An Evaluation of Community Nursing Process in the Frame of the International Classification for Nursing Practice

University of Ljubljana, University College of Health Care,
Poljanska 26a,  SI-1000 Ljubljana, Slovenia
Vladislav RAJKOVIC, Miroljub KLJAJIC
University of Maribor, Faculty of Organizational Sciences, Presernova 11, SI-4000 Kranj,
and “Jozef Stefan” Institute, Jamova 39, SI-1000 Ljubljana, Slovenia
Abstract. The evaluation of client’s/patient’s health condition is the basis for determination  of nursing problems/diagnosis and the nursing planing. Changed health condition to  better is also the indication of successful   performance  of  nurse’s  work.  The  evaluation  of  the appropriateness  of  nursing   interventions  is   based  on  positive alternations of  individual daily  activities. That is why a constant, in-fixed-intervals  monitoring of client’s changed health condition on the  basis  of   attributes  or  indicators,  determined   within  the individual  daily activities,  is suggested.  At that stage a problem, how  to  aggregate the  individual  estimates  of indicators  into  an overall   health  condition   evaluation,  emerges.   A  multicriteria decision-making  theory  is used for  that  purpose.  Practically that means  the introduction  of the measuring  scale of 3 or 5 descriptive values and the  aggregate function into the  hierarchically structured indicators,   which   are   in   accordance  with   the  International Classification for  Nursing  Practice (ICNP). In the following article the  evaluation case  using the  shell of  expert system DEX (Decision Expert) will be presented. The logical  operations and the transparent interpretation of the evaluation within  the expert system DEX enables a full presentation of the aggregated knowledge.

[title & authors]
[1. Introduction]
[2. DEX - An Expert System Shell for Multi Attribute Evaluation] [3. Evaluation of Client’s/Patient’s Daily Activity] [4. Conclusion and Further Work]

1. Introduction

Community nursing  (CN) is defined as a special form of health  care that assures an active health and social care of individuals, families and communities that are, due to their biological features or a particular disease, more exposed to harmful effects from the environment. In Slovenia it includes: health and social treatment of individuals, families and communities, nursing of women in childbed, new-born child at their homes and nursing of patients at their homes. Community nurse works on primary, secondary and tertiary prevention and for promotion of health [1][2][3i].

CN is an integral part of the primary health care and it plays an important role in implementing WHO goals. In the process of CN is the individual, his family and community the subject of treatment. V. Henderson´s and D. E. Orem´s nursing theories are theoretical basis for philosophy,  scope and the nature of the CN [4][5]. They are orientated into responsibility for individual own health and into support of risk groups. That is why their implementation value is in accordance with the WHO strategy’s goals. Both theories define fourteen daily activities but none of the theories has its objective evaluation system for evaluating the achieved or altered health condition of individual, patient or community installed [12][13]. The evaluation of the client’s health condition is the basis for the determination of nursing problems or nursing diagnosis and nursing planing. This provides CN with feedback which is not only essential for CN activity but it also enhances  it’s  quality and effectiveness. The evaluation of the appropriateness nursing activities is based on positive alternations of basic daily activities. When monitoring the client’s health conditions alternations in the fixed–intervals in daily activities we are met with the problem of aggregating the evaluated indicators into overall evaluation of the client’s  or patient’s health condition and his independence.

2. DEX - An Expert System Shell for Multi Attribute Evaluation

DEX closely follows the concept of multi-attribute decision making [6], which is based on the decomposition of a decision problem into smaller less complex problems. Options are decomposed on to different dimensions, usually called attributes, performance variables, criteria, etc. These are evaluated independently. The total utilities of options are then obtained by some aggregation procedure for example a weighted sum. The procedure is designed by the decision maker as to best represent his or her preference knowledge about the options. The obtained utilities are finally used for option evaluation.

In DEX, this approach is combined with some elements of expert systems and machine learning [7][8]. Attributes and aggregation procedures are treated as an explicit knowledge base that consists of:  (1) tree of criteria, (2) aggregation procedures expressed by decision rules, and (3) description of options. The components will be presented in next section.

DEX basically consists of two operational parts: (1) knowledge based acquisition and (2) evaluation and analysis of options. The first part supports the user in designing the criteria tree and decision rules for particular problem. This is actually the process of decision problem structuring and preference knowledge elicitation, which is continuously supervised by computer based tools for checking the consistency of rules. The second part of DEX, which is actually shown in Figure 1, utilises the so acquired knowledge base to evaluate and analyse options. At the beginning, each option is described by a set of values that correspond to the leaves  of the criteria tree. DEX then evaluates each option according to the knowledge base, i.e., according to the structure of the criteria tree and defined decision rules. For each option, an adequateness estimate is obtained. The analysis of the results can follow, which consists of one or more of the following activities:

  1. Explanation of the evaluation: DEX can explain how each particular estimate has been obtained in terms of criteria values involved in the process, triggered decision rules and verbal descriptions of computations performed by DEX.
  2. What-if analysis is performed interactively by changing the descriptions of options, re-evaluating them and comparing the obtained results with the original ones.
  3. Selective explanation of options: DEX finds and reports those subtrees of criteria that expressed the most strong or weak characteristics of a particular option. The main point is in the explanation of options using only the most relevant information.
Figure 1: A schematic structure of the evaluation system

In summary, the decision support offered by DEX is based on preference  knowledge  modelling. Such a support makes decision analysis transparent by providing the decision maker with the explanations regarding the evaluation results and the background of the evaluation process itself. The knowledge representation is based on an integration of multi-attribute decision-making approach with expert systems. This offers a user-friendly decision support, where decision knowledge is expressed simply and naturally by words, rules and hierarchies of criteria.

3. Evaluation of Client’s/Patient’s Daily Activity

The evaluation will be explained on daily activity Physical Activity. The activity according to ICNP [9][10][11] is presented in Figure 2 as hierarchical tree structure. The knowledge base that has been developed for the evaluation consists of the attributes (indicators, parameters) in that tree and the decision  rules determine in each node of the tree the interdependent effect of lower parameters on the node - aggregated parameter.

Table 1 presents a DEX print-out of linearised tree from Figure 2 together with description of  attributes. Each attribute is measured along the 5 grade  Likert-type scale (Table 2). So the problem of each attribute concerning physical activity can be expressed on the scale from no problem to a problem to a very high degree.

Table 3 presents decision rules in a complex form for aggregation of mouth, eyelid and throat paresis into estimate of overall paresis problem of a patient. For example 1st decision rule can be read as: If there is  a problem of a very high degree concerning mouth paresis then regardless of the eyelid and the throat (* means any value) the overall paresis problem is a problem to a very high degree.

Rule 11 can be read as: If mouth paresis is in the interval between 4 (to a high degree) and 3 (to some degree), regardless of the eyelid (including problem to a very high degree), and the throat problem to some degree the overall paresis problem is a problem to a high degree.

These rules are the result of professional consensus and are opened for discussion.

Figure 2: Hierarchical structure of daily activity: Physical Activity
Table 1: DEX print-out of linearised tree of attributes from Figure 2
Attribute               Description                        . 
PHYSICAL ACTIVITY       Daily activity 
+-BODY PARTS            Mobility of body parts 
I I +-MOUTH             Mouth paresis 
I I +-EYELID            Eyelid paresis 
I I +-THROAT            Throat paresis 
I +-EXTREMITY           Mobility of extremities 
I   +-UPPER             Mobility of the upper extremities 
I   +-LOWER             Mobility of the lower extremities 
+-WHOLE BODY            Mobility of the whole body 
  +-PART-IMMOBIL        Partial immobility 
  +-COMP-IMMOBIL        Complete Immobility
Table 2: Value domain of attributes
 Value      Description              Class                 . 
 1. vhd     to a very high degree    Bad 
 2. hd      to a high degree 
 3. sd      to some degree 
 4. ld      to a lesser degree 
 5. no      no problem               Good
Basically the knowledge base determines the conversion of data about client’s/patient’s daily activities into estimates which can be used for evaluation purposes (Figure 1). In our case of Physical Activity we collect 5 data from Mouth Paresis to Complete Immobility (leaves of the tree in Figure 2). DEX gives an overall Physical Activity estimate, together with estimates on intermediate nodes. These information together with complex rules can be used for the explanation of the estimates. By varying input data what-if analysis can take place. There is also a possibility of handling fuzzy and probabilistic data about client/patient so soft data (unreliable or missing) can be processed.

Table 4 presents 3 successive client’s/patient’s estimates of daily Physical Activity monitored on successive visits. There can be seen a significant improvement of his or her physical activity from visit V1 to visit V3.

Table 3: Decision rules in a complex form for aggregation of mouth, eyelid and throat paresis
into estimate of overall paresis problem of a patient
       MOUTH   EYELID  THROAT  PARESIS                     . 

    1. vhd     *       *       vhd 
    2. *       *       vhd     vhd 
    3. < sd    < sd    < hd    vhd 
    4. < hd    *       < hd    vhd 
    5. *       vhd     < hd    vhd 

    6. > ld    hd      > hd    hd 
    7. > ld    > hd    hd      hd 
    8. sd      > ld    hd:sd   hd 
    9. > hd    < hd    > sd    hd 
   10. hd      *       > sd    hd 
   11. hd:sd   *       sd      hd 

   12. > ld    > sd    sd      sd 
   13. sd      > sd    > ld    sd 

   14. > ld    sd:ld   > ld    ld 
   15. ld      > sd    > ld    ld 
   16. > ld    > sd    ld      ld 

   17. no      no      no      no

Table 4: Estimates of client’s/patient’s daily physical activity monitored on three successive visits
              Option:  v1          v2          v3         . 
PHYSICAL ACTIVITY       vhd         hd          sd 
+-BODY PARTS            vhd         hd          sd 
I +-PARESIS             vhd         hd          sd 
I I +-MOUTH             vhd         hd          sd 
I I +-EYELID            ld          ld          no 
I I +-THROAT            sd          ld          ld 
I +-EXTREMITY           sd          ld          ld 
I   +-UPPER             no          no          no 
I   +-LOWER             sd          ld          ld 
+-WHOLE BODY            no          no          no 
  +-PART-IMMOBIL        no          no          no 
  +-COMP-IMMOBIL        no          no          no

4. Conclusion and Further Work

The proposed evaluation of health condition demonstrates ICNP and contemporary information technology as a challenge to deepen the system approach in nursing. DEX as a part of cybernetic loop in nursing process offers a possibility for  better (self)control and consequently higher professional work and better quality of nursing care. The approach can be also seen as an active method for testing ICNP in the nursing process.

Knowledge-based multicriteria evaluation and ICNP is a fruitful combination for information support of nursing process which leads towards more client orientated approach using problem solving strategy. Such approach does not only assure a greater functional adequacy of the system but also stimulates the user, i.e. nurses, toward research and development work.

We believe that such an evaluation could be an integral part of an information system for community nursing [3ii][14][15]. Modern information technology (portable computing, networking and multimedia) [16] offers the possibility of integrated onsite information support. The use of IT does not merely mean that a machine operates or helps us to make what we have been doing manually. The use of IT tools is also a challenge and opportunity for new ideas and solutions.

Further work will be focused on development of appropriate knowledge bases for all 14 daily activities and their testing in practice. Later on the expert system should become an integral part of IS for community nursing, especially in connection with nursing diagnoses and interventions. Some organizational changes regarding process of nurses’ work are also expected.


The work presented in this paper was supported by The Republic of Slovenia, Ministry of Science and Technology, The Slovene Science Foundation and  INCO Copernicus Project: Co-operative Research in Information Infrastructure. We would like to thank the subproject Computer-Aided Information System for Community Nursing team members and specially Dr. Marko Bohanec for his contribution regarding DEX. We are also grateful to The Danish Institute for Health and Nursing Research and Mr. Gunnar Nielsen for his personal support and encouragement.


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