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PURPOSE: To develop the revised Belgian nursing minimum data set (B-NMDS). METHODS: The Nursing Interventions Classification (NIC, 2nd edition) was used as a framework. Six expert nurse panels (cardiology, oncology, intensive care, pediatrics, geriatrics, chronic care) were consulted. Seventy-nine panelists completed standardized e-mail questionnaires and discussed results in face-to-face meetings. FINDINGS: We initially selected 256 of 433 NIC interventions. After panel discussions, plenary meetings, and pretesting, the revised B-NMDS (alpha version) contained 79 items covering 22 NIC classes and 196 NIC interventions. CONCLUSIONS: Consensus building promoted acceptance of the B-NMDS, while the NIC provided a good theoretical basis and guaranteed international comparability. IMPLICATIONS FOR NURSING PRACTICE: The revised B-NMDS instrument can be used to visualize nursing activities in different applications (e.g., financing, staffing allocation).

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BACKGROUND: Internationally, nursing is not well represented in hospital financing systems. In Belgium a nursing weight system exists to adjust budget allocation for differences in nurse staffing requirements, but there is a need for revision. Arguments include the availability of a nursing minimum dataset and the adverse consequences of the current historically based nursing weight system. OBJECTIVES: The development and validation of nursing resource weights for the revised Belgium nursing minimum dataset (NMDS). DESIGN: Two independent cross sectional Delphi-surveys. SETTING AND PARTICIPANTS: A convenience sample of 222 head nurses from 69 Belgian hospitals participated in the cross sectional survey methods. To assess validity 112 patient case records from 61 nursing wards of 35 Belgian general hospitals representing general, surgical, pediatric, geriatric and intensive care were selected. METHODS: Nursing resource weights were constructed based on Delphi survey results by NMDSII intervention. The patient case Delphi survey results were used as the primary source for validation. A series of additional validation measures were calculated, based on the different patient classification systems. Finally, three validated nursing resource weighting systems were compared to the constructed NMDSII weighting system: the use of 'Closon', 'Ghent' and WIN weights. RESULTS: A coherent set of nursing resource weights was developed. The comparison of nurse resource weights, based on the survey per NMDS intervention versus the survey on patient cases, yielded high correlations: r=0.74 to r=0.97 (p<0.01) between three case rating questions, as an indication of reliability in terms of internal consistency, and r=0.90 (p<0.01) between summed intervention weights and patient case weights, as an indication of criterion validity in terms of concurrent validity. Other concurrent validity measures based on summed intervention weights versus patient classification dependency weights showed a correlation ranging from r=0.14 to r=0.74. The correlation of summed intervention weights with the Closon, Ghent and WIN weights ranged from r=0.93 to r=0.96 (p<0.01), as a third indication of concurrent validity. CONCLUSIONS: A system of valid nursing resource weights has been developed. The system should be further validated within an international context.

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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.

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The process of revising the Belgian Nursing Minimum Dataset (B-NMDS) started in 2000 and entailed four major phases. The first phase (June-October 2002) involved the development of a conceptual framework based on a literature review and secondary data analysis. The Nursing Interventions Classification (NIC) was selected as a framework for the revision of the original B-NMDS. The second phase (November 2002-September 2003) focused on language development for six care programs evaluated by panels of clinical experts (N=75). These panels identified the following items as priorities for the revised B-NMDS: hospital financing, nurse staffing allocation, assessment of the appropriateness of hospitalisation, and quality management. During this period, we developed a draft instrument with 92 variables using the NIC. This led to an alpha version of a revised B-NMDS. The third phase (October 2003-December 2004) focused on data collection and validation of the new tool. The revised B-NMDS (alpha version) was tested in 158 nursing wards in 66 Belgian hospitals from December 2003 until March 2004. This test generated data for some 95,000 in-patient days. The interrater reliability of the revised B-NMDS was assessed. The criterion-related validity of the revised B-NMDS was compared to that of the original B-NMDS. The discriminative power of the revised B-NMDS was also assessed to select the most relevant variables for data collection. This resulted in a beta version of the revised B-NMDS in December 2004. The records of the revised B-NMDS were linked to the Hospital Discharge Dataset and other mandatory datasets to integrate the revised B-NMDS into the overall healthcare management system. The fourth phase (January 2005-December 2005) is presently focusing on information management. Nationwide implementation is foreseen by January 2007.

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This paper describes the process of revising the Belgian Nursing Minimum Data Set (NMDS). The study started in 2000. Implementation is planned from 2006. The project is divided in 4 major phases. The first phase (June-October 2002) implied the development of the conceptual framework based on literature review and secondary data-analysis. The Nursing Interventions Classification (NIC) was selected as framework for the revision of the NMDS. The second phase focused on the language development (November 2002 - September 2003) with panels of clinical experts (N=75) for six care programs. They indicated hospital financing, nurse staffing allocation and assessment of the appropriateness of hospitalization as priorities of a revised B-NMDS. A draft instrument with 84 variables, using NIC, was developed during this period. This leads to an alpha version of a revised NMDS. The third phase (October 2003 - December 2004) focused on the data collection and validation of the new tool. The new NMDS was tested on 158 nursing wards in 66 Belgian hospitals from December 2003 until March 2004. This test generated data for some 95.000 inpatient days. The interrater-reliability of the revised NMDS is tested. The criterion-related validity of the revised NMDS is compared with the actual NMDS. The discriminative power of the revised NMDS is tested to select the most relevant items for data collection. This will result in a beta version of revised NMDS in December 2004. The records of the revised NMDS are linked with the hospital discharge dataset and other mandatory datasets to integrate the revised NMDS in the broader health care management. The fourth phase (January-December 2005) will focus on information management.

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