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The purpose of this paper is to show how a clinical decision support system can help in prescription and knowledge acquisition processes.

An integrated electronic medical records system (iEMRS) is designed to enhance the decision support quality in prescription.

By evaluating the system performance through 135 prescription records collected from a Hong Kong medical organization, iEMRS shows a satisfactory result in suggesting medicines that is properly the same as the decisions made by the physicians.

Compared with the static clinical guidelines built (manually) in the traditional clinical decision support system, knowledge in iEMRS is generated by the knowledge discovery result from professional experiences of various physicians and patient histories, which are more dynamic in nature. A treatment algorithm, designed in data mining technique, is introduced to improve information management in medical organizations by integration of decision support capability and EMRS, and supplement the deficiencies of traditional clinical decision support system.

Due to its ability to support well-informed decision-making, business intelligence (BI) has grown in popularity among executives across a range of industries. However, given the volume of data collected in health-care organizations, there is a lack of exploration concerning its implementation. Consequently, this research paper aims to investigate the key factors affecting the acceptance and use of BI in healthcare organizations.

Leveraging the theoretical lens of the “unified theory of acceptance and use of technology” (UTAUT), a study framework was proposed and integrated with three context-related factors, including “rational decision-making culture” (RDC), “perceived threat to professional autonomy” (PTA) and “medical–legal risk” (MLR). The variables in the study framework were categorized as follows: information systems (IS) perspective; organizational perspective; and user perspective. In Jordan, 434 healthcare professionals participated in a cross-sectional online survey that was used to collect data.

The findings of the “structural equation modeling” revealed that professionals’ behavioral intentions toward using BI systems were significantly affected by performance expectancy, social influence, facilitating conditions, MLR, RDC and PTA. Also, an insignificant effect of PTA on PE was found based on the results of statistical analysis. These variables explained 68% of the variance (R²) in the individuals’ intentions to use BI-based health-care systems.

To promote the acceptance and use of BI technology in health-care settings, developers, designers, service providers and decision-makers will find this study to have a number of practical implications. Additionally, it will support the development of effective strategies and BI-based health-care systems based on these study results, attracting the interest of many users.

To the best of the author’s knowledge, this is one of the first studies that integrates the UTAUT model with three contextual factors (RDC, PTA and MLR) in addition to examining the suggested framework in a developing nation (Jordan). This study is one of the few in which the users’ acceptance behavior of BI systems was investigated in a health-care setting. More specifically, to the best of the author’s knowledge, this is the first study that reveals the critical antecedents of individuals’ intention to accept BI for health-care purposes in the Jordanian context.

The purpose of this paper is to present an automatic Medical Knowledge Elicitation System (MediKES), which is designed to improve elicitation and sharing of tacit knowledge acquired by physicians. The system leverages the clinical information stored in electronic medical record systems, by representing the acquired information in a series of knowledge maps.

The system architecture of the proposed MediKES is first discussed, and then a case study on an application of the proposed system in a Hong Kong medical organization is presented to illustrate the adoption process and highlight the benefits that can be realized from deployment of the MediKES.

The results of the case study show that the proposed solution is more reliable and powerful than traditional knowledge elicitation approaches in capturing physicians' tacit knowledge, transforming it into a machine‐readable form, as well as enhancing the quality of the medical judgment made by physicians.

A prototype system has been constructed and implemented on a trial basis in a medical organization. It has proven to be of benefit to healthcare professionals through its automatic functions in representing and visualizing physicians' diagnostic decisions.

Knowledge is key to improving the quality of the medical judgment of physicians. However, researchers and practitioners are still striving for more effective ways of capturing tacit knowledge and transforming it into a machine‐readable form so as to enhance knowledge sharing. In this paper, the authors reveal that the knowledge retrieval and the visual knowledge representation functions of the proposed system are able to facilitate knowledge sharing among physicians. Thus, junior physicians can use it as a decision support tool in making better diagnostic decisions.

Improving healthcare services by developing assistive technologies includes both the health aid devices and the analysis of the data collected by them. The acquired data modeled as a knowledge base give more insight into each patient’s health status and needs. Therefore, the ultimate goal of a health-care system is obtaining recommendations provided by an assistive decision support system using such knowledge base, benefiting the patients, the physicians and the healthcare industry. This paper aims to define the knowledge flow for a medical assistive decision support system by structuring raw medical data and leveraging the knowledge contained in the data proposing solutions for efficient data search, medical investigation or diagnosis and medication prediction and relationship identification.

The solution this paper proposes for implementing a medical assistive decision support system can analyze any type of unstructured medical documents which are processed by applying Natural Language Processing (NLP) tasks followed by semantic analysis, leading to the medical concept identification, thus imposing a structure on the input documents. The structured information is filtered and classified such that custom decisions regarding patients’ health status can be made. The current research focuses on identifying the relationships between medical concepts as defined by the REMed (Relation Extraction from Medical documents) solution that aims at finding the patterns that lead to the classification of concept pairs into concept-to-concept relations.

This paper proposed the REMed solution expressed as a multi-class classification problem tackled using the support vector machine classifier. Experimentally, this paper determined the most appropriate setup for the multi-class classification problem which is a combination of lexical, context, syntactic and grammatical features, as each feature category is good at representing particular relations, but not all. The best results we obtained are expressed as F1-measure of 74.9 per cent which is 1.4 per cent better than the results reported by similar systems.

The difficulty to discriminate between TrIP and TrAP relations revolves around the hierarchical relationship between the two classes as TrIP is a particular type (an instance) of TrAP. The intuition behind this behavior was that the classifier cannot discern the correct relations because of the bias toward the majority classes. The analysis was conducted by using only sentences from electronic health record that contain at least two medical concepts. This limitation was introduced by the availability of the annotated data with reported results, as relations were defined at sentence level.

The originality of the proposed solution lies in the methodology to extract valuable information from the medical records via semantic searches; concept-to-concept relation identification; and recommendations for diagnosis, treatment and further investigations. The REMed solution introduces a learning-based approach for the automatic discovery of relations between medical concepts. We propose an original list of features: lexical – 3, context – 6, grammatical – 4 and syntactic – 4. The similarity feature introduced in this paper has a significant influence on the classification, and, to the best of the authors’ knowledge, it has not been used as feature in similar solutions.

Purpose – This chapter examines medical consumerism and the changing relations between patients as consumers and the medical system across two women's health contexts, breast cancer and infertility.

Methodology/approach – The analysis draws on two qualitative studies: The first explores the experiences of 60 breast cancer survivors through in-depth interviews and participant observation (Sulik, 2005), and the second uses in-depth interviews to analyze 18 women's experiences with infertility (Eich-Krohm, 2000).

Findings – The medical consumer is an individualized role that shifts attention away from the quality problem in health care and toward the quality of the person as a medical consumer who is characterized to be optimistic, proactive, rational, responsible, and informed.

Research limitations/implications – As medicine has become a form of mass consumption, the category of medical consumer has elevated the individual in medical decision-making. The shift from patient to medical consumer is an ongoing process that is grounded in a tension between medical control and individual agency, and is exacerbated by the intensity and incomprehensibility of modern medicine.

Practical implications – The proliferation of medical information and personal illness narratives through the Internet, advice books, and self-help groups have advanced lay knowledge about preventive medicine and medical treatment while simultaneously introducing new fears and anxiety about the multitude of options and outcomes.

Originality/value of chapter – This study contributes to our knowledge on medical consumerism and its impact on illness experience and the synthesis of lay and professional knowledge.

Since the execution of National Health Insurance system in Taiwan, the competition of medical industry is becoming more and more severe. The ways the hospital operate knowledge management (KM) concept, combine current human resources and professional knowledge by information techniques and upgrade the competitiveness through reinvention of organizational culture have become the important issues. This research is based on the relationship between KM and organizational operation, integrates the characteristic of medical institutions and framework of medical knowledge cycle and starts the research subject by questionnaires from three dimensions: current situation of KM construction in medical organizations, executive effect of KM activities and the challenges faced by KM; subsequently, from qualitative interview, this research attempts to understand how a medical organization executes and adjusts in the consideration of theory and reality as well as quality and costs when actually operates the organization. This research accesses to KM system application of medical institutions and the empirical executive benefits and difficulties through questionnaires. The research results are as follows: (1) having initial understanding toward current KM establishment of medical institutions; (2) confirming the most important items of KM establishment of medical organizations; (3) understanding the most difficulty which the medical organizations encounter when executing KM; (4) establishing medical knowledge cycle figure of the hospitals receiving interviews. Through case interview, this research profoundly accessed to the actual operation of KM application of medical organizations. The target hospitals intended to try many medical KM measures; however, during to complicated hospital organizations and cultural characteristics, the promotion was not successful and the results were not apparent. The most difficulty was to change the employees’ behavior. The targets believed that only the continuous promotion of KM can allow it to be an important aspect of organizational culture and the competitiveness could constant be upgraded.

Purpose: We analyze how medical providers use accountability processes or the regulatory means through which individuals hold themselves or others accountable to social norms, to uphold their medical authority. We use the case of trans medicine because in this medical domain, providers often have little to no expertise and few are trained specifically in delivering trans medicine or working with trans patients. As a result, providers experience uncertainty and are left without the typical tools and expertise on which they depend in most other areas of medical decision-making.

Design/methodology/approach: We conducted in-depth interviews with 23 medical providers and observations of transgender healthcare conferences in the United States between 2012 and 2015.

Findings: Our work offers insight into the provider side of patient-provider encounters and medical decision-making in gender minority health. The first accountability strategy providers employed was to invoke the language of evidence as a method to maintain their authority, in spite of the paucity of scientific evidence that undergirds this emergent medical domain. The second strategy was to mandate compliance by holding trans people accountable to the expectation of acquiescing to medical authority.

Originality/value: We contribute to the scholarship on gender minority health by examining how high power actors use accountability processes to restore order in interactions with trans and nonbinary patients. We demonstrate how enforcement to expectations through accountability processes is a plausible, though oft-overlooked, dimension of health inequalities.

This study aims to provide an ontology based Baysian network for clinical specialty supporting. As a knowledge base, ontology plays an essential role in domain applications especially in expert systems. Interactive question answering systems are suitable for personal domain consulting and recommended for real-time usage. Clinical specialty supporting for dispatching patients can assist hospitals to locate desired treatment departments for individuals relevant to their syndromes and disease efficiently and effectively. By referring to interactive question answering systems, individuals can understand how to alleviate time and medical resource wasting according to recommendations from medical ontology-based systems.

This work presents an ontology based on clinical specialty supporting using an interactive question answering system to achieve this aim. The ontology incorporates close temporal associations between words in input query to represent word co-occurrence relationships in concept space. The patterns defined in lexicon chain mechanism are further extracted from the query words to infer related concepts for treatment departments to retrieve information.

The precision and recall rates are considered as the criteria for model optimization. Finally, the inference-based interactive question answering system using natural language interface is adopted for clinical specialty supporting, and indicates its superiority in information retrieval over traditional approaches.

From the observed experimental results, we find the proposed method is useful in practice especially in treatment department decision supporting using metrics precision and recall rates. The interactive interface using natural language dialogue attracts the users’ attention and obtains a good score in mean opinion score measure.

Adverse drug events and medication errors can occur because of decisions made during prescribing. The New Zealand Herald reported that preventable medical errors by doctors and other health workers are thought to be killing more than 1,500 patients a year in New Zealand. This article is a small‐scale case study which examines the perceptions of paediatric doctors in a New Zealand hospital regarding adverse drug events/reactions and their views on how they may or may not be avoided with the use of a clinical decision support system (CDSS) in a prescribing environment. It was found that doctors welcomed the use of a CDSS for prescribing. The doctors stated three critical factors for their use of such a system: time constraint, limited knowledge, and misreading.

The purpose of this paper is to investigate the elements that health care personnel in Thailand believe are necessary for successful adoption of electronic medical record (EMR) systems.

Initial qualitative in-depth interviews with physicians to adapt key elements from the literature to the Thai context. The 12 elements identified included things related to managing the implementation and to IT expertise. The nationwide survey was supported by the Ministry of Public Health and returned 1,069 usable questionnaires (response rate 42 percent) from a range of medical personnel.

The key elements clearly separated into a managerial dimension and an IT dimension. All were considered fairly important, but managerial expertise was more critical. In particular, there should be clear EMR project goals and scope, adequate budget allocation, clinical staff must be involved in implementation, and the IT should facilitate good electronic communication.

Thailand is representative of middle-income developing countries, but there is no guarantee findings can be generalized. National policies differ, as do economic structures of health care industries. The focus is on management at the organizational level, but future research must also examine macro-level issues, as well as gain more depth into thinking of individual health care personnel.

Technical issues of EMR implementation are certainly important. However, it is clear actual adoption and use of the system also depends very heavily on managerial issues.

Most research on EMR implementation has been in developed countries, and has often focussed more on technical issues rather than examining managerial issues closely. Health IT is also critical in developing economies, and management of health IT implementation must be well understood.