10th Annual Utah's Health Services Research Conference - Data from EHRs in Outpatient Practice Settings: An Emerging but Immature Resource. By: Jeff Black
The 10th Annual Utah Health Services Research Conference: Data from EHRs in Outpatient Practice Settings: An Emerging but Immature Resource. By: Deepthi Rajeev and Jeff Black - HealthInsight
Health Services Research Conference: March 16, 2015
Patient Centered Research Methods Core, University of Utah, CCTS
Dr. Douglas Fridsma, Presentación Simposio Salud 2017
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10th Annual Utah's Health Services Research Conference - Data from EHRs in Outpatient Practice Settings: An Emerging but Immature Resource. By: Jeff Black
1. Data from EHRs in Outpatient
Practice Settings: An Emerging
but Immature Resource
Deepthi Rajeev and Jeff Black
March 16th, 2015
10th Annual Utah Health Services
Research Conference
2. Objectives
• Present an overview of the scope of our
collaborations in the community
• Describe real-world challenges in deriving
meaningful information from EHRs of
outpatient practices
• Share lessons learned
• Future implications in the community
3. Who is HealthInsight?
• A private, non-profit, community based
organization dedicated to improving health
and health care in Utah, New Mexico, and
Nevada
• We currently serve as:
– Medicare Quality Innovation Network / Quality
Improvement Organization (UT, NV, NM, OR)
– State External Quality Review Organization (NM)
– Regional Extension Center for HIT (UT, NV)
– NRHI Regional Health Improvement Collaborative
– AHRQ-designated Chartered Value Exchange (UT,
NV)
– RWJ Foundation Aligning Forces for Quality
community (NM)
4. Scope in the Community
• 353 clinics and 1256
providers
• Quality Improvement
• Assistance with
Meaningful Use and
HIT
5. Project Examples
• Beacon cooperative agreement
– In 2010, the Office of the National Coordinator
awarded 17 Beacon communities across the US
– Improve care provided to adult patients with
Diabetes in the Salt Lake MSA
• Quality Improvement Task
– Aligned with the Million Hearts Initiative
– Focused on ABCS: Aspirin therapy, Blood pressure
control, Cholesterol management , Smoking
cessation
6. EMR Systems of Beacon and
Cardiac Participants
22
19
16
7
7
7
5
4
4
4
4
4
2
2
2
2
2
1
1
1
1
1
1
1
1
eClinicalWorks (eClinicalworks, LLC)
Epic Care EHR (Epic, Inc)
Help2 (Intermountain Healthcare)
Amazing Charts (Amazing Charts)
e-MDs (e-MDs, Inc)
PrimeSuite (Greenway)
Vitera Intergy EHR (Vitera Healthcare)
AllScripts Enterprise (AllScripts)
Aprima (Aprima Medical Software)
GE Centricity Enterprise (GE Healthcare)
Other
Practice Partner (McKesson)
AllScripts Professional (AllScripts)
Care360 EHR (MedPlus, Inc)
NextGen EHR (NextGen)
Practice Fusion (Practice Fusion)
Red Planet EHR (ArcSys)
Advanced MD (Advanced MD)
AltaPoint (AltaPoint)
CADURx (CADURx)
MyWay (AllScripts)
Noteworthy (Noteworthy Medical)
Sevocity EHR (Sevocity)
SOAPware (SOAPware, Inc.)
Spring Charts (Spring Medical Systems)
7. 8 Diabetes Quality Measures
• HbA1c screening
• Diabetes in control (HbA1c<8%)
• LDL-C screening
• LDL-C in control (<100mg/dl)
• BP in control (<140/90)
• Medical attention for nephropathy
• Retinal eye exams
• Foot exams
8. 9%
2% 2% 2% 5%
25%
39%
16%
5% 2% 2% 2%
57%
32%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 3 4 5 6 7 8
Number of the 8 Diabetes CQMs Available
Clinic Count by Number of Number
of Diabetes CQMs Available
Clinic Count Initial Clinic Count Final
14. Clinical Workflow: Lessons
Learned
• Improving data documentation critical to
measure calculation and subsequently QI
• Workflow changes may be needed:
– Use of standard templates vary across
clinics using the same EHR system
– Free text (e.g., DM foot exam, DM eye
exam, L/R to denote the side BP was
measured)
– Identification of ‘inactive’ patients and
classification of ‘urgent care’
15. Measures: Lessons Learned-1
• Interpretation of measure specifications
vary across vendors
• Trial and error and reverse
engineering to calculate valid
denominators and numerators
• Custom reporting is usually possible but
complex
– Often doesn’t meet standard measure
specifications (e.g., 2 visits in two years for
diabetes denominators)
16. Measures: Lessons Learned-2
• Measures that are not core ‘MU’: less likely
to be available (e.g., A1c and LDL
screening)
• Vitals based measures more likely to be
available and valid (e.g., BP)
• Measures relying on lab test results require
substantial effort
–Point of care entry for in-house tests and
multiple lab interfaces
17. MU and QI: Lessons Learned
• Most EHR systems (even MU-certified) are
not designed to support analysis for QI and
population management
• MU reports helpful for provider-level data
but limited capability to present data at the
clinic-level
– Clinic level reporting may not be valid due to
duplication of patients among providers
• Burden on clinics to create run charts and
generate patient lists
18. Implications for the Community
• EHRs have the potential to be rich data
sources but important to recognize their
limitations
• Move towards Transparency and
Accountability in healthcare outcomes
– Practices need to assess and improve their
capacity in using EHRs to support
population health and care management
• Growing awareness of the value of
data integration