Nursing administration is the work process carried out by nursing care staff to assist and increase the quality of nursing care and medical treatment for patients and their families [16] [17] . Nurse administrators must create an optimal nursing care environment for patients by making the necessary instruments available, and ensuring that there are enough nursing care staff with all the necessary skills for the volume of nursing care work. Data needed for nursing administration [8] [18] [19] includes information on bed occupancy rate, length of hospital stay, severity of patient symptoms/illness, patient outcomes, the degree of pressure of work of hospital ward, working conditions and work schedule of nurses in the such a ward, skills of nurses and more.

Nurse administrators use this data to analyze the status of patients, patient dynamics, ward characteristics and staff information, and use the results to continuously manage the work of nurses, personnel distribution and the quality of nursing care services. Specifically, the data is used to confirm whether or not hospital criteria for treatment fees are being filled and working conditions comply with the Labor Standards Law. In addition, ward characteristics are determined from nursing care implementation and patient information, and efforts are made to ensure correct personnel distribution based on the appropriateness of nursing staff for the ward.

In clinical practice that changes from day to day, nurse administrators that are required to make decisions to suit each situation must correctly comprehend and evaluate large volumes of information [20] . Nurse administrators in psychiatric wards would be greatly benefit from the development of a nursing administration analysis system that could perform personnel arrangement simulations, manage information on nursing staff and mange ward operations [21] . This methodology focuses on collecting and analyzing the data which nurse administrators need for decision-making in nursing administration.

Nurse administrators can view outcomes for information to be analyzed by simply having an operator input categories and conditions, from registered information to electronic health records, nursing care logs, nursing care plans, staff information management, work schedule management and clinical pathways system databases in PSYCHOMS^® that was developed from a nursing perspective (Figure 1).

This analysis system uses two different display methods: 1) one is data classified into types of information as desired by the nursing administrator; 2) the other is extracted data required by the nursing administrator. This system can classify information registered in the database based upon categories (S_category) and conditions (S_condition) established by the user, and extract the information that corresponded to the condition. To view classified data, when there are multiple categories and/or conditions, records matching each category and condition must all be extracted from the data tables of the database.

Categories as used here refer to those used to classify information, as shown in Figure 2. Conditions refer to the key words used in searches.

Normally a search formula such as an SQL search formula for the SQL server is created to classify data, but SQL specifications and mathematical functions vary somewhat with the type of software being used. Following are conceptual formula of this methodology.

In Equation (1) below, inf (x) shows the information that user wants to know and S_category, S_condition shows the categories and conditions that have been selected.

shows that record i is only extracted if it matches the category selected S_category. Similarly, shows that record j is only extracted if it matched the condition selected

S_category.

The value m shows the overall number of data. The operator ■ represents the extraction of overlapping parts of records extracted in the former and latter parts on the right side of the equation.

[Formula 1]

For example, when a “character trait” of nursing staff information, such as the category (S_category) was selected, and a “hospital ward” of nursing staff information, such as the condition (S_condition) was selected, those were referenced in the nursing staff administration database and classified based upon the character trait (gregariousness, warmth, excitement seeking, activity, assertiveness, and positive emotions) of nursing staff and the result was obtained as shown in the Figure 3.

Classifications such as this can be realized by using the following SQL search formula:

SELECT hospital ward, character trait, COUNT (*)

FROM nursing staff administration database

GROUP BY hospital ward, character trait

In the above search formula, the conditions and categories are specified after SELECT and the conditions based on which the classification is made are specified after GROUP BY. COUNT (*) is a function to count the number of data that fits the category and condition.

Figure 3 shows an example of the SQL search formula for obtaining the number of nurses in each ward for each type of character trait and the results retrieved.

This methodology is constructed so that it memorizes the previous analytical result and makes an analysis based on the new condition (S_condition) in addition to the previous analytical result EF (1).

In other words, as shown in Formula (2) mentioned below, if the first analytical result is EF (1), it is possible to make an analysis with a new condition (S_condition) in addition to the previous analytical result EF (1).

[Formula 2]

Where EF (1) is the first analytical result, x is the selected condition, i = the factor meeting the condition, j is the factor meeting condition x, m = the total number, and S_condition is the selected condition.

For example, in order for a new condition (character trait) to be added to the first analytical result EF (1), a new analytical result classified by “gender” can be expressed as shown in Figure 4. In this case, the SQL search formula is as follows:

SELECT hospital ward, character trait, gender, COUNT (*)

FROM datatable_1

GROUP BY hospital ward, character trait, gender

The datatable_1 in SQL search formula is memorized information from the previous analytical result EF (1). Similarly, at the n-th analysis, it is possible to make a new analysis by adding a new condition (S_condition) to EF (n − 1), the datatable converted from the previous analytical data.

The following is an explanation on how to extract data by limiting the conditions: In this case, it is possible to further reduce conditions by adding a new condition (S_condition) to the previous analytical result EF (n − 1).

For example, in the SQL search formula, specify the items to be shown after SELECT and specify the condition to obtain staff information after WHERE. If there is more than one condition, connect them with “AND”. Formula (3), mentioned below, shows a new restriction by adding a new condition to the previous analytical data EF (2).

[Formula 3]

EF (2) is a table converted from the second analytical results. For example, when a restriction was made by adding the gender “male” to the previous analytical result EF (2), the SQL search formula was as follows:

SELECT hospital ward, character trait, gender, COUNT (*)

FROM datatable_2

WHERE gender = male

The datatable_2 in SQL search formula memorizes information from the previous analytical result EF (2). As shown above, it is possible to limit the results by adding a new condition (S_condition) to the data table converted from the previous analytical result EF (n − 1).

[Formula 4]

EF (n − 1) is the n − 1 analytical results.

The following figures are shown as an example of analysis result. To extract data after further narrowing down the conditions, the name of the item is specified after SELECT. Conditions for retrieving staff information are specified after WHERE. AND is used to specify more than one condition. The results are displayed as shown in Figure 5.

As the result of information analysis, these images which the nurse administrator will perform are shown below. When “bed occupancy rate, all hospital wards” is selected as the conditions, the bed occupancy rate for each hospital ward is displayed in a list. The graphical display can be selected to show the results of the graph, as in Figure 6(a). When “all hospital wards, number of direct care patients per level of care need” is selected as the conditions, the results will look like those shown in Figure 6(b).

For example, when selecting the conditions, “overtime hours per month, staff name, number of years’ experience” gives the search results shown in Figure 7.

The following figures show the results of analysis of the variants of clinical pathways. For example, selecting, “variance in examination on admission, computed tomography (CT) and magnetic resonance imaging (MRI) findings, clinical pathway for schizophrenia, used in acute treatment wards” as search conditions gives the result shown in Figure 8(a). Adding “reasons for variance” as a further condition gives the results shown in Figure 8(b).