mission, strategy, values - ihi
TRANSCRIPT
10/20/2015
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IHI ExpeditionPutting your Patient Experience Data to Work
Session 2: Understanding Potential Pitfalls and
How to Avoid Data Craziness: Formal Survey Data
October 20, 2015
These presenters have
nothing to disclose
Kevin Little PhDKristine White RN, BSN, MBAAngela Zambeaux
Today’s Host2
Rebecca Goldberg, Project Coordinator, Institute for
Healthcare Improvement (IHI), coordinates multiple
projects focused on increasing value in health care by
improving quality and reducing costs. Currently,
Rebecca’s primary responsibility is coordinating and
hosting IHI’s Expeditions, monthly virtual support
programs focused on specific topic areas. Rebecca is a
recent graduate of Georgetown University in
Washington, D.C., where she obtained her Bachelor of
Science degree in human science with a minor in public
health.
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Expedition Director
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Angela G. Zambeaux, Senior Project Manager, Institute
for Healthcare Improvement (IHI), has managed a wide
variety of IHI projects, including a project funded by the US
Department of Health and Human Services that partnered
with the design and innovation consulting firm IDEO
around shared decision-making and patient-centered
outcomes research, the STAAR (STate Action to Reduce
Avoidable Rehospitalizations) initiative, virtual
programming for office practices, and in-depth quality and
safety assessments for various hospitals and hospital
systems. Prior to joining IHI, Ms. Zambeaux provided
project management support to a small accounting firm
and spent a year in France teaching English to elementary
school students.
Expedition Objectives
At the conclusion of this Expedition, participants will be able to:
List the variety of patient experience data available in your organization
Identify and avoid wasted effort in use of required data
Discuss the use of complaint data for improvement
Place patient stories in context
Define fast and slow feedback and provide examples of when each is appropriate
Explain the role of leaders in interpreting and using data to drive improvement
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Expedition Sessions
Session 1 – Data Sources: What’s Out There and What Do
You Have?
Session 2 – Understanding Potential Pitfalls and How to
Avoid Data Craziness
Session 3 –Using Surveys, Letters, and Complaints as Data
Session 4 –Storytelling: Patient, Clinician, and Staff Stories
Session 5 – Fast and Slow Feedback – Best Practices for
Both
Session 6 – Leadership from Where You Are and Where You
Are Going
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Session Objectives
List the variety of patient experience data
available in your organization (continuation
from Session 1)
Identify and avoid wasted effort in use of
required survey data
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Faculty
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Kevin Little, PhD, Improvement Advisor, Institute for Healthcare
Improvement (IHI), is a statistician specializing in the use of
information to study, understand, and improve system
performance. His experience in application of statistical methods
includes direct work with scientists and engineers in a range of
disciplines. He has also coached improvement teams in a range of
industries. Dr. Little served as Improvement Advisor to the
National Health Disparities Collaboratives from 2001 to 2006, and
to IHI's Hospital Portfolio of projects from 2010 to 2012. Recently,
he has worked on the measurement strategy for the Healthier
Hospitals Initiative and led a pilot to improve physician
communication behaviors.
Faculty
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Kristine K. S. White, RN, BSN, MBA, Principal, Aerate Consulting, and
Co-Founder, Aefina Partners, LLC, previously served in senior leadership
roles at Spectrum Health. Her areas of expertise include leadership and
system design for cultures of excellence and innovation, integrating
innovation practices and skills into organizations, and readying cultures
and organizations to solve problems and identify new tools and
processes for the future. Ms. White has worked with physicians to
increase the effectiveness of physician communication efforts and with
leaders and teams to drive meaningful improvement in the patient and
family experience in organizations of all types and understand and utilize
patient experience data sets. She has also coached senior teams to
strategically focus and prioritize efforts that yield value to patients within
their systems. Ms. White is passionate about integrating patient and
family advisors into the design and evaluation of health care and has
helped many organizations build the infrastructure and processes to do
so. Her aim is to connect leaders and health care teams to a clear
purpose, with measurable and sustainable impact and value to patients
and their families.
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Agenda
Review: Patient Experience Data Sources and Action Period Assignment #1
KL's Measurement Perspective
Jargon Check: Top Box
Survey Data: Top Four Advice to maximize impact and reduce wasted effort
Case Vignette: Using Survey Data to Assess Impact of a Change Guest Speaker Amanda Griffiths
Percentiles, Correlations, How n Matters
Assignment for Session 3
Review: Sources of Patient Experience Data
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Patient Experience Data Overview
No single perfect measure of patient experience
exists
“Good enough” data, multiply sourced, drives
improvement
Focus on things that matter
Understand organizational opportunities AND
team specific opportunities
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Patient experience is multi-faceted
Each data source gives
you one view of patient
experience.
Multiple sources provide
different views and
details, enable you to
build a coherent picture.
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Sources of Patient Experience Data
CAHPS: respecting its influence, understanding its limitations
Press Ganey, NRC Picker, Gallup, Avatar, etc.
Focus groups
Patient Relations
Patient/Family advisors
Billing
Physicians
Safety culture surveys
Staff and provider engagement surveys
“Hot” comments- a gold mine
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Data Source Data TypeDirect or Indirect
Patient Experience
CAHPS surveys (national government-sponsored patient experience surveys in U.S.)
Survey data Direct3rd party formal surveys, linked to common set of
questions across multiple organizations
In-house Comment Cards/Open Ended questions of patients
Staff vitality surveys, safety culture surveys Indirect
Patient/Family AdvisorsFocus groups, conversations
DirectPatients and Families
StaffIndirect
Physicians
Front line process/service performance data Workplace (“Gemba”) data
Indirect/Direct
Rounding observations Indirect
Patient Relations data (grievances, complaints and positive letters)
Admin/Operations data
DirectBilling complaints and issues (U.S.)
Dashboard metrics: LWBS, errors, safety performance etc.
Indirect
A Table to Organize Patient Experience Data
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Review of Action Period Assignment #1
Looking at the survey data at a high level, it is clear that
while the majority of organizations have the data listed,
as we drill down to accessibility, understanding and
summary by senior leaders, and link back to point of
care staff, it is clear that there are opportunities for
improvement.
Most units/departments are reviewing data monthly,
many organizations are also reviewing data monthly but
there are also a large number doing so annually.
For nearly all groups of people, you are reviewing more
than 30 surveys in each time period listed.
Measurement Perspective
Measurement is necessary but only a means to a greater purpose
We focus on formal, designed surveys of patient experience
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STOPDATA
CRAZINESS
Three faces of Measurement
– Research
– Accountability/Judgment
– Improvement
P22
More explanation in Appendix 1
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Informing Ecological Design, LLC • Madison, WI
Lord Kelvin's advice
• "… when you can measure what
you are speaking about, and
express it in numbers, you know
something about it; but when you
cannot measure it, when you
cannot express it in numbers,
your knowledge is of a meager
and unsatisfactory kind…"
• "the more you understand what is
wrong with a figure, the more
valuable that figure becomes.“
Sir William Thomson, Baron Kelvin of Largs
What's "wrong" with government
mandated survey figures?
Low and variable response rate opens door for bias in
estimates
– a challenge for judgment, ok for improvement
Differential responses by demographic groups may
obscure patterns and opportunities
– Patients by payer type (in U.S.!)
– Preferred Language
– Ethnicity
– Comorbidities
Sampling variation: Is 78.6% really different from
81.9%?
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Jargon Check: Top Box
Why does it matter? What are the implications?
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Top Box
For survey data,
“top box” refers
to most positive
choice on a
ordered scale*
*Exception: On the CAHPS survey questions that use a 0 to 10 point scale, top box refers to evaluation of a
service as 9 or 10 out of 10 point scale, with 10 the best score possible.
https://cahpsdatabase.ahrq.gov/CAHPSIDB/Public/Files/Doc4_How_Results_are_Calculated_CG_2014.pdf
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Meaning and Use of "Top Box"
Common way for U.S. government agencies* and 3rd-party vendors to report information
Top-box responses are linked to strong customer loyalty
Allows a simple focus on best service and performance
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*AHRQ, CMS….what about other countries??
Formal Survey Data: Top Four Advice
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CAHPS* survey data:
"Top Four" to Know and Do
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In U.S. *Consumer Assessment of Healthcare Providers and Systems" https://www.cahps.ahrq.gov/ …our
points apply to every other formal patient experience survey data we know
Understand Percentiles
Interpret Correlations
Remember effect of "n"
Plot dots in time order
Case Vignette: Using Formal Survey Data in Improvement
Amanda Griffiths, Patient Experience Leader
UnityPoint Health Trinity
Rock Island, IL
Case Vignette: Using Formal Survey Data in Improvement
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Context for Our Intervention
HCAHPS scores are low
We give good clinical care
Communication isn’t good
Rounding can improve this
6th floor rounding pilot program
Intervention June 2015Communication with Nurses
Responsiveness of Staff
Pain Management
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How should the June 2015 data be interpreted?
Degree of Belief?
11 Month Avg
thru May 2015
June 2015 Point Increase
over 11 mo.
average
%age
increase
Communication with Nurses 70.7 97 26.3 37%
Responsiveness of staff 53.8 82.5 28.7 53.4%
Pain Management 65.2 85.0 19.8 30.4%
Hospital Environment 52.2 68.2 16.0 30.6%
Communication with Doctors 80.5 84.8 4.3 5.2%
Communication about
Medications
51.2 68.8 17.6 34.4%
Percentiles
Why do percentiles matter? What are the implications?
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Why percentiles matter
You get a sense of what other organizations are able to achieve. Can be a shock and motivator.
Survey performance is improving by "everybody" on most measures. 80% Top Box can be relatively poor; interpret raw scores in context.
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Alice running with the Red Queen, just to stay in one place
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https://cahpsdatabase.ahrq.gov/CAHPSIDB/Public/CG/CG_Topscores.aspx#Percentile
accessed 15 October 2015
Excerpt: CAHPS Clinician and Group (Primary Care)
2014 Visit Adult 2.0 Top Box Scores
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https://data.medicare.gov/data/hospital-compare accessed 11 Oct 2015; data
tables updated 8 Oct 2015 for calendar year 2014
Example using a picture: U.S. national HCAHPS 2014 survey results
https://data.medicare.gov/data/hospital-compare accessed 11 Oct 2015; data
tables updated 8 Oct 2015 for calendar year 2014
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https://data.medicare.gov/data/hospital-compare accessed 11 Oct 2015; data
tables updated 8 Oct 2015 for calendar year 2014
A six point change in Top Box % above or below the median translates to a ~40% change in percentile score!
Variability by Service Type 90th Percentile42
Data is based on the Press-Ganey Means and Ranks report for FY11Q4
Survey Type Mean Score
Outpatient Oncology 95.1
Outpatient Services 94.5
Ambulatory Surgery 94.2
Home Health 93.3
Adult Medical Practices 92.9
Pediatric Medical Practice 92.6
NICU 91.5
Urgent Care 91.3
Pediatric Inpatient 89.4
Emergency Services 88.8
Adult Inpatient 88.2
LTACH 87.6
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43Percentiles are increasing over time: HCAHPS example
Based on summary tables 2008-2014,
http://www.hcahpsonline.org/SummaryAnalyses.aspx accessed 10 October 2015
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2014
Wait times in
primary care not
improving;
confounded with
increasing numbers
of providers
reporting CG-
CAHPS
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Correlations
While "correlation does not imply causation", "Causation does not
exist without correlation."
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Why correlations matter
Correlations are a first step to making sense of
relations among multiple survey questions
The lowest score on a panel of questions may not
be strongly associated with overall evaluation
Tackling the lowest score may not be good use of
organization resources
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See the Data Tools Self Assessment for more details about correlation
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What is correlation?
Correlation, based on either scores or ranks, measures strength of association and ranges from 1 (perfect positive linear or rank order relationship) to 0 (no linear or rank relationship) to -1 (perfect negative linear or reverse rank order relationship.)
Here’s a picture that shows some invented data, with the correlation coefficient ranging from 0.96 to 0.55
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Specific Case of General Pattern48
http://circoutcomes.ahajournals.org/content/3/2/188.full.pdf+html accessed 15 Oct 2015
…we found that hospitals that score high on questions such as “skill of nurses (physician),” “how well the nurses (physician) kept you informed,” “amount of attention paid to your special or personal needs,” “how well your pain was controlled,” “the degree to which the hospital staff addressed your emotional needs,” “physician’s concern for your questions and worries,” “time physician spent with you,” and “staff efforts to include you in decisions about your treatment” also tended to score high on patient overall satisfaction. In contrast, there was no association with scoring high on questions concerned with the room (eg, “room temperature and pleasantness of room decor”), meals (eg, “quality of food, temperature of food”), tests (eg, “waiting time for tests or treatment”), and discharge (eg, “speed of discharge process”) and the patient overall
satisfaction score. Moreover, patient satisfaction with nursing care was the most important determinant of patient overall satisfaction,
thus highlighting an important area for further quality improvement efforts and underscoring the role of the entire health care team in the in-hospital treatment of patients with AMI.” (p. 193, emphasis added.)
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Adult Inpatient Correlations
Press Ganey National database – through June 30, 2012
Staff addressed emotional needs .79
Staff sensitivity to inconvenience .78
Teach/instruct self-care, med, treatment .78
Staff attitude toward visitors .74
Nurses kept you informed .73
How well your pain was controlled .69
Skill of physician .67
Room cleanliness .62
Noise level in and around room .52
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http://www.hcahpsonline.org/Files/Report_April_2015_Corrs.pdf
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How n matters
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Why "n" matters
Survey samples have built-in variation, just from
sampling
– This is in addition to the variation that arises from different
experiences of care, the variation you need to control
When you examine formal survey responses for
departments or units within your organization, n can get
"small."
Knowledge of n should inform comparisons, month to
month or across units or providers ("control chart
thinking")
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Web app:https://iecodesign.shinyapps.io/survey_simulator
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Knowledge of the “n effect” should
Dampen or eliminate management cycles of
despair or celebration, based on a single
reported percent.
Cause you to interpret one month unit-level
results (really small n!?) with caution
Help make the case for plotting survey results in
time order
Inspire you to learn and use control charts--see
Provost and Murray (2012)
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Return to UnityPoint Example
For June 2015, 18 surveys were returned in the latest report for a med/surg acute care unit.
One of the questions of the nursing communications composite is "During this hospital stay, how often did nurses listen carefully to you?"
17 of 18 patients responded "Always" (Top Box) = 94.4%.
If the Top Box average for this question for the previous year was 82%, how unusual is it to observe 94.4%?
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How do you interpret these data?Table for 4 Quarters
Chart of Top Box ValuesPhys IDN
surveys
count of
Top BoxTB %
4 2 1 50.0
13 2 2 100.0
16 3 1 33.3
17 4 4 100.0
3 5 5 100.0
5 7 6 85.7
9 8 7 87.5
6 9 7 77.8
11 11 9 81.8
2 11 10 90.9
19 13 11 84.6
14 15 11 73.3
8 21 16 76.2
1 22 18 81.8
7 22 20 90.9
12 23 15 65.2
18 23 18 78.3
15 25 21 84.0
10 27 19 70.4
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Plot survey data in time order
Start with run charts, move on to control charts
57
Why plot data in time order?
Single survey numbers provide very little useful
guidance for improvement
You need time order to make “before and after”
comparisons to assess progress
If “n” is about the same for each survey number
in the series, you can look for striking patterns
over time to signal improvement with run charts
See Perla et al. (2011) for run chart shift and trend “rules” useful for fewer than 20 time periods
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59
n = 20 each month, sampled from adjusted model, 10% sampling fraction
No
change,
points
just
bounce
around!
Plot survey data in time order 60
Collabstart
Baseline median
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61
Compressed
Percentile
scale is
good
news/bad
news.
Do you know
which is
which?
Return to UnityPoint Example
Strengths Enhancements to consider
Showed three survey items plotted in one page
You can show many items plotted on one page; use medians.
Showed 12 monthly values Show 24 months to help you understand seasonal effects
Asked for survey data by month of service not month of survey completion
Annotate chart with notes of interventions
Recorded the number of surveys, month by month
Construct a control chart to account for variation in sample sizes
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To learn more
Perla, R., Provost, L., and Murray, S. (2011), “The run chart: a simple analytical tool for learning from variation in healthcare processes”, BMJ Quality & Safety. 2011 Jan; 20(1):46-51. Note: the shift and trend rules cited are appropriate only for data series no more than 20 points long. See http://www.iecodesign.com/index.php/217-run-charts-in-quality-improvement-work
Provost, L. and Murray, S. The Health Care Data Guide: Learning from Data for Improvement. Jossey-Bass Publishers, 2011, especially chapter 12.
http://www.iecodesign.com/index.php/223-rank-and-funnel-plots
Data Tools Self-Assessment with Answers
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Assignment for Session 3
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Assignment for Session 3
1. Make a simple flow chart (three to five steps) that shows
how formal patient complaints are handled by your
organization.
2. Annotate the flow chart so that it is clear:
who receives the complaints
who reviews the complaints
who determines actions
how the information is stored for analysis and reference
3. Are verbal or written comments that do not rise to the level
of formal complaint handled the same way as formal
complaints? Why or why not?
Appendix 1: Three faces of measurement
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Why are you measuring?
The answer to this question will guide your entire quality
measurement journey!
Improvement?(improving the effectiveness or
efficiency of a process or system)
Accountability Judgment?
(making comparisons; no change focus)
Research?(testing theory and building
new knowledge)
The Three Faces of Performance Measurement
Aspect Improvement Accountability Research
Aim Improvement of care
(efficiency & effectiveness)
Comparison, choice,
performance management
New knowledge
(efficacy)
Methods:
• Test Observability Test are observable No test, evaluate current
performance
Test blinded or controlled
• Bias Accept consistent bias Measure and adjust to
reduce bias
Design to eliminate bias
• Sample Size “Just enough” data, small
sequential samples
Obtain 100% of available,
relevant data
“Just in case” data
• Flexibility of
Hypothesis
Flexible hypotheses,
changes as learning takes
placeNo hypothesis
Fixed hypothesis
(null hypothesis)
• Testing Strategy Sequential tests No tests One large test
• Determining if achange is animprovement
Run charts or Shewhart
control charts
(statistical process control)
No change focus
(maybe compute a percent
change or rank order)
Hypothesis, statistical
tests (t-test, F-test,
chi square, p-values
• Confidentiality ofthe data
Data used only by those
involved with improvement
Data available for public
consumption and review
Research subjects’
identities protected
Reference: Solberg, L., Mosser, G., and McDonald, S. “The Three Faces of Performance Measurement:
Improvement, Accountability and Research” Journal on Quality Improvement vol. 23, no. 3, (March 1997), 135-147.
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Expedition Communications
• All sessions are recorded
• Materials are sent one day in advance
• Listserv address for session communications:
• To add colleagues, email us at [email protected]
69
Session 370
Using Surveys, Letters, and Complaints as DataTuesday, November 3rd, 2015, 1:00-2:00 PM ET
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Thank You!
71
Angela Zambeaux
Rebecca Goldberg
Please let us know if you have any questions or
feedback following today’s Expedition webinar.
Action Period 1 Survey Results
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Survey Results 73
Does your organization have this information?
Yes– No– I'm not sure–
Government-defined patient experience survey data (e.g. CAHPS in the U.S.)
80.00%28
17.14%6
2.86%1
Externally designed and managed patient experience data other than government defined surveys (e.g. Press-Ganey surveys)
52.94%18
38.24%13
8.82%3
Internally designed and managed patient survey data (e.g. 90-day follow up phone call on patient experience)
68.57%24
20.00%7
11.43%4
Formal patient complaints 100.00%35
0.00%0
0.00%0
Patient letters 85.71%30
2.86%1
11.43%4
Leadership rounding/direct observation 71.43%25
17.14%6
11.43%4
Spoken patient complaints or comments
83.33%30
2.78%1
13.89%5
Survey ResultsIs the information organized and accessible to you?
Yes– No– I'm not sure–
Government-defined patient experience survey data (e.g. CAHPS in the U.S.)
83.33%25
6.67%2
10.00%3
Externally designed and managed patient experience data other than government defined surveys (e.g. Press-Ganey surveys)
60.00%15
28.00%7
12.00%3
Internally designed and managed patient survey data (e.g. 90-day follow up phone call on patient experience)
41.94%13
45.16%14
12.90%4
Formal patient complaints 73.53%25
20.59%7
5.88%2
Patient letters 63.64%21
21.21%7
15.15%5
Leadership rounding/direct observation
39.39%13
57.58%19
3.03%1
Spoken patient complaints or comments
61.76%21
26.47%9
11.76%4
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Survey ResultsIs the information summarized and understood by senior leaders?
Yes– No– I'm not sure–
Government-defined patient experience survey data (e.g. CAHPS in the U.S.)
63.33%19
6.67%2
30.00%9
Externally designed and managed patient experience data other than government defined surveys (e.g. Press-Ganey surveys)
48.00%12
28.00%7
24.00%6
Internally designed and managed patient survey data (e.g. 90-day follow up phone call on patient experience)
32.26%10
35.48%11
32.26%10
Formal patient complaints 67.65%23
11.76%4
20.59%7
Patient letters 53.13%17
21.88%7
25.00%8
Leadership rounding/direct observation
50.00%16
25.00%8
25.00%8
Spoken patient complaints or comments
51.52%17
18.18%6
30.30%10
Is the information linked back to front-line staff?
–Yes– No– I'm not sure–
Government-defined patient experience survey data (e.g. CAHPS in the U.S.)
58.06%18
25.81%8
16.13%5
Externally designed and managed patient experience data other than government defined surveys (e.g. Press-Ganey surveys)
46.15%12
34.62%9
19.23%5
Internally designed and managed patient survey data (e.g. 90-day follow up phone call on patient experience)
31.25%10
40.63%13
28.13%9
Formal patient complaints 34.29%12
22.86%8
42.86%15
Patient letters 42.42%14
27.27%9
30.30%10
Leadership rounding/direct observation
42.42%14
30.30%10
27.27%9
Spoken patient complaints or comments
42.42%14
24.24%8
33.33%11
Survey Results
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Additional Data SourcesPress Ganey, Leadership Rounds, looking into a new administration and management patient experience rounding app.
NSQIP data for 30 day patient outcomes after surgery
Community Health Assessment-informs programs and services regarding population health needs.
Patient satisfaction surveys
Discharge phone calls 24-48 hrs post discharge include service issues and names of staff
Patient focus groups run regularly with inpatients on subacute site, information gathered is themed and fed back to frontline teams. Patient stories are also used in a similar way.
We have a Patient Advisory Council that meets every other month who provide us with feedback on "focus areas" and we have consumer board members who also provide feedback.
A Incident-reporting system
Additional mock CAHPS/HOS Survey data
We accept comments (complaints and commendaitons) via a web application from all pts and families - managed similar to patient letters
Automated discharge phone calls to all inpatients
FaceBook comments, local press
The information may be inconsistently shared among front line staff.
78Survey ResultsTime Period: How frequently are these surveys reviewed by the groups/people in the left column?
Annual– Quarterly– Monthly–
Whole organization 29.03%9
22.58%7
48.39%15
Unit or department 9.38%3
15.63%5
75.00%24
Care team or individual provider
7.41%2
29.63%8
62.96%17
1-10– 11-20– 21-30– More than 30–
Whole organization 12.90%4
3.23%1
0.00%0
83.87%26
Unit or department 16.13%5
6.45%2
12.90%4
64.52%20
Care team or individual provider
30.77%8
7.69%2
11.54%3
50.00%13
Typical number of surveys completed and analyzed in each time period