3.1 Approach support for building multidisciplinary teams

This first approach presents a new model for multidisciplinary team building. It takes professional's preferences into account when a team building processes. The proposed approach is presented in four main steps explained below.

Assumption 1. If a proposer professional does not complete a form, the proposer would accept being a member of any team without complaint. Thus, the rows of these proposers in the matrix are filled with 1 (highest preference level) for each

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams…

Assumption 2. If an author does not give a priority level for acceptors, the author agrees that all these acceptors have the same priority level. Thus, the priority level of the acceptor (columns) is set to the lowest priority given by the

gular matrix by adding the weights of each cross proposer and acceptor

When the preference matrix is constructed, it is transformed into a lower trian-

The algorithm of the proposed model is straightforward, and it is similar to Prim's minimum spanning tree algorithm and Sahin algorithm. Figure 3 presents the pseudocode of the algorithm. We begin by traversing all the elements of the first discipline in order to find the two groups who have the minimum weights. For this, we use the function FindMinRelationShip. The function chekGroupComposition permits to verify if it is possible to merge the two groups (e.g., if we merge the two groups, the total number of surgeons is less than maximum surgeon authorized in one group). If the merging of the groups is possible, we remove the second group and we recalculate the new weights. If the merging is not possible, we put a negative value in a matrix of preferences. We repeat the same steps until all the weight values are negative. When this first phase is finished, i.e., we can no longer create a new group using the first discipline, we add the individuals of the second discipline. We recall the same function, FindMinRelationShip, chekGroupComposition, merge, until no way to merge groups. This last phase is repeated until all disciplines

A sample example is presented in Figure 4, for application of the proposed algorithm. Suppose we have nine employees with three disciplines (three surgeons, three anesthetists, three instrumentalists) and we need to compose teams with three members (one surgeon, one anesthetist, and one instrumentalist). We apply the algorithm above; we obtain this composition (Step 4—Figure 4) of three teams. This developed approach represents an improvement for Sahin algorithm [14].

We have developed our computer algorithm on the Java platform within the Eclipse. The next step of procedure is selection teams, as detailed below.

posed model is presented in four main steps explained below.

Once we have teams already built, we are going to apply this second approach that helps the decision maker to find more appropriate team; which means, the team that is adapted to his preferences and the need of each operation. The pro-

The presentation of the base depends strongly on the structure and content of such cases. A case base contains problems and solutions that can be used to derive solution for a new situation. In our work, cases contain a vector of attributes that define the problem and the solution, which correspond to the best team that satisfies exactly the needs of the operation and the preferences of the decider. A case is

acceptor (column), P(j) = 1, for j = 1 to n1.

DOI: http://dx.doi.org/10.5772/intechopen.80934

author plus 1. P(j) = P(i) + 1 for j = i + 1 to n1.

Step 3: The team building algorithm

(Mij = Mij + Mji).

are added.

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3.2 Approach for selection teams

Step 1: Case base construction

described by the criteria and also the solution.

Step 1: Completion of preference form

At the beginning of the year, the professionals in the operating theater (proposers) are asked to complete a form (Figure 2) for ranking their colleagues (acceptors) using a preference scale from 1 to 6 (1 being highest and 6 being lowest) according to their willingness to be in the same group. This process should be finalized within a period of 7 days. Although proposers are completing the forms, they should agree to the following rules:


Step 2: Constructing the preference matrix

We transform the forms into a preference matrix. Several revisions are made on the matrix according to Assumptions 1 and 2.

Figure 2. Transfer sequence of the preference forms to a preference matrix.

Assumption 1. If a proposer professional does not complete a form, the proposer would accept being a member of any team without complaint. Thus, the rows of these proposers in the matrix are filled with 1 (highest preference level) for each acceptor (column), P(j) = 1, for j = 1 to n1.

Assumption 2. If an author does not give a priority level for acceptors, the author agrees that all these acceptors have the same priority level. Thus, the priority level of the acceptor (columns) is set to the lowest priority given by the author plus 1. P(j) = P(i) + 1 for j = i + 1 to n1.

When the preference matrix is constructed, it is transformed into a lower triangular matrix by adding the weights of each cross proposer and acceptor (Mij = Mij + Mji).

Step 3: The team building algorithm

3.1 Approach support for building multidisciplinary teams

Step 1: Completion of preference form

Industrial Engineering

they should agree to the following rules:

priority with the highest level.

Step 2: Constructing the preference matrix

Transfer sequence of the preference forms to a preference matrix.

the matrix according to Assumptions 1 and 2.

Figure 2.

80

proposed approach is presented in four main steps explained below.

This first approach presents a new model for multidisciplinary team building. It takes professional's preferences into account when a team building processes. The

At the beginning of the year, the professionals in the operating theater (proposers) are asked to complete a form (Figure 2) for ranking their colleagues (acceptors) using a preference scale from 1 to 6 (1 being highest and 6 being lowest) according to their willingness to be in the same group. This process should be finalized within a period of 7 days. Although proposers are completing the forms,

1. All professionals must submit a form at the beginning of the year; otherwise the proposer agrees that all the acceptors will be regarded as having the same

2. They cannot give the same preference order for more than one acceptor.

We transform the forms into a preference matrix. Several revisions are made on

The algorithm of the proposed model is straightforward, and it is similar to Prim's minimum spanning tree algorithm and Sahin algorithm. Figure 3 presents the pseudocode of the algorithm. We begin by traversing all the elements of the first discipline in order to find the two groups who have the minimum weights. For this, we use the function FindMinRelationShip. The function chekGroupComposition permits to verify if it is possible to merge the two groups (e.g., if we merge the two groups, the total number of surgeons is less than maximum surgeon authorized in one group). If the merging of the groups is possible, we remove the second group and we recalculate the new weights. If the merging is not possible, we put a negative value in a matrix of preferences. We repeat the same steps until all the weight values are negative. When this first phase is finished, i.e., we can no longer create a new group using the first discipline, we add the individuals of the second discipline. We recall the same function, FindMinRelationShip, chekGroupComposition, merge, until no way to merge groups. This last phase is repeated until all disciplines are added.

A sample example is presented in Figure 4, for application of the proposed algorithm. Suppose we have nine employees with three disciplines (three surgeons, three anesthetists, three instrumentalists) and we need to compose teams with three members (one surgeon, one anesthetist, and one instrumentalist). We apply the algorithm above; we obtain this composition (Step 4—Figure 4) of three teams.

This developed approach represents an improvement for Sahin algorithm [14]. We have developed our computer algorithm on the Java platform within the Eclipse. The next step of procedure is selection teams, as detailed below.

### 3.2 Approach for selection teams

Once we have teams already built, we are going to apply this second approach that helps the decision maker to find more appropriate team; which means, the team that is adapted to his preferences and the need of each operation. The proposed model is presented in four main steps explained below.

Step 1: Case base construction

The presentation of the base depends strongly on the structure and content of such cases. A case base contains problems and solutions that can be used to derive solution for a new situation. In our work, cases contain a vector of attributes that define the problem and the solution, which correspond to the best team that satisfies exactly the needs of the operation and the preferences of the decider. A case is described by the criteria and also the solution.


Criteria:

Figure 4.

Solution:

in the case base.

are coherent and vice versa.

Figure 5.

83

The criteria which characterize the team choice are:

• The competence (Ct): the technical competence of the team.

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams…

• The communication (Co): the communication in the team.

preferences of the decision maker. That is defined by a set of criteria.

It is represented by the best team which satisfies exactly the needs and the

In this step, the AHP method is used to determine the weights of criteria for case similarity analysis. This weight is the key to case retrieval. For this reason, we use the analytic hierarchy process (AHP) to determine the relative weight of each attribute according to its importance and use these important weights to calculate the similarity among the new coming case and each case

The first step is to compose our problem in three hierarchical levels presented by

The next step is to conduct a questionnaire survey handed to each member. The value assigned is based on the scale in interval of 1–9. Then, create square pair-wise comparison matrices of the selection criteria. Table 2 [15] presents the scale of

The consistency of results obtained is found by calculating the consistency index (CI). More consistency index becomes bigger and more the judgments of the user

• The risk criticality (R): the criticality degree of the risk.

• The time (T): the duration of the operation

Sample steps for the multidisciplinary team building algorithm.

DOI: http://dx.doi.org/10.5772/intechopen.80934

Step 2: Calculate the weights of criteria

preference in the pair-wise comparison process.

Figure 3. The algorithm's pseudocode.

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams… DOI: http://dx.doi.org/10.5772/intechopen.80934

#### Figure 4.

Sample steps for the multidisciplinary team building algorithm.

### Criteria:

The criteria which characterize the team choice are:


#### Solution:

It is represented by the best team which satisfies exactly the needs and the preferences of the decision maker. That is defined by a set of criteria.

Step 2: Calculate the weights of criteria

In this step, the AHP method is used to determine the weights of criteria for case similarity analysis. This weight is the key to case retrieval. For this reason, we use the analytic hierarchy process (AHP) to determine the relative weight of each attribute according to its importance and use these important weights to calculate the similarity among the new coming case and each case in the case base.

The first step is to compose our problem in three hierarchical levels presented by Figure 5.

The next step is to conduct a questionnaire survey handed to each member. The value assigned is based on the scale in interval of 1–9. Then, create square pair-wise comparison matrices of the selection criteria. Table 2 [15] presents the scale of preference in the pair-wise comparison process.

The consistency of results obtained is found by calculating the consistency index (CI). More consistency index becomes bigger and more the judgments of the user are coherent and vice versa.

Figure 3.

82

The algorithm's pseudocode.

Industrial Engineering

Sim T; <sup>T</sup>′ <sup>¼</sup> <sup>∑</sup><sup>n</sup>

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams…

new case will be selected.

in the case base.

operation.

size of each discipline.

cases in base, described in Table 5.

type of operation.

listed in Table 7.

The experimental departments.

Table 3.

85

Step 4: Construction of the new case solution

DOI: http://dx.doi.org/10.5772/intechopen.80934

4. Experimental results and data analysis

parameters of scale for the three departments.

where T is the case in memory, T′ is the target case, and n is the number of attributes of each case. Finally, the case having the biggest global similarity with the

The objective of this phase is to evaluate the retrieved solution. Thus, the decision maker must judge if the selected case is well or no. If yes, this case solution will be adapted to the new case. Otherwise, he passes to the second more similar case, to the third, etc. Finally, the new case and its validated solution are integrated into the case base. It is then necessary to know which information can be important to retain, how to index the case for a future retrieve, and how to integrate the new case

To assess the computational tractability and efficiency of the developed model, we tested the operation of our model on a set of department of operating theater in "Habib Bourguiba" hospital in Tunisia. We report the results obtained on three departments of different sizes. The comparative Table 3 shows the relevant

The table shows the number of professionals for each discipline (D1: surgery, D2: anesthesia, D3: instruments) in the second column. The third column lists the possible size of team for each department which depends on the nature of the

Then, we apply the second support for selection team. Within our framework of aid to the choice of the best team which satisfies the preferences of decision maker and operation need. Our case base is formed by 20 operations which satisfied this

Our objective consists on searching the best team of a new case arising to the case base. This new case is described by the same attributes that those of the other

The objective of similarity measures is to look for the nearest case which satisfies the most preferences of the new operation in the case base. Indeed, by applying

The relative importance weighting attributes obtained by AHP method, Wi, are

Department Nb professionals D1 D2 D3 Team size 22 8 8 6 5-6 36 10 12 14 4-5-6 42 12 15 16 6-7

Eq. (1), we calculate all local similarities between attributes (Table 6).

During 3 months, the team performance of the first support for building multidisciplinary teams is identified by seven tests. Respectively, two tests in orthopedics department, three tests in urology department, and two tests in the neurology department. Table 4 shows respectively the team size for each test and

<sup>i</sup>¼<sup>1</sup>sTi∗Wi ∑<sup>n</sup> <sup>i</sup>¼<sup>1</sup>Wi

(2)

Figure 5.

An AHP structure for selection teams.


#### Table 2.

AHP comparison scale.

#### Step 3: Retrieving phase

The objective of the retrieving phase is to find the most similar previous cases in case base, and retrieving them for analysis, in order to select one and reuse it in the next phase. The similar case retrieval depends on the case representation and their indexing in the case base. The objective is to measure the similarity between the new case (operation) and the stored cases in the case base.

The question in our model is which one of the previous teams is the most similar to the new operation (case) that must be treated. In order to evaluate the similarity, the similar attribute collection S = {sT1,…, sTn} should be determined first. Let us denote the new operation (case) to be considered by T′. By T, we denote operation (case) stored in the case base. We also denote by Sim the similarity degree between the new operation and the operation stored.

In the first step, we calculate the local similarity sTi between attribute. We define this similarity in the following way:

$$\sigma T\_i = \left(\mathbf{1} - \frac{\left|\mathbf{T\_i} - \mathbf{T\_i'}\right|}{\mathbf{T\_i^{\max}} - \mathbf{T\_i^{\min}}}\right) \tag{1}$$

where Ti is the ith attribute of the case in memory, T′<sup>i</sup> is the ith attribute of the current case, and Ti max and Ti min are the maximum and minimum values between all the cases for the ith attribute.

In the second step, we calculate overall similarity by using the weights associated with each attribute. We thus introduce the importance of the attributes as a new variable. It measures the importance of the ith attribute, which we express as Ti. In our model, the weights Wi were calculated by using the AHP method. A general form of similarity measure function is shown in Eq. (2).

An Integrated Approach for the Building and the Selection of Multidisciplinary Teams… DOI: http://dx.doi.org/10.5772/intechopen.80934

$$\text{Sim}\left(\mathbf{T}, \mathbf{T}'\right) = \frac{\sum\_{i=1}^{n} \mathbf{s} \mathbf{T}\_{i} \* \mathbf{W}\_{i}}{\sum\_{i=1}^{n} \mathbf{W}\_{i}} \tag{2}$$

where T is the case in memory, T′ is the target case, and n is the number of attributes of each case. Finally, the case having the biggest global similarity with the new case will be selected.

Step 4: Construction of the new case solution

The objective of this phase is to evaluate the retrieved solution. Thus, the decision maker must judge if the selected case is well or no. If yes, this case solution will be adapted to the new case. Otherwise, he passes to the second more similar case, to the third, etc. Finally, the new case and its validated solution are integrated into the case base. It is then necessary to know which information can be important to retain, how to index the case for a future retrieve, and how to integrate the new case in the case base.
