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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2

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?

3

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?

Questions?

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