HOW NURSES IDENTIFY HOSPITALIZED PATIENTS ON THEIR PERSONAL NOTES: FINDINGS FROM ANALYZING 'BRAINS' HEADERS WITH MULTIPLE RATERS.

The overarching objective of this research is to reduce the burden of documentation in electronic health records by registered nurses in hospitals. Registered nurses have consistently reported that e-documentation is a concern with the introduction of electronic health records. As a result, many nurses use handwritten notes in order to avoid using electronic health records to access information about patients. At the top of these notes are patient identifiers. By identifying aspects of good and suboptimal headers, we can begin to form a model of how to effectively support identifying patients during assessments and care activities. The primary finding is that nurses use room number as the primary patient identifier in the hospital setting, not the patient's last name. In addition, the last name, gender, and age are sufficiently important identifiers that they are frequently recorded at the top of handwritten notes. Clearly distinguishable field labels and values are helpful in quickly scanning the identifier for identifying information. A web based annotator was designed as a first step towards machine learning approaches to recognize handwritten or printed data on paper sheets in future research.