patient linking

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Patient Linking Hearing – Allscripts – Richard Elmore

U.S. Department of Health and Human Services Office of the National Coordinator for Health Information Technology

HIT Policy Committee Privacy & Security Tiger Team

Patient Linking Hearing Thursday, December 9, 2010

Allscripts

Written Public Testimony

Richard ElmoreVice President, Strategic Initiatives

Introduction

To Deven, Paul and the Privacy & Security Tiger Team members – thank you for

the opportunity to participate in in this vitally important hearing.

Allscripts provides electronic health records and revenue cycle managementsystems – both inpatient and ambulatory - as well as analytics, ePrescribing,

financial and clinical transaction services, clinical trials, care management, EDand home health software and services to over 180,000 physicians, 1,500

hospitals, and many thousands of post-acute care organizations across the

country. In working to best serve our clients and help advance our collectivegoal of improving health for the nation’s citizens, Allscripts promotes a vision of a

connected community of health built on this foundation. As you can imagine,with a client base this size, we do a lot of matching of individuals across

heterogeneous platforms. Identity management and matching are critical toAllscripts.

A Clarification on Matching / Linking

The published purpose of this hearing is “to learn about experiences in linkingor matching patients to their information”. In the technical community, therehas been a debate when a patient match is established regarding the relative

merits of dynamically linking the patient information versus the merging of thepatient information. As a result, in this testimony, the word “matching” has been

used to establish when information from two systems are determined to refer to

the same person. The word “linking” is used to refer to links to information forthe same person.




Summary

In preparation for this hearing, we surveyed eleven larger provider organizations(with patient populations as large as 4 million) as well as a similar number of

internal and external experts on the topic of patient matching.

The most important requirement identified by Allscripts clients when it comes tomatching is high quality patient demographic information. Data governance and

management, user workflows, provider verification and a clean patient registryare vitally important to patient matching.

From a technology perspective, electronic health records, master patient indices,

and patient identification exchange standards are critical components to ensuring

and improving the high accuracy levels reported by our clients. Based on oursurvey, electronic health records are serving as a positive safety and privacy

mechanism.

General Topics for All Panels

1. Standards for identifying individuals

Individual identification standards are established by the provider. Typically

these include “out of band” verification of identification through cards such asdriver’s licenses and health insurance certificates. Minimal basic demographics

include full name, date of birth, sex, address, zip and preferably the last 4 digits

of the social security number. For pediatrics and other special workflows,additional demographics are required. Some states and organizations, however,do not collect social security numbers.

One note is that discipline and completeness of demographics tends to be betterin organizations that see patients on a recurring basis, and better in revenuecycle departments than in clinical departments. With the exception of certainurgent care workflows, identity usually flows from the revenue cycle

demographics data capture process to the clinical departments.

2. Ensuring accuracy in matching a patient with his/her data

There are no perfectly accurate matching approaches, and there is no one-size-

fits-all approach to patient matching. The best approach for a given healthcareorganization depends on a number of factors related to the population

characteristics, the way the information is used and managed, data quality andalgorithms employed by various systems involved in information exchange,

among other factors.




Patient information is matched in two basic ways:

• Statistical patient matching based on demographic factors, with moreadvanced systems including rules, demographic weightings and

probabilistic algorithms with configurable attributes.• Unique invariant non-disclosing patient identifiers

These linkages can be established at any number of points in the workflow, but

there are two basic methods:• Dynamic linking, which is real-time linking based on demographics with

records kept separate, or

• Static merge, which means a link is established, with the data potentially

combined and maintained at that point in time

There are two types of errors associated with patient matching:

• A false positive – which incorrectly links or merges clinical information tothe wrong patient.

• A false negative – which fails to match information to a patient and mayresult in an additional record for the same patient, with each record

missing some information.

Most legacy systems in use in the U.S. today use deterministic matching (themost basic statistical matching looking for exact matches over 4 or 5

demographic variables). This may have been a workable solution for smallerpatient populations that resided in a society where demographic factors like

name and address were more stable, and where the collection of uniqueidentifiers like social security number was better tolerated. All of that has rapidlychanged, however, and many of the legacy systems haven’t kept up with the

need for advances in patient identity. As the distance between the settings of care gets smaller, and as there is more

interconnectedness, the opportunity for error rises rapidly. In an interconnectedworld, the borders get fuzzier. Patient matching was important in the 90’s as

hospitals consolidated and now, with ACO’s, Community Health Teams and otherpayment reform initiatives rapidly gaining momentum, the pace of consolidation

is quickening even more, with the importance of accurate linking growingexponentially alongside.

Patient matching technologies are employed in a variety of systems. Reviewing

recent strategic decisions by Allscripts clients, we can generalize how thesematching approaches are being applied today including:

• Patient identity management systems (community patient registries andEMPI’s) with robust probabilistic matching are found in health information




exchange settings – both public and private. Health information exchangecan rely on dynamic linking of patient records, thus ensuring as

demographics are updated, patient record matches resolve to the mostcurrently appropriate individual (recognizing that in some cases,

demographic changes can also degrade the accuracy of a match, as in legalname or address changes).

• Strong statistical matching and fuzzy logic algorithms can be applied to

traditional operational interfaces and bulk uploads.

• Unique patient identifiers are applied for system-to-system patientmatching, in the limited cases where there is strong organizational control

of the patient identifier to ensure it is unique, invariant and non-disclosing.

• Patient communication solutions start with a unique invariant patient

identity established in the EHR and result in an email invitation being sentto the patient to participate in a portal / EHR. The patient email address is

established outside of the system in communication between the providerand the patient.

• Providers with low tolerance for matching errors have implemented unique,

invariant, non-disclosing patient biometric identification techniques likepalm vein scanning.

• Patient identification for devices is typically driven from the EHR to the

device using web services.

3. Problems with patient-matching, internally and/or for informationexchange. Source of those problems.

Issues that have adversely affected patient matching performance include:

o Source data quality, completeness and consistency – these are by far and

away the most often reported sources of patient matching problems.

o Local population characteristics and social factors that impede good qualitydata including common names, sharing of identity information (or in the

case of drug abuse – the use of multiple identities), and other factors

o State level differences such as opt-in or opt-out consent requirements forhealth information exchange and varying policies regarding exclusions of

sensitive health information (e.g., Psych, HIV, etc.).




o Accuracy of the management of demographics information in sourcesystems

o Acute care environments with numerous ancillary systems and medical

devices (key problem is that there are multiple points of demographic dataentry with varying degrees of accuracy)

o Pediatric workflows

o Multi-organization sharing where many organizations are sending patientinformation into a single repository.

o Changes in marital status (with name change)

o Dirty MPI’s resulting historically from less sensitivity to patient identity

accuracy

As an example, Steven Anderman, Bronx Lebanon Hospital’s COO has led major advances in care coordination and technology at Bronx Lebanon, as well as

health information exchange through the Bronx RHIO. Many of the “externalities” listed above are applicable at Bronx Lebanon and these adversely impact patient

registry quality. Bronx Lebanon Hospital’s patient population is two thirdsMedicaid and is subject to frequent moves, often provide bad addresses and

phone numbers, and creates a tremendous burden in terms of collecting good demographics. And patient identity sharing is common. The process repeats

itself at each health care organization in the community. This places anundue burden on the provider to be the regulator of patient identity.

3a) Handling patient matching problems (wrong/ambiguous match)

Providers use a variety of tools and processes for handling patient matchingproblems, including:

o Workflow and reporting solutions to evaluate possible duplicated patients.

o Follow-up user workflow solutions to analyze, identify and resolve potential

duplicates.

o Corrections in source systems.

o Improvements to the rules / weightings based on specific usage, dataquality and population characteristics.




o Upstream workflow process improvement to accurately capturedemographic information the first time.

o False negatives can be resolved through dynamic linking solutions or

merge capabilities.

o Logic to detect common mistakes and alert users of possible match ormismatch.

o When no match is found, user workflows and tools for research andresolution are typically applied.

o Also, "cleansing" techniques can be applied such as removal (from

database and/or from searches) of obsolete records (e.g., deceasedpatients)

Who’s doing this well today?

One example is North Shore Long Island Jewish. They identify the potential

duplicates centrally and then are able to communicate with various Medical Records Departments electronically to obtain the information necessary to

resolve them. They can also assign the resolution to the respective Medical Records departments. Currently they have at least 8 systems feeding the

Allscripts EMPI and are adding additional participants at a steady pace.

3b) Consequences of a Wrong Match – to Patient Safety, Privacy

We can find a powerful real world example of the dangers of wrong matches in a

recent case where a patient had an EKG at her local clinic. While she was at theclinic, they misplaced her EKG and mistakenly evaluated her based on another

person’s EKG. The wrong EKG, with her name hand-written on top, incorrectlyindicated that she was on the verge of a heart attack. The ensuing clinical

response ultimately resulted in coma and then her death. In this simple case,where the patient match involved data only intended to move from the device to

the physical patient in the same clinical setting, during the same encounter,without an EHR and without any health information exchange technology, had

deadly consequences.

This story is very revealing for those of us who are engaged in the policyconversation. In fact, it’s a bit of a Rorschach test. As you tell this story, there

is a tendency for listeners to jump to their pre-conceived notions of what’simportant about patient matching depending on their role in the healthcare

ecosystem.




Providers recognize the risks to safety and privacy, but up until now, amongthose polled, error rates have been generally extremely low. Providers also

recognize that as they engage with more outside entities, the risk of patientmatching errors rises.

Providers are very aware of the potential for patient safety issues in the event of

a wrong match. Typical of the issues include the need to review/changemedications, inform pharmacies, manage rework of the records, and

communicate with providers, consulting providers and patients.

The providers did identify privacy concerns, as well, in the event of a wrongpatient match. This includes the risk of privacy exposures when the wrong

information is entered into a patient’s chart and the potential need to explain this

to a patient.

There are privacy considerations, as well, in connection with larger data bases,unique patient identifiers (not including VUHID – more on this below) and

potentially non-essential information being shared in connection with patientmatching.

4. Level of Accuracy for Patient Matching

The fact of the matter is that the industry doesn’t have consistent measurement

and performance standards for patient matching accuracy. The Allscriptsproviders interviewed on this topic generally reported overall accuracy rates of

99.9+% or 100% after human review and ~97+% on automated match.However, they acknowledge the presence of some errors, and this moreconsistently aligns with the findings of the RAND study, in which statistical

matching generated a false-negative error rate of approximately 8%, meaningthat there can be data gaps needed for identification around 8% of the time.

A false positive that results in incorrectly merged patient information is more

likely to be a hidden error and therefore more serious for patient safety. Tostrictly limit false positives, standards for matching are high. So generally, in

our client base, a false negative is more likely to occur than a false positive.

Allscripts’ goal is to avoid any false positives, to minimize false negatives, toensure upfront accuracy and to provide resiliency by putting the tools in place to

cost-effectively and timely handle issue resolution and merges.

Matching accuracy is favorably affected by the presence of more uniquedemographic attributes. The Rand study found that in a database of 80 million

unique patients, false positives could be avoided with a composite key consistingof name, DOB, zip code and the last 4 digits of the social security number.




Conversely, Rand found that removing the unique 4 digits from the SSN reducedaccuracy from 1 in 39 million, to 1 in 8. Allscripts in general provides more

demographic fields, options and rules than this to ensure false positives areavoided. Unique demographic factors are vitally important to patient matching

accuracy.

Note that this also points out that when social security number is shared among

patients, or otherwise mis-used, the risk substantially increases of mixedrecords.

5. Lessons Learned

Demographic data quality: Secure technology exists for accurate matching –

the key is the data behind the match. Source data quality, completeness andconsistency are by far and away the most often reported sources of patient

matching problems.

Process: Workflows, processes, training and best practices including the

individual provider as a last stop-gap are vitally important to accurate sourcedata used for patient matching.

As an example, Scott Whyte at Catholic Healthcare West is very aware of theneed to be on top of processes to ensure data quality. In addition to deployingthe technology, Catholic Healthcare West has placed a major emphasis on

people, policies, executive sponsorship and monitoring.

Dynamic linking: Changes in demographics can resolve in different matching.This can improve accuracy when the changes are corrections in the underlying

data and can diminish accuracy when the changes are, for example, changes inname or address. Holding individual records separately and dynamically linking

– effectuated by the HIE strategies of record locator and linking to records offers

greater transparency than merging of records.

Regional differences: Population and regional differences result in disparateproblems. For example, a community with a large uninsured population, a large

immigrant population, or a large transient (e.g., university) population all haveslightly different weightings based on their population characteristics.

Persons: Guarantors & Subscribers are typically difficult to match withtraditional statistical / deterministic techniques. While the Tiger Team hearing isfocusing on the patient, Allscripts would recommend that you consider any

individual person matching, whether they’re a patient or fill a financial role inconnection with the patient.




Demographic data management: Data quality management is challenging inenterprises and even more so in collaborating, decentralized organizations. Data

ownership is ambiguous when multiple organizations are seeing the samepatient. The reputation and quality of participating organizations enters into

providers’ willingness to accept another’s information. Best practice involvesensuring that there is zero tolerance for errors at the source, due to the

extremely high cost of rework and low level of capability for undoing propagatedchanges. The very credibility of the HIEs (or any EMPI service for that matter)

rests in large part on ensuring a high quality error free patient registry.

Priorities for Health Information Exchange using IHE Profiles: Allscripts isvery supportive of the identity-related IHE profiles for Health Information

Exchange. Like MU, where there is a logical progression for the industry, the

following progression should be considered:

• PIX for HL7 v2 typical use case involves an HIT system with a registration

system communicating with the HIE. It is a well-established matureprofile. PIX V3, using Web Services, is widely used internationally and is

gaining market share in the US. • PDQ (demographic query) typical use case involves using patient

demographic information to communicate with the registry to establish thepatient’s identifier. It is less well established than PIX.

• Pediatric Profiles typical use case involves a hospital maternity or pediatricdepartment already using PIX communicating with the HIE. These profiles

are undergoing trial implementations.• Cross Community Patient Discovery (XCPD) typical use case involves

support for snow birds, patient moves, multiple homes, etc., where theprovider needs to establish a new patient’s global identifier across thenetwork of HIE’s. Through gateways, using XDPD, in combination with

IHE’s Cross Community Access Gateway (XCA) – a request can be sent outacross HIE’s to determine a patient’s id in other exchange networks. XCPD

is promising technology for these “edge cases”, which should be prioritizedonce core HIE functions are well established.

• With all patient matching, as with any exchange of PHI, there is the needfor secure transmissions as well as auditing.

Support for the small provider: Patient registries for health information

exchange will of necessity be dealing with multiple and various clinical andadministrative systems, large and small, that all must be able to participate.

70% of healthcare is provided in small practices, and these providers must haveaccess to the same levels of capability, performance and supporting tools for

patient matching as those available to an enterprise.




Innovation isn’t necessarily government work: Much of the innovationaround patient matching (including EMPI’s and surround workflows and tools)

has its roots in advanced healthcare organizations.

For example, Bill Spooner’s IT team at Sharp Healthcare supports sevenhospitals and 2,500 physicians. A small team of five handles patient matching

and duplicates, running a tight ship, where any mismatch is a critical error. In2009, on a base of four million patients, they identified and managed 260

manual registration errors. In the same timeframe, 2,740 duplicates weregenerated off of enrollment tapes. As more of healthcare becomes risk bearing,there is clearly a lesson to be learned around Sharp’s experience withenrollment.

The bottom line is, ONC should look to these experienced healthcareorganizations for continuing innovation. As the rest of this testimony suggests, it

isn’t about the match as much as it is about the quality, consistency, resilienceand recovery capabilities around the match. ONC should consider how to provide

the platform for innovation and allow the market to develop.

6. Cost Implications of Various Solutions

In all cases cited, the high performance solutions will be cost-effective comparedto current operational costs managing errors and rework.

7. Recommendations for ONC to address patient matching problems in

information exchange

ONC recommendations in support of Meaningful Use Stage 2 & 3 should be made

as early as possible to ensure that vendors and providers have time to developand implement the recommendations. In the context of patient matching, in

support of public and private health exchanges, the extended rules shouldinclude, in our opinion:

• Robust probabilistic patient matching capabilities

• “Possible duplicate” identification

• Merge (or link) functionality to correct the MPI and through HL7 ADT-type

transactions to communicate the corrections to participating organizations.

• Progressive adoption over several stages of the IHE profiles (in sequence:PIX, PDQ, Pediatric, XCA/XCPD).




ONC should provide support to the innovators in complex environments that arepiloting the new generation of patient registries.

These are emerging at places like Hartford Healthcare System where Steve

O’Neill’s IT team is in pilot with four acute care facilities, several federal qualified health centers, and independent Connecticut physician organizations. The

strategy calls for leveraging the patient registry and health information exchangeinfrastructure to create a patient throughput platform, providing a variety of

services across heterogeneous provider platforms.

ONC may also want to give due consideration to two excellent published workson this topic: Connecting for Health’s 2005 Linking Health Care Information:

Proposed Methods for Improving Care and Protecting Privacy and the HIMSS

2009 Patient Identity Integrity (PII) work group recommendations whichincluded developing demographic data standards, medical device standards for

identification and HIT workflow support for analysis and correction of duplicates.

Universal Patient Identifiers

As we all know, the subject of unique identifiers is one that engenders a strongresponse from different constituencies, and there is even a law prohibiting simple

discussion of a national patient identifier. However, it is our sense that suchlimits were applied at a time when health information technology and the ability

to securely exchange health information was in a very different place, and it’stime to revisit the conversation

The industry should not give up on further exploration of a unique voluntarypatient identifier inasmuch as it has the potential to reduce false positives and

false negatives and thus improve safety for those patients who opt in to it. It’snot, however, the “silver bullet.” With any voluntary approach, like the ASTM-

based Voluntary Universal Healthcare Identifier (VUHID) that is being discussedin many circles, there will always be a need for patient matching technologies.

VUHID needs mass adoption seeded by a national catalyst, and well-researchedbest practices/policy guidance to support its implementation and use.

Should Congress elect to remove its restrictions on the conversation about

patient identifiers, a private / public partnership using organizational techniquessimilar to the Direct Project could be a productive means of encouraging

innovation and practical approaches to the implementation.

Specific Topics for Panel 2

1. Solutions:




ONC can and should play a leadership role to:

• Ensure high quality patient demographic information at the source using

workflow, training, strong attention to process and EHR/MPI technology.

• Standardize on data definitions and performance standards for algorithmsfor patient matching.

• Encourage methods for resilience and recovery to perfect the informationsystem.

• Encourage innovation with biometrics to get five 9s plus accuracy – with

opt-in / opt-out to protect the providers and provide privacy options (atvarious levels of safety) to the patients. Multi-factor look-up could include

biometrics, photos, etc.

• Standardize on health information exchange, using stages/roadmap tofoster adoption of Integrating the Healthcare Enterprise (IHE) PIX, PDQ,

Pediatric Profiles, XCPD/XCA.

• Encourage patient demographic self-verification methods.

• Establish best practices for governance of data quality in a distributedenvironment.

• Support implementation of the HIMSS PII workgroup recommendations forstandards, interfaces, algorithms for matching, business processes, data

accuracy, data quality, training and medical devices.

• Standardize device communication for patient identification.

In addition to these solutions, ONC should seek to ensure similar levels of support for small providers and support innovation that is emerging in private as

well as public exchanges.

2 & 3: Status of solutions and gaps for healthcare:

While basic matching technology is well established, there are significant gaps inthe solutions outlined above. These include:

• Standards for source data quality, completeness and consistency are

lacking and vitally important as the healthcare system becomes moreconnected.




• High performance technology for patient matching exists today. Allscripts

is deploying this technology to providers and health information exchangesnationally.

• Methods for resilience and recovery are inconsistently applied and

inconsistently available.

• Biometrics are well established (and in fact required in Ohio). Biometricsare not however part of the usual standards for health informationexchange.

• IHE profiles for PIX are well established. PDQ, Pediatric profiles and

XCA/XCDP are emerging.

• Patient demographic self-verification has been implemented in severalpatient portal solutions.

• Best practices for governance around data quality in a distributed

environment are not well established.

• HIMSS PII workgroup recommendations are generally yet to be delivered,with the exception of the strong work on EHR deployment.

• Device standardization for patient identification is a significant gap.

In summary, not all Health IT vendors deploy these technologies today.Standards and best practices aren’t in place. An improved understanding of best

performing algorithms for patient matching is needed. And of course, ONCshould foster the platform for experimentation, innovation and performance

improvement around patient identification.

Conclusion

In conclusion, Allscripts believes that accurate patient identification isfundamental to health care quality, efficiency, and safety, especially as EHRs and

HIEs become more embedded in the healthcare system. Adoption of appropriatestandards, identifiers, technology, and processes must be put in place to

minimize the costs and consequences of false positives, false negatives and anongoing remediation of problems.



Thank You

To the Privacy and Security Tiger Team members – thank you for the terrific

progress that you’ve made to date, your ever mindful work to gain and keep thepublic’s trust, and your continued leadership on key issues in connection with

privacy and security in healthcare. It will take your leadership to make theimperfect “near perfect” for patient matching. Resilience and recovery will be

the key in lieu of a “perfect solution”.

patient linking

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