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

Summary

ObjectivesWe 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.

MethodsWe 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.

ResultsThe 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).

ConclusionsDiverse hospital admissions can be consistently described with nursing-documentderived 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.

Citation: Kim K, Jeong S, Lee K, Park H-A, Min YH, Lee JY, Kim Y, Yoo S, Doh G, Ahn S. Metrics for electronicnursing-record-based narratives: cross-sectional analysis.

Keywords

Electronic health records - nursing informatics - nursing records - narrative evaluation - narrative analysis

• References

• 1 Weng C, Appelbaum P, Hripcsak G, Kronish I, Busacca L, Davidson KW, Bigger JT. Using EHRs to integrate research with patient care: promises and challenges. J Am Med Inform Assoc 2012; 19 (05) 684-687.
• 2 Capan M, Wu P, Campbell M, Mascioli S, Jackson VE. Using electronic health records and nursing assessment to redesign clinical early recognition systems. Health Systems. 2015
• 3 Cai X, Perez-Concha O, Coiera E, Martin-Sanchez F, Day R, Roffe D, Gallego B. Real-time prediction of mortality, readmission, and length of stay using electronic health record data. J Am Med Inform Assoc 2016; 23 (03) 553-561.
• 4 McCoy TH, Castro VM, Cagan A, Roberson AM, Kohane IS, Perlis RH. Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study. PLoS One 2015; 10 (08) e0136341.
• 5 Mathias JS, Agrawal A, Feinglass J, Cooper AJ, Baker DW, Choudhary A. Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data. J Am Med Inform Assoc 2013; 20 (e1): e118-124.
• 6 Marier A, Olsho LE, Rhodes W, Spector WD. Improving prediction of fall risk among nursing home residents using electronic medical records. J Am Med Inform Assoc 2016; 23 (02) 276-282.
• 7 Castro VM, McCoy TH, Cagan A, Rosenfield HR, Murphy SN, Churchill SE, Kohane IS, Perlis RH. Stratification of risk for hospital admissions for injury related to fall: cohort study. Bmj 2014; 349: g5863.
• 8 Collins SA, Cato K, Albers D, Scott K, Stetson PD, Bakken S, Vawdrey DK. Relationship between nursing documentation and patients’ mortality. Am J Crit Care 2013; 22 (04) 306-313.
• 9 Min YH, Park HA, Chung E, Lee H. Implementation of a next-generation electronic nursing records system based on detailed clinical models and integration of clinical practice guidelines. Healthc Inform Res 2013; 19 (04) 301-306.
• 10 Park HA, Cho I, Ahn HJ. Use of narrative nursing records for nursing research. Nurs Inform 2012; 2012: 316.
• 11 International Classification of Diseases (ICD). Geneva: World Health Organization [Internet]; Available from: http://www.who.int/classifications/icd/en
• 12 Cohen R, Elhadad M, Elhadad N. Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies. BMC Bioinformatics 2013; 14: 10.
• 13 Wrenn JO, Stein DM, Bakken S, Stetson PD. Quantifying clinical narrative redundancy in an electronic health record. J Am Med Inform Assoc 2010; 17 (01) 49-53.
• 14 Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J Am Med Inform Assoc 2005; 12 (05) 505-516.
• 15 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med 2010; 363 (06) 501-504.
• 16 Spellman SKennebeck, Timm N, Farrell MK, Spooner SA. Impact of electronic health record implementation on patient flow metrics in a pediatric emergency department. J Am Med Inform Assoc 2012; 19 (03) 443-447.
• 17 Sittig DF, Singh H. Electronic health records and national patient-safety goals. N Engl J Med 2012; 367 (19) 1854-1860.
• 18 Poulin C, Shiner B, Thompson P, Vepstas L, Young-Xu Y, Goertzel B, Watts B, Flashman L, McAllister T. Predicting the risk of suicide by analyzing the text of clinical notes. PLoS One 2014; 09 (01) e85733.
• 19 Reed M, Huang J, Brand R, Graetz I, Neugebauer R, Fireman B, Jaffe M, Ballard DW, Hsu J. Implementation of an outpatient electronic health record and emergency department visits, hospitalizations, and office visits among patients with diabetes. JAMA 2013; 310 (10) 1060-1065.
• 20 Hanauer DA, Ramakrishnan N, Seyfried LS. Describing the Relationship between Cat Bites and Human Depression Using Data from an Electronic Health Record. PLoS One 2013; 08 (08) e70585.
• 21 Makam AN, Nguyen OK, Moore B, Ma Y, Amarasingham R. Identifying patients with diabetes and the earliest date of diagnosis in real time: an electronic health record case-finding algorithm. BMC Med Inform Decis Mak 2013; 13: 81.
• 22 Association CN. Measuring nurses’ workload. Nursing Now: Issues and trends in Canadian Nurs. 2003
• 23 Morris R, MacNeela P, Scott A, Treacy P, Hyde A. Reconsidering the conceptualization of nursing workload: literature review. J Adv Nurs 2007; 57 (05) 463-471.
• 24 Weis JM, Levy PC. Copy, paste, and cloned notes in electronic health records: prevalence, benefits, risks, and best practice recommendations. Chest 2014; 145 (03) 632-638.
• 25 Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013; 309 (13) 1351-1352.