Journal of Healthcare Management - January/February 2014 - (Page 39)
t H e v alue
of
p atIents ' H andwr Itten c o MMents
on
HcaHps s urveys
ta B l E 2
incidence of Patient Comments and Number of Words by Coded Category
Mean Number of
Words
Number
%
473
80.3
.00
.000
Positive
17
2.9
9.71
7.776
Neutral
53
9.0
3.72
8.977
Negative
28
4.8
12.25
15.253
Mixed
18
3.1
16.50
11.618
No comment
the surveys contained at least one missing response. One way to handle this
problem is to delete all surveys that contain missing data, but as the preceding
example indicates, this option would
eliminate patients who have answered
most or all of the relevant questions in
good faith and we would be deprived of
their responses. The problem was handled instead by employing a multiple
imputation procedure (Graham, 2009)
whereby missing values are imputed for
each case, and thus a complete data set
can be used.
The comment score variable is
not normally distributed, so dummy
codes were created for the analysis,
with no comment coded as zero. Thus,
the estimated coefficients resulting
from the regression analysis effectively
compared the positive, neutral, negative, and mixed codes to the no comment category (Cohen, Cohen, West, &
Aiken, 2002). The HCAHPS data were
aggregated into subscales, resulting in
six composite measures: communication with nurses, communication with
doctors, responsiveness of hospital staff,
SD
pain management, communication
about medicines, and discharge information (Giordano, Elliott, Goldstein,
Lehrman, & Spencer, 2010), along with
two individual items relating to the
hospital environment: clean room/
bathroom and quiet room. These measures were used as quantitative independent variables in a regression analysis.
Ordinary least-squares regression was
performed on the HCAHPS measures
and the comment score dummy-coded
variables. The two dependent variables
were overall hospital rating and intent
to recommend, which correspond to
HCAHPS Questions 21 and 22. Such
attitude and intention variables have
been used as outcome measures in similar analyses performed in a variety of
studies (e.g., Elliott et al., 2012; Otani,
Kurz, Burroughs, & Waterman, 2003;
Otani et al., 2012; Press Ganey, 2009;
Qu et al., 2008; Rathert, May, & Williams, 2011).
Results from our study show that
negative comments significantly affect
the prediction of the two global outcome measures (Table 3). Coefficients
39
Table of Contents for the Digital Edition of Journal of Healthcare Management - January/February 2014
Journal of Healthcare Management - January/February 2014
Contents
Interview With Kenneth R. White, PhD, FACHE, Associate Dean for Strategic Partnerships and Innovation and the University of Virginia Medical Center Professor of Nursing, University of Virginia School of Nursing
Team-Based Care at Mayo Clinic: A Model for ACOs
The Management Springboard: Eight Ways to Launch Your Career as a Healthcare Leader
The Role of a Public–Private Partnership: Translating Science to Improve Cancer Care in the Community Donna M. O’Brien and Arnold D. Kaluzny
The Value of Patients’ Handwritten Comments on HCAHPS Surveys John W. Huppertz and Robert Smith
Can Inbound and Domestic Medical Tourism Improve Your Bottom Line? Identifying the Potential of a U.S. Tourism Market
Success Factors for Strategic Change Initiatives: A Qualitative Study of Healthcare Administrators’ Perspectives
Journal of Healthcare Management - January/February 2014
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