1. Brought to you by
Improving Clinical Outcomes
Through Technology
How Six Sigma and Business Intelligence Support
CAMC Initiatives for Reducing Medication
Reconciliation Failure Rates

2. Thank You to Our Sponsor
The outcomes achieved by the Siemens customers described herein were achieved in the customers’ unique setting. Since
there is no “typical” hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption), there can be no
guarantee that others will achieve the same results.

3. How to Participate
• Submit your questions in the GoToWebinar presentation
window.
• This webinar will be recorded and available for
download a few days after the webinar. The slides will
also be available.

4. About the Panel
Karen Miller, RN, MSN, MBA; Lean Six Sigma Director; Master Black Belt; Charleston Area Medical
Center
Karen Miller has over 20 years’ experience in healthcare administration, program development and project
management with proven expertise in the ability to create strong functional teams for the development and
execution of quality and cost improvement projects. She supervises, manages, coordinates, and integrates
quality initiatives and process improvement activities to facilitate achievement of organizational goals at
Charleston Area Medical Center system using Lean Six Sigma tools, methodologies and philosophy. She
reports to and works closely with the Chief Quality Executive and administrators to effectively initiate and
manage change.
Janet Hickman, R.T. (R),(N)(ARRT), NMTCB; Systems Analyst, Siemens Managed Services
I’ve worked in the healthcare industry for over 38 years and am currently employed by Siemens Managed
Services as a DSS System Analyst. I am based at Charleston Area Medical Center (CAMC) and act as the
DSS team lead. Before working for Siemens, I was employed by CAMC as a Decision Support
Consultant, Laboratory System Analyst, Nuclear Medicine Technologist, and Radiologic Technologist.
Janet M. Kennedy, Moderator
Janet M. Kennedy , CMD is the Director of Marketing and Digital Strategy for Health Vue, a big data analytics
firm working in healthcare. Janet creates and presents webinar and in-person social media training for
healthcare organizations through a partnership with EHR2.0. She is also the host of “Get Social Health” a
podcast about social media for healthcare. Follow her @GetSocialHealth.

5. At the conclusion of this presentation participants should be able to:
 Describe the Define, Measure, Analyze, Improve, and Control
(DMAIC) Six Sigma methodology and benefits
 Describe the key role Healthcare Intelligence (HI) plays in the
measure phase
 Describe how information technology, Six Sigma, and clinical staff
collaborate to develop clinical documentation, work lists, and
education for process changes
 Describe how to develop and use Crystal reports to monitor
improvements and maintain projects in control
Objectives
5

6. Charleston Area Medical Center
 Non-profit, 908-bed, 4-campus
teaching hospital system and
tertiary regional referral center
 Servicing 557,328 mostly rural
population
 550,000+ outpatient visits
 100,000+ ED visits
 40,600+ Cancer Center Visits
 38,000+ inpatient discharges
 5,000+ employees
 600+ physicians
 500+ health professional students
daily
Memorial Hospital
6
General Hospital

7. Charleston Area Medical Center
 Primary Stroke Center of Excellence
 Bariatric Surgery Center of
Excellence
 Heart & Vascular Center of
Excellence
 9,469 cardiac cath procedures
 1,289 open heart bypass procedures
 Level I Trauma Center
 Only free standing Women &
Children’s Hospital in state
 Level III Neonatal Intensive Care Unit
 3,000+ births
Women & Children’s
7
Teays Valley

8. Six Sigma Methodology
DMAIC: To improve any existing product or process
Define Measure Analyze Improve Control
Who are the
customers and
what are their
priorities?
How is the process
performing and how
is it measured?
What are the most
important causes of
the defects?
How do we remove the
causes of the defects?
How can we
maintain the
improvements?
8

9. Six Sigma Methodology
DMADV : To redesign a bad process by improving the
average and variation in the process
Define Measure Analyze Design Verify
Who are the
customers and what
are their priorities?
How is the process
performing and how
is it measured?
What are the most
important causes of the
defects?
How do we design or
redesign a process
with minimal defects?
How do we verify
that the design
meets the goal?
9

10. Allocation of Six Sigma Resources
 8 full time black belts
 Online request for complex problem solving
 Request must include scoped problem statement, aligned
strategic goal, and estimated Return on Investment (ROI)
 Online requests are presented by Vice Presidents at
monthly executive meetings
 Start dates are determined and assigned based on
current workload and strategic priorities
 Chief Information Officer frequent requestor of resources
10

11. Define Phase
 Identify key stakeholders
 Executive sponsor, physician champion, and team
members assigned
 Concise problem statement and business case
 Team defines and prioritizes possible causes of problem
 Brainstorming and fishbone diagrams
 Six Sigma assigned staff contacts Healthcare
Intelligence for data
D M A I C
11

12. Define Example:
Medication Reconciliation
D M A I C
Project Start Date: January 2011
Executive Sponsor: CQO
Project Process Owner: CMO
Physician Champions: Medicine,
Hospitalist, and Information Services
Clinical Directors
Master Black Belt: Karen Miller
Team Members: CPOE Design Team,
Information Services, Transcription
Services
Project Description/Problem Statement:
Discharge medication list defects account for
50% of the total Center for Medicare & Medicaid
Services (CMS) discharge instructions defects.
Project Scope:
Discharge medication reconciliation CMS
indicators:
1. Provider dictated discharge summary
medications match discharge meds ordered
2. Med list given to patient match physician
discharge meds ordered
3. Documentation med list given to patientAlignment:
Strategic Vision Pillar: Best place to
receive patient centered care
Strategic Goal: Evidenced Based Care/
CMS Reliability
TJC standards: CMS Congestive Heart
Failure (CHF) discharge instruction
indicator
What is the project business case?
Patient Safety: Incomplete discharge medication
reconciliation contributes to readmissions,
patient mortality and morbidity.
Financial: Part of Meaningful Use criteria for
potential $6 million.
12

13. Define the Current Process
D M A I C
Physician uses Home &
Current Medication Order
form that prints from Soarian®
to order meds
Key Takeaway: Defining the
current process identifies the
data elements required for the
measure phase and is the
basis for discussion with
Healthcare Intelligence
resource staff
Physician uses home med and
clarification lists to order meds
by circling continue or
discontinue?
Physician only uses physician
blank order form and writes
discharge meds?
Physician uses Med
Administration Check™ to
identify inpatient meds?
Nurse adds, deletes, and revises
discharge med list based on
discharge orders from multiple
forms
Physician dictates
discharge summary up to
30 days past discharge
Physician uses multiple
forms to dictate
medications in summary 13

14. Measure Phase
 Develop Excel document for data elements needed for the
Analyze Phase
 Meet with Healthcare Intelligence resource to explain the
project business case and expected outcomes
 Suggestions made for additional data elements
 Many of the data elements can usually be supplied by Decision
Support
 Revise data collection plan and send to Healthcare
Intelligence resource
Key Takeaway: Often I don’t know all data elements
needed or available until my project team member asks
“would you want to know this”?
D M A I C
14

15. Healthcare Intelligence
Resource
 Compares data in Soarian® and Data Warehouse for
availability of each data element
 Helps team to import new Soarian data into warehouse if needed
 Validates data by comparing warehouse data to Soarian
 Sends draft data to Six Sigma
 Six Sigma reviews data with team members and
collaborates if revisions are needed
 Approval received by Six Sigma to develop report
 Final report formatted in Excel and sent to Six Sigma for
statistical analysis
D M A I C
Key Takeaway: Frequent communication and
collaboration required to meet customer needs 15

16. Analyze Phase
 Warehouse Excel data copied to statistical software for data
analysis
 Data elements supplied by warehouse allow for
segmentation
 Segments targeted in the improvement phase for cycles of
change
 Physician department, nurse department, time, admission source,
documenter names, etc.
 Example: 72% of defects were physician discharge summary
dictation of med lists and 46% of those were Hospitalists
D M A I C
Key Takeaway: Healthcare Intelligence data helps identify
root causes of defects
16

17. Improve/Design Phase
 Process mapping of future state and potential failure
identification
 Identify pros/cons for different options
 Potential failure mitigation strategies using electronic
documentation and workflows
 Identify education needs for process changes
 Implement pilots for tests of change
 Meet with BI people to develop data for analysis of pilots
 Spread of successful improvements/design
D M A D V
D M A I C
17

18. Improve/Design Outcomes
 Dictated provider discharge summary medication lists
matching the discharge orders is now at 2.68% defects
compared to 34.21% with baseline data, which represents
a 92% decrease in defects.
 Initially targeted Hospitalists and spread success
D M A D V
D M A I C
18
10-13
07-13
04-13
01-13
10-12
07-12
04-12
01-12
10-11
07-11
11-10
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Audit Date
Proportion
_
P=0.0268
UCL=0.0893
LCL=0
Baseline
Hospitalists
All Physicians
Automation
Tests performed with unequal sample sizes
Provider Discharge Med Reconciliation Defects by Stage
Electronic DC Instructions

19. Improve/Design Outcomes
 Decrease in provider dictated discharge summary med
defects resulted in improvement of discharge med
reconciliation CMS compliance
D M A D V
D M A I C
19

20. Control/Verify Phase
 Development of monitoring reports to obtain outcome
data
 Example: Physician CHF discharge summary Web Publishing
report
 Discharge department, status, account number, patient
name, admission and discharge date/time, discharge med list
date/time, person completing med list, discharge summary
date/time, physician completing, attending physician at
discharge
 Development of control plan
 Handoff to operations
D M A I C
D M A D V
20

21. Implementation of Electronic
Medication Collection, Admission &
Discharge Reconciliation
 Address physician concerns about home medication
collection
 Address defects from nursing transcription of discharge
med orders to the patient discharge med list
 Executives chartered team consisting of physicians,
nurses, pharmacists, IS, and Six Sigma
 Defined the problem
 Measured the current process for med collection and admission
reconciliation by completing process mapping
 Analyzed risks
D M A D V
D M A I C
21

22. Improve/Design Phase:
Electronic Medication
Reconciliation Implementation
 Multidisciplinary team collaboration
 Future state process mapping
 Risk identification and mitigation strategy
 28 process issues identified for mitigation
 21 of the 28 were new process issues for mitigation
 Pilot on large medicine nursing department
 Feedback and redesign of process
 Education prioritization based on risks
D M A D V
D M A I C
22

23. Future State Process Risks:
Electronic Discharge Med
Reconciliation
Risk: Med reconciliation
not completed.
Solution: Alert when
discharge order placed.
Risk: Duplicate inpatient and
home med ordered.
Solution: Default by drug sort.
Risk: Chooses inpatient home med instead
of “house icon” med – prints on med list to
stop home med and then start same med.
Solution: Default by drug sort “house "first.
Risk: Provider adds new med
under Soarian orders instead of
Discharge Med Reconciliation
Solution: Education, monitoring, &
feedback.
Risk: Meds added in “complete”
status – how will nurse know when to
print? Solution: Nurse alert if DMR
changed after placed in a “complete”
status.
D M A D V
D M A I C
23

24. Risk Mitigation
Implementation Strategies
 Training for nurses, physicians, pharmacists
 Mandatory computer based training
 Whole house adoption on a single day
 Pre loading inpatients home med list on the morning of
the conversion
 Two weeks of live support including Six Sigma staff
 Job Instructional Training (JIT) competency validation within one
month of go-live
 Development of 13 Single Point Lessons (SPL) placed at work
stations for visual aids
D M A D V
D M A I C
25

25. DMR Single Point Lesson D M A D V
D M A I C
26

26. Control/Verify Example
 Developed home med collection entry tips for high frequency problems
associated with expansive Soarian drop down menus (51% OTC meds)
 Web Publishing reports for daily and weekly nurse and individual provider
defect monitoring for performance management
 Cycles of analysis and improvements implemented from home med list
collection report
 50% of patients admitted through EDs
 58% of ED home med lists had missing required med components
 Implemented LPN home med collection in EDs resulting in 3-5%
missing required med components, representing a 131%
improvement
 Weekly data patient file for CHF chart audits and monthly control charts for
CMS medication compliance sent to administration
D M A I C
D M A D V
27

27. Web Publishing Reports
Daily report for home med list
completion and completion
within 4 hours of admission
percentages
Daily report for home meds
missing required components that
need corrections before discharge
reconciliation
Weekly report for identification of
nurses using free text and entry of
home meds with missing components
for follow up
Weekly report for physician
completion rates for discharge
medication reconciliation between
8:00 a.m. – 5:00 p.m.
D M A I C
D M A D V
28

28. D M A I C
D M A D V
Patient Home Med List
Key Takeaway: Executive patient data view for
medications entered on the home medication list
% patients with completed status
Home Med List (HML)
% patients with free text meds on
HML
% patients with a required med
component missing on HML
(excludes free text)
% meds with required med
component missing on HML
29

29. Patient Home Med List
Key Takeaway: Drill down to department level data to
identify patients currently in the department that need
home med list corrections
D M A I C
D M A D V
30

30. Patient Home Med List
Key Takeaway: Drill down to patient to identify home med
lists that need correction for hospitalization continuity of
care and prep for discharge reconciliation
D M A I C
D M A D V
31

31. Patient Home Med List
Patient location nurse dept and
location HML collected
Missing required components on
HML
Key Takeaway: Drill down to patient med components
that need corrected
D M A I C
D M A D V
32

32. Home Med Collection
Key Takeaway: Executive view for home med list
collection performance management
D M A I C
D M A D V
33

33. Home Med Collection
Key Takeaway: Drill down to department then to
nurse/provider data for performance management
D M A I C
D M A D V
34

34. % patients with HML not completed
Key Takeaway: Drill down to department then to nurse
data for timeliness performance management
Home List Completion
% patients with HML not completed
within four hours of admission
D M A I C
D M A D V
35

35. Provider Discharge
Reconciliation
Key Takeaway: Executive view of provider completion
rates
% with Discharge Med
Reconciliation (DMR) in complete
status and any status
% initial DMR entry by physician or
midlevel
% last DMR entry by physician or
midlevel
% initial DMR entry by Privately
Employed RN (PERN)
% final DMR entry by Privately
Employed RN (PERN)
% initial DMR entry by staff RN% final DMR entry by staff RN% final DMR entry by staff RN
5:00 p.m. – 8:00 a.m.
D M A I C
D M A D V
36

36. Provider Discharge
Reconciliation
Key Takeaway: Medical Affairs and medical staff
leadership view for provider completion rates
D M A I C
D M A D V
37

37. Provider Discharge
Reconciliation
Key Takeaway: Drill down to physician for identification of
top performers, performance management and
credentialing
D M A I C
D M A D V
38

38. Six Sigma and Healthcare
Intelligence Projects
 Nursing admission
assessment
 Skin assessment/actions
 Fall assessment/actions
 Severe sepsis
 Telemetry classifications
 ED wait times and boarder
hours
 Patient discharge call
backs
 Glucose value < 60
 Consult orders
 Mortality
 Complications and
comorbidities
 Readmissions
 DRG physician detail
 Case mix index
 ALOS by nursing
department
Key Takeaway: 71 current Web Publishing reports
being used for Six Sigma projects 39

39. Healthcare Intelligence
Resources Benefits
 Access to100% of patient population
 Eliminate manual data abstraction for key elements
 Decrease measure phase time
 Improve analysis for critical variables
 Sustain gains after improvements
Key Takeaway: Collaborating with BI team member
improves efficiency & effectiveness in all project phases
I ask the impossible and Healthcare Intelligence
delivers! I simply couldn’t do my job without them!
40

40. Six Sigma Benefits
$-
$20,000,000
$40,000,000
$60,000,000
$80,000,000
$100,000,000
$120,000,000
$140,000,000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Financial Impact
2001 - 2012
Cumulative Total Annual Improvements
Key Takeaway: $132,603,856 over 11 year period with
$13 million in expenses. Net ROI of $119,603,856.
41

41. Questions ?
42