PROJECT NAME: Reducing No-Show rates in
Ambulatory Psychiatric Clinic
Institution: University of Texas Health Science Center
Primary Author: Dr. Prashant Gajwani
Secondary Author: Rachna (Priya) Khatri, MBA, MPH
Project Category: Patient Centered Care
Please complete all of the following sections.
Submission is limited to a maximum word count of 1500 (not including text in
graphs).
Overview: Describe 1) where the work was completed (in what type of
department/unit); 2) the reason the change was needed; 3) what
faculty/staff/patient groups were involved, and 4) the alignment to organizational
goals.
UT Health’s Ambulatory Psychiatric Clinic suffers from very high no-show rates
(31%). Patient no-shows compromise patient safety, medication compliance,
quality of care, employee productivity, employee morale, medical education,
revenue generation, and the effective use of emergency and after hour services.
The cost of poor quality is significant. With approximately 800 patient appointments
per month, a 31% no show rate equates to 248 patients who fail to receive
recommended care and 248 appointment slots that are wasted. Because this
project aims to increase the percentage of patients who arrive for their scheduled
appointment in the psychiatry clinic, the project aligns well with the organization’s
clinical, operational, and financial goals.
We started with a target to reduce the patient no-show rate to 20%, and hopefully,
share the knowledge gained in this project to improve no-show rates in other
departments at UT. Dr. Prashant Gajwani, associate professor and vice chairman
of clinical affairs in the Department of Psychiatry and Behavioral Sciences at the
UTHealth Medical School and student in the CS&E course, led this project and
assembled a team of medical faculty, residents, nursing personnel, and front office
staff who were involved at each phase of the project.
Aim Statement (max points 150): Describe the problem that you sought to
address.
To improve patient care and efficiency of the UT ambulatory psychiatric clinic by
reducing no-show rate to 20%.
1
Measures of Success: How did you measure the impact of your proposed
change?
Retrospective data collection for a one and a half week period took place to
establish baseline no-show rate of 31%. At this time, we re-defined operational
definitions to ensure common understanding of a “no-show” versus a
“cancellation.” We counted the number of no-shows and divided that number by
the total number of appointments scheduled to obtain a daily no-show rate. The
total number of appointments scheduled included patient arrivals, same day
cancellations, and no-shows. Several interventions to reduce no-shows were
implemented over a one month period in the month of June. To measure the
impact of our interventions, we collected data daily for one week followed by
surprise data collections one day per week on an ongoing basis to assess
performance.
Use of Quality Tools (max points 250): What quality tools did you use to
identify and monitor progress and solve the problem? Provide sample QI tools,
such as fishbone diagram or process map.
After completing a basic process map, a team of ambulatory service providers
which included residents, medical faculty, therapists, front office staff, and nursing
personnel were assembled to develop a detailed cause and effect diagram.
Cause and Effect Diagram to Understand No-Shows
Patient
Lack of understanding
(importance of appt &
timeliness, health illiteracy)
Illness too severe
Addiction,
unable to obtain
Anxiety,
prescription
Depression, etc
Patients feels better, does
not seek treatment
Finances
No “chemistry”
with MD
Unaware of
options
Expensive
Co-Pay Expensive
Parking
Transportation
No sense of
partnership
Resident changes
each year
issues
Forget
appointment
No reminder,
incorrect contact info
Long Wait
Times
Institutional look
not welcoming
Expensive
Parking Lot
Location hard
to find
Only one
metro line route
Environment
No
directions
Lack of
professionalism
Previous
Pt. Late
Staff
busy
Staff
busy
Need for staff
training/support
Staff forget
to update
cancellations
Busy
Bump
patients
Plans change
at last minute
Staff
High Rate
of No-Shows
No award/penalty
for good/poor attendance
Failure to comply
with no-show
policy (>3)
Prescription
refill policies
Patients show
up for re-fills
New MD
(resident)
every year
Academic
Institution
Lack of
Patient
Education
Lack of
Awareness,
Inconsistent
practices
Policies
&
Procedures
Using the “causes” listed in the cause and effect diagram session, the group
brainstormed and identified several interventions to pilot.
2
To measure our progress, daily and then weekly data were collected and
presented in a control chart (presented on page 5).
Interventions (max points 150 includes points for innovation): What was
your overall improvement plan? How did you implement the proposed change?
Who was involved in implementing the change? How did you communicate the
change to all key stakeholders? What was the timeline for the change? Describe
any features you feel were especially innovative.
Intervention Summary:
The improvement plan included several interventions, which were identified
through brainstorming and best practices research. While Dr. Prashant Gajwani,
the department administrator, Pauline Stapleton, and Priya Khatri (CS&E mentor)
decided on the interventions to be administered, most of the brainstorming
occurred during the latter half of the cause and effect meeting that included
medical faculty, residents, therapists, the department administrator, and the nurse
manager.
The first intervention was the introduction of a “personal” appointment reminder call
one day in advance of patient appointments. While UTHealth utilizes an automated
reminder service that calls patients two days before their appointment, our
intervention is a personalized appointment call one day before the appointment.
We worked with volunteer services to develop a script and schedule for
appointment reminder calls. Each day the department administrator emailed
volunteer services an appointment list. The volunteers made the calls and
annotated the list to identify which patients confirmed, cancelled, or did not pick up
the phone. The updated list was then sent back to the department administrator. In
order for this intervention to be effective, the front office staff needed updated
patient contact information. Therefore, a complementary front office process
change to verify patient contact information at every patient visit was rolled out as
well.
In addition to the appointment reminder calls, some educational practices were
introduced. Dr. Gajwani coached faculty and residents on the importance of
reminding patients to attend follow up appointments. A patient handout detailing
cancellation, no show, prescription refills, and urgent call policies was developed
as well for distribution to new and follow up patients via email or in person.
During the cause and effect diagram session, some residents and physicians
indicated that no shows could be attributed to low patient satisfaction and long wait
times. To better understand patient satisfaction, a short survey was developed and
administered for two weeks. The survey indicated that patients for the most part
are satisfied with the service they receive, but that some would prefer shorter wait
times. While improving customer service is a continuous process, one intervention
developed to ameliorate patient concerns during long waits was hourly RN
rounding in the waiting room to keep track of patients and assuage their concerns.
3
Communication:
Dr. Gajwani took responsibility for updating clinical staff, medical faculty, residents,
and nursing regarding process analysis and interventions. The department
administrator kept the front office informed, and Priya Khatri kept UT Physicians’
COO aware of upcoming changes and performance.
As interventions were rolled out, Priya analyzed and shared performance data with
all stakeholders. If the no-show rate increased, Priya immediately sent an email to
Dr. Gajwani, the department administrator, and volunteer coordinator to problem
solve.
Timeline:
Process changes were slowly implemented in the month of June. Although
solutions were identified at the beginning of June, finalizing action plans took some
time. The first changes, patient appointment calls and the collection of up-to-date
patient contact information began on June 20th, and nurse rounding in the waiting
room began the following week. The group is awaiting legal review of the new
patient information handout, which should be ready for distribution by the end of
August.
Results (max points 250): Include all results, using control charts, graphs or
tables as appropriate.
The no-show rate in the Psychiatric Clinic decreased from 31% to 12%. The
average number of no-shows per day decreased, and the standard deviation of the
number of no-shows per day decreased as well.
Please see performance data and control chart below:
4
Psychiatric Clinic No Show Data-Before Interventions
Day
Date
# No Shows # Cancellations
# Arrived Total # Appointments No Show Rate
Monday
5/21/2012
27
5
61
93
29%
Tuesday
5/22/2012
17
0
58
75
23%
Wednesday
5/23/2012
12
0
32
44
27%
Thursday
5/24/2012
20
0
46
66
30%
Friday
5/25/2012
18
0
19
37
49%
Tuesday
5/29/2012
27
0
45
72
38%
Wednesday
5/30/2012
14
0
37
51
27%
Summary Stats
135
5
298
438
31%
Average
Standard Deviation
Sigma
19.3
5.88
2
Psychiatric Clinic No Show Data-Post Interventions
Day
Date
# No Shows # Cancellations
# Arrived Total # Appointments No Show Rate
Wednesday
6/20/2012
3
0
33
36
8%
Thursday
6/21/2012
3
0
47
50
6%
Friday
6/22/2012
1
0
16
17
6%
Monday
6/25/2012
6
0
56
62
10%
Tuesday
6/26/2012
12
3
44
59
20%
Monday
7/2/2012
7
0
30
37
19%
Monday
7/9/2012
8
0
43
51
16%
Tuesday
7/18/2012
5
0
45
50
10%
Wednesday
7/25/2012
4
0
38
42
10%
Thursday
8/2/2012
3
0
37
40
8%
Summary Stats
52
3
389
444
11.71%
Average
5.2
Standard Deviation
3.19
Sigma
2.69
p Chart: No Show Rates before and after Interventions
0.600
0.500
UCL
# No-Shows
0.400
CL
0.300
0.2743
0.200
LCL
0.1201
0.100
0.0000
0.000
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Summer 2012
5
Revenue Enhancement /Cost Avoidance / Generalizability (max
points 200): What is the revenue enhancement /cost avoidance and/or savings
for your project? Did you implement this project in multiple sites after determining
that your change was successful?
A decrease in patient no-shows equates to an increase in the number of patients
seen. With an average patient charge of $163 year to date and a collection rate of
approximately 53%, each additional patient seen contributes $86 to the bottom
line. With the intervention in place, we saw the average no-shows per day fall from
19.3 to 5.2. The increase in patients seen results in an additional $1,200 in
charges collected over one day. Over one week, the increase in charges collected
would be $6,000, and over an entire year the increase amounts to over $300,000.
Not only did the project interventions increase charges collected, but it enabled
better utilization of medical and office staff.
CS&E mentor, Priya Khatri, and CS&E professor, Dr. Eric Thomas, have begun
discussions to implement the project in General Medicine, Pediatrics and any other
ambulatory service line with high no-show rates. We hope to perform some
analysis and inform and gain buy-in from key stakeholders in the month of August
and roll out interventions in September. UT Physicians’ COO, Andrew Casas, has
expressed interest in spreading the success of this project to other clinics at UTP.
Conclusions and Next Steps: Describe your conclusions drawn from this
project and any recommendations for future work. How does this project align with
organizational goals? Describe, as applicable, how you plan to move ahead with
this project.
Overall, this project proves to UTP that no-shows can be reduced with
concentrated effort and positive teamwork. The effectiveness of personalized
appointment reminder calls cannot be overlooked, and the psychiatry department
agreed to hard wire this process change by assigning responsibility for making
these calls to one of its own staff members.
The project aligns with many organizational goals. First, reduced no-shows
represent an increase in the number of patients receiving treatment in a timely
manner, and this increase translates to an increase in charges collected.
Furthermore, as an academic institution, the number of patients that medical
students, residents, and faculty see influences their educational experience and
research aspirations. High no-shows result in less opportunity for resident learning
and research.
As mentioned before, news of the project’s success has spread throughout UTP,
and the COO has agreed to spread the project to other departments over the next
few months. After achieving positive results in other departments with reduced noshow rates, we will consider more sophisticated management engineering
interventions such as the design of appointment scheduling algorithms that
leverage predicted no-show and cancellation rates to “overbook” and maximize the
number of patients seen.
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