title: Identification of Hypertension Management-related Errors in a Personal Digital Assistant-based Clinical Log for Nurses in Advanced Practice Nurse Training.
abstract: OBJECTIVE
  The purposes of this study were to develop a taxonomy for detection of errors related to hypertension management and to apply the taxonomy to retrospectively analyze the documentation of nurses in Advanced Practice Nurse (APN) training.
  
  
  METHODS
  We developed the Hypertension Diagnosis and Management Error Taxonomy and applied it in a sample of adult patient encounters (N = 15,862) that were documented in a personal digital assistant-based clinical log by registered nurses in APN training. We used Standard Query Language queries to retrieve hypertension-related data from the central database. The data were summarized using descriptive statistics.
  
  
  RESULTS
  Blood pressure was documented in 77.5% (n = 12,297) of encounters; 21% had high blood pressure values. Missed diagnosis, incomplete diagnosis and misdiagnosis rates were 63.7%, 6.8% and 7.5% respectively. In terms of treatment, the omission rates were 17.9% for essential medications and 69.9% for essential patient teaching. Contraindicated anti-hypertensive medications were documented in 12% of encounters with co-occurring diagnoses of hypertension and asthma.
  
  
  CONCLUSIONS
  The Hypertension Diagnosis and Management Error Taxonomy was useful for identifying errors based on documentation in a clinical log. The results provide an initial understanding of the nature of errors associated with hypertension diagnosis and management of nurses in APN training. The information gained from this study can contribute to educational interventions that promote APN competencies in identification and management of hypertension as well as overall patient safety and informatics competencies.
authors.affiliation: Full-time Instructor and Researcher, Research Institute of Nursing Science, Seoul National University College of Nursing, Seoul, Korea.
authors.forename: Nam-Ju
authors.initials: NJ
authors.lastname: Lee
authors.affiliation: Assistant Professor and Researcher, Nursing Policy Research Institute Yonsei University College of Nursing, Seoul, Korea.
authors.forename: Eunhee
authors.initials: E
authors.lastname: Cho
authors.affiliation: Alumni Professor of Nursing and Professor of Biomedical Informatics, Columbia University School of Nursing, New York, USA.
authors.forename: Suzanne
authors.initials: S
authors.lastname: Bakken