CIN: Computers, Informatics, Nursing

& Vol. 32, No. 4, 174–181 & Copyright B 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins

F E A T U R E A R T I C L E

Prioritizing Factors Influencing Nurses’ Satisfaction With Hospital Information Systems A Fuzzy Analytic Hierarchy Process Approach KHALIL KIMIAFAR, MSc FARAHNAZ SADOUGHI, PhD ABBAS SHEIKHTAHERI, PhD MASOUMEH SARBAZ, MSc

Our aim was to use the fuzzy analytic hierarchy process approach to prioritize the factors that influence nurses’ satisfaction with a hospital information system. First, we reviewed the related literature to identify and select possible factors. Second, we developed an analytic hierarchy process framework with three main factors (quality of services, of systems, and of information) and 22 subfactors. Third, we developed a questionnaire based on pairwise comparisons and invited 10 experienced nurses who were identified through snowball sampling to rate these factors. Finally, we used Chang’s fuzzy extent analysis method to compute the weights of these factors and prioritize them. We found that information quality was the most important factor (58%), followed by service quality (22%) and then system quality (19%). In conclusion, although their weights were not similar, all factors were important and should be considered in evaluating nurses’ satisfaction. KEY WORDS

Despite huge investments, many hospital information system (HIS) implementations are neither productive nor successful.1,2 Therefore, many researchers have emphasized the thorough evaluation of these systems3 and believe that the users’ satisfaction with an information system is an important success factor and should be continuously evaluated.2–4 Different factors contributing to users’ satisfaction have been introduced.2 For example, some researchers believe that users’ satisfaction depends on system quality, information quality, and service quality.3,4 Researchers have also studied various subfactors of these three main factors for different technologies, which in some cases do not correspond with each other.1,2,5–10 Many factors discussed in the literature have been considered from the viewpoint of researchers and evaluation experts. However, the participation of the users themselves should also be encouraged so that a set of factors they agree on can be considered when evaluating their satisfaction.11 Moreover, the importance of each factor and the weight of each factor are not the same. However, this issue has been considered only in a limited number of evaluation tools.9 Generally, the importance of these factors has been considered equal.12,13 The importance of these factors should be considered in developing questionnaires for users’ satisfaction, developing an ideal information system for users, and selecting an appropriate information system. 174

Analytic hierarchy process & Chang’s fuzzy extent analysis & Evaluation factors & Fuzzy set theory & Hospital information system & Nurses & Nurses’ satisfaction & Users’ satisfaction

Nurses have an important role in managing patient care and data. They are the largest proportion of healthcare specialists and are among the most important HIS users.11,14 Author Affiliations: School of Health Management and Information Sciences, Department of Health Information Management, (Mr Kimiafar and Dr Sadoughi), and School of Allied Medical Sciences, Department of Health Information Management, (Dr Sheikhtaheri), Tehran University of Medical Sciences, Tehran; and School of Paramedical Sciences, Faculty of Medicine and Medical Records Department, Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad (Ms Sarbaz), Iran. This study was supported by Tehran University of Medical Sciences. The authors have disclosed that they have no significant relationship with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Farahnaz Sadoughi, PhD, School of Health Management and Information Sciences, Department of Health Information Management, Tehran University of Medical Sciences, Yasemi St, Near Niayesh Highway, Valey-e-Asr St, Tehran, Islamic Republic of Iran ([email protected]). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.cinjournal.com). DOI: 10.1097/CIN.0000000000000031

CIN: Computers, Informatics, Nursing & April 2014 Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

The successful implementation and adoption of an HIS are highly dependent on nurses’ adoption and satisfaction,15–17 so a hospital manager should consider their perspectives regarding this technology.14,18,19 Many studies have been conducted about nurses’ satisfaction with health information technologies and the factors influencing their satisfaction,11,14–17,20–27 but there has been little research about how they view the importance of these factors. To remedy this omission, our research attempted to prioritize the factors that affect nurses’ satisfaction with an HIS.

METHODS Selecting the Weighting Method Because various factors affect users’ satisfaction with an information system, multiple criteria decision-making (MCDM) approaches are appropriate when evaluating these factors. In this regard, various methods such as Simple Additive Weighting, Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), ELECTRE, VIKOR, and PROMETHEE have been developed.28–30 We can also use a simple agreement percentage,9 in which we ask the decision makers to rate the importance of each factor and then calculate its agreement percentage.9 This method does not compare different factors with each other, so the results may not be precise. Simple Additive Weighting, ELECTRE, VIKOR, PROMETHEE, and TOPSIS are used to evaluate different alternatives (eg, different HISs) and select the best one.28–30 These methods do not weigh each factor, even though their calculations depend on these weights. Therefore, these weights should be calculated through another method. One of the best approaches of MCDM is the AHP method, which was developed by Thomas Saaty,10,31 because it allows users to express preferences about the importance of factors in a pairwise comparison and also decomposes a complex decision-making problem into several simple subproblems (a hierarchy).32,33 In addition, we can use AHP to drive the weights of factors and to rank the different alternatives.28 Like other MCDM methods, AHP uses crisp data. However, human judgment is believed to be affected by uncertainty and fuzziness, so crisp data are not appropriate for modeling these judgments.7 Scientists consider the fuzzy set theory, developed by Professor Zadeh,34 to be a proper way to model human judgment and have proposed the use of methods such as fuzzy AHP (FAHP) to prioritize different factors.5,8,35–37 We selected FAHP as our weighting model because studies have indicated its effectiveness in determining the priority of factors affecting the success or quality of information technologies.1,5,7,8,10 Fuzzy AHP can be implemented in several ways. We used Chang’s fuzzy extent analysis because it is one of the most frequently used approaches, is similar to traditional

AHP, and has simpler calculations compared with other FAHP approaches.5,10,37,38 The stages of AHP and Chang’s FAHP are explained in the literature.5,8,36–38 The next section describes the stages of FAHP we used in our case study.

Defining the Problem and Developing the Analytic Hierarchy Process Structure In the first stage, the problem, purpose, factors, subfactors, and alternatives are defined in terms of a hierarchical structure.5,38 Based on the literature, we selected information quality, system quality, and service quality as the main factors affecting users’ satisfaction.2–4,9 We reviewed the literature and identified subfactors of these main factors for different information systems and technologies.1,2,5–17,20–22,24,26,27,39,40 Increasing the number of factors results in numerous questions when making pairwise comparisons and decreases the efficiency of the AHP model,10 so we determined the final factors as well as their hierarchical structure and definition during three 1-hour sessions with three experts in evaluating HISs (Table 1). To select the final factors, we considered criteria such as efficiency of the AHP model, frequency of a factor in the literature, clarity of its definition, and its similarity (ability to easily differentiate between factors). For example, factors such as content, which is similar to information quality, and ease of use, which is sometimes considered a subfactor of usability,21 were omitted. We decided to use the questionnaire developed by Ribie`re et al9 as a basis for our hierarchical structure (Figure 1).

Designing the Analytic Hierarchy Process Questionnaire We developed a questionnaire based on a pairwise comparison, in which participants were asked to anonymously indicate the importance of each factor compared with other factors at the same hierarchical level. We asked the participants to answer 34 questions in terms of linguistic variables (See Appendix, Supplemental Digital Content 1, which presents the questionnaire example, http://links.lww.com/CIN/A14) as well as questions about demographics and their experience with HISs. We provided the definitions of factors as an appendix to the questionnaire to ensure that the participants shared a common understanding of the factors. To evaluate the validity of the questionnaire’s content, we asked three experts in evaluating HISs to review the questionnaire. Following their suggestions, we clarified the questions by making minor syntax changes. To check the questionnaire’s reliability, we used the test-retest method and asked three nurses (who were excluded from the final study) to complete the questionnaire once and again after 10 days. The test-retest reliability was 85%. We also included an instruction section in the questionnaire that provided information about the purpose of the study and example answers.

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FIGURE 1. The AHP hierarchy of factors affecting nurses’ satisfaction with an HIS.

Selecting the Participants According to Saaty,50 a small number of participants (usually three to seven) are sufficient for the AHP method.7 For our study, we asked a supervisor at a teaching hospital to participate and to introduce other nurses, identified through snowball sampling, who met our inclusion criteria: a willingness to participate, availability, and at least 5 years of experience with using an HIS. Of those, 10 nurses (including the supervisor) agreed to participate. We distributed the questionnaires in the hospital and collected them 1 week later.

Developing Pairwise Comparison Matrices In the AHP method, linguistic variables are changed to numerical variables in pairwise comparison matrices.5,38 We used nine scales for comparison. For the FAHP, Chang suggests the use of triangular fuzzy numbers (TFNs) instead of crisp numbers.37 A TFN is represented as , (l, m, u), and l e m e u and l 9 0, where l, u, and m represent the lower value, the higher value, and the modal value of a TFN, respectively.37 We developed a pairwise comparison matrix for each nurse using the scales shown in Table 2 and used the geometric mean to form aggregated matrices.10,38

consistency of each participant’s judgments, and the overall consistency.28 If CR is less than 0.1, the judgments are said to be consistent. When we calculated the CR for each participant’s judgments, if the CR was less than 0.1, we asked the nurse to modify his/her comparisons. We also checked the consistency of the aggregated matrices based on Saaty’s method.5,8,36

Determining the Factors’ Weights and Ranks Next, we used Chang’s method to calculate the weights of the factors.36–38 Following is a brief description of the steps in this method. In step 1, we computed the value of fuzzy synthetic extent related to each factor of every matrix by using Equation 1: 1 n n S˜ i ¼ ~nj ¼ 1 aij ˜ ` ½~k ¼ 1 ~j ¼ 1 aij ˜

ð1Þ

where ` is the multiplication of two fuzzy numbers, and S˜i is a TFN (li, mi, ui). In step 2, we calculated the degree of possibility of S˜1 greater than S˜2 by using Equation 2: 8 > >
ðu  mi Þ þ ðmi  lj Þ > : i 0; otherwise ð2Þ

In the AHP method, the consistency ratio (CR) is used as a control for the accuracy of the comparative weights, the

In step 3, we calculated the degree of possibility of S˜i greater than K convex fuzzy numbers by using equation 3:

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T a b l e 1 Definitions of Selected Factors and Subfactors Factors and Subfactors Information quality Right information Security Availability System quality Performance Flexibility Response time Reliability Functions Integration of new functions and features Reversibility Integration with current duties Interface Usability Screen Online assistance Data entry Language Service quality

Training Maintenance Communication with IT staff Processing change requests Hotline assistance

Definitions High-quality and secure information available when and where it is needed Accurate and complete information The protection of all information from access by unauthorized individuals The accessibility of information from systems and subsystems in a timely manner The technical quality of an HIS, including the quality of interfaces and functions as well as system performance The technical performance of the system, including the system’s speed in responding to a request, reliability, and flexibility The capability to easily change and adapt the HIS in regard to new conditions and requests as well as organizational changes The time taken between the initiation of a request for an action or a special activity (such as viewing or printing information) and the HIS response The reliability of the HIS when damaged and during sudden cutoffs The main functions of the system, including its capability to reverse information, add new functions, and correspond to daily tasks The capability to add, with few problems, functions such as statistical data retrieval and clinical decision support features The capability of the system to reverse information and allow users the option to correct errors and wrong information How the system corresponds to routine user tasks The interaction between users and the computer system when entering, processing, and retrieving data The system’s ease of use and whether users can learn, understand, use, and remember the system The quality of the screen, including its color, graphics, and output volume The availability of documentation for help, guidance, and information about errors and possible problems The availability and quality of different input devices, such as bar-code reader, keyboard, and mouse The simplicity, understandability, and consistency of words and terms used by the system The IT department’s quality of services and assistance in different subjects such as selecting hardware and software, providing training services, and modifying the system The quantity, kind, quality, and understandability of training, including instructions to enhance the users’ ability to use the system System maintenance, including maintaining software and hardware to improve its functions and performance The quality and manner of communication of HIS staff with users (including managers and clinicians) Fulfillment of users’ change requests to develop and update the system, such as adding new functions 24-h Telephone help to answer users’ questions and help with their problems

2, 9, 21, 45

vðS˜ i Q S˜ j k j ¼ 1; .... ; n; j m iÞ ¼ minðS˜ i Q S˜ j Þ; i ¼ 1; ::::; n ð3Þ

In step 4, we defined the priority vector of weights in each fuzzy matrix and then normalized the final weights by using equation 4:

Wi =

References 2–5, 9, 14, 39, 41, 42

ðS˜ i Q S˜ j Þ; j ¼ 1; ::::; n; j m i ~nk ¼ 1 ðS˜ i Q S˜ j Þ; j ¼ 1; ::::; n; j m k

(4)

5, 8, 9, 14, 42, 43 2, 3, 5, 8, 9, 44 2, 3, 9, 14, 42, 43

2–4, 9, 14

2, 3, 9, 39, 44, 45

2, 3, 5, 9, 39, 40, 45

2, 3, 9, 39, 44, 45

3, 5, 8, 9 3, 9, 39, 45

3, 9, 39, 45, 46

9, 47

3, 9, 13, 39

2, 3, 21, 39, 40, 44, 45

9, 21 5, 9

3, 9, 42

9, 21

2–4, 8, 9, 39, 44, 46

2, 8–10, 48, 49

8, 9, 44

2, 9, 10, 49

2, 3, 9, 39, 40, 45, 49

9

where Wi is the final normalized, defuzzified weight of each factor or subfactor in its hierarchy.

Ethical Considerations This study was supported by the research committee of Tehran University of Medical Sciences. We also obtained

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Ta b l e 2 Conversion of Linguistic Variables to Fuzzy Numbers37 Linguistic Variables for Importance

Crisp Numbers

Equal importance

1

Moderate importance Strong importance Very strong importance Extreme importance

3 5 7 9

TFNs (1, (1, (1, (3, (5, (7,

authorization from the hospital manager and supervisor. We used anonymous questionnaires and obtained each nurse’s consent before his/her participation in the study.

1, 1, 3, 5, 7, 9,

Reciprocal Numbers

1) if diagonal; 3) otherwise 5) 7) 9) 11)

(1, 1, 1) if diagonal; (1, 1, 3) otherwise (0.2, 0.33, 1) (0.14, 0.2, 0.33) (0.11, 0.14, 0.2) (0.09, 0.11, 0.14)

respectively) were the first-rank factors for evaluating system performance, functions, and interfaces, respectively.

DISCUSSION RESULTS Eight of the 10 participants were female (80%), five (50%) were younger than 40 years (mean age, 37 [SD, 6.7] years), and eight (80%) had more than 6 years of experience working with an HIS (mean, 7.1 T 2.1). The majority of the participants had experience entering and using clinical data in an HIS (80%), but only 40% had experience entering administrative data (40%). In addition, 70% had used an HIS to conduct research, and 50% used the decision support capabilities of an HIS. Table 3 shows a sample pairwise comparison matrix with regard to the main factors. Information quality was determined to be the main factor (58%) affecting nurses’ satisfaction, followed by service quality (22%) and system quality (19%). Table 4 shows the final weights obtained for all factors and subfactors. Providing right (complete and accurate) information, a subfactor of information quality, was perceived as the most important (with a weight of 0.416). The most important subfactors of service quality were providing adequate training and maintenance services by the HIS department (with weights of 0.241 and 0.235, respectively). The most important subfactor of system quality was system performance (with a weight of 0.366). In addition, system flexibility, integrating new functions and features, and usability (with weights of 0.36, 0.396, and 0.293,

The results of our study showed that among the main factors, information quality was perceived as the most important factor. A study in Greece reported that system quality and information quality had a significant effect, and service quality did not have a significant direct effect on users’ satisfaction with an HIS.2 A study performed by Low and Hsueh Chen showed that the most important factors in choosing an HIS and its vendor were system quality (with a weight of 0.261) and service quality (with a weight of 0.223).8 The results of the study of Low and Hsueh Chen8 are similar to ours in terms of service quality but not in terms of system quality. Salmeron and Herrero33 showed that information and technology played the most important roles in users’ satisfaction with an executive information system (with a weight of 0.598). Salmeron and Herrero33 concluded that technical factors (such as proper hardware and software or system flexibility) were less important than soft factors and information (such as information quality and user support).33 Our results also indicated that soft factors (such as the quality of information and services) were more important than factors related to the technical quality of the system. Nurses in our study rated information quality (58%) more than twice as important as service quality (22%). Another study showed that information quality (P G .05) had a significant effect on nurses’ perception of an

Ta b l e 3 Fuzzy Evaluation Matrix With Respect to Nurses’ Satisfaction With HISs (Geometric Mean of Paired Comparisons)a Service quality System quality Information quality a

Service quality

System quality

Information quality

Weight

(1, 1, 1) (0.7353, 0.9169, 2.102) (1.9482, 2.5582, 4.4229)

(0.9169, 1.0873, 2.6185) (1, 1, 1) (1.984, 2.4526, 4.9165)

(0.3115, 0.3878, 0.7108) (0.3495, 0.4065, 0.8686) (1, 1, 1)

0.224 0.190 0.585

CR = 0.002.

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T a b l e 4 Priority Weights of Factors and Subfactors Affecting Nurses’ Satisfaction With an HIS Main Factors Service qualitya

System qualityb

Information qualitya

Local Weights 0.224

0.190

0.585

Subfactors

Local Weights

Training services Maintenance Communication of IT staff with users Processing change requests Hotline assistance Performancea

Functionsb

0.329

Local Overall Overall Sub-subfactors Weights Weights Ranks

0.241 0.235 0.227

— — —

— — —

0.054 0.053 0.050

4 5 6

0.185





0.041

7

0.111 0.366

— Flexibility Response time Reliability Integration of new functions and processes Reversibility Integration with current duties Usability Screen Online assistance Data entry Language — — —

— 0.360 0.320 0.319 0.396

0.024 0.025 0.022 0.022 0.025

9 8 10 10 8

0.315 0.289

0.019 0.018

11 12

0.293 0.238 0.170 0.150 0.150 — — —

0.017 0.014 0.010 0.008 0.008 0.243 0.20 0.142

13 14 15 16 16 1 2 3

Interfacec

0.305

Right information Information security Information availability

0.416 0.342 0.243

a

CR = 0.01. CR = 0.00. c CR = 0.02. b

HIS.14 Nurses have diverse duties (such as making diagnoses and planning, executing, and monitoring patient care), and these duties depend mainly on information.43 Other studies have indicated the diverse information needs of nurses.42 Nurses spend about 25% of their daily work on the documentation of patient data, although they believe that manual systems and paper records do not meet the quality requirements.42,43 According to a study, about 98% of nurses considered information quality a very important factor both before and after the introduction of a nursing information system. Other studies have also shown that nurses need better information after the implementation of an information system.42,43 Therefore, many researchers consider quality of information a motivator for adoption of an HIS by nurses,14,42,43 which confirms our results. Regarding information quality, our results indicated that providing right (complete and accurate) information and security were the most important factors for nurses (41% and 34%, respectively). The weight for accessibility was 24%. Other studies have indicated that nurses need more complete, accurate, and accessible patient information. According to these studies, nurses expect more complete and accurate patient information from an HIS; however,

they are not completely satisfied with these quality requirements. In addition, only 50% of nurses believe that a nursing information system makes patient information more accessible anytime and anywhere.42–43 The nurses’ concern regarding the confidentiality and security of patient data in electronic health records has also been reflected in previous studies.16 The results suggest that information quality requirements should be considered more when developing and implementing an information system for nurses. Our study showed that the quality of services offered to nurses was the second most important factor. In this regard, offering training and maintenance services and communication with the information technology (IT) team were the most important factors (with almost similar weights). A study reported that training services (the first priority) and system maintenance (the third priority) should be considered when selecting an HIS.8 Previous studies on nurses’ attitudes support our results and suggest that IT support is an important predictor for nurses’ satisfaction with health information technologies; however, nurses’ dissatisfaction with such supports has also been reported.15,20,23 Regarding IT support, previous studies have emphasized providing adequate IT training services

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to nurses.15,16 Huryk et al24 concluded that inadequate training increased discomfort with the system. They also suggested that managers should consider nurses’ suggestions for improving and maintaining the system. Consequently, developing adequate training programs appropriate for nurses’ needs (considering format, content, and training time), receiving nurses’ feedback, and maintenance services should be considered priorities of an HIS team. The technical quality of a system is one of the important factors in evaluating an information system.3,4,8 Ribie`re et al9 emphasized that system quality should be studied regarding a system’s interface, functions, and performance. Our data showed that system quality was almost as important as service quality (19% vs 22%). In addition, we found that the three subfactors (interface, functions, and performance) had almost the same weight (0.305, 0.329, and 0.366, respectively). Moreover, flexibility, response time, capability of integrating new functions and features to the system, and usability were the most important subfactors. According to the available literature, 61% of nurses have reported software problems,16 and half have reported serious problems with the performance, reliability, and usability.43 Furthermore, researchers have shown that a poor HIS design, a difficult-to-use interface, and low usability of an HIS reduce nurses’ satisfaction.15,17,22 Therefore, nurses’ feedback regarding the technical features of an HIS should be continually monitored. Some limitations should be considered when interpreting our results. This research was carried out in just one country and with the participation of only 10 experienced nurses. Therefore, our study may not be generalized to other populations. Moreover, previous studies have considered different factors, classifications of these factors, users, and technologies. In addition, many previous studies of nurses’ attitudes and satisfaction were based on the technology acceptance model. These studies showed that many of these factors have a direct or indirect effect on nurses’ satisfaction but did not compare these factors with each other. Therefore, we cannot easily compare the results of our study with previous research. Moreover, we did not consider all possible factors because including numerous factors and subfactors in an AHP study results in a complex questionnaire. Each factor and subfactor considered in our research may have numerous other subfactors warranting a more in-depth study.

CONCLUSION AND IMPLICATIONS Our results indicated that all factors were important in evaluating nurses’ satisfaction with an HIS, and none of them should be neglected. However, the weights of the factors were not similar. In this respect, more attention should be paid to information quality (providing right information, security, and availability). 180

The implications of this research can be applied to different tasks. One, awareness of the importance of factors based on the viewpoints of HIS users provides an opportunity for HIS managers to judge users’ expectations and more effectively manage the process of selecting or developing an HIS. Two, this information enables vendors to learn about users’ expectations and develop a more desirable HIS. Third, the weights of these factors can help HIS managers assess the strong and weak aspects of an HIS. Four, by considering the weights of these different factors, HIS researchers and evaluators can redevelop their evaluation tools with regard to users’ needs and participation.

Acknowledgments The authors thank all the nurses who freely and kindly participated in this study.

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Prioritizing factors influencing nurses' satisfaction with hospital information systems: a fuzzy analytic hierarchy process approach.

Our aim was to use the fuzzy analytic hierarchy process approach to prioritize the factors that influence nurses' satisfaction with a hospital informa...
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