7 tips to improve patient identity processes

Matching people to their medical records is crucial in improving continuity of care.

With provider organizations merging and creating affiliations with other providers as accountable care organizations expand, accurate matching of patients to a unique medical record is crucial; yet, it’s challenging as patients receive care at various points within a highly complex healthcare system. Critical and potentially fatal errors can be made when a patient or the surgical area of a patient is incorrectly identified.

7 tips to improve patient identity processes

A new report from health information management consultancy Just Associates examines the patient-matching landscape and identifies seven critical issues providers need to get right.

1. Capture data

Data capture plays a tremendous role in the creation of duplicate patient information. Front-end registration processes and optimizing algorithms on the back-end are equally important. If data is not captured accurately, according to policy and complete in all required fields, the patient’s incorrect health information is initially fractured and subsequently cascades throughout multiple downstream health information systems.

2. Standardize data

Standardizing data capture by increasing the number of primary data values and incorporating secondary data values will support sharing accurate patient information across electronic health records to downstream systems and to multiple data partners, such as insurance payers or health information exchange organizations.

3. Trusted data

Data analytics and informatics rely on trusted data that is complete and reliable, no matter the source of truth. When there are significant amounts of duplicate and overlaid records, the value of analytics is significantly diminished.

4. Biometrics beckon

Biometrics, such as iris recognition and palm vein scanning techniques, hold great promise in minimizing the creation of new duplicates. Implementation considerations should address historical records or multiple duplicates that already exist within a health system, as these would not likely contain biometric images until many years down the road. The real value of the new technology will be realized once critical mass has been achieved.

5. Algorithm challenges

Advanced record matching algorithms are an important piece to the overall patient-matching solution, but they are not the “silver bullet.” Most provider organizations do not have access to such algorithms. Further, the “black box” in these products works differently across various vendors and have varying degrees of record-match accuracy and error tolerance. In addition, the effectiveness of these advanced algorithms is at the mercy of the quality and completeness of the patient identity data attributes captured.

6. Police your policies

Policies are critical, but they must be reviewed and upheld. Some healthcare organizations have excellent policies regarding patient medical record number creation processes. However, unless they are monitored for compliance and updated as needed, they may become outdated and disregarded.

7. Data integrity team

Training is essential to patient matching “baseline” practices. The data integrity team must be updated on current tools and expectations, including topics like feedback protocols for the patient registration/access team.

For additional information on this topic

The full report is available here.

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