patient identification the challenges facing community hospitals presentation to the bipartisan...
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Patient Identification
The Challenges Facing Community Hospitals
Presentation to theBipartisan Policy Center Collaborative on Health IT and Delivery System ReformMay 16, 2012
Indranil (Neal) Ganguly, CHCIO, FCHIME, FHIMSS
Vice President / CIOCentraState Healthcare SystemFreehold, New Jersey
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Patient Identification
Background & History
A Statement of Fact
How Does It Work?
Why Should We Care?
The Challenge
Is There Any Hope?
What’s Happening?
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About Me
Community Hospital CIO for 13+ years
Vice Chair, CIO StateNet, CHIME
Member, Policy Steering Committee, CHIME
Member, Board of Directors, HIMSS
Past Chair, Public Policy Committee, HIMSS
Active in Advocacy Efforts
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About CentraState
282 bed Community Medical Center
143 bed Skilled Nursing Facility
82 unit Assisted Living Facility
430 unit Continuous Care Retirement Community
500 Board Certified Physicians
Teaching program in Family Medicine
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About CentraState
Voluntary Medical Staff
Private Health Information Exchange Installed
Participate in Regional HIE
Successfully Attested for Stage 1 Meaningful Use
EMRAM Stage 6
2010 & 2011
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Some History
HIPAA requires a unique healthcare identifier for each individual, employer, health plan, and health care provider
NCVHS hearings raise privacy concerns for individual patient identifiers
Appropriations rider prohibits HHS study / leadership for a nationwide patient identity solution
1996
1998
1999
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Some History
Development of National Health Information Network (NHIN) proposed
ARRA Stimulus Bill provides incentives for EMR deployment and health information exchange
ONC requires State HIT plans to address health information exchanges but does not address the UPIN leaving patient matching as only alternative
2004
2009
2010
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A (Problem) Statement
A uniform, standard method of
identifying patients does not exist
in the United States at this time
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A (Problem) Statement
12 years of data from Harris County, TX
3.4 million patients in hospital district’s database
249,213 patients have same first & last name
76,354 patients share both names with 4 others
69,807 pairs share both names and birth date
2,488 patients named Maria Garcia
231 ‘Maria Garcia’s have the same birth date
Source: Houston Chronicle, 4/5/11
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How Does it Work?
Patient Matching Methodologies
Deterministic – Key data must match exactly
Fuzzy Logic – Key data must match established logic
Probabilistic – Key data is weighted and scored
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How Does it Work?
Patient Matching Methodologies
Deterministic
• Rapid Implementation
• Simple calculations
• Relies on accurate and consistent data
Probabilistic
• Complex implementation
• Sophisticated algorithms
• Adjusts for minor data errors
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Why Should We Care?
Patient Safety Implications
Reimbursement Implications
Operational Cost Implications
Privacy Implications
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Why Should We Care?
Patient matching methods are error prone
Types of errors include:
False positives - linking to the wrong records
False negatives - missing the link between a patient and some part of the record
Published analyses have found false-negative error rates of about 8 % in medical databases, trending higher in databases with millions of records.
Identity Crisis : An Examination of the Costs and Benefits of a Unique Patient Identifier for the U.S. Health Care System, RAND Corporation 2008
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• All data elements are not always accurately available
• Data element capture subject to human error in transcription
• Matching methodologies can vary widely between organizations
• HIEs potentially increase spread of errors
The Challenge
Patient Matching Challenges
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• 200,000 total patient visits per year
• Matching accuracy rate approximately 95%+ False Negatives = 4% (0.2 hrs) = 1,600 hrs / yr to correct
+ False Positives = 1% (2.0 hrs) = 2,000 hrs / yr to correct
• No adverse patient impacts reported to date
• Risk of negative impact exists in both cases
The Challenge
A Community Hospital’s Experience
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• False Negatives are considered the ‘lower risk’ error but can yield sub-optimal care since clinicians can not take advantage of existing information
• False Positives are much more difficult to correct and can cause harm by having clinicians rely on inappropriate historical information
The Challenge
A Community Hospital’s Experience
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• Private HIE introduces physician office data
• Error rates not yet known - Physician offices have fewer resources - Errors can rapidly disseminate - Error correction may exceed office capacity to
handle
• Regional HIE further compounds potential issues
The Challenge
Going Beyond the Hospital
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The Challenge
CentraStateHIE (P)
RegionalHIE (P)
CentraState
(D)MD
(D)
MD
ED
(P)
OR
(D)OP
(D)
D = DeterministicP = Probabilistic
MD
(P)
MD
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• High costs of matching – MPI systems costly
• High risks of errors – False + / -
• Lack of patient matching standards makes regional exchange challenging
• Risk of dissemination of erroneous data and costs for correction
• Patients and public poorly educated regarding the benefits of positive identification
Is There Hope?
Challenges for CIOs
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Hospitals have been dealing with the patient
identification challenge for decades. 128
hospitals responded to CHIMEs brief survey
and the following slides highlight the results
What’s Happening?
CHIME Surveyed CIOs
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What’s Happening?
CHIME Surveyed CIOs
Deter
mini
stic
Proba
bilist
ic
Biomet
ric
Unique
Pat
ient I
dent
ifier
Unkno
wn0.0%
20.0%40.0%60.0%
What technologies or strategies does your organization use to match patient
data?
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What’s Happening?
CHIME Surveyed CIOs
False Positive
False Negative
0 10 20 30 40 50 60 70 80
76
68
33
35
14
17
4
5
1
3
above 25 percent21-25 percent15-20 percent8-14 percentLess than 8%
In your experience, approximately what percent of health records have patient data-matching errors?
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What’s Happening?
CHIME Surveyed CIOs
Yes19%
No81%
Has your hospital incurred an adverse event due to a patient
mismatch in the last year?
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What’s Happening?
CHIME Surveyed CIOs
Yes76%
No24%
Are you involved with any local, regional, or national organization(s), including an HIE, who facilitate interoperability
among providers, states and other stakeholders?
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What’s Happening?
CHIME Surveyed CIOs
Deter
mini
stic
Proba
bilist
ic
Biomet
ric
Unique
Pat
ient I
dent
ifier
Oth
er
Unkno
wn0.0%
10.0%
20.0%
30.0%
40.0%
What technologies or strategies does your health information exchange (HIE)
use to match patient data?
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Questions?
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