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The aim of this study was to evaluate the effects of a data‐driven clinical productivity system that leverages Electronic Health Record (EHR) data to provide productivity decision support functionality in a real‐world clinical setting. The system was implemented for a large behavioral health care provider seeing over 75,000 distinct clients a year.

The key metric in this system is a “VPU”, which simultaneously optimizes multiple aspects of clinical care. The resulting mathematical value of clinical productivity was hypothesized to tightly link the organization's performance to its expectations and, through transparency and decision support tools at the clinician level, affect significant changes in productivity, quality, and consistency relative to traditional models of clinical productivity.

In only three months, every single variable integrated into the VPU system showed significant improvement, including a 30 percent rise in revenue, 10 percent rise in clinical percentage, a 25 percent rise in treatment plan completion, a 20 percent rise in case rate eligibility, along with similar improvements in compliance/audit issues, outcomes collection, access, etc.

A data‐driven clinical productivity system employing decision support functionality is effective, because of the impact on clinician behavior relative to traditional clinical productivity systems. Critically, the model is also extensible to integration with outcomes‐based productivity.

EHR's are only a first step – the problem is turning that data into useful information. Technology can leverage the data in order to produce actionable information that can inform clinical practice and decision‐making. Without additional technology, EHR's are essentially just copies of paper‐based records stored in electronic form.

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.

The purpose of this paper is to develop a framework for the application of evidence-based management to chronic disease healthcare.

Chronic healthcare is specially characterized by recursive patient-physician interactions in which evidence-based medicine (EBM) is applied. However, implementing evidence-based solutions to improve healthcare quality requires managers to effect changes in many different areas: organizational structure, procedures, technology and in physician/provider behaviors. To complicate matters further, they must achieve these changes using the tools of resource allocation or incentives. The literature contains many systematic reviews evaluating the question of physician and patient behavior under various types and structures of incentives. Similarly, systematic reviews have also been done regarding specific changes to the healthcare process and their effectiveness in improving patient outcomes. Yet, these reviews uniformly lament a lack of appropriate data from well-organized studies on the question of “Why?” solutions may work in one instance while not in another. The authors present a new theoretical framework that aids in answering this question.

This paper presents a new theoretical framework (Influence Model of Chronic Healthcare) that identifies: the critical areas in which managers can effect changes that improve patient outcomes; the influence these areas can have on each other, as well as on patient and physician behavior; and the mechanisms by which these influences are exerted. For each, the authors draw upon, and present the evidence in the literature. Ultimately, the authors recognize that this is a complex question that has not yet been fully researched. The contribution of this model is twofold: first, the authors hope to focus future research efforts, and second, provide a useful heuristic to managers who must make decisions with only the lesser-quality evidence the literature contains today.

This model can be used by managers as a heuristic either ex ante or ex post to determine the effectiveness of their decisions and strategies in improving healthcare quality. In addition, it can be used to analyze why actions or decisions taken achieved a given outcome, and how best to proceed to effect further improvements on patient outcomes. Last, the model serves to focus attention on specific questions for further research.

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.

We investigate whether information technology (IT) can help physicians more efficiently acquire new knowledge in a clinical environment characterized by information overload. We combine analysis of data from a randomized trial with a theoretical model of the influence that IT has on the acquisition of new medical knowledge. Although the theoretical framework we develop is conventionally microeconomic, the model highlights the non-market and non-pecuniary influence activities that have been emphasized in the sociological literature on technology diffusion. We report three findings. First, empirical evidence and theoretical reasoning suggests that computer-based decision support will speed the diffusion of new medical knowledge when physicians are coping with information overload. Second, spillover effects will likely lead to “underinvestment” in this decision support technology. Third, alternative financing strategies common to new IT, such as the use of marketing dollars to pay for the decision support systems, may lead to undesirable outcomes if physician information overload is sufficiently severe and if there is significant ambiguity in how best to respond to the clinical issues identified by the computer. This is the first paper to analyze empirically and theoretically how computer-based decision support influences the acquisition of new knowledge by physicians.

The progress of initiatives concerned with implementing evaluated clinical research (such as evidence based medicine and clinical effectiveness) is dependent on the way individual health professionals actually acquire, use and value clinical knowledge in routine practice. The findings of two research projects, the Value and EVINCE projects, are compared with studies of the consolidation and application of clinical knowledge in clinical decision making. The Value project was concerned with the ways in which information from NHS libraries might be used in present and future clinical decision making. EVINCE was a similar impact study for nursing professionals. Both studies confirmed the importance of personal clinical knowledge. Health information services need to use a variety of strategies and knowledge management skills to ensure that the evaluated research evidence is assimilated and implemented into practice.

This paper looks at a number of approaches to health informatics that support decision‐making relevant to the integrated development and management of information systems with clinical and managerial practices in healthcare. Its main aim is to explore three such approaches for integrated development, the soft information systems and technologies methodology, participative simulation modelling and stakeholder analysis. A description of the health informatics research and development environment in the UK is given as necessary background to the paper. Organisational and social aspects are examined through these approaches including information and clinical process development, telemedicine, ethical issues of drug use and management, health policies and information management and strategies, tele‐education and modelling structures. In the conclusion the synergies between the three approaches are discussed and some principles are extracted for future research and development in integrated approaches to health informatics research.

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.

States that risk and risk management in general practice in the UK are now integral parts of the clinical effectiveness and clinical governance agenda, rather than being primarily concerned with negligence and malpractice. Notes that this has led to the introduction of a variety of technologies for improving care and thus reducing risk. Considers the frequency, nature and causes of adverse incidents in general practice, and the rise of evidence‐based practice and clinical practice guidelines, and then looks at some of those technologies currently in use. Concludes that technology seems to have a growing impact on the practice of primary care medicine and the management of clinical risk.

Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.