Achieving HIM Optimization After Implementing a System

Achieving HIM Optimization After
Implementing a System-Wide EHR
February 26, 2014
John Showalter, MD, MSIS
Chief Health Information Officer
Leigh Williams, CPC, CPHIMS
Director, Revenue Cycle / HIM
DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.
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Conflict of Interest Disclosure
John Showalter, MD, MSIS
Has no real or apparent conflicts of interest to report.
Leigh Williams, CPC, CPHIMS
Has no real or apparent conflicts of interest to report.
© 20142HIMSS
Learning Objectives
• Evaluate methods for performing process improvements within
health information management during an EHR implementation
• Identify metrics to monitor process improvement in revenue cycle
during EHR implementation
• Identify methods for automating chart merges during a Master
Patient Index (MPI) cleanup project
• Explain methods for engaging physicians in revenue cycle
initiatives using health information technology
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University of Mississippi Medical Center
UMMC includes Mississippi’s only Level 1 trauma hospital. With a total
of 722 beds, it is the largest diagnostic, treatment and referral care
system in the state. Inpatient stays total about 29,000 annually with
more than 209,000 outpatient and emergency visits every year. More
than 450 University Physicians providers see approximately 584,000
patients each year in 170 locations in 38 counties.
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EHR Implementation
• Converted from paper to electronic health record (EHR) on June 1, 2012
– Former system did involve scanning some clinical documentation
– There was some discrete clinical data capture
• Epic Systems
• “Big Bang” approach
• Integrated revenue cycle including PB and HB
• Simultaneously integrated the physician ambulatory practice with 17
independently governed medical record systems
– Some were electronic while some practices were wholly on paper
– Had a separate Master Patient Index (MPI) from UMMC
• Also integrated our critical access hospital in Holmes County
– Paper to electronic
– Had a separate MPI
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HIM Department Overview
• In 2011, HIM department had 88 staff in Medical Records covering:
– Incomplete medical records
– Scanning & indexing
– Chart assembly and loose filing
– Chart merges
– Birth certificates and hospital statistics
• Release of Information is outsourced, but managed internally
• Department had 20 in Hospital Coding covering all services
– Inpatient
– Outpatient
– Ambulatory surgery
• Clinical Documentation Improvement (CDI) was in our Quality group
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Stages of EHR Conversion
Go-live
• Months 0 - 3
• Break-fix
• Learning new processes
Stabilization
• Months 4 - 12
• Stabilizing processes and procedures
• Understanding the electronic environment
Optimization
• Months 12 and beyond
• Optimizing and customizing
• Working in the electronic environment
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Key Metrics for “Optimization”
• Medical Records targeted:
– Patient safety initiatives
– Cost savings initiatives
– Culture change and employee engagement
• Hospital Coding targeted:
– Productivity metrics
– Overall DNB reduction and stabilization
– Documentation accessibility in the EHR
• Clinical Documentation Improvement targeted:
– Realigning around ways physicians work
– Real time physician education and support
– Collaboration with Hospital Coding
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Medical Records Initiatives
• Patient safety initiatives
– Master Patient Index (MPI) cleanup
• Cost savings initiatives
– Transition to only partial dictation
• Culture change and employee engagement
– Redefining titles, roles, and expectations
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Patient Safety: MPI Cleanup
• Merged 3 Master Patient Indexes
– Reconciled 135,000 of 140,000 potential duplicates in 12 months
• Export from original systems to delimited text file merged into Epic
• Prioritized potential duplicate reconciliation by:
1. Patients scheduled for upcoming visits
2. Frequent fliers
3. Long-term chronic care patients
4. Everyone else prioritized in reverse chronological order from last visit
• Enabled Epic’s auto-merge system
– Uses a weighted index to predict appropriate matches
– Certain fields like last name must match
– Reconciled 70,000 duplicates when first turned on
– Continues to run as a nightly process
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Transition to Only Partial Dictation
Metric
Pre-Go-Live
Post-Go-Live
Count per month
9,160
1,842
Cost per month
$81,232
$10,556
% without blanks
77.6%
81.2%
Turn around time
98.3%
98.3%*
Scope
Inpatient
Inpatient &
ambulatory
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Significant Cost Savings
• Cost savings of $1.3 Million over the first 18 months post implementation
$90,000.00
$80,000.00
$70,000.00
$60,000.00
$50,000.00
$40,000.00
$30,000.00
$20,000.00
$10,000.00
$0.00
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Culture Change & Employee Engagement
• Redefined roles based on the electronic environment
– Learned from AHIMA’s e-HIM standards
– “HIS Tech” became an “EHR Analyst”
– Market analysis informed salary ranges
• Partnership with physician leadership
– Understanding of physician learning and communications styles
• See one. Do one. Teach one.
– Integrated remote support shifted IT support work to HIM analysts
• Encouraged staff to embrace the technology side of HIM
– Organizational affiliate member of HIMSS
– CAHIMS credential offered to staff
– Frequent webinars focusing on EHR topics for HIM professionals
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Right-Sizing the Staff for an EHR
100.00
FTE Count
80.00
88.00
59.00
60.00
40.00
30.30
24.30
20.00
0.00
18 Months
Before
Go-Live
Month 12
Month 18
Months on EHR
• Net decrease of 63.70 FTEs over 36 months
• Minus 29 FTEs through attrition leading up to the EHR implementation
• Minus 34.70 productive FTEs over the 18 months post implementation
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Operating Expense per
Adjusted Admission
Significant Cost Savings
$150.00
$121.00
$93.32
$100.00
$61.19
$57.55
$50.00
$0.00
18 Months
Before
Go-Live
Month 12
Month 18
Months on EHR
• Net decrease of $50.45 per adjusted admission over 36 months
• Minus $27.68/AA leading up to the EHR implementation
• Minus $35.77/AA over the 18 months post implementation
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Hospital Coding Initiatives
• Productivity metrics
– Rate of coded charts per hour
• Overall DNB reduction and stabilization
– Days of revenue in uncoded accounts post min days
• Documentation accessibility in the EHR
– Customizing the EHR to ease finding information in the record
• Achieved all this through continuous process improvement
– Work queue process redesign
– DNB goal setting and monitoring
– Understanding what is appropriate in an EHR
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Work Queue Process Redesign for
Revenue Optimization
1.
Review the big picture: target high dollar / high volume concerns first
2.
Look at every Work Queue (WQ) impacting that area of concern
3.
Redesign individual WQs
4.
Think about how they work in tandem as a system
5.
State your expected results in numerical terms
6.
Continuously monitor WQs for performance and results
• Fix the right problem
– Are accounts available to coders at the point in the process?
– Are there work flows for addressing problem accounts?
– Does everyone in the revenue cycle understand their responsibilities?
– Are teams using consistent procedures for transferring accounts?
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Uncoded DNB in Millions
EHR
Go-live
Process
Redesign
Months on EHR
18
Number of Uncoded
Outpatient Accounts
Process Redesign at 18 Months:
A New Issue
Process
Redesign
Months on EHR
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Clinical Documentation Improvement
Initiatives
• CDI was integrated into HIM 12 months post go-live
– Emphasizes partnership with coding and revenue cycle
• Realigning around ways physicians work
– Physician engagement strategy
• Real time physician education and support
– ICD-10 focused
• Collaboration with hospital coding
– Allowing post-discharge queries
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CDI Realignment & ICD-10 Focus
• Clinical Documentation Improvement specialists reassigned
– Were organized by hospital floor (had multiple specialties)
– Now are organized by provider team as defined in the EHR
– Abandoned ICD-9 work, focused solely on ICD-10 requirements
• Focus is on elbow-to-elbow support
– Real time interaction during patient care
– Leveraging mobile devices to access the EHR while teaching
– Developing multiple educational modes for ICD-10
• Migrated to EHR-based queries
– Physicians see queries while they are working in the patient record
– Communications facilitated by shared access to the record
– Developed post-discharge queries to enable hospital coding to catch any
that “slip through the cracks” while CDI focuses on education rather than
querying every single chart
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Questions?
Thank You!
John Showalter, MD, MSIS
Chief Health Information Officer
[email protected]
@JohnShowalterMD
Leigh Williams, CPC, CPHIMS
Director, Revenue Cycle / HIM
[email protected]
@leightw
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