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Charge Nurse Expertise: Implications for Decision Support of the Nurse-Patient Assignment Process
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Charge Nurse Expertise: Implications for Decision Support of the Nurse-Patient Assignment Process. |
| 개인저자 | Meyers, Elizabeth Lillian. |
| 단체저자명 | University of Minnesota. Health Informatics. |
| 발행사항 | [S.l.]: University of Minnesota., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 202 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085610018 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Pieczkiewicz, David. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | Each day, across thousands of medical-surgical inpatient nursing units, charge nurses make decisions about which nurse will care for each patient. Recent attempts have been made to introduce health information technology (HIT) solutions to automate the nurse-patient assignment process. This research investigated charge nurse decision making during the nurse-patient assignment process as an exemplar of the larger question: How can we leverage information technology to improve decision making in healthcare, while respecting individual clinician expertise and the unique context of individualized patient care? Four primary questions were used to guide research of the process, decision factors, goals and context of nurse-patient assignments. A mixed-methods approach of qualitative interviews (N = 11) and quantitative surveys (N = 135) was used.Findings related to the charge nurse decision making process indicate that measurable, nurse-sensitive indicators of patient outcomes have not yet been standardized for nurse-patient assignments. HIT solutions and quality improvement efforts should define, collect and analyze measurable outcome criteria prior to attempting to improve or augment existing nurse-patient assignment practices to prevent unintended consequences.When clear outcome measurements have been identified, informatics researchers and professionals should investigate the ability of machine learning to recognize goal priorities and factor weighting from patient, nurse and environmental factors within existing HIT solutions. Until that time, HIT solutions augmenting the nurse-patient assignment process should be designed with flexible configurations, to enable goals, decision factors and factor weights can be varied by hospital, unit, charge nurse and shift, in order to best meet the needs of charge nurses. |
| 일반주제명 | Information technology. Health sciences. Cognitive psychology. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
