Health Information Technology: Approaches and Challenges to Electronically Matching Patients' Records across Providers
Released: 2019-01-15T00:00:00


Highlights: section_title: Why GAO Did This Study
paragraphs: Health care providers are increasingly sharing patients' health records electronically. When a patient's records are shared with another provider, it is important to accurately match them to the correct patient. GAO and others have reported that accurately matching patient health records is a barrier to health information exchange and that inaccurately matched records can adversely affect patient safety or privacy. At the federal level, ONC is charged with coordinating nationwide efforts to implement and use health IT.

The 21st Century Cures Act included a provision for GAO to study patient record matching. In this report, GAO describes (1) stakeholders' patient record matching approaches and related challenges; and (2) efforts to improve patient record matching identified by stakeholders.

To do its work, GAO reviewed reports by ONC and others about patient record matching. GAO also interviewed various stakeholders that play a role in exchanging health records, including representatives from physician practices, hospitals, health systems, health information exchange organizations, and health IT vendors. GAO also interviewed other stakeholders, such as ONC officials, provider and industry associations, and researchers. GAO selected stakeholders based on background research and input from other stakeholders, and interviewed 37 stakeholders in total. The information from stakeholders is not generalizable. HHS provided technical comments on a draft of this report, which GAO incorporated as appropriate.

section_title: What GAO Found
paragraphs: Stakeholders GAO interviewed, including representatives from physician practices and hospitals, described their approaches for matching patients' records—that is, comparing patient information in different health records to determine if the records refer to the same patient. Stakeholders explained that when exchanging health information with other providers, they match patients' medical records using demographic information, such as the patient's name, date of birth, or sex. This record matching can be done manually or automatically. For example, several provider representatives said that they rely on software that automatically matches records based on the records' demographic information when receiving medical records electronically. Stakeholders said that software can also identify potential matches, which staff then manually review to determine whether the records correspond to the same patient. Stakeholders also said that inaccurate, incomplete, or inconsistently formatted demographic information in patients' records can pose challenges to accurate matching. They noted, for example, that records don't always contain correct information (e.g., a patient may provide a nickname rather than a legal name) and that health information technology (IT) systems and providers use different formats for key information such as names that contain hyphens.

Stakeholders GAO interviewed identified recent or ongoing efforts to improve the data and methods used in patient record matching, such as the following:

Several stakeholders told GAO they worked to improve the consistency with which they format demographic data in their electronic health records (EHR). In 2017, 23 providers in Texas implemented standards for how staff record patients' names, addresses, and other data. Representatives from three hospitals said this increased their ability to match patients' medical records automatically. For example, one hospital's representatives said they had seen a significant decrease in the need to manually review records that do not match automatically.

Stakeholders also described efforts to assess and improve the effectiveness of methods used to match patient records. For example, in 2017 the Office of the National Coordinator for Health Information Technology (ONC) hosted a competition for participants to create an algorithm that most accurately matched patient records. ONC selected six winning submissions and plans to report on their analysis of the competition's data.

Stakeholders said more could be done to improve patient record matching, and identified several efforts that could improve matching. For example, some said that implementing common standards for recording demographic data; sharing best practices and other resources; and developing a public-private collaboration effort could each improve matching. Stakeholders' views varied on the roles ONC and others should play in these efforts and the extent to which the efforts would improve matching. For example, some said that ONC could require demographic data standards as part of its responsibility for certifying EHR systems, while other stakeholders said that ONC could facilitate the voluntary adoption of such standards. Multiple stakeholders emphasized that no single effort would solve the challenge of patient record matching.

section_title: Letter
paragraphs: As health care providers increasingly use electronic health records  (EHR), a greater share of patients’ medical records are stored and  exchanged electronically. This exchange can occur among various types  of providers—including hospitals, primary care physicians, specialty  physicians, pharmacies, and laboratories—and has increased in recent  years. An American Hospital Association (AHA) survey found that in  2017, 74 percent of hospitals reported sharing clinical or summary of care  records electronically with other hospitals, whereas in 2012 only 30  percent of hospitals reported doing so. The 2017 survey also found that  58 percent of hospitals indicated physicians at their facilities “always or  sometimes” used patient health information received electronically from  outside sources while treating a patient.

When a patient’s medical records from one health care provider are  shared with another provider, it is important that those records belonging  to the same individual are accurately matched. Patient record matching is  the process of comparing patient information in different health records to  determine if the records refer to the same patient. Inaccurate patient  record matching can adversely affect the care patients receive as well as  their privacy. For example, inaccurately matched records can adversely  affect patients’ safety if incorrect patient data are used to make medical  decisions; the ECRI Institute reported the case of a patient in cardiac  arrest who was mistakenly not resuscitated because the care team  adhered to the wrong patient’s do-not-resuscitate order. We and others  have reported that patient record matching has caused difficulties for  providers. A 2014 study found that as few as 50 percent of records are  accurately matched when organizations exchange information. In AHA’s  2017 survey, 45 percent of large hospitals reported that difficulties in  accurately identifying patients across health information technology (IT)  systems limited health information exchange.

1. the patient record matching approaches used by selected  stakeholders and related challenges they identified, and  2.  efforts to improve patient record matching identified by ONC and  selected stakeholders.

To describe the patient record matching approaches used by selected  stakeholders and related challenges they identified, we reviewed  published reports (including our own past reports) and other  documentation about patient record matching, such as ONC’s 2014  Patient Identification and Matching Final Report. We conducted  interviews with relevant stakeholders that we identified through this  background research and from input from other stakeholders. Specifically,  we interviewed representatives from organizations involved in exchanging  health information, including providers such as physician practices and  hospitals; health information exchange (HIE) organizations, which  facilitate the exchange of health information among providers and other  types of organizations, often at a regional level; and health IT vendors.  We also interviewed representatives from organizations that represent  providers (AHA, the American Medical Association, and the Medical  Group Management Association); health information management  professionals (the American Health Information Management Association  and the College of Health Information Management Executives (CHIME)  ); HIE organizations (the Strategic Health Information Exchange  Collaboration); and health IT vendors (the Electronic Health Records  Association), as well as individuals or entities with expertise or experience  with patient record matching issues. In addition, we interviewed officials  from ONC and HHS’s Agency for Healthcare Research and Quality. In  total, we conducted 35 interviews with stakeholders. The information we  obtained from these stakeholders is not generalizable to the experiences  of other health providers, organizations, vendors, and other stakeholders  regarding patient matching approaches.

To describe efforts to improve patient record matching identified by ONC  and selected stakeholders, we reviewed ONC documents and reports that  describe the agency’s efforts related to patient record matching. For  example, we reviewed ONC’s 2014 Patient Identification and Matching  Final Report, ONC’s Interoperability Roadmap, as well as documentation  of recent ONC studies related to patient record matching. In addition, we  discussed patient record matching efforts with the stakeholders  mentioned previously. We supplemented those discussions by conducting  two additional interviews with stakeholders about their efforts to improve  patient record matching; we identified these stakeholders through  background research and input from other stakeholders. In addition, we  reviewed documentation related to those stakeholders’ efforts. The  information we obtained from stakeholders is not generalizable to the  experiences or views of other stakeholders regarding efforts to improve  patient record matching.

We conducted this performance audit from November 2017 to January  2019 in accordance with generally accepted government auditing  standards. Those standards require that we plan and perform the audit to  obtain sufficient, appropriate evidence to provide a reasonable basis for  our findings and conclusions based on our audit objectives. We believe  that the evidence obtained provides a reasonable basis for our findings  and conclusions based on our audit objectives.
subsections: section_title: Background
subsections: section_title: Patient Record Matching
paragraphs: Patient record matching is the process of comparing patient information in  different health records to determine if the records refer to the same  patient. This matching generally relies on the use of demographic  information such as a patient’s name, date of birth (DOB), sex, Social  Security number (SSN), or address, among other information.

Many types of stakeholders can be involved in patient record matching.  Examples of stakeholders include the following:

Health care providers, such as physicians, hospitals, and their staffs  may receive records from another provider that need to be matched to  existing patient records. When treating a new patient, for example, a  provider might obtain records from other providers that previously  cared for the patient. Similarly, a provider caring for a patient with  multiple chronic conditions (e.g., heart disease, diabetes) might obtain  information from other providers that are also caring for the patient.  The providers must ensure that the records they obtain from other  providers are matched to the correct patient and therefore properly  linked with the patient’s existing records.

HIE organizations match patient records as part of their role in  facilitating the electronic exchange of health information among  hospitals, physicians, and other organizations. They can offer a range  of services, such as allowing providers to access the medical records  for a patient who has received care from other providers in the HIE  organization’s network. They may also obtain information from  hospitals when a patient is admitted or discharged, and they then  notify the patient’s other providers when those events occur. In these  cases, HIE organizations must accurately match records from multiple  organizations to the correct patient. HIE organizations generally serve  a specific state or region and match records among a network of local  or state-wide providers and other entities; some, however, operate  nationally.

Health IT vendors also play a role in matching patient records. Some  IT vendors, for example, provide record matching tools as part of their  EHR systems; these tools allow providers to electronically search for  patient records that are available from other providers that use the  same IT vendor. Other IT vendors offer tools that allow providers or  HIE organizations to leverage third-party data, such as credit-bureau  data, when matching patients’ medical records.

section_title: Importance of Accurate Patient Record Matching
paragraphs: ONC and others have reported that the ability to accurately match patient  medical records across different providers is a critical part of effective  health information exchange, which can benefit patient care. For  example, accurate record matching can help ensure that providers have  current information about patients’ laboratory or other diagnostic test  results; their medications; their diagnosed medical conditions, such as  allergies; and their family medical histories.

In contrast, when a patient’s records are not accurately matched, it can  adversely affect the patient’s care. There are two ways in which records  can fail to be accurately matched.

Records for different patients are mistakenly matched. When  medical records for different patients are mistakenly matched (known  as a “false positive”), it can present safety and privacy concerns for  patients. For example, a provider may inadvertently use information  about the wrong patient, such as diagnoses or medication lists, to  make clinical decisions. In addition, if the wrong patient’s medical  information is added to a patient’s record, it could result in disclosure  of that information to a provider or patient who is not authorized to  view it.

Records for the same patient are not matched. When medical  records for the same patient are not matched (known as a “false  negative”), it can affect patient care. For example, providers may not  have access to a relevant part of the patient’s medical history—such  as current allergies or prior diagnostic test results—which could help  them avoid adverse events and also provide more efficient care, such  as by not repeating laboratory tests already conducted.

section_title: ONC Responsibilities and Patient Record Matching
paragraphs: ONC leads federal efforts to promote interoperability, including setting  requirements for the information that EHRs and other health IT systems  should collect. ONC developed certification criteria for EHRs and other  health IT systems that include the ability for health IT systems to capture  and exchange various types of information, including clinical data such as  information on patients’ allergies, as well as the patient’s name, sex, and  date of birth. ONC also compiles an Interoperability Standards Advisory,  which suggests certain standards that developers should incorporate into  their products.

section_title: Stakeholders Described Patient Record Matching Approaches and Associated Challenges
subsections: section_title: Providers and HIE Organizations Described Using Both Manual and Automated Approaches to Patient Record Matching
paragraphs: All seven provider representatives we interviewed described manual  matching as one of the ways that they match patient records when  exchanging health information with other providers. With manual  matching, an individual reviews a medical record in order to match it to  the correct patient. For example, an outpatient practice representative  said that to match records that the practice receives by fax, a staff  member must manually review information such as name and DOB to  identify the correct patient and add the new information to the correct  patient’s electronic record. All of the provider representatives we  interviewed told us that they receive health records from other providers  by fax.

Six provider representatives told us they also use health IT tools to help  automatically identify and match patients’ records stored in other data  systems. These tools generally use algorithms that compare  demographic data in a patient’s separate electronic records. For  example, representatives from four of the six providers told us they used  a module offered by their EHR system vendor to match records and  exchange information with other providers that use the same vendor’s  EHR systems. The module includes an algorithm that compares  patients’ demographic information and, if the information in two or more  records is identical or very similar, can automatically link the records.  Automated matching can also involve some degree of manual review, as  algorithms can identify potential matches by providing information about  the likelihood that two records with similar information refer to the same  individual. Afterwards, provider staff manually review the demographic  information in the records and assess whether these potentially matching  records should be linked as belonging to the same patient.

Representatives from the five HIE organizations we spoke with said they  use a range of automated and manual approaches to match patients’  records when exchanging information. Representatives from all five of  the HIE organizations said that they use software with algorithms to  locate and match records using demographic information provided by the  providers in their networks. Though these HIE organizations’ algorithms  vary, they all use name, sex, DOB, and address to match patients’  records. Representatives said that when the patients’ records contain  similar but not identical demographic information, the HIE organizations  rely on staff or additional software to review potential matches and  determine whether the records belong to the same patient. For example,  one HIE organization representative said that his organization leverages  third-party data, such as credit databases that store past names or  addresses, to update demographic information for records that cannot be  matched automatically.

When describing their approaches to patient record matching when  exchanging information, six of the seven provider representatives said  that they sometimes used HIE organizations to exchange and match  records. However, none of them relied on HIE organizations as their  primary way to match records and exchange health information. Five of  the provider representatives we spoke with, including one provider that  does not participate in an HIE organization, noted that they only  exchange health information with a few providers. They explained that  they were able to connect to these providers in ways other than through  an HIE organization.

According to stakeholders we interviewed, it is difficult to determine the  accuracy of the health IT tools used to match patients’ medical records  automatically. While the algorithms typically match records belonging to a  patient and identify potential matches that need to be manually reviewed,  users of these algorithms do not know how many matches the algorithm  may have failed to make. These stakeholders expressed concern that it  is not possible to assess the accuracy of algorithms without independent  testing to identify matches that the algorithm may have missed. HHS  stated that the proprietary nature of many patient matching algorithms  makes it difficult to assess their effectiveness.

section_title: Stakeholders Said That Inaccurate, Incomplete, and Inconsistently Formatted Data Can Pose Challenges for Patient Record Matching
paragraphs: Representatives from providers, HIE organizations, and the other  stakeholders we interviewed emphasized the importance of using quality  patient demographic data when matching patients’ medical records.  These stakeholders noted that inaccurate, incomplete, or inconsistently  formatted demographic information in patients’ medical records can make  it challenging to identify and match all the records belonging to a single  patient. Figure 1 illustrates how the demographic information for a  hypothetical patient can be recorded inaccurately, incompletely, and  inconsistently across the patient’s providers.

Stakeholders described the ways in which providers or their staff can  collect inaccurate demographic information from patients. According to  stakeholders, provider staff sometimes make transcription errors when entering  information into electronic records, patients do not always provide correct information (e.g., they register  with a nickname rather than a legal name), and patient demographic information can change, such as when a patient  moves to a new address or changes her last name, but this  information is not consistently updated in all of the patient’s medical  records.

Provider representatives identified several reasons that patients’  demographic information can be incomplete or contain different data  elements across the medical records maintained by multiple providers. In  particular, provider representatives explained that providers collect  different information from their patients, and health IT systems can collect  demographic data differently. Examples include the following:

Two provider representatives said that their organizations do not  collect patients’ SSN because many patients choose not to provide  that information or the information is not available. However, other  provider representatives said they do collect SSNs. A health IT vendor  said that the algorithms in its software do not rely on SSN as a key  factor for matching records because SSN is not consistently available.

One provider representative explained that the IT system used by the  provider’s laboratory does not contain fields for the same  demographic information that the provider’s EHR system contains. As  a result, laboratory results often contain too little information to reliably  match records, even if the tests were ordered using complete  information.

One provider representative explained that they do not collect  patients’ mothers’ maiden names, though other organizations collect  and use this information for patient matching.

According to stakeholders, the inconsistencies in formatting across  medical records can reflect differences in health IT systems or the  policies of the health care organization creating the records:

A 2014 ONC report noted that one health IT system may list  addresses in a single field, while another may separate street names  from the city and state.

A 2018 report noted that providers use different standards for  recording names with spaces, hyphens, or apostrophes, and that  some health IT systems include special characters in phone numbers  (i.e., (123) 456-7890), whereas others only allow for numbers (i.e.,  1234567890).

Representatives from one HIE organization explained that providers  handle missing data for fields differently; for example, one provider  may enter all 9s into an SSN field when it is not available for a patient  and another will enter all 0s.

Provider representatives and other stakeholders identified some patient  populations for which matching is particularly challenging, due in part to  data issues. Three provider representatives said that medical records for  newborns often contain temporary names that are not updated with the  child’s legal name after it is determined, which makes it difficult to locate  these records. Further, provider representatives and other stakeholders  said that multiple births (e.g., twins) result in record matching challenges,  as these children can have the same DOB and address, and may be  named similarly. A few provider representatives said that records can be  inaccurate across providers for patients from certain nationalities. For  example, according to stakeholders, some east-Asian cultures use the  “family name” as the first name, and some Hispanic cultures use multiple  last names. Another provider representative said that a few times a  month, a transgender patient’s photo ID lists the wrong gender, yet the  organizational policy is to record the gender exactly as it appears on a  state-issued photo ID.

section_title: Stakeholders Identified Efforts Underway to Improve Patient Record Matching as Well as Additional Efforts ONC and Others Could Undertake
subsections: section_title: Stakeholders Have Undertaken Efforts to Improve the Demographic Data and Methods Used to Match Records
paragraphs: Officials from ONC, selected provider representatives, and other  stakeholders we interviewed described a variety of efforts they have  undertaken or are currently undertaking to improve the ability to match  patients’ medical records accurately. In general, these efforts focus on  improving demographic data and improving the methods used for  matching. These efforts are discussed in more detail below.
subsections: section_title: Efforts to Improve Demographic Data Used for Matching
paragraphs: ONC has reported that quality demographic data is important for  effectively matching patients’ medical records, and in 2017 the agency  published the Patient Demographic Data Quality Framework. The  Framework is a tool to help providers and other organizations assess  their processes for managing data quality and improve the quality of the  demographic data they use in matching. It includes, for example,  questions that providers can use to identify any gaps in how they manage  their demographic data. In 2016, before ONC published the Framework,  the agency began a pilot study to assess how the Framework could work  in a clinical setting. As part of this pilot study, ONC provided training on  demographic data quality to staff from two community health centers,  during which it shared best practices for collecting these data. After the training, researchers who collaborated on the pilot with ONC found that  there were improvements at the community health centers in indicators of  how they managed data quality. According to ONC officials, this pilot  highlighted the effect that data quality and training have on effective  patient record matching. In addition, officials said it underscored  difficulties in implementing data quality improvement efforts when health  care organizations have limited resources and high staff turnover. ONC  officials plan to issue a final report on the pilot study; however, they said  ONC is not currently planning to assess the impact of the Framework or  to conduct future studies on how it works in clinical settings.

Several stakeholders told us they have worked to improve the  consistency with which they record and format demographic data in their  EHRs. According to ONC officials and hospital representatives, as well as  other stakeholders with whom we spoke, implementing common  standards for how certain demographic data should be formatted—such  as names and addresses—could improve the consistency of data across  providers and thus make it easier to match records. Representatives from  four hospitals told us that they collaborated with other providers in their  regions to implement common standards for recording patients’  demographic data. They told us the following:  In 2017, 23 providers in Texas reached agreement on, and then  implemented, standards for how staff should record patients’ names,  addresses, and other data in order to improve record matching and  facilitate health information exchange. We spoke with  representatives from three hospitals that were part of this effort, who  all told us that the effort resulted in an increased ability to accurately  match patients’ medical records automatically without the need to  manually review the records. (See text box.) For example,  representatives from one hospital said that when patient records are  not matched automatically or when there are questions about the  accuracy of record matching, staff must then conduct a manual review  to resolve the issue. They said that they have seen a significant  decrease in the need for those manual reviews since implementing  the data standards. Representatives from all three hospitals estimated  that the amount of manual review to resolve matching issues and  match incoming records to the right patient had decreased by about  90 percent. Representatives from one hospital added that they are  now better able to prevent records from being matched to the wrong  patient.

One children’s hospital in California worked with other local hospitals  in recent years to implement a standard for how staff should record a  temporary name for newborns who do not have their own name at  birth. According to representatives from this hospital, after  implementing this standard, clinical staff are able to more easily match  patients’ records and therefore have access to real-time information  on the care newborns received in other hospitals.

Lessons Learned from One Regional Effort to Standardize Patient Demographic  Data across Multiple Providers  In 2017, 23 providers in Texas implemented agreed-upon standards for capturing patient  name, address, and other data. Representatives from three participating hospitals shared  with us lessons for others interested in standardizing data, such as:

Allow sufficient time to get buy-in from staff and test for any downstream effects on

Communicate the benefits of standardizing data to clinical and administrative staff; and

Train staff on how to enter data, and then assess compliance to identify any  opportunities for improvement.

In a related 2017 effort, Pew Charitable Trusts sponsored a study to  measure how standardizing specific types of patient demographic data  could improve patient record matching. As part of this study, researchers  used four data sets to test the effect that standardizing patient names,  addresses, DOBs, telephone numbers, and SSNs had on record  matching accuracy. As of September 2018, the full findings from this  study had not been published; however, according to Pew, the findings  indicated that standardizing some demographic data, such as address,  shows promise for increasing the likelihood that patients’ records will be  matched.

Two stakeholders we spoke with have examined ways to boost patients’  ability to electronically share data with their providers using smartphone  applications or other tools. According to these stakeholders, these types  of tools could improve the accuracy of the demographic data providers  receive from patients, reduce manual data entry errors by providers’ staff,  and allow patients to update their information as changes occur, such as  if they move.

In 2015, the Workgroup for Electronic Data Interchange (WEDI)  initiated a “Virtual Clipboard” project to explore the development of a  mobile tool to automate the transmission of demographic, insurance,  and clinical information to providers. WEDI representatives told us  that they had engaged with stakeholders such as providers, vendors,  patient advocates, and health plans about the potential benefits of  such a tool, but had not yet identified organizations prepared to move  forward with developing specific applications.

In 2017, Pew Charitable Trusts funded a RAND study on “patient- empowered” patient record matching approaches—specifically, to  identify ways that patients could play an additional role in patient  record matching and to select a promising solution for further  development. In its August 2018 report, RAND proposed a solution in  which patients could verify their mobile phone number and other  identifying information with providers and then use a smartphone  application to share this information with providers.

Representatives from both WEDI and Pew told us that, when developing  these types of tools, it is important to consider the practical implications  for the providers that would need to be able to accept data in this way.  For example, Pew representatives said that it would be important to  understand whether these tools present any workflow challenges in  provider settings, such as with any IT tools that providers would need to  access the data stored via smartphone applications, or with the steps  needed to incorporate that data into their EHR systems. Representatives  from both organizations also noted that not all patients would be willing or  able to use these types of tools to share data with providers. In addition,  RAND reported on a range of security considerations for these types of  tools. For example, RAND noted that a smartphone app that gathers  health data—like its proposed patient matching solution—would introduce  risk because it would contain private demographic and health information  and would therefore be a target for individuals looking to steal data.

section_title: Assessing and Improving Matching Methods
paragraphs: Officials from ONC and other stakeholders described various efforts to  assess and improve the effectiveness of the methods used in matching  patients’ medical records. These efforts include hosting competitions,  conducting studies, and issuing guidance. For example, ONC officials  described the following two efforts to improve patient record matching  methods:  In 2017, ONC held a Patient Matching Algorithm Challenge in which  participants competed to develop an algorithm that most accurately  matched patient records in a test data set. According to ONC officials,  the goals of the exercise were to bring about greater transparency on  the performance of existing patient record matching algorithms, spur  the adoption of performance metrics for algorithm developers, and  improve other aspects of patient record matching, such as resolving  duplicate patient records. Over 140 teams used varying methods to  match patient records using an ONC-provided test data set, and ONC  selected six winning submissions based on various measures of  matching accuracy. As of July 2018, ONC was analyzing data from  the challenge to learn more about algorithm performance. Officials  told us that the challenge highlighted limitations of commonly used  matching algorithms and demonstrated that extensive manual review  is often needed to accurately match patients’ medical records. ONC  officials told also us they plan to publish a report on their analysis of  the challenge data.

In 2017, ONC also conducted a patient record matching Gold  Standard and Algorithm Testing pilot study. According to ONC  officials, there is no widely used standard for assessing the accuracy  of patient record matching algorithms, so the pilot was intended to  create a data set with known duplicate records (that is, multiple  records for the same individual) and then use it to evaluate how well a  commonly-used algorithm matched those records. ONC officials told  us that the pilot demonstrated how much effort is needed to evaluate  the matching algorithms providers and others use, as well as the  importance of using standard metrics to assess matching accuracy.  ONC expects to issue a final report on the results of the study.

Among the examples other stakeholders described were the following  efforts to improve patient record matching methods:  In 2018, the Sequoia Project published A Framework for Cross- Organizational Patient Identity Management to provide guidance to  help providers and other types of health care entities improve patient  record matching across organizations. The report, for example,  suggests ways organizations can improve their matching algorithms,  and it identifies practices that organizations can use to improve how  they use patient demographic data and other information when  matching records. Representatives from the Sequoia Project told us  they plan to speak with organizations that have voluntarily adopted  this guidance to learn how doing so affects record matching. These  representatives also said they are looking into how ONC’s Patient  Demographic Data Quality Framework relates to their own framework,  as it may be beneficial if there were a way to link these two efforts.

HHS’s Agency for Healthcare Research and Quality funded a study  that began in 2017 to evaluate patient record matching approaches,  with the goal of identifying different approaches to improving the  accuracy of patient record matching algorithms. As part of this  ongoing study, researchers are measuring how different changes to  matching methods—including changes that have and have not been  recommended or evaluated previously—improve matching  accuracy. The study is expected to run through 2022. According to  researchers, their initial work tested the use of different combinations  of demographic data elements, among other things. They identified a  modest improvement in the accuracy of matching algorithms, and  determined that further research was needed.

In 2016, CHIME sponsored a National Patient ID Challenge that  offered a monetary award for the development of a tool that matched  patients’ medical records with 100 percent accuracy. Although the  challenge was not specific to matching patient records across  providers, several CHIME members who were involved with the  challenge told us that they hoped to identify a patient record matching  approach that could be widely adopted and easily integrated into  existing EHR and HIE platforms without significant cost. They noted  the challenge also was an opportunity to encourage organizations to  develop effective matching methods, and to identify a matching  method that did not rely solely on demographic patient information.  CHIME assessed submissions from a range of organizations, but  suspended the challenge in November 2017, reporting that the effort  did not achieve the results it had sought. CHIME members said that  the challenge nonetheless helped draw attention to patient record  matching issues.

In addition, several stakeholders have worked to improve the matching of  medical records specifically for newborns and multiple-birth siblings such  as twins, for whom matching can be particularly challenging:

Representatives we spoke with from one children’s hospital told us  they have implemented indicators in their EHR to highlight when a  child has a twin or other multiple-birth sibling, so that staff know that  another child has similar demographic information. Representatives  said that this helps prevent medical records from one child being  incorrectly matched with the medical records of a sibling. In 2017,  this hospital began working with its health IT vendor to explore the  broader use of a multiple birth indicator to improve the probability of  accurate matching for the multiple birth population between different  vendors’ EHRs. The representatives said that while there is a  standard indicator that can be used for multiple births, many  organizations are not aware of it.

In addition, one researcher we spoke with is studying how using  information such as physicians’ names and parents’ demographic  data could help address record matching challenges for newborns. As  noted earlier, one children’s hospital worked with other local hospitals  to implement a standard for how staff record a temporary name for  newborns.

section_title: Stakeholders Identified Additional Efforts That ONC or Others Could Undertake to Improve Patient Record Matching
paragraphs: Stakeholders we spoke with said more could be done to improve the  ability to accurately match patients’ medical records. The stakeholders  identified several efforts that could improve matching, and had varying  views on the roles ONC and others should play in these efforts. Among  the examples of efforts stakeholders identified that could improve  matching were implementing common standards for demographic data;  developing a data set to test the accuracy of matching methods; sharing  best practices and other resources; implementing a national unique  patient identifier; and developing a public-private collaboration effort to  improve patient record matching. Multiple stakeholders noted that no  single effort would be sufficient to improve matching, given the factors  that contribute to matching challenges. These potential additional efforts  are described below.
subsections: section_title: Implementing Common Standards for Recording Patients’ Demographic Data in Health IT Systems
paragraphs: Several stakeholders told us that implementing common standards for  recording patients’ demographic data in health IT systems could improve  the ability of providers to match patients’ medical records. Stakeholders  said that if providers implemented such standards, it could increase the  extent to which they collect the same types of demographic data or use  the same format for names and addresses as other providers, for  example. However, stakeholders had differing views on how to reach  agreement on and implement common standards among providers, as  well as how feasible it would be to do so. Some said it would be helpful if  ONC established requirements regarding demographic data—such as the  types of data collected, and how it is formatted—potentially through the  EHR certification process. In contrast, other stakeholders saw an  opportunity for industry organizations to voluntarily agree to implement  standards for demographic data. Some stakeholders advocated for EHR  vendors to take steps to standardize the data their products allow  providers to collect. A representative with one hospital said that having  demographic data standards built into EHRs could minimize the amount  of time needed to train staff on how to format the data they collect—and  then to monitor whether they format the data correctly. A number of  stakeholders said that ONC could play a role in getting industry groups to  agree on and implement common data standards. ONC officials noted  that as part of their role in coordinating health IT efforts, they have worked  with industry groups in a number of ways and expect to continue their  coordination efforts.

Some stakeholders we spoke with told us that efforts to implement  common demographic standards could face challenges, such as the  following:

Several said it could be difficult to reach consensus across various  industry organizations on what standards to adopt and implement.

Multiple stakeholders noted that patient preferences could affect the  effectiveness of efforts to standardize data. Patients might not always  be willing to provide some types of data even if providers wanted to  collect it. For example, one provider noted that patients may want to  use their middle name instead of their legal name.

Some stakeholders said it could be time-intensive for providers to  train their staff on how to collect data in accordance with standards, or  that staff might not always follow the standards. For example, a  representative from one hospital that implemented demographic  standards told us that they continuously train staff and perform audits  to ensure that staff follow those standards.

Some said that EHR systems differ in how they allow staff to record  demographic data, which can affect providers’ ability to implement  standards. Some stakeholders said it can be costly for providers to  update or upgrade their EHRs.

Stakeholders cited other potential limitations of data standardization  efforts. Several, for example, said that standardizing data would not  prevent inaccurate or outdated data. In addition, some stakeholders  did not think that data standardization would yield significant  improvements.

section_title: Developing a Data Set to Test the Accuracy of Methods Used to Match Patients’ Medical Records
paragraphs: Several stakeholders told us that developing a standard data set that  organizations could use to evaluate matching methods would be helpful.  Stakeholders noted that such a data set would allow health IT vendors,  providers, or others to assess matching methods independently (instead  of relying on vendors’ reported accuracy rates, for example) and in a  standardized way (by using the same data source). While stakeholders  did not always specify who should develop such a data set, an official  from one stakeholder involved with patient record matching and data  sharing efforts said that the most useful thing ONC could do to address  patient record matching would be to develop a master data set to allow  testing in a uniform way. This official added that without a way to  accurately and uniformly test patient record matching methods, efforts to  improve patient record matching are hindered. A number of stakeholders  did not specifically mention the utility of a data set, but nonetheless  highlighted the importance of testing how well matching methods work.

For its part, ONC officials said that the lack of a data set for evaluating  matching methods is a challenge to efforts to improve matching, and that  developing such a data set would be difficult. They noted that the  agency’s 2017 Patient Matching Algorithm Challenge had highlighted the  difficulties of creating a test data set that closely mimics real world patient  data and that could be used to assess the accuracy of matching  algorithms. ONC officials cited a number of challenges to developing one  test data set for assessing a range of patient matching algorithms. For  example, they said the data set would need to be very large; would  require an extensive and expensive effort to develop; could be difficult to  implement from a practical perspective; and that, because data varies  widely across patient populations and organizations, might have limited  application for assessing algorithms that are designed to match specific  data sets. HHS also stated that the development of a data set would  need to include a “key” of known duplicate patient records—that is, an  indicator of which records in the data set should be matched to the same  individual.

section_title: Sharing Best Practices and Other Resources Used in Matching Patients’ Medical Records
paragraphs: According to a number of stakeholders we spoke with, more could be  done to encourage the sharing of best practices and other patient record  matching resources. For example, representatives from some HIEs said it  would be beneficial to bring organizations together to share lessons  learned and collaborate on best practices for using patient data to match  records. Representatives from one industry association noted that  disseminating information on patient matching errors could help  organizations better understand the extent of matching errors and what  causes them; for example, if information were shared about whether  certain data elements are more likely to cause matching errors or  problems, then organizations could work to prevent the errors or  problems related to those data elements. A few stakeholders said that  efforts to identify and share effective matching algorithms could expand  resources to a broader range of providers. While stakeholders did not  always specify who they thought should identify and share matching  resources, several stakeholders saw the potential for ONC to play a role  in these types of efforts. For example, representatives from one industry  association said that ONC could provide information about the types of  identifiers that could be used to facilitate matching, such as cell phone  numbers or driver’s license numbers. These representatives also said  that ONC could provide information on how to address matching patient  records for children and other individuals who might not have those types  of identifiers. ONC officials noted that they have shared information and  resources about patient matching in a number of ways, such as through  the agency’s Patient Demographic Data Quality Framework. They added  that other organizations, such as the Sequoia Project and Pew Charitable  Trusts, have worked to communicate best practices in this area.

section_title: Implementing a National Unique Patient Identifier
paragraphs: A number of stakeholders noted that implementing a new national, unique  patient identifier specifically for use in health care settings could improve  the ability to match patients’ medical records. For example, having a new  unique number assigned to an individual would reduce the reliance on  demographic data for record matching, according to several stakeholders.

However, stakeholders had differing views on the potential benefits and  feasibility of implementing a new unique patient identifier for health care:

Some stakeholders said that it is unlikely that any new identifier could  be implemented nationwide; they cited reasons such as the  prohibition on federal funds being used to develop a national unique  health care identifier, as well as potential privacy concerns.

Multiple stakeholders cited potential limitations to using a national  patient identifier, noting for example that—as with SSNs—patients  may not be willing to share their identifier, and identifiers could still be  subject to manual data entry errors, data breaches, or fraud.

Some stakeholders said that a unique identifier would be the most  effective way to improve matching. However, others said they did not  believe a new identifier was needed, or did not think a new identifier  would significantly improve matching, given the potential limitations.

HHS stated that health care systems currently rely on a number of  identifiers to match patient records and that a new government- generated identifier would improve matching only if other technical  and non-technical challenges were solved before it was implemented.  The creation, transmission, and capture of a single national patient  identifier across many systems could take decades and would  encounter implementation difficulties, according to HHS.

In addition, a few stakeholders said that patients might be willing to  voluntarily obtain a unique identifier to use across health care settings if  one were available. A representative from one provider association, for  example, said that people with chronic conditions who obtain care from  multiple providers might opt to obtain a unique identifier, if available, to  help match their records. In its 2018 report on patient-empowered  approaches to matching, RAND described various considerations for  implementing a voluntary unique identifier issued by a non-federal entity.  The report cited, for example, one organization’s work to develop a tool to  allow health care providers to offer patients a unique identifier. RAND  stated that although this solution would greatly improve matching if  adopted, there is uncertainty that providers or patients would adopt it.  Representatives from the organization that developed this tool told us that  they had tested it in one location, but that it had not yet been adopted by  providers.

section_title: Developing a Public-Private Collaboration Effort to Improve Patient Record Matching
paragraphs: Multiple stakeholders we spoke with saw a need for a collaborative  public-private effort to help identify and implement efforts to improve  patient record matching. For example, several stakeholders saw a  specific need for a national strategy or approach for addressing patient  record matching issues. Representatives from the Pew Charitable Trusts,  for example, stated that a national strategy—led by the private sector,  with the federal government providing support—could help reach  consensus on ways to improve matching. In addition, one researcher  said that ONC should help facilitate a strategy for addressing patient  record matching at the provider, vendor, and national levels—and that it  would be beneficial for ONC to foster collaboration among private sector  organizations to address matching issues. More generally,  representatives from several provider associations stated that ONC could  play an important role by convening stakeholders to identify ways to  improve patient record matching. As noted earlier, some stakeholders  said that ONC could help industry groups agree on common data  standards for EHRs. While some stakeholders we spoke with said that  ONC should collaborate by supporting private-sector efforts to improve  matching instead of directing those efforts, others said that ONC could  potentially play more of a leadership role. Representatives from one HIE,  for example, said that ONC could lead an overall effort to improve patient  record matching and that private-sector organizations could lead specific  actions within that larger effort.

For their part, ONC officials said that public and private stakeholders  should play a role in efforts to improve patient record matching. According  to ONC officials, while the agency does not have sufficient resources to  support broad implementation of efforts to improve patient record  matching, ONC has collaborated with other stakeholders on various  patient record matching issues.

ONC’s August 2018 Interoperability Forum included a “patient  matching track” where industry stakeholders, such as providers,  health IT vendors, and researchers, discussed matching challenges  and potential solutions. According to ONC officials, this track  covered topics such as patient-empowered solutions to matching,  including smartphone applications; issues when matching patient  medical records across organizations; the development of consensus  on patient matching definitions and metrics; and issues when  matching records for pediatric patients. The outcomes of this track,  according to ONC officials, were increased awareness of a range of  patient matching issues; information sharing among speakers and  participants; and an opportunity to network and potentially collaborate  with individuals on patient matching issues. ONC officials told us that  a takeaway for them was that while various approaches to patient  matching—including technical approaches such as biometrics and  referential matching; efforts regarding unique identifiers; and non- technical approaches such as data quality improvement efforts—may  enhance the capacity for matching, additional research is needed.

ONC participated in the Sequoia Project’s development of that  organization’s Framework for Cross-Organizational Patient Identity  Management. During the 2018 Interoperability Forum, ONC officials  and Sequoia Project representatives presented together about  developing consensus on patient record matching definitions and  metrics. They discussed definitions outlined in the Framework and  encouraged participants to work toward consensus and transparency  when measuring and reporting matching metrics, such as by forming  local and national workgroups, ONC officials said.

Looking forward, ONC and some stakeholders said that the agency’s  current effort to establish a national framework for exchanging health  information electronically is an opportunity for the agency to address  patient record matching challenges. As required by the 21st Century  Cures Act, ONC is taking steps to develop or support a framework for  ensuring the full exchange of health information among health information  networks. ONC has referred to this effort as establishing a “network of  networks,” and it includes the development of a common agreement  among health information networks nationally, which providers and others  can use to facilitate the exchange of electronic health information,  including patients’ health records. As part of this effort, in January 2018,  ONC issued a draft Trusted Exchange Framework that included principles  for the trusted exchange of information, as well as minimum required  terms and conditions for the Common Agreement. ONC plans to provide  funding for an industry entity to incorporate these terms and conditions  into a single Common Agreement that participating Qualified Health  Information Networks (QHIN) and their participants voluntarily agree to  adopt.

While it is too soon to tell how this ONC effort will be implemented,  several stakeholders said that it could potentially improve patient record  matching if, for example, it results in new guidance or standards about  demographic data elements. One HIE organization, for example, said that  it would be beneficial if this effort leverages non-governmental work on  matching and synthesizes this work into guidance for the industry.  According to ONC officials, the framework is expected to affect patient  record matching by requiring participating QHINs to use ONC’s Patient  Demographic Data Quality Framework to evaluate their data practices.  The agency plans to release a second draft Trusted Exchange  Framework and then release a draft Common Agreement and an updated  Trusted Exchange Framework for public comment.

section_title: Agency Comments
paragraphs: We provided a draft of this report to HHS for review and comment. HHS  provided technical comments, which we incorporated as appropriate.

We are sending copies of this report to the appropriate congressional  committees, the Secretary of Health and Human Services, and other  interested parties. In addition, the report is available at no charge on the  GAO website at http://www.gao.gov.

If you or your staff members have any questions about this report, please  contact me at (202) 512-7114 or farbj@gao.gov. Contact points for our  Offices of Congressional Relations and Public Affairs may be found on  the last page of this report.

section_title: Appendix I: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
paragraphs: Jessica Farb, (202) 512-7114 or farbj@gao.gov.

section_title: Staff Acknowledgments
paragraphs: In addition to the contact named above, individuals making key  contributions to this report include Thomas Conahan (Assistant Director),  Robin Burke (Analyst-in-Charge), A. Elizabeth Dobrenz, Krister Friday,  Monica Perez-Nelson, Vikki Porter, and Andrea Richardson.

Fast facts: Health care providers increasingly share records electronically. It can be hard to match records to the right patient, and mistakes can have consequences. For example, a doctor might use information from the wrong record when treating a patient.

Inaccurate, incomplete, or inconsistently formatted data can make record-matching difficult. These data problems can happen for many reasons, e.g., a patient can go by a full name in one record and a nickname in another.

We explored possible solutions, such as requiring a specific format for recording a patient’s name and address, and sharing best practices for record-matching.

Question-summary annotations: questions: question: How do the interviewed stakeholders approach matching patient records?
answer: Stakeholders GAO interviewed, including representatives from physician practices and hospitals, described their approaches for matching patients' records—that is, comparing patient information in different health records to determine if the records refer to the same patient.
child_questions: question: What techniques are common for matching records?
answer: Stakeholders explained that when exchanging health information with other providers, they match patients' medical records using demographic information, such as the patient's name, date of birth, or sex. This record matching can be done manually or automatically.

question: In what ways do stakeholders utilize software for matching?
answer: For example, several provider representatives said that they rely on software that automatically matches records based on the records' demographic information when receiving medical records electronically. Stakeholders said that software can also identify potential matches, which staff then manually review to determine whether the records correspond to the same patient.

question: What factors can complicate matching?
answer: Stakeholders also said that inaccurate, incomplete, or inconsistently formatted demographic information in patients' records can pose challenges to accurate matching. They noted, for example, that records don't always contain correct information (e.g., a patient may provide a nickname rather than a legal name) and that health information technology (IT) systems and providers use different formats for key information such as names that contain hyphens.

questions: question: To what extent have stakeholders improved their data consistency?
answer: Several stakeholders told GAO they worked to improve the consistency with which they format demographic data in their electronic health records (EHR).
child_questions: question: What standards did stakeholders implement?
answer: In 2017, 23 providers in Texas implemented standards for how staff record patients' names, addresses, and other data.
child_questions: question: How did these changes affect hospital operations?
answer: For example, one hospital's representatives said they had seen a significant decrease in the need to manually review records that do not match automatically.

questions: question: What actions have stakeholders taken regarding patient records?
answer: Stakeholders also described efforts to assess and improve the effectiveness of methods used to match patient records.
child_questions: question: How did ONC attempt to improve patient matching?
answer: For example, in 2017 the Office of the National Coordinator for Health Information Technology (ONC) hosted a competition for participants to create an algorithm that most accurately matched patient records.
child_questions: question: What resulted from the competition?
answer: ONC selected six winning submissions and plans to report on their analysis of the competition's data.

questions: question: How could patient matching be improved?
answer: Stakeholders said more could be done to improve patient record matching, and identified several efforts that could improve matching. For example, some said that implementing common standards for recording demographic data; sharing best practices and other resources; and developing a public-private collaboration effort could each improve matching.

question: What were the stakeholders' views on ONC's role?
answer: Stakeholders' views varied on the roles ONC and others should play in these efforts and the extent to which the efforts would improve matching.
child_questions: question: What possible roles could ONC take?
answer: For example, some said that ONC could require demographic data standards as part of its responsibility for certifying EHR systems, while other stakeholders said that ONC could facilitate the voluntary adoption of such standards.

question: How feasible is a single-effort solution?
answer: Multiple stakeholders emphasized that no single effort would solve the challenge of patient record matching.

questions: question: How are most patient records shared now?
answer: Health care providers are increasingly sharing patients' health records electronically.
child_questions: question: Why is record matching important?
answer: When a patient's records are shared with another provider, it is important to accurately match them to the correct patient. GAO and others have reported that accurately matching patient health records is a barrier to health information exchange and that inaccurately matched records can adversely affect patient safety or privacy.

question: What federal entity is in charge of health IT implementation?
answer: At the federal level, ONC is charged with coordinating nationwide efforts to implement and use health IT.

questions: question: How did GAO make use of prior reports?
answer: To do its work, GAO reviewed reports by ONC and others about patient record matching.

question: How did GAO collect information from stakeholders?
answer: GAO also interviewed various stakeholders that play a role in exchanging health records, including representatives from physician practices, hospitals, health systems, health information exchange organizations, and health IT vendors. GAO also interviewed other stakeholders, such as ONC officials, provider and industry associations, and researchers.
child_questions: question: How were stakeholders selected?
answer: GAO selected stakeholders based on background research and input from other stakeholders, and interviewed 37 stakeholders in total.

question: How did HHS respond to the draft report?
answer: HHS provided technical comments on a draft of this report, which GAO incorporated as appropriate.