Metrics for Electronic-Nursing-Record-Based Narratives: cross-sectional analysis

Appl Clin Inform. 2016 Nov 30;7(4):1107-1119. doi: 10.4338/ACI-2016-07-RA-0119.

Abstract

Objectives: We aimed to determine the characteristics of quantitative metrics for nursing narratives documented in electronic nursing records and their association with hospital admission traits and diagnoses in a large data set not limited to specific patient events or hypotheses.

Methods: We collected 135,406,873 electronic, structured coded nursing narratives from 231,494 hospital admissions of patients discharged between 2008 and 2012 at a tertiary teaching institution that routinely uses an electronic health records system. The standardized number of nursing narratives (i.e., the total number of nursing narratives divided by the length of the hospital stay) was suggested to integrate the frequency and quantity of nursing documentation.

Results: The standardized number of nursing narratives was higher for patients aged ≥ 70 years (median = 30.2 narratives/day, interquartile range [IQR] = 24.0-39.4 narratives/day), long (≥ 8 days) hospital stays (median = 34.6 narratives/day, IQR = 27.2-43.5 narratives/day), and hospital deaths (median = 59.1 narratives/day, IQR = 47.0-74.8 narratives/day). The standardized number of narratives was higher in "pregnancy, childbirth, and puerperium" (median = 46.5, IQR = 39.0-54.7) and "diseases of the circulatory system" admissions (median = 35.7, IQR = 29.0-43.4).

Conclusions: Diverse hospital admissions can be consistently described with nursing-document-derived metrics for similar hospital admissions and diagnoses. Some areas of hospital admissions may have consistently increasing volumes of nursing documentation across years. Usability of electronic nursing document metrics for evaluating healthcare requires multiple aspects of hospital admissions to be considered.

Keywords: Electronic health records; narrative analysis; narrative evaluation; nursing informatics; nursing records.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cross-Sectional Studies
  • Diagnosis
  • Documentation / methods*
  • Electronic Health Records*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Nursing*
  • Patient Admission

Grants and funding

Funding: Financial support for this study was provided by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2013R1A1A3012306); no financial relationships with any organizations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.