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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Jessica Farb, (202) 512-7114 or farbj@gao.gov. 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.
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. 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.