Transcript presentation title goes here

CHiR-AZHQ
Fall 2013 Stakeholder Meeting
Center for Health Information & Research
agenda
• Welcome/Introductions
• Status of CHiR – Bill Riley & Tameka Sama
– existing program
– what’s new & improved
• Project Updates/Results - Bill Johnson
– trauma registry
– physician, use, exchange & evaluation of EMRs
• Q&A
• Remarks
welcome & introductions
•
•
•
•
•
Name
Position
Organization
Brief statement of affiliation with CHiR
Interests going forward
status of
what hasn’t changed
• AZ Board of Regents research center
• Multidisciplinary
• Neutral source of information
• Community resource & tool
• HIPAA compliant environment
data sharing partnership principles
• Voluntary participation
• Business Associate Agreements
• Covered entities retain
ownership of data
• Flexible data submission formats
• Covered entities authorize data
uses/disclosures
• Patients never identified in
research output without
individual consent
Administrative claims data
(ED, inpatient, trauma,
outpatient/office visits)
Banner Health System
Dignity Health: Arizona
John C. Lincoln Health Network
Maricopa Integrated Health System
Mountain Park Health Center
Phoenix Children’s Hospital
Scottsdale Healthcare
HEALTH CARE
WORKFORCE
insurers
Administrative claims
data (ED, inpatient,
outpatient, office visits)
data
sources
medical
boards
labs
Licensing & Survey data
•
AZ Medical Board / AZ Board of
Osteopathic Examiners
•
•
AZ State Board of Nursing
AZ State Board of Pharmacy
Sonora Quest Labs
state
Hospital discharge data
(ED & inpatient)
State Vital Records
(birth/death certificate
info)
data repositories
Health Care Workforce
• Individual level health
encounter data on millions
of Arizona residents
• Geographically-based
• Sub-repositories
• Data sharing partnerships
• Tracks patients across
health care systems and
over time
• Licensing data
– demographics, education,
certifications
• Survey data
– Questions differ across
licensing cycles
– Current physician questions:
HIT
– Current nursing/pharmacy
questions: Employment
status/setting/role
– Physician surveys optional
with licensing applications;
Nursing & pharmacy surveys
required
what’s new, improved &
under development?
vision & objectives
Goals
Vision
• To provide
comprehensive health
care information for
Arizona.
• Develop new business
strategy
• Expand data architecture
– Incorporate clinical data
(EMR/EHR)
• Increase available
services
– CHNA, etc.
• Advance data sharing
partnerships
health professional workforce
• Practicing Physicians (in state only)
– MD -13,508
– DO – 1,700
15,208
• Active Nurses
– RN = 74,637
– LPN = 10,525
– NP = 4,336
89,498
Total
126,869
• Pharmacy
– Pharmacists – 6,179
– Technicians – 15,984
22,163
Source: http://www.azmd.gov/MediaCenter/MediaFactSheet.aspx http://www.azdo.gov/MediaCenter/FactSheet.aspx; Daily Arizona Nursing
Statistics, http://www.azbn.gov/; Arizona State Board of Pharmacy 2011-2012 Annual Report,
http://www.azpharmacy.gov/pdfs/2012%20annual%20report.pdf.
licensed health care organizations
130
Hospitals (short/long term, critical access, psych, federal,
154
Ambulatory surgical centers
1,212
Outpatient treatment centers
children’s)
101
FQHCs
21
Rural health clinics
1,249
Pharmacies
14
Individual health insurers
22
Employer group health plans
Total
2,903
service opportunities
services we provide
Clinical Trials: Subject Availability & Database Development
Community Health Needs Assessments
Community Reports
Consulting
Database Development
Data Requests
Health Outcomes Studies
Health Workforce Supply/Demand
Program Evaluations & Validations
Quality and Performance Assessments
Scholarly Publications
Specialty & Other Projects
community health needs assessments
• Useful for strategic planning, community benefit planning, grant
proposals, and federal reporting (IRS).
• Uses data at the city and/or county level for specified zip codes
• Streamlined Basic Information Report
– community demographics
– community health care facilities & resources
– community health needs (ED/IP use, insurance coverage, injuries,
deaths)
– primary and chronic disease needs (asthma, heart disease,
diabetes, stroke, cancer incidence)
– other health issues (behavioral health, risk factors, births)
• Data Sources: state hospital discharge data and birth/death
records, publicly available data
data requests
• Various requests for data from:
–
–
–
–
–
university researchers & students
community researchers
providers
insurers
others
• Customizable from simple counts of a
particular condition to more complex data sets
for analysis with multiple variables from
multiple sources.
• All HIPAA rules and business associate rules
apply
• Standardized processing fees
health outcomes studies
• Most studied area
• Defined as “research that seeks to
understand the end result of health
care delivery.”
• Our goal: assist the health sector in
reviewing the impact of health care
services and improving the quality of
care provided to consumers.
– multi business associate collaborations
health workforce
• Longitudinal data systems to track the physician,
physician assistant, nursing and pharmacy
workforce in AZ.
• Combines survey questions with license
applications.
• Analyzes and forecasts the supply and demand
for these health professionals
• Analyzes the impact of health information
technology.
• Model developed to assist in policy decisions that
address workforce needs assessments and relate
these to the available training programs in AZ.
current projects
& results
AHCCCS/ASET
HIE Cooperative Agreement
Program
September 2013
Physician Adoption & Ranking of
Electronic Medical Records
2007-2013
Highlights
William G. Johnson, PhD
Professor of Biomedical Informatics
Founder, CHiR
Gevork Harootunian
Senior Statistical Programmer, CHiR
Center for Health Information & Research
Acknowledgements
I would like to gratefully acknowledge the
contributions of Tom Betlach, the Director of
AHCCCS; Lorie Mayer, HIT Director, AHCCCS;
Jenna Jones, the Executive Director of Arizona
Board of Osteopathic Examiners in Medicine
and Surgery and Lisa Wynn, the Executive
Director of the Arizona Medical Board.
The results to be presented today would not have
been possible without their dedicated
cooperation.
Data Collection Methods
• Data collection on physicians began in 1991.
(Extended to nurses and pharmacists in 2007.)
• Survey data are merged with licensing applications.
• Scope of the survey limited by reliance on paper
forms until adoption of electronic survey in March,
2012.
• Data are collected for in-state and out-of-state
physicians. The current report is restricted to instate physicians.
• Physicians with active licenses who are retired,
semi-retired or on leave are excluded from the
study.
Data Collection March 2012 – April 2013
24,476
Total Physicians
13,938
Total Physician
License Renewals
146
Physician States
Cannot Be Identified
4,312
Licensed Physicians
Residing Outside of AZ
9,488
Licensed Physicians
Living in AZ
8,276
Physician Surveys
Received
(87% response rate)
806
Osteopathic
Physicians
7,470
Allopathic Physicians
Source: Arizona Medical Board (AMB), Arizona Board of Osteopathic Examiners (ABOE) Survey and Administrative Data, 2012-2013.
Note: Physicians who responded to the survey as retired or semi-retired/on leave were excluded.
Utilization of
Electronic Medical
Records
Center for Health Information & Research
Electronic Medical Records
• In 2012-2013, approximately 61% of
Arizona physicians who responded to
the survey used some type of
electronic medical record (EMR)
• In 2009-2011, approximately 52% of
physicians used EMRs
• In the 2007-2009 approximately 45% of
physicians used EMRs
Methods of Storing Medical Records 2012-2013 vs. 2007-2009 &
2009-2011
Source: AMB, ABOE Survey Data, 2007-2009; 2009-2011; 2012-2013.
Note: 2007-2009, Respondents who did not identify a method of storing medical records (missing): 390 for 2007-2009 and 2,567 for 2012-2013.
*Data on “EMR alone or in combination” is not mutually exclusive from other categories.
The Persistence of Paper
• Paper records alone declined from
46% to 12%
» BUT
• Paper & scanned images combined
with EMR tripled
– Problem of gradual conversion of files
– Problem of lack of HIEs so practices with
EMRs sharing data on paper/scanned
images
EMR Rates by Type of Practice, 2012-2013 (N = 5,323)
Type of Practice
Rates
Rank
Government Health (VA, Indian Health Service, etc.)
95.80%
1
Hospital or Medical School Physician Group Practice
91.70%
2
Medical School/University/Research Center
91.60%
2
Community or Rural Health Center
91.30%
3
Private Hospital System
87.40%
4
State or County Hospital System
85.90%
5
Physician Owned Group Practice
78.60%
6
Non-Hospital Private Outpatient Facility
76.50%
7
Other
69.20%
8
Physician Owned Solo Practice
53.90%
9
Source: AMB, ABOE Survey Data, 2012-2013.
Note: Rates = % of physicians within each practice type. 1,196 respondents were missing type of practice.
Trends in the Target Population of Physicians without
EMRs by County, 2012-2013 vs. 2007-2009
Location
Apache
Cochise
Coconino
Gila
Graham
Greenlee
La Paz
Maricopa
Mohave
Navajo
Pima
Pinal
Santa Cruz
Yavapai
Yuma
Total
Non- Users of EMRs as a Percent of Physicians
2012-2013
2007-2009
31.3%
17.2%
17.6%
12.5%
12.5%
0%
60.0%
19.6%
20.0%
31.0%
16.5%
14.0%
0%
10.3%
15.6%
17.8%
Source: AMB, ABOE Survey Data, 2012–2013.
47.1%
56.6%
56.8%
67.7%
57.9%
57.9%
66.7%
57.2%
64.1%
52.9%
56.0%
52.1%
77.8%
62.6%
73.3%
57.6%
Summary 2007-2013
• Sources of growth
– Incentives/penalties designed to induce
increases in use of EMRs
– As older physicians retire they are replaced by
cohorts trained in use of EMRs (see med school
rates)
– Decrease in percentage of physicians in solo
practice
Utilization of EMR
Functions
Center for Health Information & Research
Summary Utilization of EMR Functions
100%
100.0%
92.3%
100.0%
100.0%
91.4%
100.0%
100.0%
89.3%
77.9%
75.6%
80%
60%
40%
100.0%
81.5%
47.6%
35.1%
32.5%
30.9%
35.1%
19.7%
20%
0%
Included in EMR
Used by the Respondent
Exchanged with Other Providers
The Exchange Problem
• Most but not all physicians use the functions
included in their EMRs but relatively few who use
the functions also exchange the information with
others.
– Patient care summary is used by approximately 91%
of physicians but only 33% of the physicians
exchange the information.
– The comparable percentages for prescriptions are
89% using the function and 48% exchanging the
information.
– Public health information: 76% utilization and 31%
exchange.
HIE: the next frontier in HIT
• EMR use is not universal but the
upward trend is well established
• Problem: most of the benefits of EMRs
require exchanges among
organizations
• Lack of HIEs and failure of many
attempts show need for solutions
• HINAZ continues to expand
EMR Software Use and
Physician Rankings by
Brand
Center for Health Information & Research
EMR Use by Vendor ≥ 70 Users
Source: AMB, ABOE Survey Data, 2012–2013.
Note: The “Other” vendor includes all vendors contracted with government hospitals/clinics. 2,820 physicians did not
respond to the survey question on vendor name.
EMR Use by Vendor < 70 Users
Source: AMB, ABOE Survey Data, 2012–2013.
Note: 2,820 physicians did not respond to the survey question on vendor name.
EMR Users Rankings of All Vendors:
(1=awful: 5=outstanding)
Weighted Mean
Number of
Physicians
Ease of Use
3.3
4,640
Effect on Physician
Productivity
3.0
4,619
Effect on Staff Productivity
3.1
4,597
Reliability
3.5
4,604
Performance vs. Promise
3.1
4,517
Mean of the Weighted
Means
3.3
--
Criterion
Source: AMB, ABOE Survey Data, 2012–2013.
Note: Physicians practicing in government settings have been excluded from these results.
Summary of Rankings
• Despite widespread complaints about
EMRs, average ratings are either
neutral (3.0) or slightly positive (3.3)
• Rankings by vendor generally cluster
around the overall means with some
notable exceptions
Summary Ranking of Weighted Means by Vendor
(N = 4,599)
Total
Weighted Ease of
Doc
Performance
Total
Productivity Reliability
Use Productivity
vs Promise Respondents
Average
Rank
Allscripts
2.9
3.0
2.7
2.8
3.3
2.8
541
Amazing Charts
3.6
3.8
3.3
3.5
3.8
3.8
59
Aprima
3.1
3.3
2.9
3.2
3.4
3.0
46
Athena Health
3.4
3.6
3.0
3.3
3.9
3.3
122
Cerner
3.0
3.0
2.8
2.8
3.4
2.8
732
CHARTCARE
1.8
2.0
1.0
2.0
2.0
2.0
1
ClaimTrak
2.6
2.8
2.6
2.6
2.5
2.3
22
eClinicalWorks
3.8
4.0
3.6
3.8
4.0
3.8
266
eMDs
3.6
3.7
3.4
3.5
3.8
3.4
80
Epic
3.2
3.3
3.0
3.0
3.6
3.1
323
GE Centricity
3.6
3.7
3.4
3.6
3.8
3.5
122
gloStream
3.8
4.0
3.9
4.0
3.8
3.4
9
GMed
3.7
4.0
3.5
3.8
3.9
3.4
21
Name of
EMR/EHRInState
Summary Ranking of Weighted Means by Vendor
(N = 4,599) (cont.)
Total
Weighted Ease of
Doc
Performance
Total
Productivity Reliability
Use Productivity
vs Promise Respondents
Average
Rank
Greenway Medical
3.4
3.5
3.1
3.4
3.8
3.2
56
HealthPort
4.0
4.0
4.0
4.0
4.0
4.0
1
McKesson
3.0
3.0
2.8
2.9
3.3
2.8
208
Meditech
2.9
2.8
2.7
2.7
3.2
2.8
67
NextGen
2.9
2.9
2.7
2.8
3.2
2.8
374
Noteworthy
3.3
3.3
3.4
3.3
3.6
3.1
29
Office Practicum
3.7
3.8
3.5
3.7
4.0
3.8
26
Practice Fusion
3.7
4.0
3.2
3.3
3.9
3.9
82
Sage
3.3
3.4
3.1
3.4
3.5
3.1
127
SOAPware
3.8
4.0
3.6
3.7
4.0
3.6
22
Sunrise
3.4
3.3
3.4
3.4
3.7
3.2
16
Other
3.4
3.5
3.2
3.2
3.6
3.2
862
Don't Know
3.1
3.2
3.1
3.1
3.3
3.0
385
Average
3.3
3.4
3.1
3.3
3.5
3.2
-Name of
EMR/EHRInState
Source: AMB, ABOE Survey Data, 2012–2013.
Note: Physicians practicing in government settings have been excluded from these results.
Conclusion
• Percentage of physicians with EMRs is higher than
national studies suggest, but much of the difference
is due to difference in sample characteristics.
• Use of EMRs is generally limited to intra-office use
with little electronic exchange of EMR data.
• Biggest obstacle is the absence of networks for the
exchange of EMR data
• Variance among counties is very large with some
rural counties having utilization rates higher than
Maricopa and Pima.
• Many topics in the full report have been omitted
from this presentation
Next Steps
• Current licensing renewal cycle ends in April 2014
• Report on findings 2007-2014
• Create new survey questions to be operational in
April 2014 (suggestions welcome)
– Avoid asking same questions of same respondents
– Emphasize the effects of EMRs on practice
– Emphasize the nature of exchanging EMR
information & barriers to exchange
– Create some separate decision trees for AHCCCS
providers
Revising the Survey
• Organize a set of meetings to solicit suggestions for
the issues to be addressed by the revised survey
(October-November 2013)
• Draft the new survey and circulate among interested
parties for comments (December 2013)
• January –March 2014 Software development & testing
in cooperation with the licensing boards
• April 2014: implement the new survey
• YOU ARE ALL INVITED TO PARTICIPATE IN THE
DEVELOPMENT PROCESS (sign up sheet is being
circulated)
database development and health
outcomes study
Collaborative effort with the 8
Level 1 Trauma Centers and the
Arizona Burn Center to:
Collect and merge trauma data into
a repository to be used for
reference and research as needed
for:
Trauma Registry
• comparing processes and
outcomes
• establishing benchmarks
Sponsor: Arizona Biomedical
Research Commission
2010-2014
•
improving trauma care
• meeting the certification
requirements of the American
College of Surgeons.
Trauma 1 Registry: Falls Mortality
William G. Johnson, PhD
Professor of Biomedical Informatics
Founder, CHiR
Gevork Harootunian
Senior Statistical Programmer, CHiR
Center for Health Information & Research
Acknowledgements
I would like to gratefully acknowledge the contributions
of the participating organizations: AZTRACC, ABRC,
John C. Lincoln, St. Joesph’s, MIHS, and Scottsdale
Trauma.
I would also like to specifically acknowledge the
contributions of Tracey Sotelo, Executive Director of
ABRC, Alicia Mangram, Medical Director of Trauma
Services/Critical Care, JCL; James Dzandu, Trauma
Research Manager, JCL; Michael Corneille, Trauma
Medical Director of Research, JCL and Melissa
Moyer, Trauma Registrar, JCL.
The Trauma 1 project would not have been possible
without their dedicated cooperation.
Overview
• Data collected from JCL, Scottsdale, St. Joseph’s,
and MIHS trauma centers from 2008-2011.
• Patients 60+ with ground level falls are identified
and merged across to the ADHS Discharge and
Vital Stats datasets.
• Trauma level data, demographic data, previous
medical history, and death records will be utilized to
examine factors associated with mortality after falls.
– Relationship of underlying cause of death with fall.
– Nature of the cause of fall: mechanical or medical.
– Predictive factors for mortality.
remarks
contact us
William (Bill) Riley, PhD
Director & Professor
School for the Science of Health Care Delivery
[email protected]
Ph: 602.496.0878
Gevork Harootunian, BS
Senior Statistical Programmer
[email protected]
Ph: 602.496.2008
William G. Johnson, PhD
Professor of Biomedical Informatics &
Founder of CHiR
[email protected]
Ph: 602.516.4241
Tameka Sama, MBA
Senior Coordinator
[email protected]
Ph: 602.496.2009
Center for Health Information & Research
502 E Monroe St, Ste C320
Phoenix, AZ 85004-2430
Web Site: http://chir.asu.edu
Email: [email protected]
Main: 602.496.2021 | Fax: 602.496.2020