Organizational decision to adopt hospital information system: an empirical investigation in the case of Malaysian public hospitals

Development and validation of an instrument to evaluate the perspective of using the electronic health record in a hospital setting

BACKGROUND

Evaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals.

METHODS

The questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals.

RESULTS

The structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity.

CONCLUSIONS

The resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts.

Psychometric Properties of the Teleprimary Care Oral Health Clinical Information System (TPC-OHCIS) Questionnaire Using the Rasch Model

Background The Tele Primary Care Oral Health Clinical Information System (TPC-OHCIS) was implemented in Malaysia to digitalize health care and reduce numerous ground-level manual tasks. This study measures the psychometric properties of the TPC-OHCIS questionnaire among healthcare workers (HCWs) at primary healthcare clinics (PHC). Method A pilot study was conducted at PHC, which implemented the TPC-OHCIS application for service delivery. The questionnaire contained 65 items with four response categories, grouped into four scales: technology, organization, external support, and human resource. The questionnaire items were analyzed using the Rasch model in Winsteps 3.72.3. Results There were 319 respondents who participated (98.8%). The Cronbach alpha was 0.93. The construct validity was determined by a positive point measure correlation (PMC) value, with an infit and outfit mean square (MNSQ) range of 0.4-1.5 and a Z-standardized (ZSTD) range of -2.0 to 2.0. The person and item reliability were 0.93 and 0.97, respectively, indicating excellent reliability. The questionnaire was unidimensional, where the raw variance explained by measures was >40%. Conclusion The questionnaire was deemed fit for an actual survey after 18 items had been deleted. It has good psychometric properties and is practically applicable for evaluating HCWs on the TPC-OHCIS application implementation process monitoring using the local Malay language. High reliability and unidimensionality were achieved, supporting its use in digital healthcare. With this validated questionnaire, it will enhance digital healthcare implementation and streamline manual tasks.

Implementation of Health Information Systems to Improve Patient Identification

Wellbeing can be ensured in society through quality healthcare, a minimum of medical errors, and the improved performance of healthcare professionals. To this end, health information systems have been implemented in hospitals, with this implementation representing progress in medicine and information technologies. As a result, life expectancy has significantly increased, standards in healthcare have been raised, and public health has improved. This progress is influenced by the process of managing healthcare organizations and information systems. While hospitals tend to adapt health information systems to reduce errors related to patient misidentification, the rise in the occurrence and recording of medical errors in Lebanon resulting from failures to correctly identify patients reveals that such measures remain insufficient due to unknown factors. This research aimed to investigate the effect of health information systems (HISs) and other factors related to work-related conditions on reductions in patient misidentification and related consequences. The empirical data were collected from 109 employees in Neioumazloum Hospital in Lebanon. The results revealed a correlation between HISs and components and the effects of other factors on patient identification. These other factors included workload, nurse fatigue, a culture of patient safety, and lack of implementation of patient identification policies. This paper provides evidence from a Lebanese hospital and paves the way for further studies aiming to explore the role of information technologies in adopting HISs for work performance and patient satisfaction. Improved care for patients can help achieve health equality, enhance healthcare delivery performance and patient safety, and decrease the numbers of medical errors.

Artificial intelligence healthcare service resources adoption by medical institutions based on TOE framework

Objectives

This study used the Technology-Organization-Environment (TOE) framework to identify the factors involved in the decisions made by integrated medical and healthcare organizations to adopt artificial intelligence (AI) elderly care service resources.

Method

This study identified the Decision-making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) method was used to construct a multilayer recursive structural model and to analyze the interrelationships between the levels. A MICMAC quadrant diagram was used for a cluster analysis.

Results

The ISM recursive structural model was divided into a total of seven layers. The bottom layer contained the four factors of High risk of data leakage (T1), Lack of awareness of the value and benefits of AI healthcare technology (T5), Lack of management leadership support (O1), and Government policies (E1). Having a low dependency but high driving force, these factors are the root causes of adoption by healthcare organizations. The topmost layer contained the most direct factors, which had a high dependency but the low driving force, influencing adoption: Competitive pressures (E2), Lack of patient trust (E5), and Lack of excellent partnerships (E7). Healthcare organizations are more concerned with technology and their environments when deciding to adopt intelligent healthcare resources.

Conclusion

The combination of the three methods of DEMATEL-ISM-MICMAC construction models provides new ideas for smart healthcare services for hospitals. The DEMATEL method favors the construction dimension of the micro-model, while the ISM method favors the construction dimension of the macro-model. Combining these two methods may reduce the loss of information within the system, simplify the matrix calculation workload, and improve the efficiency of operations while decomposing the complex problems into several sub-problems in a more comprehensive and detailed way. Conducting cluster analysis of the adoption determinants utilizing MICMAC quadrant diagrams may provide strong methodological guidance and decision-making recommendations for government departments, senior decision-makers in healthcare organizations, and policy-makers in associations in the senior care industry.

Assessing organizational health-analytics readiness: artifacts based on elaborated action design method

Purpose

While the adoption of health-analytics (HA) is expanding, not every healthcare organization understands the factors impacting its readiness for HA. An assessment of HA-readiness helps guide organizational strategy and the realization of business value. Past research on HA has not included a comprehensive set of readiness-factors and assessment methods. This study’s objective is to design artifacts to assess the HA-readiness of hospitals.

Design/methodology/approach

The information-systems (IS) theory and methodology entail the iterative Elaborated Action Design Research (EADR)method, combined with cross-sectional field studies involving 14 healthcare organizations and 27 participants. The researchers determine factors and leverage multi-criteria decision-making techniques to assess HA-readiness.

Findings

The artifacts emerging from this research include: (1) a map of readiness factors, (2) multi-criteria decision-making techniques that assess the readiness levels on the factors, the varying levels of factor-importance and the inter-factor relationships and (3) an instantiated system. The in-situ evaluation shows how these artifacts can provide insights and strategic direction to an organization through collective knowledge from stakeholders.

Originality/value

This study finds new factors influencing HA-readiness, validates the well-known and details their industry-specific nuances. The methods used in this research yield a well-rounded HA readiness-assessment (HARA) approach and offer practical insights to hospitals.

Predictive Analysis of Hospital HIS System Usage Satisfaction Based on Machine Learning

Hospital information system (HIS) can provide a full range of information support for various hospital business activities and information collection, processing, and transmission, helping medical service providers. And HIS can reduce medical service costs and improve work efficiency, greatly reducing errors in diagnosis and treatment. Although the advantages of using the HIS are obvious, there are still some challenges in its use, the most prominent being how to make the medical staff use HIS effectively. Based on this background, this paper uses machine learning (ML) technology to predict and analyze the satisfaction of HIS use in hospitals and completes the following work: firstly, introduce the situation and development trend of HIS construction at home and abroad and provide theoretical basis for model design. The related development technologies are discussed and studied in detail. Second, the ML algorithm is used to provide a prediction strategy. The support vector machine (SVM) can handle small data sets well, and this study applies the AdaBoost technique to improve the model's generalization ability and accuracy. Lastly, a diversity metric is included to guarantee that the basic learner has good variety in order to increase the algorithm's performance. Accuracy rates may reach more than 95% in the case of tiny data sets, according to the self-built data set used for testing. This proves the superiority of the model proposed in this paper.

Selecting the most suitable organizational structure for hospitals: an integrated fuzzy FUCOM-MARCOS method

Background

Previous studies mentioned four organizational structures for hospitals, which are budgetary, autonomous, corporate, and private. Nevertheless, healthcare decision-makers are still required to select the most organizational structure specific to their circumstances. The present study aims to provide a framework to prioritize and select the most suitable organizational structure using multicriteria decision-making (MCDM) methods in Iranian hospitals.

Methods

First, a multicriteria decision-making model consisted of the respective criteria, and alternatives were developed. The pertinent criteria were identified through a systematic literature review. The coefficient weights of the identified criteria were then calculated using FUCOM-F. Finally, organizational structures were prioritized in accordance with the identified criteria using FMARCOS.

Results

The findings reveal that income is the most significant criterion in selecting organizational structures for hospitals whereas the number of outpatient visits is the least important. Also, the private structure is the most appropriate, and budgetary style is the least suitable organizational structure for Iranian hospitals.

Conclusion

Providing a framework in order to select the most appropriate organizational structure could help managers and policymakers of the healthcare sector in Iran and other countries, mainly similar developing countries.

Implementation strategies for high-performance health care simulation centres: A multicentre exploratory case study in China

INTRODUCTION

The number of health care simulation centres (HSCs) in mainland China has increased exponentially recently. However, the performance of these centres varies significantly between hospitals. The aim of this study is to address two research questions: (i) what are the critical factors for implementing high-performance HSCs and (ii) how are these critical factors used in the development of implementation strategies to achieve satisfactory performance?

METHODS

Following a literature review on information technology (IT) implementation, we identified a framework comprising four key dimensions for HSCs implementation: technology, organisation, environment and individuals. This TOEI (technology-organisation-environment-individual) framework was then used as a basis for a multicentre case study through which data collection and analysis proceeded. We collected 12 one-to-one in-depth interviews alongside secondary data from six high-performance HSCs in mainland China.

RESULTS

Our study identifies critical TOEI factors that collectively influence HSC implementation performance and major activities at the six high-performance HSCs. Three strategies for implementing high-performance HSCs are also identified: facility management platform, education and training centre and innovation centre.

CONCLUSIONS

HSC implementation is an holistic approach. The critical TOEI factors collectively build a foundation for centre activities. An HSC's implementation strategy highly relies on organisational strategic goals. Additionally, HSCs in mainland China face some common challenges such as faculty retention and future opportunities such as expanding the research scope. Our study also provides insights for hospital leadership, medical associations and policymakers.

Development, Implementation, and User Evaluation of COVID-19 Dashboard in a Third-Level Hospital in Iran

INTRODUCTION

The implementation of a dashboard enables managers to make informed and evidence-based decisions through data visualization and graphical presentation of information. This study aimed to design and implement a COVID-19 management dashboard in a third-level hospital in Mashhad, Iran.

MATERIALS AND METHODS

This descriptive developmental applied study was conducted in the second half of 2020 in three stages, using user-centered design methodology in four phases: (1) specification of the application context, (2) specification of requirements, (3) creation of design solutions, and (4) evaluation of designs. Data collection in each phase was performed through holding group discussions with the main users, nominal group techniques, interviews, and questioners. The dashboard prototype for the data display was designed using the Power BI Desktop software. Subsequently, users' comments were obtained using the focus group method and included in the dashboard.

RESULTS

In total, 25 indicators related to input, process, and output areas were identified based on the findings of the first stage. Moreover, eight items were introduced by participants as dashboard requirements. The dashboard was developed based on users' feedback and suggestions, such as the use of colors, reception of periodic and specific reports based on key performance indicators, and rearrangement of the components visible on the page. The result of the user satisfaction survey indicated their satisfaction with the developed dashboard.

CONCLUSION

The selection of proper criteria for the implementation of an effective dashboard is critical for the health care organization since they are designed with a high-tech and content-based environment. The dashboard in the present study was a successful combination of clinical and managerial indicators. Future studies should focus on the design and development of dashboards, as well as benchmarking by using data from several hospitals.

New combination of simple additive and entropy weighting criteria for the selection of best substitution box

Substitution boxes (S-boxes) are among the most widely recognized and fundamental component of most modern block ciphers. This is on the grounds that they can give a cipher fortifying properties to oppose known and possible cryptanalytic assaults. We have suggested a novel tool to select nonlinear confusion component. This nonlinear confusion component added confusion capability which describes to make the connection among the key and the cipher as complex and engaging as possible. The confusion can be obtained by using substitution box (S-box) and complex scrambling algorithm that relies on key and the input (plaintext). Various statistical and cryptographic characteristics were introduced to measure the strength of substitution boxes (S-boxes). With the help of the present objective weight methods and ranking technique, we can select an ideal S-box among other constructed confusion component to make our encryption algorithm secure and robust against various cryptographic attacks.

Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component

As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

Understanding the Drivers of Wearable Health Monitoring Technology: An Extension of the Unified Theory of Acceptance and Use of Technology

The market for wearable health monitoring technology is promising globally and in Saudi Arabia particularly. The country has a very high prevalence of chronic diseases that can be managed using wearable health monitoring technology. However, wearable devices are not fully advantageous if people do not accept them. Due to the parsimony of studies on the acceptance of wearable health monitoring technology, understanding the key drivers of using wearable health monitoring technology remains uncertain. This cross-sectional study extends the extended unified theory of acceptance and use of technology (UTAUT2) to explain the variance in the adoption intention of wearable health monitoring technology. A total of 256 responses were analyzed using the partial least squares structural equation modeling technique, in addition to the importance-performance map analysis. The results indicate that performance expectancy (PE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM) and habit (HA) significantly impact users’ behavioral intention (BI) to adopt wearable health monitoring technology. The results also demonstrate that effort expectancy (EE), price value (PV), government health policy (GHP) and trust (TR) are not important. Based on the findings, this research presents a set of recommendations for decisions makers, managers and system developers in the healthcare sector to enhance the use and quality of wearable technology.

Antecedents for enhanced level of cyber-security in organisations

Purpose

The present study aims to identify and investigate the antecedents of enhanced level of cyber-security at the organisational level from both the technical and the human resource perspective using human–organisation–technology (HOT) theory.

Design/methodology/approach

The study has been conducted on 151 professionals who have expertise in dealing with cyber-security in organisations in sectors such as retail, education, healthcare, etc. in India. The analysis of the data is carried out using partial least squares based structural equation modelling technique (PLS-SEM).

Findings

The results from the study suggest that “legal consequences” and “technical measures” adopted for securing cyber-security in organisations are the most important antecedents for enhanced cyber-security levels in the organisations. The other significant antecedents for enhanced cyber-security in organisations include “role of senior management” and “proactive information security”.

Research limitations/implications

This empirical study has significant implications for organisations as they can take pre-emptive measures by focussing on important antecedents and work towards enhancing the level of cyber-security.

Originality/value

The originality of this research is combining both technical and human resource perspective in identifying the determinants of enhanced level of cyber-security in the organisations.

Investigating medical professionals' continuance intention of the cloud-based e-learning system: an extension of expectation–confirmation model with flow theory

Purpose

The purpose of this study is to propose an integrated model based on expectation–confirmation model (ECM), flow theory and human–organization–technology fit framework to examine whether human, organizational and technology factors as antecedents to medical professionals' beliefs can affect their continuance intention of the cloud-based e-learning system.

Design/methodology/approach

Sample data for this study were collected from medical professionals at five hospitals in Taiwan. A total of 500 questionnaires were distributed, and 368 (73.6%) useable questionnaires were analyzed using structural equation modeling in this study.

Findings

Synthetically speaking, human, organizational and technology factors, as antecedents to medical professionals' continuance intention of the cloud-based e-learning system have been examined, and the results strongly support the research model with all hypothesized links being significant.

Originality/value

Particularly, it is worth mentioning that the application of capturing both ECM and flow theory for completely explaining three types of factors (i.e. human, organizational and technology factors) as external variables to medical professionals' cloud-based e-learning continuance intention is well documented, that is, information systems (IS) and nonIS determinants are simultaneously evaluated, and extrinsic and intrinsic motivators are both taken into consideration in this study's theoretical development of medical professionals' cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

Utilization of clinical practice guideline on antimicrobial in China: an exploratory survey on multilevel determinants

BACKGROUND

Nowadays, irrational use of antimicrobials has threatened public health. It's necessary to expand the use of clinical practice guideline (CPG) on antimicrobial for facilitating the proper use of antimicrobial. However, the utilization status of CPG on antimicrobial and the influencing factors, especially the ones at the organizational level, remain largely unknown.

METHODS

A cross-sectional questionnaire survey was conducted on a sample among physicians from 16 public hospitals in the eastern, central and western parts of China. A multilevel regression model was employed to examine factors associated with physicians' utilization of CPG on antimicrobial.

RESULTS

A total of 815 physicians were included in this study. About 80% of the surveyed physicians reported their adherence to the CPG on antimicrobial. Dimensions of "subjective norm", "perceived risk" and "behavioral intention" from the domain of physician belief, a dimension of "ease of use" from the domain of CPG traits, and dimensions of "top management support" and "organization & implementation" from the domain of hospital practice were significantly associated with physicians' utilization of CPG on antimicrobial. And except for working department, most demographics characteristics of the physician were not found to be significantly related to the CPG use. In addition, it also showed that region is a significant factor affecting physicians' CPG use.

CONCLUSIONS

This study depicted the current status of CPG on antimicrobial and comprehensively identified its potential determinants not only from the three domains at the individual level, such as physician belief, but also from the location region at the organizational level. The results will provide a direct reference for the implementation of CPG on antimicrobial.

Technological, Organizational, and Environmental Factors Influencing Social Media Adoption by Hospitals in Switzerland: Cross-Sectional Study

BACKGROUND

Social media platforms are important tools for hospitals. These platforms offer many potential benefits in various areas of application for hospitals to connect and interact with their stakeholders. However, hospitals differ immensely in their social media adoption. There are studies that provide initial findings on individual factors influencing social media adoption by hospitals, but there is no comprehensive and integrated model.

OBJECTIVE

This study aimed to develop a comprehensive model of social media adoption by hospitals in the context of the Swiss health care system and to test the model with empirical data from Switzerland.

METHODS

To develop our model, we applied the general technology-organization-environment framework of organizational technology adoption and adapted it to the specific context of social media adoption by hospitals in Switzerland. To test our model, we collected empirical data on all 283 hospitals in Switzerland and identified the accounts they operate on 7 different social media platforms (Facebook, Google+, Twitter, Instagram, LinkedIn, XING, and YouTube). We tested the hypotheses of our model by means of binary logistic regression (dependent variable: platform adoption) and negative binomial regression (dependent variable: number of different platforms adopted).

RESULTS

Our general model on social media adoption received broad support. Overall, hospitals in Switzerland are more likely to adopt social media if they have a higher share of patients with voluntary health insurance or have a higher patient volume. In contrast, they are less likely to operate their own social media accounts if they are associated with a hospital network. However, some hypotheses of our model received only partial support for specific social media platforms; for instance, hospitals in Switzerland are more likely to adopt XING if they provide an educational program and are more likely to adopt LinkedIn if they are located in regions with higher competition intensity.

CONCLUSIONS

Our study provides a comprehensive model of social media adoption by hospitals in Switzerland. This model shows, in detail, the factors that influence hospitals in Switzerland in their social media adoption. In addition, it provides a basic framework that might be helpful in systematically developing and testing comprehensive models of social media adoption by hospitals in other countries.

Decision to Adopt Neuromarketing Techniques for Sustainable Product Marketing: A Fuzzy Decision-Making Approach

Sustainable products and their marketing have played a crucial role in developing more sustainable consumption patterns and solutions for socio-ecological problems. They have been demonstrated to significantly decrease social consumption problems. Neuromarketing has recently gained considerable popularity and helped companies generate deeper insights into consumer behavior. It has provided new ways of conceptualizing consumer behavior and decision making. Thus, this research aims to investigate the factors influencing managers’ decisions to adopt neuromarketing techniques in sustainable product marketing using the fuzzy analytic hierarchy process (AHP) approach. Symmetric triangular fuzzy numbers were used to indicate the relative strength of the elements in the hierarchy. Data were collected from the marketing managers of several companies who have experience with sustainable product marketing through online shopping platforms. The results revealed that the accuracy and bias of neuromarketing techniques have been the main critical factors for managers to select neuromarketing in their business for advertising and branding purposes. This research provides important results on the use of neuromarketing techniques for sustainable product marketing, as well as their limitations and implications, and it also presents useful information on the factors impacting business managers’ decision making in adopting neuroscience techniques for sustainable product development and marketing.

Evaluating and prioritizing hospital service quality

PURPOSE

Hospital evaluations create competition between healthcare providers. In this study, a multi criteria decision-making (MCDM) method is used to evaluate criteria that affect hospital service quality. The paper aims to discuss these issues.

DESIGN/METHODOLOGY/APPROACH

Criteria affecting hospital service quality are identified. Four Iranian public hospitals are evaluated using these criteria. Four hybrid methods, including modified digital logic-technique for order of preference by similarity to an ideal solution, analytical hierarchy process-technique for order of preference by similarity to an ideal solution, analytical hierarchy process-elimination and choice expressing reality and modified digital logic-elimination and choice expressing reality are used to evaluate hospital service quality. Results are aggregated using the Copeland method and final ranks are determined.

FINDINGS

The four main criteria for evaluating hospital service quality are: environment; responsiveness; equipment and facilities; and professional capability. Results suggest that professional capability is the most important criterion. The Copeland method, used to integrate four MCDM hybrid methods, provides the final hospital ranks.

PRACTICAL IMPLICATIONS

The criteria the authors identified and their weight help hospital managers to achieve comprehensive organizational growth and more efficient resource usage. Moreover, the decision matrix helps managers to identify their strengths and weaknesses.

ORIGINALITY/VALUE

New and comprehensive criteria are proposed for hospital quality assessments. Moreover, a new hybrid MCDM approach is used to achieve final hospital rankings.

Factors Affecting Acceptance of Hospital Information Systems in Public Hospitals of Zahedan University of Medical Sciences: A Cross-Sectional Study

A hospital information system is used to support a wide range of operations and activities in the hospital. This study was conducted to determine the factors affecting hospital information system acceptance by users. A cross-sectional, descriptive, analytic study was performed in 2018. The study population included 550 users of the system. The data were collected using a questionnaire and analyzed using the SPSS software. A significant moderate positive correlation was found between hospital information system acceptance and perceived usefulness (r = 0.54, P < 0.01), perceived ease of use (r = 0.41, P < 0.01), human factors (r = 0.46, P < 0.01) and technological factors (r = 0.54, P < 0.01). A significant weak positive correlation was detected between the acceptance of the hospital information system and organizational factors (r = 0.35, P < 0.01). Perceived usefulness of the system, social influence, system quality, perceived ease of use of the system, and top managers' supports had the most substantial influence on the users' intention to accept a hospital information system. User education, preparation of guidelines suited to the user specialty or department, incorporating users' work needs into the capabilities of the hospital information system, and improving the system to an ideal level are important considerations.

The distribution strategy selection for an e-tailer using a hybrid DANP VIKOR MCDM model

Purpose

The purpose of this paper is to apply a multi-criteria decision-making (MCDM) framework to evaluate distribution strategies for an e-tailer. An application of MCDM method, the hybrid DANP–VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model, is used for e-tailers’ distribution strategy evaluation. The choice of distribution strategies under various dimensions is evaluated.

Design/methodology/approach

The authors used a hybrid MCDM model to solve the decision-making framework, which combines Decision-Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based analytic network process and VIKOR method. Data were collected from the experts (e-tail manager, logistics manager, operations manager and distribution center (DC) manager) using two questionnaires, first for the influential relationship among the criteria and dimensions and second for a performance rating of each alternative (distribution strategies) against each criterion.

Findings

DANP with VIKOR method prioritizes the distribution strategies in the following order: DC shipment, drop shipment, click and collect, store shipment and click and reserve. Performance gap was calculated based on the VIKOR method to provide distribution strategies to an e-tailer under different situations. The authors infer that in developing country, product characteristics and transportation have a major influence on deciding the distribution strategy.

Practical implications

Decision-making framework will provide e-tail mangers a knowledge-based understanding to select the distribution strategy under the different situations related to the performance, product, e-tailer and external characteristics for smooth order fulfillment process. The insights developed by this research provide a framework for rational decision making in distribution strategy selection in e-business.

Originality/value

This is the first kind of a study which offers a decision framework for e-tail managers on how to choose distribution strategies under different situations which are related to the performance, product, e-tailer and external characteristics.

Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps

Background

The health area is one of the most affected systems on the perspective of decision-making with multiobjectives, thus becoming prone to errors in the final solution, however, multicriteria decision analysis (MDCA) appears as an aid tool for this process decision-making. Therefore,the present study aims to analyze and synthesize articles found in the literature, involing MCDA in health care, evaluating general issues and methodological aspects, structuring them in a single work.

Methods

Surveys in the bibliographic databases SCOPUS and PUBMED indicated 1852 documents on the subject, however after a careful verificatios, 66 studies were selected to be analyzed completely. The data extracted from the included articles were organized into a spreadsheet for the preparation of analysis, and the technique used was descriptive statistics.

Results

It was possible to identify a growth trend in the application of the MCDA in the health area, but no dominance was identified in relation to the authors of the publication and the periodicals where they are published, but some countries stood out in terms of the number of published researches, such as: Canada and Turkey. In defining the decision problem, and in defining criteria, the “literature” presented the greatest demand for those who wish to structure their decision problem. Finally, it was verified by the analysis of the problem, that the MCDA to solve the problems of ranking has comprehensive application and that there is a greater incidence in the use of the AHP and Logic methods Fuzzy.

Conclusion

With this, it is possible to observe, through the data of this review, that more than the multicriteria methods, the multicriteria decision model has been highlighted, also in the health area. In addition, the study can guide new applications and techniques using MCDA in the health care.

Designing a model of hospital information system acceptance: Organizational culture approach

Background: The significance and influence of organizational culture on Information Technology acceptance, especially in healthcare field, has been recognized as a source of organizational inertia. This study aimed at developing a model of Hospital Information System (HIS) acceptance for non-teaching hospitals of Iran University of Medical Sciences to encourage the authorities to promote organizational culture and successful application of HIS.

Methods: The proposed model was developed according to Michigan Organizational Assessment Questionnaire (MOAQ), Harrison, Hofstede models, and Comparative Values Framework (CVF). The questionnaires were designed based on the model and distributed among 400 HIS users in the hospitals under study, who were selected using stratified random sampling. The structural equation modeling method was used for data analysis in LISREL software.

Results: According to the final model, the influences of developmental culture on perceived usefulness, the relationship of 4 types of organizational culture with mandatoriness according to CVF, and the relationships of hierarchical and developmental culture with system use were attested. The relationships between supervision and 4 variables of HIS acceptance were confirmed. Furthermore, the influence of process/ result oriented culture on user satisfaction was demonstrated. The normed chi square index (2.60) revealed that the final model was fitted to the data. The indices were as follow: GFI= 0.95, CFI= 0.97, AGFI= 0.88, RMSEA= 0.064.

Conclusion: The components and structural relationships in the model of this study are applicable in the related hospitals, and using this model can promote organizational culture and acceptance of HIS by the users.

A novel mixed sustainability-resilience framework for evaluating hospital information systems

Information systems (ISs) are one of the most widely used systems in different organizations especially hospitals. Improving the performance of a hospital information system (HIS) is one of the most important tasks for patient satisfaction and health. To do this, a mixed sustainability-resilience framework for evaluating HISs is proposed in this study in order to enhance their performance from a mixed sustainability-resilience view. First, a comprehensive framework including suitable sustainable and resilience indicators for performance enhancement of HISs is provided. Then, the importance weight of each indicator is achieved by using the best-worst method (BWM). Required data is obtained through a standard questionnaire. A data envelopment analysis (DEA) is applied to evaluate HIS performance in different departments of a real case study. Additionally, improvement actions are obtained by considering the effect of each selected indicator on the HIS performance through a sensitivity analysis. Also, appropriate strategies for improving the resilience and sustainability aspects of the HIS are presented using strengths-weaknesses-opportunities-threats (SWOT) matrix. Using the results, designers can build more intelligent HISs from both sustainability and resilience perspectives.

Real-Time Fault-Tolerant mHealth System: Comprehensive Review of Healthcare Services, Opens Issues, Challenges and Methodological Aspects

The burden on healthcare services in the world has increased substantially in the past decades. The quality and quantity of care have to increase to meet surging demands, especially among patients with chronic heart diseases. The expansion of information and communication technologies has led to new models for the delivery healthcare services in telemedicine. Therefore, mHealth plays an imperative role in the sustainable delivery of healthcare services in telemedicine. This paper presents a comprehensive review of healthcare service provision. It highlights the open issues and challenges related to the use of the real-time fault-tolerant mHealth system in telemedicine. The methodological aspects of mHealth are examined, and three distinct and successive phases are presented. The first discusses the identification process for establishing a decision matrix based on a crossover of 'time of arrival of patient at the hospital/multi-services' and 'hospitals' within mHealth. The second phase discusses the development of a decision matrix for hospital selection based on the MAHP method. The third phase discusses the validation of the proposed system.

The influence of dynamic capabilities on hospital-supplier collaboration and hospital supply chain performance

Purpose

The purpose of this paper is to explore the influence of hospital’s visibility for sensing (VFS), learning, coordinating and integrating on hospital-supplier collaboration. Second, it explored the influence of hospital-supplier collaboration on hospital supply chain performance. The author also explored how the technology orientation of the medical chain units influences the above linkages.

Design/methodology/approach

The study adopted a multi-unit study of different hospital supply chains. Consequently, perceptual data were gathered from seven dominant entities in a typical medical/hospital supply chain: hospitals and clinics, accommodation (i.e. hotels), chemistry and pharmaceutical, marketing/public relations/promotion, medical equipment manufacturers, food and beverage and insurance. The responses were gathered using e-mail survey and were analyzed using structural equation modeling.

Findings

Based on 192 completed responses, the author found positive influences of VFS, learning and integrating on hospital-supplier collaboration and a positive impact of hospital-supplier collaboration on hospital supply chain performance. An insignificant influence of hospital’s visibility for coordinating was noted on hospital-supplier collaboration. The study argued hospitals to invest more for enriching their dynamic capabilities to diagnose the changes in the environment so as to sustain their collaborative relationships leading to positive performance implications.

Originality/value

The study is the foremost to investigate the effects of hospital’s dynamic capabilities on its collaborative efforts with its key supplier and their influence on hospital supply chain performance. Also the study is foremost in exploring the importance of technology orientation on hospital dynamic capabilities and hospital-supplier collaboration. An important contribution of the research is the conceptualization of supply chain visibility core components (visibility of sensing, visibility of learning, visibility of coordinating and visibility of integrating) in the context of hospital supply chains.

Understanding latent structures of clinical information logistics: A bottom-up approach for model building and validating the workflow composite score

BACKGROUND AND PURPOSE

Clinical information logistics is a construct that aims to describe and explain various phenomena of information provision to drive clinical processes. It can be measured by the workflow composite score, an aggregated indicator of the degree of IT support in clinical processes. This study primarily aimed to investigate the yet unknown empirical patterns constituting this construct. The second goal was to derive a data-driven weighting scheme for the constituents of the workflow composite score and to contrast this scheme with a literature based, top-down procedure. This approach should finally test the validity and robustness of the workflow composite score.

METHODS

Based on secondary data from 183 German hospitals, a tiered factor analytic approach (confirmatory and subsequent exploratory factor analysis) was pursued. A weighting scheme, which was based on factor loadings obtained in the analyses, was put into practice.

RESULTS

We were able to identify five statistically significant factors of clinical information logistics that accounted for 63% of the overall variance. These factors were "flow of data and information", "mobility", "clinical decision support and patient safety", "electronic patient record" and "integration and distribution". The system of weights derived from the factor loadings resulted in values for the workflow composite score that differed only slightly from the score values that had been previously published based on a top-down approach.

CONCLUSION

Our findings give insight into the internal composition of clinical information logistics both in terms of factors and weights. They also allowed us to propose a coherent model of clinical information logistics from a technical perspective that joins empirical findings with theoretical knowledge. Despite the new scheme of weights applied to the calculation of the workflow composite score, the score behaved robustly, which is yet another hint of its validity and therefore its usefulness.

Critical Factors Influencing Decision to Adopt Human Resource Information System (HRIS) in Hospitals

The aim of this research is to explore factors influencing the management decisions to adopt human resource information system (HRIS) in the hospital industry of Bangladesh-an emerging developing country. To understand this issue, this paper integrates two prominent adoption theories-Human-Organization-Technology fit (HOT-fit) model and Technology-Organization-Environment (TOE) framework. Thirteen factors under four dimensions were investigated to explore their influence on HRIS adoption decisions in hospitals. Employing non-probability sampling method, a total of 550 copies of structured questionnaires were distributed among HR executives of 92 private hospitals in Bangladesh. Among the respondents, usable questionnaires were 383 that suggesting a valid response rate of 69.63%. We classify the sample into 3 core groups based on the HRIS initial implementation, namely adopters, prospectors, and laggards. The obtained results specify 5 most critical factors i.e. IT infrastructure, top management support, IT capabilities of staff, perceived cost, and competitive pressure. Moreover, the most significant dimension is technological dimension followed by organisational, human, and environmental among the proposed 4 dimensions. Lastly, the study found existence of significant differences in all factors across different adopting groups. The study results also expose constructive proposals to researchers, hospitals, and the government to enhance the likelihood of adopting HRIS. The present study has important implications in understanding HRIS implementation in developing countries.