[{"innerBlocks":[],"name":"core\/freeform","postId":1348,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"While healthcare providers are increasingly sharing electronic health records, they continue to face challenges in matching patients to their EHRs, according to the Government Accountability Office.

Patients are matched to their medical records\u2014either manually or automatically via software\u2014through demographic information, such as name, date of birth or sex.

However, a GAO audit<\/a> based on stakeholder interviews found that \u201cinaccurate, incomplete or inconsistently formatted demographic information in patients\u2019 records\u201d is posing challenges to matching medical records. Particularly problematic is the matching of medical records for newborns and multiple-birth siblings such as twins, according to the report.

A provision in the 21st Century Cures Act called on the GAO to review patient record matching efforts in the context of EHRs.

In particular, auditors noted that EHRs \u201cdon\u2019t 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.\u201d

As a result, GAO warned that inaccurately matched records can negatively impact patient safety in cases where incorrect data are used to make medical decisions.

Also See<\/b>: Why patient matching is key to interoperability and patient safety<\/a>

\u201cStakeholders said more could be done to improve patient record matching, and identified several efforts that could improve matching,\u201d states the GAO\u2019s report. \u201cAmong 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.\u201d

Specifically, some stakeholders interviewed by auditors indicated that the Office of the National Coordinator for Health IT could require demographic data standards as part of ONC\u2019s EHR certification responsibilities, while others said the agency could facilitate the voluntary adoption of these standards.

Another problem identified by the GAO is the difficulty providers experience in determining the accuracy of health IT tools used to match patients\u2019 medical records.

\u201cWhile 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,\u201d states the report. \u201cStakeholders 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.\u201d

Auditors also reported that, according to the Department of Health and Human Services, the \u201cproprietary nature of many patient matching algorithms makes it difficult to assess their effectiveness.\u201d

ONC officials referenced in the GAO\u2019s audit cited a number of challenges to developing a test data set for assessing a range of patient matching algorithms.

\u201cThe 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,\u201d auditors quoted ONC officials as saying.

Nonetheless, ONC and some stakeholders indicated in the GAO report that ONC\u2019s current effort to establish a national framework for exchanging health information electronically could address some patient record matching challenges.

\u201cAs 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,\u201d states the audit. \u201cONC has referred to this effort as establishing a \u2018network of networks,\u2019 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\u2019 health records.\u201d","saveContent":"While healthcare providers are increasingly sharing electronic health records, they continue to face challenges in matching patients to their EHRs, according to the Government Accountability Office.

Patients are matched to their medical records\u2014either manually or automatically via software\u2014through demographic information, such as name, date of birth or sex.

However, a GAO audit<\/a> based on stakeholder interviews found that \u201cinaccurate, incomplete or inconsistently formatted demographic information in patients\u2019 records\u201d is posing challenges to matching medical records. Particularly problematic is the matching of medical records for newborns and multiple-birth siblings such as twins, according to the report.

A provision in the 21st Century Cures Act called on the GAO to review patient record matching efforts in the context of EHRs.

In particular, auditors noted that EHRs \u201cdon\u2019t 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.\u201d

As a result, GAO warned that inaccurately matched records can negatively impact patient safety in cases where incorrect data are used to make medical decisions.

Also See<\/b>: Why patient matching is key to interoperability and patient safety<\/a>

\u201cStakeholders said more could be done to improve patient record matching, and identified several efforts that could improve matching,\u201d states the GAO\u2019s report. \u201cAmong 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.\u201d

Specifically, some stakeholders interviewed by auditors indicated that the Office of the National Coordinator for Health IT could require demographic data standards as part of ONC\u2019s EHR certification responsibilities, while others said the agency could facilitate the voluntary adoption of these standards.

Another problem identified by the GAO is the difficulty providers experience in determining the accuracy of health IT tools used to match patients\u2019 medical records.

\u201cWhile 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,\u201d states the report. \u201cStakeholders 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.\u201d

Auditors also reported that, according to the Department of Health and Human Services, the \u201cproprietary nature of many patient matching algorithms makes it difficult to assess their effectiveness.\u201d

ONC officials referenced in the GAO\u2019s audit cited a number of challenges to developing a test data set for assessing a range of patient matching algorithms.

\u201cThe 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,\u201d auditors quoted ONC officials as saying.

Nonetheless, ONC and some stakeholders indicated in the GAO report that ONC\u2019s current effort to establish a national framework for exchanging health information electronically could address some patient record matching challenges.

\u201cAs 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,\u201d states the audit. \u201cONC has referred to this effort as establishing a \u2018network of networks,\u2019 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\u2019 health records.\u201d","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

GAO: Patient matching hampered by demographic data

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