharged to home health care. Data sources: National Medicare administrative, claims, and patient assessment data. Study design: Patients with a full week of potential exposure to the treatments were followed for 30 days to determine exposure status, 30-day all-cause hospital readmission, other health care use, and mortality. An extension of instrumental variables methods for nonlinear statistical models corrects for nonrandom selection of patients into treatment categories. Our instruments are the index hospital's rate of early aftercare for non-HF patients and hospital discharge day of the week. Data extraction methods: All hospitalizations for a HF principal diagnosis with discharge to home health care between July 2009 and June 2010 were identified from source files. Principal findings: Neither treatment by itself has a statistically significant effect on hospital readmission. In combination, however, they reduce the probability of readmission by roughly 8 percentage points (p < .001; confidence interval = -12.3, -4.1). Results are robust to changes in implementation of the nonlinear IV estimator, sample, outcome measure, and length of follow-up. Conclusions: Our results call for closer coordination between home health and medical providers in the clinical management of HF patients immediately after hospital discharge.
Article
Background: Eliciting knowledge from geographically dispersed experts given their time and scheduling constraints, while maintaining anonymity among them, presents multiple challenges. Objectives: Describe an innovative, Internet based method to acquire knowledge from experts regarding patients who need post-acute referrals. Compare, 1) the percentage of patients referred by experts to percentage of patients actually referred by hospital clinicians, 2) experts' referral decisions by disciplines and geographic regions, and 3) most common factors deemed important by discipline. Methods: De-identified case studies, developed from electronic health records (EHR), contained a comprehensive description of 1,496 acute care inpatients. In teams of three, physicians, nurses, social workers, and physical therapists reviewed case studies and assessed the need for post-acute care referrals; Delphi rounds followed when team members did not agree. Generalized estimating equations (GEEs) compared experts' decisions by discipline, region of the country and to the decisions made by study hospital clinicians, adjusting for the repeated observations from each expert and case. Frequencies determined the most common case characteristics chosen as important by the experts. Results: The experts recommended referral for 80% of the cases; the actual discharge disposition of the patients showed referrals for 67%. Experts from the Northeast and Midwest referred 5% more cases than experts from the West. Physicians and nurses referred patients at similar rates while both referred more often than social workers. Differences by discipline were seen in the factors identified as important to the decision. Conclusion: The method for eliciting expert knowledge enabled national dispersed expert clinicians to anonymously review case summaries and make decisions about post-acute care referrals. Having time and a comprehensive case summary may have assisted experts to identify more patients in need of post-acute care than the hospital clinicians. The methodology produced the data needed to develop an expert decision support system for discharge planning.
Book
From the reviews of the First Edition."An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."—Choice"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."—Contemporary Sociology"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."—The StatisticianIn this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.
Article
Hospitalization among older adults receiving skilled home health services continues to be prevalent. Frontloading of skilled nursing visits, defined as providing 60% of the planned skilled nursing visits within the first two weeks of home health episode, is one way home health agencies have attempted to reduce the need for readmission among this chronically ill population. This was a retrospective observational study using data from five Medicare-owned, national assessment and claim databases from 2009. An independent randomized sample of 4500 Medicare-reimbursed home health beneficiaries was included in the analyses. Propensity score analysis was used to reduce known confounding among covariates prior to the application of logistic analysis. Although whether skilled nursing visits were frontloaded or not was not a significant predictor of 30-day hospital readmission (p = 0.977), additional research is needed to refine frontloading and determine the type of patients who are most likely to benefit from it.
Article
This article presents a summary and critique of the published empirical evidence between the years 2002 and 2011 regarding rehospitalization among Medicare-reimbursed, skilled home health recipients. The knowledge gained will be applied to a discussion regarding ACH among geriatric home health recipients and areas for future research. The referenced literature in MEDLINE, PubMed and Cochrane databases was searched using combinations of the following search terms: home care and home health and Medicare combined with acute care hospitalization, rehospitalization, hospitalization, and adverse events and limited to studies conducted in the United States. Twenty-five research studies published in the last eight years investigated hospitalization among patients receiving Medicare-reimbursed, skilled home health. Empirical findings indicate telehomecare can reduce hospitalizations and emergency room use. The identification of risk factors for hospitalization relate to an elder’s sociodemographic, clinical and functional status that can be identified upon admission and interventions taken in order to reduce hospitalizations. Disease management, frontloading nurse visits, the structure of home health services and OBQI are also among the interventions identified to reduce hospitalizations. However, the body of evidence is limited by a paucity of research and the over reliance on small sample sizes. Few published studies have explored methods that effectively reduce hospitalization among Medicare-reimbursed skilled home health recipients. Further research is needed to clarify the most effective ways to structure home health services to maximize benefits and reduce hospitalization among this chronically ill geriatric population.
Article
With the widespread use of health information technologies, there is a growing need to educate healthcare providers on the use of technological innovations. Appropriate health information technology education is critical to ensure quality documentation, patient privacy, and safe healthcare. One promising strategy for educating clinicians is the use of participatory e-learning based on the principles of Web 2.0. However, there is a lack of literature on the practical applications of this training strategy in clinical settings. In this article, we briefly review the theoretical background and published literature on distance education, or e-learning, of health information technology, focusing on electronic health records. Next, we describe one example of a theoretically grounded interactive educational intervention that was implemented to educate nurses on new elements incorporated into the existing electronic health record system. We discuss organizational factors facilitating nurses' in-service education and provide an example of software designed to create interactive e-learning presentations. We also evaluate the results of our educational project and make suggestions for future applications. In conclusion, we suggest four core principles that should guide the construction and implementation of distant education for healthcare practitioners.