Use of standardized nursing terminologies in electronic health records for oncology care

Abstract

The purpose of this study was to identify the characteristics of cancer patients and the most frequently chosen nursing diagnoses, outcomes and interventions chosen for care plans from a large Midwestern acute care hospital. In addition the patients' outcome change scores and length of stay from the four oncology specialty units are investigated. Donabedian's structure-process-outcome model is the framework for this study. This is a descriptive retrospective study. The sample included a total of 2,237 patients admitted on four oncology units from June 1 to December 31, 2010. Data were retrieved from medical records, the nursing documentation system, and the tumor registry center. Demographics showed that 63% of the inpatients were female, 89% were white, 53 % were married and 26% were retired. Most patients returned home (82%); and 2% died in the hospital. Descriptive analysis identified that the most common nursing diagnoses for oncology inpatients were Acute Pain (78%), Risk for Infection (31%), and Nausea (26%). Each cancer patient had approximately 3.1 nursing diagnoses (SD=2.5), 6.3 nursing interventions (SD=5.1), and 3.7 nursing outcomes (SD=2.9). Characteristics of the patients were not found to be related to LOS (M=3.7) or outcome change scores for Pain Level among the patients with Acute Pain. Specifically, 88% of patients retained or improved outcome change scores. The most common linkage of NANDA-I, NOC, and NIC (NNN), a set of standardized nursing terminologies used in the study that represents nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions, prospectively, was Acute Pain-Pain Level-Pain Management. Pain was the dominant concept in the nursing care provided to oncology patients. Risk for Infection was the most frequent nursing diagnosis in the Adult Leukemia and Bone Transplant Unit. Patients with both Acute Pain and Risk for Infection may differ among units; while the traditional study strategies rarely demonstrate this finding. Identifying the pattern of core diagnoses, interventions, and iii ACKNOWLEDGMENTS It is hard to express my gratitude to my PhD advisor, Professor Sue Moorhead. Without her inspirational guidance, her enthusiasm, her encouragement, her unselfish help, I could have never finished my doctoral work at the University of Iowa. My special thanks go also to the members of my advisory committee for their guidance and helpful discussions. I am grateful for the support from the hospital, Department of Nursing, the Cancer Center, and the Institute for Clinical and Translational Science. I want to thank the Nursing Informatics specialist, Teresa Clark in the Department of Nursing, the health information technicians, Julie Risinger and Tania Viet in the Cancer Center, the data analyst, Lisa Sturtz in the Joint Office for Compliance, and all the team that assisted me during the data collection process. I would like to thank Elena Perkhounkova, who trained me to manage the data for data analysis. I would like to express my thankfulness to Sharon Sweeney, my wa...

Nursing diagnoses among oncology patients in medical units: a retrospective study of patients’ records

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Reinterpreting the nursing record for an electronic context

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