1
Pursuing
High Value
Healthcare
Optimizing Laboratory Testing
Learning Session 3
April 2, 2015
Central Vermont Hospital Conference Center
Few Reminders
2
 Please silence your cell phones, pagers, etc
 Restroom location
 Collaborative Materials

Agenda

Worksheets
Morning Agenda
3
 Welcome and Collaborative Timeline
 Team Reports
 Break
 Research Findings- Optimizing Laboratory Testing
 PDSA- Putting Ideas into Action
 Lunch
Afternoon Agenda
4
 Data Update

NORC Data Upload

Report Reviews

Data Comparisons
 Team Working Session

-Design at least one PDSA to implement

-Determine Process Measures and create a data collection plan

-Discuss potential barriers
 Team Report Out from Working Session
 Next Steps
Welcome
5
ALLEN REPP
Kick-Off
Week 1
Collaborative Timeline
Sept 11, 2PM
Pre-work
5-6 weeks
Learning
Session 1
Oct. 22
8:30 to 3:30
Continuous Coaching/Faculty Support
Conference
Call /
Webinar
Nov 6, 2PM
Conference
Call /
Webinar
Jan. 8, 2PM
Conference
Call /
Webinar
Dec 4, 2PM
Learning
Session 4
Jun. 4 DHMC
8:30 to 3:30
6
Learning
Session 2
Feb. 12 UVM
8:30 to 3:30
Conference
Call /
Webinar
May 7, 2PM
Conference
Call /
Webinar
Mar 5, 2PM
Learning
Session 3
Apr. 2 CVH
8:30 to 3:30
Our Vision
7
 Continuous Quality Improvement
 State-wide learning, sharing & support system
 Patient Centered Improvement Efforts
 Foundation: Common Tools & Techniques

Quality Improvement Tools

Model for Improvement
 Common Lab Improvements & Site Specific Improvements
Our First Collaborative: Global Aim
8
We aim to reduce harm to patients and conserve system resources by optimizing the use of
laboratory tests for patients cared for in our region’s hospitals.
We will use a collaborative approach considering the best medical evidence and quality
improvement science.
It begins with an evaluation of current test ordering profiles and patterns followed by an
organized plan to optimize testing and ends with a plan to sustain these practices.
By doing this we expect to reduce cost and improve satisfaction and quality of care for patients
and the health system.
It is important to work on this now because as health care professionals we can play an
important role in health care reform by designing more patient-centered, efficient and high
value inpatient care.
Optimizing
Laboratory
Testing
Collaborative
Team Reports
Learning Session 3
April 2, 2015
Optimizing
Laboratory
Testing
Collaborative
Our Team Report
Team: UVMHN CVMC
Learning Session 3
April 2, 2015
Our Team
 Team Members – List Names and Institutional Roles:
 Don Weinberg, MD
 Kristin Calcagni, Lab
 Judy Perdue, Lab
 Justin Stinnett-Donnelly, MD
 Kevin Knapp, IT
 Tommie Murray, QM
 Team Meeting Frequency and members who attend:
 Monthly meetings
Our Data
 Status of Baseline Data Query:
 Run Please see next Slide
 Specific Institutional Aim(s) and other data reviewed to support your
aims: (Please use the AIM template language on the QI Monitoring
Worksheet)




Decrease the number of laboratory draws / day
Patients will not getting woken up at 6am with a needle
Reduce the possibility of anemia
Reducing patient pain and discomfort
2013-01
2013-02
2013-03
2013-04
2013-05
2013-06
2013-07
2013-08
2013-09
2013-10
2013-11
2013-12
2014-01
2014-02
2014-03
2014-04
2014-05
2014-06
2014-07
2014-08
2014-09
2014-10
2014-11
2014-12
2015-01
2015-02
# Labs / 1000 Pt Days
1400.0
1200.0
1000.0
800.0
1000
800
600.0
600
400.0
400
200.0
200
0.0
0
Patient Days
Baseline Data 1/2013 – 2/2015 (# tests/1000 pt days)
1600
1400
Patient Days
Average All Labs
1200
ALB
ALK
BILT
BUN
CA
CaIo
CL
CRE1
HCT
HGB
K
MCV
MG
600
2013-01
2013-02
2013-03
2013-04
2013-05
2013-06
2013-07
2013-08
2013-09
2013-10
2013-11
2013-12
2014-01
2014-02
2014-03
2014-04
2014-05
2014-06
2014-07
2014-08
2014-09
2014-10
2014-11
2014-12
2015-01
2015-02
2015-03
# Tests / 1000 Pt Days
1200
1000
800
1000
-5 stDev
800
400
600
400
0.3 stDev
200
200
0
0
# Patient Days
CVMC March Data
1600
1400
-2.2 stDev
1200
Patient Days
WBC
WBC Avg
BUN
BUN Avg
MG
MG Avg
+2SD
-2SD
Our Ideas/Theories for Change
 Change Ideas/Theories to meet Specific Institutional Aim:
 Modification of standardized admission orders to eliminate recurrent orders for CBC,
BMP and CMP
 Continue ability for MDs to add recurrent labs when clinically indicated
 Action Plan and Timeline for each Change Idea:
 March 2, 2015
Our Next Steps
 Data review monthly
 Calculation of total numbers of each test / patient / day
Optimizing
Laboratory
Testing
Collaborative
Our Team Report
Brattleboro Memorial Hospital
Learning Session 3
April 2, 2015
Our Team
 Team Members – List Names and Institutional Roles:
Aida Avdic – Hospitalist
 A. Shams Helminski – Hospitalist
 Michele Rowland, Charmaine Winton – Quality
 Frank Field , Doreen Lincoln, John Farina – IT
 Carolyn Allen - Laboratory

 Team Meeting Frequency and members who attend:
Monthly meetings
 Email exchange

Our Data
 Status of Baseline Data Query:

Extracts completed – data uploaded to NORC this week
 Specific Institutional Aim(s) and other data reviewed to support your
aims: (Please use the AIM template language on the QI Monitoring
Worksheet)
Minimizing routine morning laboratories (CBC and BMP) for patients admitted within 24
hours (evening and night admissions)
 Optimizing CBC ordering for stable patients (every 48-72 hours)

Our Ideas/Theories for Change
 Change Ideas/Theories to meet Specific Institutional Aim:
Hospitalist meeting (monthly update and reminders)
 Limitations of the electronic health record (no easy way to build in reminders or ‘hard
stops’)
 Effect of shift change / service change

 Action Plan and Timeline for each Change Idea:
Test period March and April
 Reminders on Hospitalist work stations

Our Next Steps
 Data analysis and comparison of pre / post intervention
 Awaiting data for March
 Feedback to Hospitalists
 Other tests to address?
Optimizing
Laboratory
Testing
Collaborative
Our Team Report
The University of Vermont Medical
Center
Learning Session 3
April 2, 2015
Our Team
 Team Members :






Allen Repp MD, Chief, Primary Care Internal Medicine
Mark Fung MD, Medical Director Lab and Pathology
Mark Pasanen MD, Program Director, Internal Medicine Hospitalist
Program and Internal Medicine Residency Program
Jill Warrington MD, Pathology
Lauren Pearson MD, Pathology Resident
Maria Burnett MD, Medicine Resident






• Meeting Frequency:
•
•
Standing weekly meeting
Meeting approximately 1x/month since our last collaborative session.
Work accomplished in sub-groups
Trace Barrett MD, Medicine Resident
Steven Jarzembowski MD, Medicine Resident
LeAnna Burgess MD, Medicine Resident
Melissa Holman RHIA, CHDA, Senior Measurement Analyst, Jeffords
Institute for Quality
Allison Kaigle Holm PhD, Senior Research Specialist, Jeffords Institute
for Quality
Heidi Guevin RN, Quality Improvement Consultant, Jeffords Institute
for Quality
Our Data
 Status of Baseline Data Query:
Preliminary data retrieved for 2013
 Required baseline data accessed from UVMMC shared drive

• Specific Institutional Aim(s) and other data reviewed to support your
aims:
Decrease unnecessary venipunctures and blood loss
 Retrieved and reviewed All Service Medicine Admissions with standing daily lab orders

• Family Practice, Medicine, PCIM
Our Ideas/Theories for Change
 Change Ideas/Theories to meet Specific Institutional Aim:









Education for healthcare providers
Modify admission order sets with daily labs
Modify “a la carte” orders with daily option
Patient lab needs discussed on rounds with rounding tool/checklist
Lab orders to identify last three results
Lab orders to identify costs of labs
Hard stop to indicate/identify need for daily lab order
Eliminate lab testing on day of discharge
Incentives for not ordering labs
 Action Plan and Timeline for each Change Idea:
 MD Education- presentations at education forums: 2/2, 2/4, 3/3
 Modify Gen Med/Family Med Admission order sets: PRISM re-engaged, meet 4-6-15
 Modify Admission order sets from other services
 Modify “a la carte” lab orders: TBD
 Resident (medicine/pathology) Survey surrounding ordering practices-draft 4-2-15
 Chart Audit Tool- draft 4-6-15
Optimizing
Laboratory
Testing
Collaborative
Our Team Report
Porter Hospital
Learning Session 3
April 2, 2015
Our Team
 Team Members – List Names and Institutional Roles:
Amber Bailey, Information Technology
Marianne Collins, Quality Director
David Rand, Hospitalist
Julie Vest, Lab Director
Rebecca Woods, Information Technology Director
 Team Meeting Frequency and members who attend:
We will meet as a team to review our data in the next week or two
Our Data
 Status of Baseline Data Query:
Data submitted. Looking forward to the results
 Specific Institutional Aim(s) and other data reviewed to support your
aims:
Create a QI culture
Decrease blood draws, patient discomfort, anemia
Our Ideas/Theories for Change
 Change Ideas/Theories to meet Specific Institutional Aim:
-Decrease frequency of reflex platelet checks when on lovenox
-Cease phlebotomy practice of from drawing extra “hold” tubes
….More to come once data is reviewed
 Action Plan and Timeline for each Change Idea:
-Not yet defined
Our Next Steps
 Review data
 Invite all stakeholders in the next two weeks to meet
Optimizing
Laboratory
Testing
Collaborative
Our Team Report
NVRH Lab Collaborative
Learning Session 3
April 2, 2015
Our Team
• Team Members – List Names and Institutional Roles:
• Michael Rousse, MD, MPH
Hospitalist Director
•
Bonnie Torres, MLS (ASCP)
Director of Laboratory Services
•
Andrea Dinneen, MBA, MPH
CIO, VP Information Services
•
Jim Coulson
Infection Control Officer
•
Ryan Cloutier
Application Support Analyst
 Team Meeting Frequency and members who attends:

Currently developing team strategy
Our Data
 Status of Baseline Data Query:
 All Baseline Data has been collected and uploaded to NORC.
 Specific Institutional Aim(s) and other data reviewed to support your
aims:

Reduce the number of unnecessary labs
Target CBC/CBCD, BMP/CMP, Albumin, and Troponins (w and w/o reflex testing)
 Study
 Number of Tests, Number of Tests per Avg Daily Census, Number of Tests per Patient per
day.
 Compare the total Number of Phlebotomies, Avg Daily Census, Avg LOS, and Annual
Discharges for 18 yo+.

Our Ideas/Theories for Change
 Change Ideas/Theories to meet Specific Institutional Aim:
 Develop a safe method to reduce unnecessary labs as identified by our data

Possible Solutions
 Modify Workflows
 Modify Order Sets
 Incorporate Order Level Decision Support
 Etc.
 Action Plan and Timeline for each Change Idea:
 TBD after receiving initial results from data analysis
Our Next Steps
 Work with the Collaborative’s Data Analysts
 Begin collecting monthly data
 Finalize Team Strategy
 Begin developing strategies to decrease the unnecessary labs
Am J Clin Pathol 143:393-397
March 2015 issue
Presentation
By
Mark Fung, MD PHD
Univ of Vermont Medical Center
Study Design
 2 month intervention in general medicine units at 400 bed hospital
 Educational flyers in offices
 Periodic email reminders of using daily lab tests only if results changed




patient care
2 month pre-intervention data (982 patients)
2 month post-intervention data (988 patients)
Measured # of daily blood tests/patient/day
Included CBC, BMP, CMP, PT/INR, PTT
Messaging
 Interactive educational sessions with providers and nurses
 Discussions at division meetings and noon conferences
 Educational flyers in provider and nurse work areas
 Weekly e-mail communication to all providers and nurses
3 Consistent Points Covered
Providers should:
 Question the utility of very blood test and order only if result will affect
patient care
 Think about the impact that costs of blood tests have on health care
expenditures
 Consider “adding on” test to blood samples already collected whenever
possible
Results
Am J Clin Pathol 143:393-397
Pros and Cons to their approach
Pros
 Low cost intervention – talks, flyers, emails, awareness
 Did not compromise provider work flow or efficiency
Con
 Unknown how much of the improvements will persist long term
http://onlinelibrary.wiley.com/doi/10.1002/jhm.2354/abstract
(in press)
Study Design – QI intervention
 53 providers across 4 inpatient facilities
 Focused on common labs ordered by a large community hospitalist
group
 Academic detailing, audit, feedback, and transparent reporting of
performance
 Pre-analysis was 10 month baseline (n=7824)
 7 month intervention period (n=5759)
Messaging
Introductory email recommending 2 changes:
 Immediate stop to practice of open-ended ordering of common labs as
daily
 Assessing the need for common labs in the next 24 hours, and ordering
based on that need, but no further into the future
Messaging (continued)
 Providers were informed that number of common labs ordered daily
would be monitored prospectively
 Monthly reports given to individual providers
 Top 5 hospitalists with highest frequently ordered labs sent a personal
e-mail notification of their “top five” status
Messaging (continued)
Monthly report included:
 Reminder of the recommendations and reasoning in the original e-mail
 List of all members and their frequency of lab ordering as daily (open
ended) per month
 Recommendation to discontinue daily ordering of common labs
 At least one example of a patient with 5 days of daily labs with no
mention in the progress notes
Limitations of this study
 Patient characteristics were statistically different between pre-analysis
and intervention period (but small)
 Choosing Wisely Report was made public during same time of start of
intervention period
 Baseline and Intervention time periods were not seasonally matched.
Quality Improvement Process
Global Aim
3
Measure(s)
Change
Ideas
Step 8- Test Your Changes
Step 7 –Determine Process Measures
Success
Measure
2
SDSA
1
3
2
1
PDSA
Step 6- Brainstorm Change Ideas
Specific Aim
Global Aim
Theme
Step 5 –Determine Cause and Effects
Step 4- Create Specific Aim
Step 3- Create Global Aim
We are here!
Step 2- Get Baseline Data
Assessment
Step 1- Form Team
52
Modified from Quality By Design, A Clinical Microsystems Approach
Nelson, Batalden and Godfrey© Trustees of Dartmouth College
Model for Improvement
53
1. What are we trying to
accomplish?
2. How will we know that a change
is an improvement?
3. What changes can we make that
will result in an improvement?
Avoid this turning
into a viscous
cycle.
Small short term
tests of change.
TAKE ACTION!
Setting
Aims
Establishing
Measures
Selecting
Changes
Testing
Changes
Important Things to Know about the Model for Improvement
• Can be used for most types of changes
• Focus on small tests of change
• New tests are based on what you learn
• Approach should not create chaos
• Only spread to others when you are ready
Why Test?
• Increase your belief (and others!) that the
change will result in improvement
• Document how much improvement can be
expected from the change
• Understand the effects – upstream &
downstream
• Minimize resistance to change
Tips for Testing Changes
•
•
•
•
•
•
Team approach (interdisciplinary)
Understand where you are today – baseline
Do not try to get buy-in, consensus, etc.
Strive for Ownership not buy-in!
Test with engaged volunteers
Scale down size of test
•
# of patients, location, # of providers
• Run tests in a small period of time
• Test over a wide range of conditions
• Different days, ages, genders, locations
Small RAPID Tests of Change
Act
• Adopt, adapt or abandon
based on what was learned
• Build knowledge into next
PDSA cycle
Study
Plan
• State objectives
• Make predictions
• Who will do what by when
Do
• Complete analysis •Carry out the test
• Compare data to prediction •Document problems,
• Summaries what surprises, observations
was learned
Reminder: Balanced Cycles
Too Much
Planning
Act
Do
Too Much
Studying
Act
Act Plan
PLAN
Study
Too Much
Doing
Plan
Study
DO
STUDY
Do
Too Much
Acting
ACT
Plan
Too Much
Studying
Study Do
Reminder: Push Yourself & Your Team
 If you think you can test it
in…




1 Day……..few hours
2 Weeks….few days
1 Month….1 week
1 Year……wrong model!
Form Team-Rutland Regional Hospital
Step 1- Form Team
Team Members Name
Role
Team Members Name
Joe Walker
Role
Project Admin and
Lab Lead
Dr. Rick Hildebrant
MD Lead
Wendy Bixby
Admin Support Sherry Ravlin
IT Data Mgr
Deborah Roy
IT Project Mgr
Daniel Michel
IT Report Writer
Rob Mcginness
IT Interface
Elizabeth Mahar-Kyhill
Nursing Lead
Dr. Susan Blish
MD Alternate
Angela Murphy
Performance
Improvement/QM
Assessment
Step 2- Baseline Data- Inpatients > 18 yrs. of age calendar yr. 2013
Baseline Data
Hemo
Lytes
# of Tests
#Tests/Avg. Daily Census
# Tests/Patient Day
Total Inpatient Lab Tests
# Annual Inpatient Phlebs
AVG Daily Census
AVG Length of Stay (LOS)
Annual # of discharges 18yr+
EMR Based Order Protocols?
BUN
Creat
PTT
TBD
Institutional Aim
Step 3- Create Institutional Aim
We aim: To improve the quality and value of the care we provide to our patients by ensuring we order
the appropriate laboratory tests during their stay.
In/at: Rutland Regional Medical Center
The process begins with: Analyzing how our laboratory ordering and collection data compares to the
rest of the collaborative.
The process ends with: A workflow to minimize unnecessary laboratory testing.
By working on the process, we expect: To reduce unnecessary testing and phlebotomy and to improve
patient satisfaction with their care.
It is important to work on this now because: We have an opportunity to improve the quality and value
of the care we provide to our patients.
Specific Aim
Step 4 –Create Specific Aim
We will: Decrease
30% of the contracted hospitalists will reduce the percentage of day 2 CBC’s
ordered on patients with a normal admission CBC for COPD.
From: our current value to be provided by the collaborative
To: less than X% of the current value
By: June 1, 2005
Fishbone/Cause and Effect Diagram
Step 5- Determine Cause and Effect
Equipment
People
Resident vs attending
Daily Labs Tab in EHR
Central Line Draws
Why do we
have so many
repeat HCT’s on
HCT Stable
patients?
Can’t see previous Lab
Ordering Process
Rounding Process
Patient/Family Request
Order Sets
EHR
Way we have always done it
Process
Environment
Report Monitoring
Report Monitoring
Change Ideas
Step 6- Brainstorm Change Ideas
List Possible Change Ideas:
Plan
Step 7- Determine Process Measures/Plan
What will be
Measured
Who will Measure?
How will it be
measured?
When
Plan
Step 7- Determine Process Measures/Plan
What will be
Measured
Who will Measure?
How will it be
measured?
When
DO
Step 8- Test Your Change
Cycle 1
Date: February 16, 2015
What
Who
How
When
Study
What was learned?
What will be changed?
ACT
Cycle 2
What
What was learned?
What will be changed?
Date: February 23, 2015
Who
How
When
Lunch!!
DATA UPDATE
Data
Definitions
 Patient
 Patient Days: (“date/time discharge – date/time admit”)/24
 Patient Stays
 Hospital Days: 0 (admission), 1, 2, 3, 4, 5, ….
Data
Initial Measures: Admissions
 Number of Patient Days: sum((“date/time discharge – date/time admit”)/24)
 Number of Unique Patient Stays
 Number of Unique Patients
 Length of Stay: average, standard deviation, median, 25th & 75 percentiles
 15 Most Frequent DRGs
Data
Initial Measures: Labs
 Number of Unique Date/Time Collections
 Total Number of Date/Time Collections
 15 Most Frequent Lab Test Codes
Data
Initial Measures: Refining (HELP!)
 What DRGs do we want to exclude?




775 vaginal delivery w/o complications
765 cesarean section with complications
766 cesarean section w/o complications
others?
 What Lab Test Codes should we focus on?




collaborative vs. institution specific
distinguishing lab panels vs. individual tests – is there a need?
do we know what these test codes mean?
are these all blood tests?
Data
Pending Measures
 Distribution of Hospital Days




Day 0 (day of admission)
Day 1 (starts 12am after day of admission)
Day 2
And so on….
 Number of Unique Date/Time Collections by Hospital Day




Day 0
Day 1
Day discharge
Day discharge -1, etc
Data
Pending Measures
 Sequential Date/Time Collections

Do patients get the same labs every day?
 Normal lab results


How can we define if a value is within a standard normal range?
If you have two normal values in a row, what is the probability the third draw will be abnormal?
 Stable lab results



If values are not within a normal range, how we can tell if there is change over time?
Delta checks
Focus on the abnormal values
Data
Collaborative Comparison Measure
 The number of unique date/time collections per patient day
# Unique date/time collections in a given month
Total Number of Patients Days in a given month
Data
Collaborative Comparison Measure
 The number of unique date/time collections per patient day
 UVM MC: 25,223/8,667.08 = 2.91 (Oct 2014)
 CVMC: 931/1,568.21=0.59 (Oct 2014)
 RRMC: 2,305/2,687.31= 0.86 (Oct 2014)
 PMC: 101,422/25,755.15=3.94 (All Baseline)
 NVRH: 13,804/14,232.37=0.97 (All Baseline)
 BMH: 557/471=1.18 (Dec 2014)
 Bennington: pending
 DHMC: pending
Working Session
83
ALL TEAMS
&
ORGANIZATIONS
Supported Team Working Session
84
 Review Tracking Tool & Action Plan
 Review Institutional/Specific Aims
 Create at least one PDSA to implement
 Determine Process Measures and data collection plan
 Consider possible barriers
Team Report Outs
85
 PDSA Plan
 Process Measures
 Data Collection plan
 Barriers


What might limit your ability to change?
What support will you need?
86
 We wanted to make it easy for teams across institutions
to easily share documents with each other


Protected, but not difficult to access
Admin approval needed before documents can be seen
 Created a section on the website where team members
can upload, and download files



Each institution has its own username and password
Used across the institution
Will provide username and passwords to team leaders
87
 How do you use the Shared Team Document




function?
Start at VMSFoundation.org
Click on the Optimizing Laboratory Testing
Collaborative image (home for all
Collaborative information)
Click on Shared Team Documents
Option to Upload or Download files, make
choice and enter username and password
88
Uploading a file:
File Title - Give it a name
File Description - Summary paragraph
Click Browse and then select the file on your
computer
Click upload
Categorize document
1.
2.
3.
4.
5.


6.
Institution: Select your institution
Document Category: Data, Interventions, Measurement, Other,
References
Click preview and/or save
It will not appear in the database immediately. An
admin will have to approve. Five of us, so should
happen quickly.
89
Downloading a file:
Click Download Documents
1.
Documents are sorted by Document
Category and Project
2.
Click parameters to sort
3.
Click on desired file name and then apply
to download.
Next Steps
 Keep meeting with your team!
 Determine frequency time and location of future meetings- Schedule them!
 Collect and/or Validate Baseline data
 Finalize your site specific improvement aims
 Finalize your change ideas
 Finalize your plans to measure your success
 Plan your PDSA cycles
 Check out our website

Work in progress

VMSfoundation.org: click on High Value Care for Vermonters box
 We will be contacting you to set up a time for one of us to join a team meeting at
your location.