The fundamentals
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== Background ==
== Background ==
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There
is currently no standard set of patient identifying or demographic data mandated for use to identify patients at the time of service or used for record matching within and across healthcare information systems.
Organizations rely on internal patient access policies and data governance principles to maintain the fidelity of their internal master patient index. The risks and the failure rate of current patient matching algorithms is underrecognized.
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IHE (Integrating the Healthcare Enterprise)has developed two profiles for use by HIE's for querying patient data. These are the Patient Identifier Cross Referencing (PIX)/Patient Demographics Query (PDQ) and Cross-Community Patient Discovery (XCPD). These profiles govern the transmission of a patient’s demographic information and the querying in search of matching patient data at the target organization and the formulation of a response to that query. Yet there
is currently no standard set of patient identifying or demographic data mandated for use to identify patients at the time of service or used for record matching within and across healthcare information systems.
Organizations rely on internal patient access policies and data governance principles to maintain the fidelity of their internal master patient index. The risks and the failure rate of current patient matching algorithms is underrecognized.
With an error rate of less than 8% considered the industry standard, contemporary patient matching algorithms fall well short of the 0.1% error rate advocated by the HIT Standards commitee.<ref name="patient"></ref><ref name="HIT"></ref>
Health Information Management professionals and patient safety advocates have long recognized the importance of strong patient identification methodology. With the promotion of data exchange and interoperability fostered by the ONC’s Meaningful Use incentive program, the need to address the gaps in patient identification and matching has gained increased focus across the HIT industry.
Health Information Management professionals and patient safety advocates have long recognized the importance of strong patient identification methodology. With the promotion of data exchange and interoperability fostered by the ONC’s Meaningful Use incentive program, the need to address the gaps in patient identification and matching has gained increased focus across the HIT industry.
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Many in the healthcare IT industry (but not all<ref name="wheatley">Wheatley V. National Patient Identifier: Why Patient-Matching Technology May be a Better Solution. HISTalk. March 3, 2014. Accessed 4/27/2015. http://histalk2.com/2014/03/03/readers-write-national-patient-identifier-why-patient-matching-technology-may-be-a-better-solution/></ref>) have advocated for the development of the National Patient Health Identification Number (PHIN) as a panacea for the patient matching challenges. The 1996 Health Insurance Portability and Accountability Act (HIPAA) directed Health and Human Services (HHS) to develop a PHIN to support the legislation’s primary goal of portable and private healthcare records. Funding for this HHS work was withdrawn by Congress in 1998. Quashing of PHIN development was precipitated by privacy rights advocates and libertarians concerned about the undue intrusion on the lives of US citizens and the threat of hacking and identity theft.
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Many in the healthcare IT industry
<ref name="rand">Identity Crisis? Approaches to Patient Identification in a National Health Information Network. Rand Corporation. http://www.rand.org/pubs/monographs/MG753.html></ref>
(but not all<ref name="wheatley">Wheatley V. National Patient Identifier: Why Patient-Matching Technology May be a Better Solution. HISTalk. March 3, 2014. Accessed 4/27/2015. http://histalk2.com/2014/03/03/readers-write-national-patient-identifier-why-patient-matching-technology-may-be-a-better-solution/></ref>) have advocated for the development of the National Patient Health Identification Number (PHIN) as a panacea for the patient matching challenges. The 1996 Health Insurance Portability and Accountability Act (
[[
HIPAA
]]
) directed Health and Human Services (HHS) to develop a PHIN to support the legislation’s primary goal of portable and private healthcare records. Funding for this HHS work was withdrawn by Congress in 1998. Quashing of PHIN development was precipitated by privacy rights advocates and libertarians concerned about the undue intrusion on the lives of US citizens and the threat of hacking and identity theft.
In the context of this void, health information exchange organizations, EHR vendors, healthcare delivery organizations, and payers, have developed algorithms, either proprietary or open-source, to perform patient matching. And while patient matching is a cornerstone of contemporary interoperability and data sharing strategies, there is relatively little appreciation for how complex and error-prone the process really is. Moreover, there is insufficient governance or standardization around the data used by these algorithms. In the last year, the ONC convened a patient identification and matching initiative to identify opportunities around this critical area of health information technology.
In the context of this void, health information exchange organizations, EHR vendors, healthcare delivery organizations, and payers, have developed algorithms, either proprietary or open-source, to perform patient matching. And while patient matching is a cornerstone of contemporary interoperability and data sharing strategies, there is relatively little appreciation for how complex and error-prone the process really is. Moreover, there is insufficient governance or standardization around the data used by these algorithms. In the last year, the ONC convened a patient identification and matching initiative to identify opportunities around this critical area of health information technology.
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== The fundamentals ==
== The fundamentals ==
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Record matching algorithms are often embedded into core systems and are largely assumed to be sufficiently accurate. Before the push to exchange data across disparate systems, that assumption may have been safe. But as regional
HIE’s
have grown into national players and as vendors have consolidated their customer base, the volumes of patients and records to be matched has grown exponentially. This growth is challenging algorithms that had heretofore functioned well within a local framework or for tightly fused integrated delivery network.<ref name="landsbach">Landsbach G, Just BH. Five Risky HIE Practices that Threaten Data Integrity. Journal of AHIMA 84, no.11 (November–December 2013): 40-42. http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_037463.hcsp?dDocName=bok1_037463></ref> One integrated delivery network reported 90% matching within individual instances of a vendor EHR but when matching was attempted across the IDN's 17 different instances of the same vednor's EHR, the accuracy of the patient matching dropped to 50 - 60%.<ref name="patient"></ref> Additionally, our increasingly multicultural and mobile society with fluid names and addresses result in any one patient's demographics changing possibly many times over a lifetime.<ref name="HIT"></ref>
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Record matching algorithms are often embedded into core systems and are largely assumed to be sufficiently accurate. Before the push to exchange data across disparate systems, that assumption may have been safe. But as regional
[[HIE]]’s
have grown into national players and as vendors have consolidated their customer base, the volumes of patients and records to be matched has grown exponentially. This growth is challenging algorithms that had heretofore functioned well within a local framework or for tightly fused integrated delivery network.<ref name="landsbach">Landsbach G, Just BH. Five Risky HIE Practices that Threaten Data Integrity. Journal of AHIMA 84, no.11 (November–December 2013): 40-42. http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_037463.hcsp?dDocName=bok1_037463></ref> One integrated delivery network reported 90% matching within individual instances of a vendor EHR but when matching was attempted across the IDN's 17 different instances of the same vednor's EHR, the accuracy of the patient matching dropped to 50 - 60%.<ref name="patient"></ref> Additionally, our increasingly multicultural and mobile society with fluid names and addresses result in any one patient's demographics changing possibly many times over a lifetime.<ref name="HIT"></ref>
In a 2015 opinion piece, the author reports that the Harris County, Texas Hospital District’s database contains medical records for close to 2,500 people named Maria Garcia and 231 of them have the same birth date.<ref name="healthcare">Healthcare Informatics. Why You Should Care About Patient-Matching Algorithms. February 4, 2015. Accessed 4/28/2015. http://www.healthcare-informatics.com/blogs/david-raths/live-ehealth-initiative-why-you-should-care-about-patient-matching-algorithms></ref> This example brings the issue of accuracy in patient matching into focus.
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== How does the accuracy of patient matching algorithms impact on key priorities of
the
US HealthCare reform? ==
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== How does the accuracy of patient matching algorithms impact on key priorities of US HealthCare reform? ==
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===Costs:===
===Costs:===
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Remediation of inaccurate patient matching is costly to healthcare organizations and health information exchanges alike. Most organizations employ teams of staff to work error queues of suspected mismatches. The process to rectify record duplication and erroneous merging is labor-intensive and expensive. Matching errors in one organization can replicate exponentially when those same mismatched records are conveyed via HIE to downstream consumers of the data.
One organization reported
that it costs $60 to remediate one incorrect record match. Exacerbating the problem, while most EHR’s accommodate merging records, many do not have an easily support undoing an incorrect merge making that remediation task particularly costly.
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Remediation of inaccurate patient matching is costly to healthcare organizations and health information exchanges alike. Most organizations employ teams of staff to work error queues of suspected mismatches. The process to rectify record duplication and erroneous merging is labor-intensive and expensive. Matching errors in one organization can replicate exponentially when those same mismatched records are conveyed via HIE to downstream consumers of the data.
Organizations report
that it costs
between
$60
- $90
to remediate one incorrect record match.
<ref name="patient"></ref>
Exacerbating the problem, while most EHR’s accommodate merging records, many do not have an easily support undoing an incorrect merge making that remediation task particularly costly.
== Types of patient matching algorithms and other related terms ==
== Types of patient matching algorithms and other related terms ==
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===
basic algorithms
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===
Basic Algorithms
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:*also called the deterministic matching
:*also called the deterministic matching
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===
intermediate algorithms
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===
Intermediate Algorithms
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:*can incorporate fuzzy logic
:*can incorporate fuzzy logic
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::*example - 1st name “GOERGE” will tentatively match for patients records with the 1st name of “GEORGE”.
::*example - 1st name “GOERGE” will tentatively match for patients records with the 1st name of “GEORGE”.
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=== Advanced
algorithms
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=== Advanced
Algorithms
: ===
:*use probabilistic and mathematical models to determine likelihood of a match
:*use probabilistic and mathematical models to determine likelihood of a match
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*cultural factors influence compilation of children’s last names
*cultural factors influence compilation of children’s last names
*newborns names change on subsequent admissions
*newborns names change on subsequent admissions
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*hyphenated names are problematic
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*hyphenated names
and names with apostrophes
are problematic
and lack consistent formatting
*handling of the middle name field:middle initial versus entire name
*handling of the middle name field:middle initial versus entire name
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*These '''will''' improve matching accuracy but many information systems cannot handle this type of data element in the ADT feed
*These '''will''' improve matching accuracy but many information systems cannot handle this type of data element in the ADT feed
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== Other Areas of Risk Associated with Patient Matching Algorithms: ==
== Other Areas of Risk Associated with Patient Matching Algorithms: ==