What HIOs can Learn from the ONC Patient Matching Report
When the subject of healthcare comes up in conversation, nearly everyone has a story to share about a mistake that could potentially have impacted their care or that of a loved one. Indeed, more than 195,000 deaths occur in the U.S. each year due to medical errors, a whopping 60 percent of which are attributable to improper patient identification.
When the subject of healthcare comes up in conversation, nearly everyone has a story to share about a mistake that could potentially have impacted their care or that of a loved one. Indeed, more than 195,000 deaths occur in the U.S. each year due to medical errors, a whopping 60 percent of which are attributable to improper patient identification.
Compare that to the airline industry. U.S. carriers flew nearly 737 million passengers domestically and internationally. Of 132 million U.S. flights, 41 included high risk mistakes by air traffic controllers.
If the airline industry can support a zero-tolerance policy when it comes to passenger safety, so too can healthcare. A key driver is a better, industry-wide approach to patient matching.
A good start took place on December 16, 2013, when the Office of the National Coordinator for Health Information Technology (ONC) released its draft report on patient matching challenges and best practices. The report, which assessed current industry capabilities and best practices, delivered eight recommendations designed to address the most vexing issues faced by large health systems and health information organizations (HIOs).
1. Require standardized patient identifying attributes.
2. Introduce certification criteria requiring certified EHR technology to capture standardized patient identifying attributes.
3. Study the ability of additional, non-traditional data attributes to improve patient matching.
4. Develop or support an open source algorithm for vendors to use in building or testing the accuracy of patient matching algorithms.
5. Introduce certification requiring certified EHR technology to perform patient matching and generate potential duplicate patient records reports.
6. Convene industry stakeholders to consider a more formal structure for establishing best practices for the matching process and data governance.
7. Develop best practices and policies to encourage consumers to keep their information current and accurate.
8. Work with professional associations and the Safety Assurance Factors for EHR Resilience Guide initiative to develop and distribute educational and training materials for accurately capturing and consistently verifying patient data attributes.
Perhaps the most important lesson we can derive from ONCs report is that HIOs do, in fact, have a responsibility to manage data to ensure accurate patient matching. Until now, there wasnt a broad awareness within many of these organizations that they held this responsibility. As a result, many implemented policies with the very real potential to damage data integrity and jeopardize patient safety.
For example, some HIOs expect participating organizations staffs to log directly into the HIOs system to merge duplicates and fix overlaid records. This creates a liability for the HIO and the participating organization when audit logs arent accurate or systems are out of sync. A smarter, safer approach is to correct data issues in the source system and send an electronic message with the correction to the HIOs system.
HIOs can extrapolate and implement a number of other best practices from ONCs recommendations. This includes thorough investigation of a system algorithms real record matching capabilities by threshold testing to eliminate auto-linking of false-positive matches and optimize functionality to reduce both false-alarm and false-negative matches.
During front end record searches, HIO database users should be required to enter several identifying data points, such as Last Name, First Name, DOB, Gender or a combination of First Name, DOB and phone number. The threshold or match weights to include a patient record in the list returned by the search should be set very high. A conservative approach minimizes false positives and lessens the potential for inadvertent disclosure. Another protective measure to optimize patient matching is to store and utilize the last four digits of a patients Social Security Number but never reveal them to the HIO user.
Every HIO should establish a data governance sub-committee of the HIOs board of directors to put in place a strong information/data governance program. This program should 1) define data fields to populate and set minimum percentages that must be populated with valid data, 2) determine a formula for calculating intra-facility duplicate record rates and establish the maximum allowed and 3) include a comprehensive data sharing agreement. Compliance with data governance should be a condition of participation.
Data management policies and procedures should also be developed and responsibilities established based upon industry best practices. For example, source facilities should merge same system confirmed duplicates and correct demographic data errors in their systems, then send electronic update messages to the HIO for processing. The same should occur when the source facility corrects overlaid records in its systemsubmit an electronic update message to the HIO for processing for those records that can be corrected by an update message or contact the HIO for those that cannot.
HIO staff should be trained on correcting cross-system overlaps and the systems record matching algorithms effectiveness should be audited periodically. Dashboard metrics should be reported both to the HIO governing body and each source organization to allow source organizations to benchmark data, providing value to each HIO member.
Finally, HIOs should ensure sufficient staff resources to review potential cross-facility matches and communicate same-system duplicates to the originating facility for correction. Data integrity errors should also be communicated, including records without enough data to verify identify and potential overlays. This communication is another value-add the HIO can provide to its member organizations.
Patient matching is a challenge for the healthcare industry as a whole and HIOs in particular because of the need to ensure accuracy across organizations. By leveraging the lessons learned from the environmental scan, input currently being solicited on the ONCs draft report and subsequent recommendations from ONC, it is possible to support a zero tolerance error policy for patient record matching and identification.
Beth Haenke Just, MBA, RHIA, FAHIMA is founder and CEO of Just Associates (www.justassociates.com), a healthcare data integration consulting firm focused on patient matching, data integrity, data migration and health information exchange.
Compare that to the airline industry. U.S. carriers flew nearly 737 million passengers domestically and internationally. Of 132 million U.S. flights, 41 included high risk mistakes by air traffic controllers.
If the airline industry can support a zero-tolerance policy when it comes to passenger safety, so too can healthcare. A key driver is a better, industry-wide approach to patient matching.
A good start took place on December 16, 2013, when the Office of the National Coordinator for Health Information Technology (ONC) released its draft report on patient matching challenges and best practices. The report, which assessed current industry capabilities and best practices, delivered eight recommendations designed to address the most vexing issues faced by large health systems and health information organizations (HIOs).
1. Require standardized patient identifying attributes.
2. Introduce certification criteria requiring certified EHR technology to capture standardized patient identifying attributes.
3. Study the ability of additional, non-traditional data attributes to improve patient matching.
4. Develop or support an open source algorithm for vendors to use in building or testing the accuracy of patient matching algorithms.
5. Introduce certification requiring certified EHR technology to perform patient matching and generate potential duplicate patient records reports.
6. Convene industry stakeholders to consider a more formal structure for establishing best practices for the matching process and data governance.
7. Develop best practices and policies to encourage consumers to keep their information current and accurate.
8. Work with professional associations and the Safety Assurance Factors for EHR Resilience Guide initiative to develop and distribute educational and training materials for accurately capturing and consistently verifying patient data attributes.
Perhaps the most important lesson we can derive from ONCs report is that HIOs do, in fact, have a responsibility to manage data to ensure accurate patient matching. Until now, there wasnt a broad awareness within many of these organizations that they held this responsibility. As a result, many implemented policies with the very real potential to damage data integrity and jeopardize patient safety.
For example, some HIOs expect participating organizations staffs to log directly into the HIOs system to merge duplicates and fix overlaid records. This creates a liability for the HIO and the participating organization when audit logs arent accurate or systems are out of sync. A smarter, safer approach is to correct data issues in the source system and send an electronic message with the correction to the HIOs system.
HIOs can extrapolate and implement a number of other best practices from ONCs recommendations. This includes thorough investigation of a system algorithms real record matching capabilities by threshold testing to eliminate auto-linking of false-positive matches and optimize functionality to reduce both false-alarm and false-negative matches.
During front end record searches, HIO database users should be required to enter several identifying data points, such as Last Name, First Name, DOB, Gender or a combination of First Name, DOB and phone number. The threshold or match weights to include a patient record in the list returned by the search should be set very high. A conservative approach minimizes false positives and lessens the potential for inadvertent disclosure. Another protective measure to optimize patient matching is to store and utilize the last four digits of a patients Social Security Number but never reveal them to the HIO user.
Every HIO should establish a data governance sub-committee of the HIOs board of directors to put in place a strong information/data governance program. This program should 1) define data fields to populate and set minimum percentages that must be populated with valid data, 2) determine a formula for calculating intra-facility duplicate record rates and establish the maximum allowed and 3) include a comprehensive data sharing agreement. Compliance with data governance should be a condition of participation.
Data management policies and procedures should also be developed and responsibilities established based upon industry best practices. For example, source facilities should merge same system confirmed duplicates and correct demographic data errors in their systems, then send electronic update messages to the HIO for processing. The same should occur when the source facility corrects overlaid records in its systemsubmit an electronic update message to the HIO for processing for those records that can be corrected by an update message or contact the HIO for those that cannot.
HIO staff should be trained on correcting cross-system overlaps and the systems record matching algorithms effectiveness should be audited periodically. Dashboard metrics should be reported both to the HIO governing body and each source organization to allow source organizations to benchmark data, providing value to each HIO member.
Finally, HIOs should ensure sufficient staff resources to review potential cross-facility matches and communicate same-system duplicates to the originating facility for correction. Data integrity errors should also be communicated, including records without enough data to verify identify and potential overlays. This communication is another value-add the HIO can provide to its member organizations.
Patient matching is a challenge for the healthcare industry as a whole and HIOs in particular because of the need to ensure accuracy across organizations. By leveraging the lessons learned from the environmental scan, input currently being solicited on the ONCs draft report and subsequent recommendations from ONC, it is possible to support a zero tolerance error policy for patient record matching and identification.
Beth Haenke Just, MBA, RHIA, FAHIMA is founder and CEO of Just Associates (www.justassociates.com), a healthcare data integration consulting firm focused on patient matching, data integrity, data migration and health information exchange.
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