ABSTRACT
BACKGROUND: The Belgian Nursing Minimum Data Set (B-NMDS) is a nationwide registration of 23 nursing activities. It was developed for the measurement of nursing care in acute hospitals. It is used to support healthcare management & policy decision making such as hospital financing and nurse staffing decisions. OBJECTIVE: To develop a measure of the intensity of nursing care based on information that is available in the B-NMDS. DESIGN-SETTING-PARTICIPANTS: Retrospective analysis of the B-NMDS from all Belgian acute hospitals (n=115) for the year 2003. The sample included 690,258 inpatient days for 298,691 patients that were recorded from 1637 acute care nursing units. METHODS: The 23 nursing activities were synthesized into one new latent variable by PRINQUAL analysis. This variable was evaluated as a measure of nursing care intensity using key characteristics of the San Joaquin patient classification system, which was designed to measure the intensity of nursing care. RESULTS: The main NMDS component from the PRINQUAL analysis accounted for 26.8% of the variance. The distribution of inpatient days over all four San Joaquin categories is: 11.1% (self-care); 40.4% (average care); 30.8% (above average care); 17.7% (intensive care). In 97.5% of the nursing units the intensity of nursing care score of the B-NMDS (main NMDS component) followed the ordinality of the San Joaquin classification system. Furthermore, the San Joaquin categories alone explained more variability--70.2% in the intensity of nursing care measure (main NMDS component) than did other determinants, such as department type, age, diagnostic-related groups (DRG) and severity of illness, hospital type and hospital size together. CONCLUSION: The B-NMDS is an instrument that produces a measure of the intensity of nursing care in acute hospitals.
Subject(s)
Nursing Care , Belgium , Nursing Staff, Hospital , Retrospective StudiesABSTRACT
Hospital governance refers to the complex of checks and balances that determine how decisions are made within the top structures of hospitals. This article explores the essentials of the concept by analysing the root notion of governance and comparing it with applications in other sectors. Recent developments that put pressure on the decision-making system within hospitals are outlined. Examples from the UK, France and the Netherlands are presented. Based on an evaluation of the current state of affairs, a research framework is developed, focusing on the determinants of governance configurations within the national healthcare systems and the wider legal and socio-economic context, as well as on the impact of governance configurations on the efficiency of the governing bodies and overall hospital performance. The article concludes with a preview of the European Hospital Governance Project, which follows the outlines of the described research framework. New techniques of data mining that are used in this project are explained by means of a real data example.
Subject(s)
Decision Making, Organizational , Governing Board , Health Care Reform/trends , Health Services Research/methods , Hospital Administration/standards , Management Audit , Medical Audit , Models, Organizational , Total Quality Management/methods , Efficiency, Organizational , Europe , Facility Regulation and Control/trends , Foundations/economics , France , Hospital Administration/trends , Humans , Netherlands , Social Responsibility , United KingdomABSTRACT
The Ministry of Public Health commissioned a research project to the Catholic University of Leuven and the University Hospital of Liège to revise the Belgian Nursing Minimum Dataset (B-NMDS). The study started in 2000 and will end with the implementation of the revised B-NMDS in January 2007. The study entailed four major phases. The first phase involved the development of a conceptual framework based on a literature review and secondary data analysis. The second phase focused on language development and development of a data collection tool. The third phase focused on data collection and validation of the new tool. In the fourth phase the validity and reliability of the dataset was tested. The new dataset is without avail if it is not leading to new information. Four applications of the dataset has been defined from the beginning: evaluation of the appropriateness of stay (AEP) in the hospital, nurse staffing, hospital financing and quality management. The aim of this paper is to describe how the B-NMDS can contribute to each of these applications.
