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The glossary of recommended primary and secondary data elements in can be used to ensure consistency of data elements and provide structure for data entry where free text is required. Lusk, mhsm, rhia neysa noreen, rhia godwin okafor, rhia, fac-ppm, fac-cor kimberly peterson, mhim, rhia, chts-ts and erik pupo, mba, cphims, fhimss nationwide initiatives designed to improve the efficiency, safety, and quality of the delivery of healthcare are driving the adoption of interoperable health information exchange (hie). Adoption of sophisticated patient matching algorithms and integration profiles a fundamental and critical success factor for hie is the ability to accurately link multiple records for the same patient across the disparate systems of the participating organizations.

It is paramount that organizations seek to establish a real-time automated patient matching process. Algorithms can support many of the patient matching functions envisioned in hie. Audacious inquiry, llc, prepared for the office of the national coordinator for health information technology, september 30, 2012.

Many hies have adopted patient identification approaches that use a unique identifier data element to establish identification within the boundaries of the hie itself. Data integrity improves with the elimination of free text and the utilization of national data standards. For hie to be successful, standards for data capture, definitions, and formatting must be developed to allow an electronic system to accurately identify patients across disparate ehr systems.

The authors thank patricia buttner, rhia, cdip, ccs melanie endicott, mbahcm, rhia, cdip, ccs, ccs-p, fahima beth just, mba, rhia, fahima annessa kirby harry b. . A upi can be provided to a patient by a regulatory body or authority.

For example, can never be used to identify a patient except in conjunction with more reliable information. In addition to patient care concerns, sharing inaccurate information also poses the risk of privacy breaches and erodes consumer confidence in the benefits of hie. Standardizing data capture through the use of existing national standards, increasing the number of primary data elements, and incorporating secondary data elements will provide a means to accurately identify participants in hie.

The xcpd profile has seen widespread adoption as a standard for query-based exchange of patient records, and, in addition to a patient matching algorithm, xcpd andor pix and pdq transactions can be used to help link multiple patient identities within or across healthcare communities. Lusk, mhsm, rhia, is the chief health information management and exchange officer at childrens health system of texas in dallas, tx. A patient match error could result in significant patient safety events, corrupt an organizations medical records, and put lives at risk. This trend results in an increased need for organizations to share data, but the lack of a patient matching standard has prevented successful exchange. Neysa noreen, rhia, is a data integrity and applications manager at childrens hospitals and clinics of minnesota in minneapolis, mn.


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Research paper buy Australia As health it innovation and system interoperability needs continue to grow, ensuring that patient data are accurate will be a key concern of many healthcare providers, Structured values (such as gender, race, or marital status) can help facilitate patient matching with a deterministic algorithm, but the process becomes more challenging when dealing with variations in free-text elements, such as a persons name, or when demographics may have been captured incorrectly, such as an incorrect number in a patients date of birth or social security number. This growing demand for hie brings the challenges of accurate patient identification to the forefront. By genevieve morris, greg farnum, scott afzal, carol robinson, jan greene, and chris coughlin. Privacy and security solutions for interoperable health information exchange perspectives on patient matching approaches, findings, and challenges httpwww. Statisticalmathematical algorithms assign weights to near matches of data elements and then determine the probability of a match between the patient records.
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    The adoption of a nationwide patient matching strategy that standardizes a set of patient demographic elements stored in a standard format would support existing models of patient matching such as the federated identity knowledge discovery model one of the most common solutions for patient matching has been to create a unique patient identifier. By genevieve morris, greg farnum, scott afzal, carol robinson, jan greene, and chris coughlin. Two primary types of algorithms can be used to determine matching patient records deterministic and statisticalmathematical algorithms. One of the most well-known of these profiles is the integrating the health enterprise (ihe) cross-community patient discovery (xcpd) profile, which allows for patient identification (pix) and patient demographic query (pdq) transactions to be conducted to facilitate patient matching across multiple organizations within a single hie. No consensus exists regarding patient matching accuracy thresholds, and each organization employs its own matching algorithm and patient matching methods, resulting in inconsistent results across the industry.

    Basic algorithms that compare selected data elements, such as name, date of birth, and gender, are the simplest technique for matching records. One of the largest unresolved issues in the safe and secure electronic exchange of health information is a nationwide patient data matching strategy that would ensure the accurate, timely, and efficient matching of patients with their healthcare data across different systems and settings of care. Audacious inquiry, llc, prepared for the office of the national coordinator for health information technology, september 30, 2012. Existing standards that are widely accepted in the marketplace, such as the united states postal service (usps) address definitions and the council for affordable quality healthcare (caqh) and uniform hospital discharge data set definitions, provide a means to normalize data across disparate systems. In this approach, mathematical calculations and predefined rules are applied to pairs of patient records to facilitate matching of patient identifiers.

    The primary challenge with this type of algorithm is that data elements must be exact for a match to be recognized, and any variation in elements is considered nonmatching, resulting in many false negatives and duplicate patient records. Health level 7 (hl-7), accredited standards committee x12 (asc x12), and caqh standards and recommendations from organizations including the national committee on vital and health statistics, the healthcare information and management systems society (himss), the onc, the office of management and budget, and the usps. Integrity of patient identity in health information exchange (updated). Standardizing data element capture across the market will affect vendors financially and result in some time constraints in ehr architecture building. Connecting health and care for the nation a 10-year vision to achieve an interoperable health it infrastructure httphealthit. Another challenge in the us healthcare system is that names are not unique and often change during a persons lifetime or are presented differently. Ihe it infrastructure (iti) technical framework supplement 2009-2010 cross-community access (xca) httpwww. While the organizational impact of increased data entry is a consideration, the capture of additional data elements enables significant improvement of patient linking accuracy until a unique patient identifier becomes available or biometric technology improves, providing a more cost-effective matching method. Standardization is also needed at the source of the data because individual healthcare organizations have different patient naming conventions, use different methods for identifying duplicate patient records in their own systems, and may have multiple records for a patient within their own ehr systems. Lusk, mhsm, rhia, is the chief health information management and exchange officer at childrens health system of texas in dallas, tx.

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    Standardizing data element capture across the market will affect vendors financially and result in some time constraints in ehr architecture building. The adoption of a nationwide patient matching strategy that standardizes a set of patient demographic elements stored in a standard format would support existing models of patient matching such as the federated identity knowledge discovery model one of the most common solutions for patient matching has been to create a unique patient identifier. The creation of local hie patient matching architectures has generally not been successful in the united states because of the contention over the use of a universal patient identifier. Lusk, mhsm, rhia neysa noreen, rhia godwin okafor, rhia, fac-ppm, fac-cor kimberly peterson, mhim, rhia, chts-ts and erik pupo, mba, cphims, fhimss nationwide initiatives designed to improve the efficiency, safety, and quality of the delivery of healthcare are driving the adoption of interoperable health information exchange (hie) Buy now Research paper buy Australia

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    Increasing the data elements utilized and incorporating standard data definitions into technical requirements for person capture provides a solid foundation regardless of the algorithm. For hie to be successful, standards for data capture, definitions, and formatting must be developed to allow an electronic system to accurately identify patients across disparate ehr systems. Rhodes, mba, rhia, chps, cdip, cphims, fahima and sheldon h. This approach has long been one of the most contentious issues in healthcare privacy because of uncertainty as to who provides and maintains control of the patient identifier. Statisticalmathematical algorithms assign weights to near matches of data elements and then determine the probability of a match between the patient records Research paper buy Australia Buy now

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    This approach has long been one of the most contentious issues in healthcare privacy because of uncertainty as to who provides and maintains control of the patient identifier. Advanced algorithms contain the most sophisticated set of tools for matching records and rely on mathematical theory and statistical models to determine the likelihood of a match. Lack of a standard data set can lead to patient records not being linked to one another in the hie, resulting in an incomplete health record being available to the provider for the patient being treated, thereby defeating the purpose of the hie. Regardless of which algorithm is used, healthcare organizations use of consistent standards for patient identification will facilitate accurate patient matching Buy Research paper buy Australia at a discount

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    Secondary data recommendations increase matching probability in the pediatric population and also serve as an additional level for data triangulation in the adult population. The primary challenge with this type of algorithm is that data elements must be exact for a match to be recognized, and any variation in elements is considered nonmatching, resulting in many false negatives and duplicate patient records. Master data management within hie infrastructures a focus on master patient indexing approaches. Currently, organizations are matching patient records within their own system but face challenges in incorporating patient matching techniques across care settings and different ehr systems Buy Online Research paper buy Australia

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    The authors thank patricia buttner, rhia, cdip, ccs melanie endicott, mbahcm, rhia, cdip, ccs, ccs-p, fahima beth just, mba, rhia, fahima annessa kirby harry b. When all ehr systems capture and store patient demographic elements in the same format, algorithms will be able to match patient records consistently within and across healthcare organizations. For hie to be successful, standards for data capture, definitions, and formatting must be developed to allow an electronic system to accurately identify patients across disparate ehr systems. Even more concerning is the potential for different patients being identified as the same, resulting in the possibility of improper care rendered on the basis of inaccurate patient information Buy Research paper buy Australia Online at a discount

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    Lusk, mhsm, rhia neysa noreen, rhia godwin okafor, rhia, fac-ppm, fac-cor kimberly peterson, mhim, rhia, chts-ts and erik pupo, mba, cphims, fhimss nationwide initiatives designed to improve the efficiency, safety, and quality of the delivery of healthcare are driving the adoption of interoperable health information exchange (hie). In the vision statement, the onc reported that it will also address critical issues such as data provenance, data quality and reliability, and patient matching to improve the quality of interoperability, and therefore facilitate an increased quantity of information movement. Neysa noreen, rhia, is a data integrity and applications manager at childrens hospitals and clinics of minnesota in minneapolis, mn Research paper buy Australia For Sale

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    . Thousands of different algorithms use statistical and mathematical constructs for patient record matching, and advanced algorithms often utilize a combination of many different algorithms. This goal can be accomplished with the standardization of primary and secondary data elements, and adoption of a uniform data capture methodology. Instituting a standard format and accepted definitions for data element capture minimizes the burden on staffing in routine business operations, providing long term financial relief. Erik pupo, mba, cphims, fhimss, is a specialist leader of federal health at deloitte consulting llp in arlington, va.

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    Regardless of which algorithm is used, healthcare organizations use of consistent standards for patient identification will facilitate accurate patient matching. Errors in patient matching will only be compounded as healthcare organizations contend with advances in technology and the development and expansion of the ehealth exchange (formerly known as the nationwide health information network). The xcpd profile has seen widespread adoption as a standard for query-based exchange of patient records, and, in addition to a patient matching algorithm, xcpd andor pix and pdq transactions can be used to help link multiple patient identities within or across healthcare communities. Many of the current hie architecture designs revolve around control being placed into the hands of the hie Sale Research paper buy Australia

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