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10/22/2018
1
Dashboards, Scorecards, & Performance Metrics
Christopher Caspers, MD, FACEP
Chief, Observation Medicine
NYU Langone Health
Associate Professor
NYU School Medicine
President-Elect
Observation Medicine Section
American College Emergency Physicians
Disclosures
• I have no conflicts of interest or disclosures.
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• Define key observation metrics and learn their importance
• Use outcomes data to identify the best delivery model for observation
care
• Learn how new technology can be used to establish surveillance
systems in observation care
• Build dashboards to improve metrics in observation medicine
• Understand how to synchronize protocolized observation care into the
electronic health record (EHR)
Objectives
Key Metrics
• Volume
• Length of Stay
• Conversion Rate
• Recidivism
• Patient satisfaction
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Volume
• The number of patients cared for in the OU, influenced by:
• Inclusion Criteria
• Patients requiring the active management of their condition following the initial ED visit to
determine the need for inpatient admission or discharge
• Exclusion Criteria
• No clear working diagnosis
• No clear management plan
• Acute exacerbation of psychiatric condition
• Acutely altered mental status
• Hemodynamic instability
• Sepsis
• Requirement for nursing evaluation more frequently than every 4 hours
• Agitated, combative or acutely intoxicated patient (may be placed in Observation Services after
clinical sobriety achieved in ED)
Volume
• Monitor by protocol
– Overutilization
– Underutilization
• Evaluate need for new protocol
– ‘General protocol’
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Volume → How many beds?
• Size matters
• Clinical breadth
• Simple vs Complex observation
• Resource justification
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Smaller Units → Less Protocols Relatively Simple Observation
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Protocols at go-live were a best-guess guess of what our patients would be like
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Predictable Daily Ebb and Flow1159pm
Full
overnight
Less full in
early
afternoon
Rapidly
filling in
evening
•OU rapidly fills in
the evening and
overnight
•Majority of
dispositions
occur in the late
morning and
afternoon
1200am
Average Hourly Arrivals/Dispositions
Arrivals decrease -- ‘Down time’ -- Rounding and Dispos -- Arrivals increase.
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Unit Structure
Key Metrics
• Volume
• Length of Stay
• Conversion Rate
• Recidivism
• Patient satisfaction
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Length of Stay
• Therapeutic protocols have longer LOS
• Troubleshoot prolonged obs stays
– Priority testing
– STAT turnaround time
• Diagnostics
• Labs
• Consults
– Intrinsic factors
• Staffing, workflows
• Patient selection, management
Key Metrics
• Volume
• Length of Stay
• Conversion Rate
• Recidivism
• Patient satisfaction
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Conversion Rate
• The percent of patients admitted to inpatient status at the end of OU
care
• Marker of OU efficacy and resource matching
– Goal 15-20%
• Too high: patient selection, workflow issue
• Too low: patient selection, missed opportunities
• Exception:
– Complex observation: trend towards higher rate
Key Metrics
• Volume
• Length of Stay
• Conversion Rate
• Recidivism
• Patient satisfaction
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72-Hour Revisit after ED Obs Discharge:
(16% returned to Obs)
72-hr revisit rate after Obs discharge: 4-5%
30-day return to IP rate after Obs discharge: 3.5%
72 Hour Revisit after ED Obs Discharge: Causes of Revisit
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Key Metrics
• Volume
• Length of Stay
• Conversion Rate
• Recidivism
• Patient satisfaction
Patient Satisfaction is Higher in a Dedicated Obs Unit
8381
94 95
70
75
80
85
90
95
100
15-bed T1 OU 35-bed T1 OU
Patient Satisfaction Scores (Press Ganey)
Type 4 OU Type 1 OU
Patient satisfaction higher
when observation services are
provided in a Type 1 OU setting.
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Patient Satisfaction
Started with all OU discharges. 358 comments
in total.
41 comments were identified as being negative and consequently omitted from qualitative analysis.
116 of the remaining comments contained just a
single word such as “Excellent” or “Good”, just a staff member’s name or
other non-substantive text.
201 of the remaining
positive comments were coded based on theme.
123 comments were in some
way related to hospital staff.
49 comments referenced the staffing overall without
specifying the department. -
“The staff were so caring and kind. I felt very safe.”, “The staff was excellent in every
regard.”
46 comments specifically cited
the nursing staff. – “The nurses were incredible - very
professional and efficient - but comforting
and calm. I had complete confidence in them.”
17 comments cited the
physicians/PAs. – “the PA and the doctor were both
incredible, empathetic and sweet.”
Remaining 11 comments about
staff included those about thosewho drew blood and
performed tests such as CT scans.
78 comments were on their stay overall. - “Can’t think of anything. It’s really first-rate”, “Could not have
been better.”
Dashboards
• Measurement
– Automatic
• Surveillance
– Early recognition
• Key to quality improvement
– PDSA
• Upward and outward management
– Tell your story
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Dashboards:Clinical Performance
High frequency, high
rate
Protocol performance is continuously monitored to ensure desired
outcomes and to identify opportunities for improvement.
Example: Gastrointestinal Bleed Protocol Inpatient Conversion Rate Too High (57%)
Conversion rate for protocol is higher than expected (57%; goal 15-
20%)
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Example: Gastrointestinal Bleed Protocol
• Exclusion criteria: a function of resources, capabilities of unit, desired
outcomes
• GI Bleed Protocol Exclusion Critieria: use data to derive exclusion
criteria, created decision support tools
Gastrointestinal Bleed Protocol – Improved Patient Selection
Conversion rate for protocol corrected and increased volume of
patients on protocol through data-driven inclusion/exclusion criteria.
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Venous Thromboembolism Prophylaxis Guidelines
VTE Prophylaxis Ordering
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CHF Protocol ‘Problem State’
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Metric Performance
Volume 87
Length of Stay 35hrs
Conversion Rate 32%
Diuretic dosing 80%
Pathway launch 46%
Follow-up w/HF Cardiologist 32%
Integrating an Evidence-based Clinical Protocol into the EHR
• Prompt providers to select protocol
• Streamline protocolized care
– Time, clinical parameters, etc
• Incorporate safeguards to prevent readmissions
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Unit-level Dashboard
• Real-time monitoring of patients on unit
• Rapid identification of patient on HF pathway
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CHF Protocol After Pathway Intervention
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Metric Pre Post
Volume 87 62
Length of Stay 35hrs 36hrs
Conversion Rate 32% 17%
Diuretic dosing 80% 85%
Pathway launch 46% 95%
Follow-up w/HF Cardiologist 32% 90%
Thank you!
Christopher Caspers, MD
Chief, Observation Medicine
NYU Langone Health