Supporting Nurses’ Decisions with a Multi-Attribute Model for Patient Health Evaluation

Abstract

Nurses are required to make many important decisions, for instance on determining the level of the nursing problem, setting nursing diagnoses and interventions. The model presented in this paper is a tool for better and easier decision making is such situations. Multi-attribute modeling of patients’ basic living activities is used for evaluation and explanation of their health status. It offers also visualization and quantification of the data which facilitate decision making in the framework of the process work method. The model can be viewed as an active check-list as it helps us reduce the possibility of “overlooking the queen on the chess board”. The model was critically evaluated in practice.

1. Introduction

Holistic evaluation of a patient’s health status is an essential precondition for making adequate decisions in health care4. Decisions need to be transparent, that is clear and comprehensive not only to the members of the nursing team but also to the patients8. The proposed hierarchical multi-attribute decision models (HMADM) can be applied both when analyzing patient’s health status and when explaining the results of the evaluation. The model is base on the Henderson’s theoretical model of basic living activities (BLA). This model is a foundation for describing a patient’s health status in nursing care3. Despite the holistic nature of the BLA approach, these models are not being sufficiently used in clinical practice5. A possible reason for limited use is the fact that a large number of parameters are used to describe a patient’s health status. This means that it is difficult to gather information on all the parameters. Furthermore, the process of drawing a conclusion becomes very complex. However, the HMADM methodology helps to address these problems2.

2. Method

Multi-attribute decision models are primarily developed for option evaluation: each option, in this case a patient, is described by values of basic attributes and is evaluated according to the model. The final outcome is an overall evaluation for each option (patient). Furthermore, an evaluation result of the option makes it possible to investigate how the result was obtained. It also provides an insight into what specific attributes can be managed to improve the outcome. The majority of current multi-attribute decision methods are aimed at the development of quantitative decision models6,7. In such models, all the attributes are continuous and utility functions are typically defined in terms of the attributes’ weight, e.g. as a weighted-sum of lower-level attributes. In practice, however, the difficulty of understanding the underlying data behind the numerical results proves to be a common problem. The relationships between the attributes are linear, although the nature of the attributes often requires non-linear interdependence. In other words, different weights can be assigned to a single attribute depending on its relative importance. In contrast, Decision Expert (DEX) methodology1, which is used in our approach, deals with discrete attributes, usually represented by words rather than numbers. The corresponding utility functions are represented by decision rules. Each rule represents one point or several equi-utility points of function values. The utility (aggregation) functions used in the DEX model are therefore not represented by formulae, such as weighted-sum, but are presented as tables of function values, i.e. decision rules. This way, a HMADM can be built on non-linear discrete utility functions. It can be compared with the relative weights approach, where the weights depend on parameter values. If a parameter value changes, its relative importance (weight) can also be changed if required. By using the DEX interface, expressing and understanding such utility functions as sets of decision rules becomes suitable for being used in practice.

3. Model description

The model uses indicators, which are measurable BLA criteria for describing a patient’s health condition, as basic attributes. In the criteria tree, the BLA are hierarchically organized and, therefore, interconnected. Utility functions are presented as tables of decision rules. In accordance with those rules, the values and definitions of the higher level criteria are defined by the combination of the values and definitions of the lower level criteria, which enter the model as their predecessors. As a result, a comprehensive evaluation of a patient’s health is obtained at the top of the criteria tree. The hierarchical structure of a comprehensive evaluation of the BLA is shown in Table 1, which represents a DEX output in tree form, including criteria descriptions. Each attribute is measured on a five-point scale: “problem to a very high degree” (vhd), “problem to a high degree” (hd), “problem to some degree” (sd), “problem to a lesser degree” (ld) and “no problem” (no).

Table 1.

DEX printout of hierarchical structure of a comprehensive evaluation of BLA

The comprehensive evaluation model has 20 attributes comprising of 13 basic and 7 aggregate attributes. These correspond to the main attributes of the BLA criteria, covering items relating both to the patient’s physical and psychosocial condition. They include: oxygenation, elimination, body temperature (OEBt); nutrition and hydration, physical activity, sleep and rest (NPaS); avoidance of danger from the environment, hygiene and tidiness, dressing (AdHD); relationships with people, communication, psychological and social needs, including religious beliefs (RRb); and purposeful activity, leisure time, health education (PLtE). The model also includes seven utility functions, one for each aggregate criterion, including the root of the tree which represents the final evaluation result. Each utility function is defined by a table of decision rules that determine the value of each aggregate attribute on the basis of the mutual dependence on lower-level attributes (immediate predecessor). Here is an example of a decision rule on how the value of aggregate attribute is defined: if OEBt is assessed as a problem ‘to some degree’, NPaS as a problem ‘to some degree’ or worse and AdHD as being on the boundary between ‘to a high degree’ and ‘to some degree’, the overall patient’s evaluation of physical BLA is evaluated as ‘to a high degree’.

Most of the tree leaves in Table 1 need to be further refined in order to reach the BLA indicators. These can be measured and evaluated for each patient. Figure 1 presents the example of the “Oxygenation” criterion.

Figure 1.

Hierarchical tree structure for a selected BLA Oxygenation

4. Results of testing in practice

Testing of the HMADM took place in two community health care centres at two different locations, namely the main national health care centre in the capital Ljubljana and a regional one in Ajdovščina. During a two month period, 5 registered nurses were involved from each location, all with minimum 10 years of professional experience. There were two main objectives of the testing: (i) to validate the nursing knowledge embedded in the model, especially with regard to the decision rules; and (ii) to test the actual operability of the model in practice, i.e. by testing during home visits. During practical use of the model, transparent decision rules encouraged nurses to critically assess and to validate the nursing knowledge expressed by the rules. Nurses provided comments regarding the appropriateness of the proposed decision rules and the results of the evaluation of the BLA model. The extent to which the HMADM encouraged assessment of the interdependencies between various criteria was also observed.

Testing of the model was monitored during successive home visits. Each nurse used the model to evaluate their patients’ health status on regular daily visits. Each nurse carried out between five and seven home visits daily, which were of a preventive and curative nature. At each visit, special emphasis was placed on analyzing the patient’s health with the set of BLA indicators. Different patient groups were included, ranging from newborns to the elderly, either due to health promotion, prevention or recovery. Observed were different health problems such as nutrition problems, chronic diseases, wound management etc.

The example of the patient evaluation according to the BLA indicators of the “Oxygenation” criterion in three subsequent observations in presented in Table 2. Changes in health indicators can also be followed graphically. Figure 2 presents the changes, during the three visits, in the evaluation of “Oxygenation” and its five improved sub-attributes: Blood pressure, Bradycardia, Circulation, Air clearance and Ventilation frequency. The nurses themselves could choose the indicators which were then presented as time series. This customization of data eliminates the need for further data analysis. Presentation of changes in the values of the indicators from one visit to another can also be viewed as a helpful tool in the quality assurance processes. In order to omit personal bias in the evaluations, each nurse is assigned to a patient for the whole duration of the nursing care process in order to assure that the progress of a patient is most accurately monitored and followed.

Table 2.

DEX printout of evaluation of three visits for a selected patient

Figure 2.

Transcript of changes of the selected criteria of a patient’s oxygenation during three visits

Nurses who took part in the testing prepared their reports with the following structure: (1) validity of decision rules, (2) suitability of computer use, user interface and transparency, (3) how the model supports nurse’s decisions in each phase of the process work method, (4) ease, accuracy and continuity of documenting nurses’ work (5) patients’ response. Afterwards, structured group interviews were also carried out, led by a senior nurse and the software engineer. The reports and interviews served as the basis for a strengths, weaknesses, opportunities and threats (SWOT) analysis. The nurses indentified three main strengths: (i) the model supports holistic understanding necessary for identification of the nature and level of nursing problems; (ii) the computerized hierarchical structure enriches nursing documentation; and (iii) the possibilities of overlooking something important are reduced. Among the weaknesses, they emphasized the increased amount of work that was involved in using the model because of the need for consistently following a methodical work process. Opportunities suggested by the nurses were the development of electronic documentation and the consistent holistic evaluation of a patient’s health status in the framework of the nurse’s competencies. It could contribute to improved patient safety and also to the safety of members of the health team. On the basis of indicators and their values some adverse events can be predicted. If an adverse event occurs, we can explain the reasons and help determine personal responsibility. As the main threat, they listed their apprehension about computer-supported models being perceived as replacing nurses’ creative thinking and work.

5. Discussion

The proposed decision support system uses the BLA model for assessing patients’ health. It is built on multi-attribute decision-making theory in order to assess a patient’s observed condition. The outcome is a computer-calculated comprehensive evaluation based on the model. The structured approach based on the BLA tree assists nurses decisions by keeping up with a large data set and their inter-relationships. This is especially vital when relatively small changes in some indicators may in combination with other indicators result in serious problems for the patient. The added value of our model is reflected in the larger scope of knowledge on interdependencies of individual indicators (parameters), their values and visual presentations, thus enabling an overview of more data. During the clinical testing of the model in practice it became apparent that the nurses demonstrated more competent decision making by being able to manage and handle complete data without sacrificing details. Transparency appeared to increase along the whole process and not only, as it was initially expected, during the phase of assessing patient health. This implies that patient data can be followed upward through the tree of criteria to the final estimate and an explanation can be provided of how the estimate was obtained and from where it originated. This way a contribution is made to an efficient evaluation that provides a holistic and transparent overview of the patient’s condition. A better understanding of the outcomes reduces the possibility of overlooking clinically important information. Together with appropriate nursing documentation, this computerized model can contribute to a higher quality of nursing and increased patient safety, as well as increased safety for health team members.