How Nurses Identify Hospitalized Patients on Their Personal Notes: Findings From Analyzing ‘Brains’ Headers with Multiple Raters

Abstract

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.

Value and Usage of a Workaround Artifact: A Cognitive Work Analysis of “Brains” Use by Hospital Nurses

“…The interrater agreement was confirmed to be acceptable (Cohen’s kappa = 0.94) using the codebook for content categories on the “brain.” We divided the analysis into content included in the “header” and “body” of the “brain.” All of the “brains” included a header which was unique to the patient (see Sarkhel et al, 2018, for the characteristics of a good and bad header design based on multiple raters analyzing the “brain” headers). The header is defined as the information displayed in an area at the top of the page, visually separated with white space or a line from the remaining body content, which included patient identifier information and other patient information deemed of high importance by the nurse.…”

Nursing Handover: A Content Analysis of Nurses’ Handwritten Notes

“…Although using room numbers is an unacceptable method for identifying a patient (WHO, 2007), they were frequently handwritten on handover tools. Sarkhel et al (2018) found hospital nurses primarily use room numbers as patient identifiers due to geographic location serving as a static memory aid. Nurses often are assigned a group of rooms on a given shift regardless of patient turnover due to admissions and discharges.…”

Interpretable Multi-headed Attention for Abstractive Summarization at Controllable Lengths

“…how the summaries came to be, the answer still remains illusive. Explaining how a machine-generated summary was constructed, has become a necessity under the newly introduced General Data Privacy Regulation Act (ITGP, 2017), especially for applications in enterprise (Sarkhel and Nandi, 2019;Keymanesh et al, 2020) and biomedical domain (Moradi and Ghadiri, 2018;Sarkhel et al, 2018). Some recent efforts have proposed using interpretable heatmaps (Baan et al, 2019) generated from the attention distribution over an input sequence for interpreting model behaviour.…”