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TRANSCRIPT
Changing Medical Outcomes with Data Analytics
March 3, 2016
Harun Rashid, VP Global Health Services & CIO Dawn Jamison, Project Director Srinivasan Suresh MD, CMIO Fereshteh Palmer, BSN, MSIS, Analytics Lead
Agenda
• Introduction • World of Big Data • Evolution of Healthcare Analytics • Data Analytics – Success Stories
– Preventing CLABSI – Reducing the Use of Vancomycin – Condition E – Predicting cardiac and respiratory decline – SHARP – Real-time Readmission Prediction – ED monitoring Dashboard
• Value Realized with Health IT • The Future of Data Analytics • Implementation guidelines
Annual Stats
Outpatient visits 1,000,000+
Inpatient visits
20,000
Emergency visits 80,000
Surgeries 25,000+
HIMSS Stage 7
Children’s Hospital of Pittsburgh of UPMC
Hollywood Paradigm: 4 Quadrants
Traditional Hollywood Four Quadrant
Legendary Applied Analytics
Micro Segment
Four Quadrants: 4 groups of 80 million Micro Segment: 80 million groups of 4
Case Study: Godzilla
• $180M production budget
• Built trailer based on analytics
• 10% less in media spend
• Found persuadable points
• Exceeded opening box office estimates by $30M
$525M worldwide box office and substantial home entertainment sales
Growth of Healthcare Analytics Data Warehouse
• The EMR compiles the data
• Government regulation requires the data
• Quality improvement necessitates the data
• Research is enhanced by the data
• Business analysis is accurate and actionable with the data
Healthcare Analytics - Challenges
• Growing demand for advanced business and clinical analytics
• Traditional system design- Unstructured • Lack of integration / interoperability • Real-time information • Extraction of meaningful information • Vendor limitations • Expensive • IT priorities / Staffing (‘data scientist’)
Data Warehouse Technology
Tools: Business Objects, Informatica, Qlikview
Data: Inpatient, Ambulatory, Biomedical, Financial
Uses: Clinical, Quality, and Business reporting use the same technology solutions
Governance
Quality and
Clinical Informatics
Privacy &
Compliance
Requestor
Multi-Disciplinary
Team
Clinical and Quality Impact
Economic Impact
HIPAA Legal
Impact
Regulatory Impact
Impact + Need + Risk = Urgency
Data Analytics – Success Stories
• Avoiding CLABSI • Reducing the use of Vancomycin • Predictive Medicine - ConditionE • Real-time Readmission Prediction – SHARP • Dashboards for clinical value - ED
Preventing CLABSI (Central Line-Associated Blood Stream Infection)
CLABSI is one of the most deadly and expensive hospital associated infections leading to sepsis. It increases the length of hospital stay by up to 10 days.
Goal Decrease the frequency of CLABSI
Outcome 39% decreased frequency 21 less patients each year
Clinical Documentation Procedure Report Accountability Success!
CLABSI – The Financial Outcome
Year Rate Average Cost* Estimated Cost Savings or Loss
Base Year – FY 2012 1.39 (53 infections) $55,000 -
Year 1 – FY 2013 1.47 (57 infections) $55,000 (165,000)
Year 2 – FY 2014 0.879 (32 infections) $55,000 $1,100,000
Year 3 – FY 2015 0.84 (32 infections) $55,000 $1,155,000
Total Savings
$2,090,000
CLABSI Rate per 1000 central line days * Becker’s Hospital Review
Monitoring Vancomycin Usage
Addresses concerns about the serious and permanent side-effects of Vancomycin including damage to hearing and kidneys.
Goal Only use Vancomycin when clinically necessary.
Clinical Documentation
Education Awareness
Report Accountability Success!
Outcome 27.2% decrease
2420 patients
Analyze Use of Vancomycin Name MRN FIN Age Admit Date Disch Date
Medical Service
Order Location
Order Status
Order
18 years 6/20/2015 6/29/2015
Critical Care Medicine PICU Completed caspofungin
PICU Ordered caspofungin
Emergency Dept Holding Ordered vancomycin
Vancomycin Day 3 ASP Note
06/24/2015 01:29:03 PM
No Proven Infection Continue Vancomycin For critically ill patient
No Proven Infection Continue Vancomycin OTHER === monitor serum trough levels closely as clearance is delayed and accumulation may occur
No Proven Infection Continue Vancomycin Per approved treatment guidelines
Caspofungin Day 3 ASP Note
06/24/2015 01:26:10 PM
No proven
Use of Caspofungin alone indicated Critically ill
Use of Caspofungin alone indicated Per protocol
Vancomycin Financial Outcome
Fiscal Year Patients Ordered Decrease Per Patient
Savings Total
Baseline- 2011 2225
2012 1578 647 $4,704 $3,043,210
2013 1749 476 $4,704 $2,238,899
2014 1528 697 $4,704 $3,278,388
Estimated -2015 1618 607 $4,704 $2,855,067
Total Savings $11,415,564
Alert Clinicians using Condition E
Electronic Alert Clinical Deterioration
Time Value of Predictive Tool
(Courtesy: PeraHealth, Charlotte NC)
Condition E - Predictive Early Warning Signs
Goal Avoid readmission to critical care
Clinical Documentation Notification Procedure Clinical Action Success!
Outcome Of 69 patients (in 3 months), only 16 experienced a critical condition
and were transferred to ICU. 76.8% (53 patients) avoided critical condition
Condition E opens a window of opportunity to intervene before the patient arrives to a critical condition on average of 8 hours and 50 minutes in advance. The patient prognosis drastically improves and rate of unplanned ICU admission decreases when action is taken to reverse the declining medical condition.
Projected Savings Based on studies, each *pediatric ICU admission costs $9,150.00 per day and mean length of stay for pediatric ICU is 3.5 days. (Please see the reference below). For each patient for which the application helps prevent ICU admission, hospital saves $32,025.00. From April – July 2015, 53 conditions were avoided for a savings of $1,697,325. Extrapolating that to an entire year, it is expected that there will be a savings of $5,091,975.00. The platform built at Children’s Hospital can easily be replicated to other adult and pediatric hospitals with very little modifications.
http://www.himss.org/ValueSuite
Value Realized with Health IT
3 Year Projection • Predictive Early Warning
• 477 patients • $15 Million
• Vancomycin
• 1780 patients • $8 Million
• CLABSI
• 38 patients • $2.2 Million
• Condition E / SHARP
• TBD
• Medical treatment methods will change • Patients will be better informed • Physician roles could change to more of a consultant
• Integration of wearable technology • Population Health
Future of Data Analytics
Implementation Guidelines
• Culture • Governance
• Vision • Funding
Technology Staff Knowledge
Process Database Organization