Many organizations struggle with outcome measures including identifying relevant measures, finding where the data resides, presenting the data in a meaningful way to different stakeholders and developing processes to isolate problem areas and improve them. One of the biggest barriers is the retrospective nature of most outcome data. This presentation will explain ways to identify, collect and display outcomes that are real-time and actionable.
2. Conflict of Interest
Scott M. Klein, MD, MHSA
Has no real or apparent conflicts of interest to report.
Roni H. Amiel, CIO / CISO / CTO
Has no real or apparent conflicts of interest to report.
3. Agenda
1. Blythedale’s Trek into Real Time, Actionable Outcomes
2. Designing Meaningful Outcome Measures
1. Dashboards vs Scorecards
2. Structure, Process, Outcome
3. Descriptive, Predictive, Proscriptive
3. Achieving/Maintaining Results and the Need to Engage
Clinicians
4. IT/Clinician Partnerships
5. Why Data Warehousing?
6. Real Time Dashboard Examples
4. Learning Objectives
• Explain fundamental concepts of outcome design and
measurement
• Identify key performance measures for the organization
• Create real-time, actionable outcome dashboards
• Illustrate ways to engage clinicians in the use of the dashboards
5. An Introduction of How Benefits
Were Realized for the Value of
Health IT
http://www.himss.org/ValueSuite
Clinician
and IT Staff
Satisfaction
Increased
Higher
Quality
and More
Efficient
Care
Delivery
Enhanced
Communication
and Data
Reporting
6. Our Trek
Joint Commission preparation consultant identified:
"Reassessment and staging of wounds not clearly documented"
7. Action Plan
1. Identifying gaps in wound rounding practice
2. Define “wounds”
3. Distinguish between pressure ulcers and all other wounds
4. Educate MD’s and RN’s
5. Revise Policy and Procedure
6. Monitor process and outcomes
8. Follow Up Tasks
• IT - Wound rounding application:
– Identify in real-time wounds documentation in the EMR
– Push notifications - Notify the staff
– Escalation engine
– Track and complete wound rounding cycle.
– Measure & Report to clinical leadership
• EMR - Build template for documentation by physicians
• Clinical Leadership Team - Define process for consult by wound
nurses
9. Milestones Along the Quality
Measurement Journey
Lloyd in The Healthcare Quality Book, 2008
10. Dashboard vs. Scorecard
• Dashboard – real time / near real time
– Ongoing performance of critical processes
• Lead to organizational success but not the success itself
– Dashboard in Car
• Fuel, Speed, Temperature
• How you get to destination, but not the destination itself
• Scorecard - retrospective
– Record and report past performance not real-time performance
– Outcome measures rather than process measures
– Investment disclaimers
• “Past performance does not guarantee future results…”
11. Donabedian
• Structure = Wound Care Nurses Exist
• Process = Policy on Wound Management, Consults and
Frequency
• Outcomes = % of patients with wounds seen by Wound Team
within policy timeframe
12. Next Steps
• Dashboard shows Outcome
• Can’t intervene on things that already happened
• Want measures to be actionable NOW
• How to use real-time data to facilitate process…
…and change outcomes before they even show up on the dashboard?
• Ask Wayne Gretzky
14. Identifying Key Performance Measures
1. Relevant
2. Reliable
3. Valid
4. Cost-effective
5. Under the control of the provider
6. Precisely defined and specified
7. Interpretable
8. Risk adjusted or stratified
15. Kotter “Leading Change”
1. Establishing a sense of urgency
2. Forming a powerful guiding coalition
3. Creating a vision
4. Communicating the vision
5. Empowering others to act on the vision
6. Planning for and creating short term wins
7. Consolidating improvements and producing still more change
8. Institutionalizing new approaches
Kotter, “Leading Change”, HBR Press, 2012
16. Clinician Engagement
• Shift the culture:
“Punitive” “Opportunity for Improvement”
“Reactive” “Proactive”
“It’s our policy” “It’s best practice”
• Evidenced Based Practice drives the outcomes
• Front line staff are the KEY
17. IT and Clinician Partnership
1. Clinicians and IT need to understand how to frame questions in
a way they each will understand
2. Clinicians come up with variables to study
3. IT identifies where the data resides and how to access,
manipulate and present the data
4. Clinicians and IT work together to find best ways to present the
data in order for clinicians to know “what to do next”
18. Implementing New Systems
1. Analysis of current workflows
2. Training of end users
3. Implementation
4. Post-Implementation Issues
5. Ongoing support and follow-through
6. Messaging to the CEO and Board
19. Data Strategy
• High Value Data Set - Administrative and Clinical
• Data Ingestion – Aggregated, Targeted insight
• Data Normalization – Unique, related and identifiable
• Real-time / Scheduled data capturing from clinical systems.
• Reporting - Push / Pull to Intranet
• Role Based Access / SSO & RFID login at point of care
25. Telling a Story with Dashboards
• Visual vs Raw Data Learners
• Visuals should help tell the story not be the story
• Will front line clinicians know what to do with the information?
26.
27.
28. Pitfalls
• People, Process, Technology
• Scope creep
• Time availability of staff for training
• Time commitment by leadership
• Is the process realistic?
• Are the tools being used? Is there Drift?
• Measuring metrics on the metrics
29.
30. An Summary of How Benefits Were
Realized for the Value of Health IT
http://www.himss.org/ValueSuite
Clinician and IT
Engagement
Led to a Front-
Line Driven
Process
Patients Seen
within 72
Hours
Increased from
61% to 100%
within 2
Quarters
Transparent
and Real
Time Metrics
Drove
Process
Change
31. Questions
• Scott M. Klein, MD, MHSA
• sklein@blythedale.org
• http://www.linkedin.com/in/scottkleinmd
• Roni H. Amiel
• roniamiel1@gmail.com
• https://www.linkedin.com/in/roniamiel
• Blythedale Children’s Hospital
• http://www.blythedale.org
Editor's Notes
(Should we show a sample Dashboard at this point of the presentation?)
Descriptive Analytics, which use data aggregation and data mining techniques to provide insight into the past and answer: “What has happened?”
Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”
Hoyt, RE, Yoshihashi, A, Eds. (2014). Health Informatics: Practical Guide for Healthcare and Information Technology Professionals, Sixth Edition