• Journal of Korean Academy of Nursing
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• v.31 no.3
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• pp.401-416
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• 2001

현재의 보건의료체계에서는 모든 것이 급박하게 변화하고 있으며 또 구체적인 자료를 요구한다. 컴퓨터의 보급과 함께 이러한 변화에 능동적으로 대처하기 위해 간호학에서도 표준화된 대규모 데이터베이스의 개발이 필수적이다. Nursing Minimum Data Set (NMDS)은 간호학분야에서 개발된 최초의 표준화된 대규모 데이터 베이스로서, 간호가 일어나는 모든 상황에서 반드시 수집되어야 할 핵심적인 간호요소를 포함하고 있다. 따라서 본 논문에서는 NMDS 개발의 역사적인 배경, 목적, 요소, 그리고 간호계의 세계적인 동향과 관련하여 NMDS가 이루어야 할 방향, 그리고 NMDS를 완성하기 위해 선행되어 할 문제로 표준화된 분류체계에 대해 논의하였다. 그리고 미국이외에도 몇몇나라에서 NNDS나 혹은 유사한 데이터베이스가 개발 중이거나 이미 수집되고 있는 나라들이 있으므로 이들에 대한 비교와 분석도 제시하였다. 그리고 보다 최근에 개발된 데이터 베이스로 주로 행정적인 목적을 위해 개발된 Nursing Management Minimum Data Set (NMMDS)을 소개하였다. 즉 NMDS가 임상적인 자료의 수집에 초점을 맞춘 데 비해, NMMD는 효과적인 간호관리에 필수적인 요소들을 포함시켰다. 그래서 간호행정가들이 의사결정에 필요한 재정적자원, 환경적자원, 간호자원에 대한 정보를 수집할 수 있게 고안되었다. 이러한 데이터 베이스들은 관계형 데이터베이스로 서로 연결되어야 하며, 다른 학문분야와도 연계되어 활용되어져야 할 것이다. 만약 이러한 대규모 데이터베이스 들이 한국에서도 개발되고 사용되어 진다면 환자간호에 더욱 비용 효과적인 관리가 가능하게 될 것이다. 마지막으로 우리나라에서 NMDS나 NMMDS 같은 대규모데이터 베이스의 개발이 시급히 요청됨을 강조하였다.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.1
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• pp.14-22
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• 2006

Purpose: The purpose of this study is developing the nursing information system which contains the core elements of nursing practice, the Nursing Minimum Data Set (NMDS) that should be collected and documented all the settings in which nursing care is provided. Method: The program was developed under the hospital information system by TCP/IP protocol and used NANDA, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC) to fill out the elements of NMDS. The Oracle was used as DBMS under the Windows 98 environment and Power Builder 5.0 was used as a program language. Results: This study developed linkage among the NANDA-NOC-NIC to facilitate choosing correct nursing diagnosis, interventions, and outcomes and stimulate nurses' critical thinking. Also the system developed includes nursing care sensitive patient outcomes, so nurses can actively involve in nursing effectiveness research by analyzing the data stored in the database or by making relational databases with other health care related databases. Conclusion: The program developed in this study ultimately can be used for the nursing research, policy development, reimbursement of nursing care, and calculating staffing and nursing skill mix by providing tool to describe and organize nursing practice and measure the nursing care effectiveness.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.652-661
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• 2006

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. Conclusions. This study demonstrated the utilization of data mining method through a large data set with stan dardized language format to identify the contribution of nursing care to patient's health.