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A Comparison of Nursing Minimal Data Sets Essay.

A Comparison of Nursing Minimal Data Sets Essay.

Although many systematic collections of health care data exist around the world, nursing data are usually absent from these systems.
Clark and Lang argue that it is becoming important for professional nurses all over the world to make visible what nurses contribute to health care. A powerful yet limited set of nursing data, often promoted as a nursing minimal data set (NMDS),
could be useful in making delivered nursing care visible. An NMDS has been defined
by Werley and others as ”a minimum data set of items of information with uniform definitions and categories concerning the specific dimension of nursing,
which meets the information needs of multiple data users in the health care system. The NMDS includes those specific items of information that are used on a regular basis by the majority of nurses across all types
of settings in the delivery of care.” Thus, an NMDS would add specific information to existing minimal data sets (MDSs) and statistics in health care.
Five aspects are important with respect to an NMDS.
First, pertinent data items need to be identified as the
Affiliations of the authors: School of Nursing, Noordelijke
Hogeschool Leeuwarden, Leeuwarden (WTFG), Leidse
Hogeschool, Leiden (PJMME), University Hospital Nijmegen,
Sint Raboud (TF), University of Groningen (TWND, WJAvdH),
University of Maastricht (AH), The Netherlands.
Correspondence and reprints: William T. F. Goossen, RN, BSN,
CertEdN, School of Nursing, Noordelijke Hogeschool Leeuwar-
den, P.O. Box 1080, 8900 Leeuwarden, The Netherlands.
variables we want information about. Second, each variable needs to be defined accurately—what is it and what is it not. Third, the universe of possible values for each variable or data item must be deter-
mined; in nursing these can be lists of agreed terminology, for example. Fourth, the actual patient data
can be documented in the patient record with use of the appropriate terminology for particular variables.
Finally, these patient data from individual records can be aggregated and coded into databases for different purposes of health care management, research, and policy.
The possibility of an NMDS for the Netherlands has
not been investigated. Several health care organizations in this country have, however, expressed an interest in a database that includes nursing data. The purpose of this paper is, therefore, to compare several
NMDS systems to see what can be learned from them
and applied to the successful development and implementation of an NMDS in the Netherlands. The
questions addressed in this selected review are: 1)
What are advantages and disadvantages of NMDSs,
and is there empirical evidence for these? 2) What are the nursing data currently included in the different
NMDS systems; how are the data collected, stored,
and analyzed; and what feedback information is aggregated and used?
To answer these questions we first describe developments toward national and international NMDS systems. Next, we examine the benefits and limitations of existing NMDSs. We then compare the data elements, the purpose and scope of the data sets, and the methods of data management, analysis, and feedback of five NMDSs. Finally, we discuss future developments in the area of NMDS systems and a possible strategy for implementation in the Netherlands.

Nursing Minimal Data Sets
The Purpose and Development of NMDS Systems
Within health care, there is an urgent need for the systematic collection of nursing care data in order to
”make visible what nurses do” and to facilitate comparison, quality assurance, management, research,
and funding of nursing care.
discuss the advantages for the health care com- munity and for patients of the inclusion of nursing
data into health care statistics. They argue that nursing information provides insight into patient care that
has never been available before, including information about health problems as they relate to patients’
life processes. These extend beyond the disease and
the costs of providing medical and surgical services to include the costs of nursing care directed toward
health problems. They also extend beyond morbidity and mortality outcomes measurement to include details about functional status.


Two countries that have NMDS systems in use are the United States and Belgium. The initiative for an
NMDS started in the U.S., and different uses of the U.S. NMDS have been reported.
Since January 1, 1988, all Belgian general hospitals are required by law
to collect data for an NMDS four times a year (the
”Minimale Verpleegkundige Gegevens (MVG)/Re´sume´ Infirmier Minimum (RIM)”).

In addition, an MDS for Belgian psychiatric hospitals—the Minimale
Psychiatrische Gegevens (MPG)—has recently been
established. This is a multidisciplinary data set that includes patient problems and nursing care.

Other countries are also developing NMDS systems.
In Australia, the objective of the Community Nursing Minimum Data Set Australia (CNMDSA)

is to introduce standardization and comparability into the collection of a minimal set of data to describe community
nursing. No actual data collection has yet been reported from there. Anderson and Hannah assert the
need for an NMDS in Canada. The Alberta Association of Registered Nurses suggested the inclusion of
nursing components into the Hospital Medical Records Institute (HMRI) database, which are addressed
as Health Information: Nursing Components.

In Switzerland, the need for an NMDS is recognized, as is the need to first standardize nursing terminology.
A Swiss NMDS is under development.
Other initiatives in the systematic collection of data
about nursing do not include nursing care items at
this stage. Germain describes the Programme de
Me´dicalisation due Syste`me d’Information (PMSI), an
experiment in the collection of data about the intensity of nursing care in France. This French system
does not include data that describe nursing care with respect to patient problems, nursing interventions, and patient outcomes. The National Health Service (NHS) in England has established an Information
Management and Technology Strategy in which clinical data about nursing care are included.

There is
no explicit description of an NMDS in this strategy, however. Wheeller
19,20 provides more detail on MDS in the UK, but essential nursing care description items  seem to be absent at this stage.
Another development in the area of nursing data includes multidisciplinary databases and health information strategies. In the UK, the Core Community
Minimum Data Set Scotland (EPPIC/CCMDS) includes nursing data in a multidisciplinary data set for
use in automated records.

Modern database and information technology offer possibilities to collect nursing data once and use them for the different purposes, which were identified earlier.
Also, the Information Management and Technology Strategy of the
NHS in England focuses on data use from clinical systems without the need for other systems to capture
information specifically for management purposes.
Epping et al.

suggest a similar approach for the development of a nursing information strategy for the
Two other projects with a broader, international scope
are ongoing: the first is the project and the
second is the international development of the Resident Assessment Instrument (RAI). The
project, funded by the European Union, contains components for: 1) the development of nursing vocabulary and classifications; 2) an NMDS; 3) clinical systems to record nursing data (as nursing diagnoses,
interventions, and outcomes); 4) information systems to collect and aggregate these data; and 5) systems to
analyze data, produce information, and provide feedback about nursing care for decision making on different levels.
Individual institutions in the following countries participate in the project: Belgium, Denmark, Finland, Greece, Iceland, Italy, Portugal, Switzerland, Great Britain, and The Nether-
lands. Other institutions and countries have expressed
their interest.
The RAI is a multidisciplinary data set that contains nursing care data about patients and clients in nursing homes, among others.
The RAI provides a structure and language for understanding long-term care and

., Comparing Nursing Minimal Data Sets design care plans, evaluating quality, and describing the nursing facility population for planning and policy efforts.
This RAI data set is currently used in nursing homes internationally, including in the Netherlands.
Nursing Data in the Netherlands
In the Netherlands, different professional organiza-
tions have expressed an interest in developing an
NMDS. Goossen and Epping
proposed the devel-
opment of a Dutch NMDS system that could be used
for the purposes identified by Werley et al.
budgeting, determining the effectiveness of care, presenting epidemiologic data on nursing problems, and
supporting policy making.
In the Dutch situation
it is not possible, however, to test all the possible benefits and uses of an NMDS in a single project. A cur-
rent project develops an initial version of an NMDS
that can be used by acute-care hospitals in the Netherlands for resource planning. In future projects other
uses and sectors can be addressed.
There is a rationale for this approach. The National
Organization for Nursing and Care in the Netherlands
investigated the possibility of developing a system to
compare nursing care data for policy development,
funding, budgeting, and staff allocation in the Dutch
health-care system.
The current way of determining
budgets for nursing care in the Netherlands includes
the many different systems and methods in use
among different health sectors, different institutions in
each sector, and different types of wards in each institution. This situation shows various results on the
actual allocation of nursing staff.
Thus, there is an urgent need for Dutch nurses to adopt comparable data to influence policymaking and financing of nursing care.
The development of a single national budget parameter for nursing care in the Netherlands is considered unfeasible, however, since the Dutch government allows consumers and insurance companies to influence the costs and quality of health care in local and regional markets.
Furthermore, one hospital in the Netherlands is participating in the project to test the nursing
interventions terms from the International Classification for Nursing Practice (ICNP).* The
pilot will thus gain experience that could be relevant for further work on a Dutch NMDS. Since there are no results available at this stage, it is not possible to tell whether the initiative can be adopted in
*In a joint effort with the International Council of Nurses in Geneva, the ICNP alpha version is established in the project.
the Netherlands. On the other hand, international cooperation and comparison is one of the goals of an
NMDS and is of interest for Dutch nurses.
Benefits and Pitfalls of NMDS
Advantages of Systematic Collection of Nursing Data Werley et al.
describe a number of benefits that an
NMDS for nursing might provide:
䡲 An NMDS would make it possible to describe pa-
tient problems across settings, clinical populations,
geographic areas, and time; identify nursing diagnoses; determine what nursing interventions or
nursing actions are taken; observe nurse-sensitive patient outcomes; and assess what nurse resources are used in the provision of nursing care.
䡲 If the data were part of ongoing nurse documentation and were computerized in such a way that they could be readily retrieved, nursing professionals for the first time would be in an excellent position to compare and contrast nursing practice at different levels; offer testimony on critical nursing and health care issues; develop databases needed to conduct clinical research; assess the cost-effectiveness of nursing interventions for different nursing diagnoses; and assess the costs of nurse resources and provide data to influence health
policy making.
䡲 Through the linkages between nursing and other
professional databases, nurses could share data
with various health providers and researchers and
at the same time have access to their own data.
Besides these expectations, there is empirical evidence
of the benefits of an NMDS
䡲 In Belgium it is possible to make nursing data vis-
ible in figures and graphic representations; base
staff allocation; partly on the MVG/RIM data; per- form clinical, quality assurance, and epidemiologic studies
; allocate resources based on MVG/RIM in general hospitals
; and, since 1994, base financing of nursing care at the national level on MVG/RIM
䡲 Saba and Zuckerman
found that medical diagnoses in nursing homes did not sufficiently predict the
intensity of nursing care and the necessary allocation of staff. The NMDS elements in the Home
Health Care Classification System could do this.
䡲 Delaney et al.
established the research value of an NMDS, which proved to be: 1) a cost-effective data abstraction tool, which reduced the costs of record retrieval from $20.20–$82.50 per patient record for manual retrieval to $0.05–$0.50 per patient record for electronic retrieval; 2) a valuable instrument for producing patient profiles by nursing diagnosis group; 3) a tool for establishing retrospective validation of the defining characteristics of nursing diagnoses; 4) a useful means of determining the costs

of direct nursing care; and 5) a means of forecasting
frequency and trends in nursing diagnoses. A Comparison of Nursing Minimal Data Sets Essay.
䡲 Ryan et al.


compared the prevalence of nursing
diagnoses for several medical diagnoses and sur-
gical procedures by analyzing a sample from 13,135
hospital admissions during 1991. The five most fre-
quent nursing diagnoses for each medical diagnosis
and surgical procedure—of which pain and injury
potential were most prevalent—suggest that only
specific nursing diagnoses were used for each med-
ical diagnosis and procedure.
Problems and Limitations of an NMDS
Although NMDS systems have many advantages,
they are not always beneficial. Identified problems in
the areas of comparability and systematic collection of health care data are as follows:
䡲 Sometimes the concepts of MDS data items (variables) match, but the definitions of the MDS elements (in data dictionaries, for example) differ. Wheeller
19,20 presents examples, such as the concept
of ”date of admission.” Possible definitions for this
concept are the date of first contact with a health care provider, the date of entering an emergency room, and the date on which a patient is admitted to a ward in a hospital. Those differences can cause problems when actual patient data from different sources are collected and compared. An actual value for patient G could be admission to ward X
in Hospital L on December 6, 1997, whereas patient H is admitted to the emergency room of hospital M on December 6, 1997. The data are equivalent on a
conceptual level, but the instantiations are not perfectly comparable. Furthermore, MDSs in one coun-
try can differ.
䡲 A unified and standardized nursing vocabulary and terminology system for nursing diagnoses, interventions, and outcomes is a prerequisite for an NMDS.
Although such material is currently available for inclusion in nursing databases and in- formation systems,
there are still problems to be addressed, such as the lack of defined relationships between nursing diagnoses, interventions, and outcomes.
䡲 Vocabularies differ in specificity and detail. For example, a very fine grained vocabulary has been derived from patient care records and represents the
terms nurses actually use, whereas a much less detailed vocabulary has been incorporated into an elaborate nursing classification.
It has been suggested that the Metathesaurus of the Unified Medical Language System (UMLS) could map linkages
between the specific terms and the classification
䡲 A single vocabulary that fits all purposes, or links
all terms, is considered a myth, and we need to look carefully at the purpose of the data we need and the vocabulary useful for those data.
䡲 The issue of ownership may be a problem. The CNMDSA project showed that developers often are not eager to ”throw away” their ”own” definitions and codes.
To adhere to national and international standards, it might be necessary to change the existing consensus on the nursing care items.
䡲 Architects of an NMDS must create mechanisms
that address informed consent issues and take measures to protect privacy.

argue that approval from committees for the protection of human subjects should be obtained prior to abstraction of nursing minimum data from patient records.
䡲 Ryan and Delaney
also assert that the reliability
and validity of the database need to be assessed.
Often these are confused with the validity and re-
liability of the classification system, but the authors stress the importance of looking at the actual data.
Relevant measures would be to inspect the data and coding and to calculate the frequencies of each element. For computerized data, a few cases should be checked against actual patient records.
䡲 High costs of updating NMDS systems include: upgrading existing data collections; changing the methodology, instruments, or classifications; changing information systems; and educating new users.
䡲 Few electronic patient record systems allow direct retrieval of nursing minimal data.
Comparison of Different NMDS Systems
A comparison of NMDS systems at national and international levels seems feasible so long as differences in stages of development and use are taken into account and made explicit. For this comparison, NMDS systems were selected on the basis of literature searches using Medline, RN-Index, and EMBASE. Using the key words ”nursing minimum data set,”

., Comparing Nursing Minimal Data Sets
Table 1

Comparison of Nursing Minimal Data Sets (NDMSs): Purpose and Population
USA Belgium Australia Canada Europe
Name of data set
Nursing Minimum
Data Set (NMDS)
Minimale Verpleeg-
kundige Gegevens/
Re´sume´ Infirmier
Minimum (MVG/
Community Nursing
Minimum Data Set
Health Information:
Nursing compo-
nents (HI:NC)
& International Classification of Nursing Practice (ICNP)
Purpose Describe and compare nursing care
Demonstrate and analyze trends in nursing care
Support nursing research
Base policy on factual data
Bridge gap between variability of daily nursing practice and policymaking
Describe health status
Allow for clinical nursing research
Determine costs and effectiveness of nursing care
Determine intensity of nursing care
Determine hospital budgets and staffing
Compare performance of institutions
Allocate resources
Monitor and compare health status of the population
Deliver information
Deliver information about nursing care
Demonstrate unique contribution of nurses to the health of Canadians
Determine feasibility of nursing data collection and comparison in Europe
Make visible what nurses do
Collect nursing data that have been documented with use of the ICNP

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