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Concept: Decision engineering

167

BACKGROUND: Pain management is a critical but complex issue for the relief of acute pain, particularly for postoperative pain and severe pain in cancer patients. It also plays important roles in promoting quality of care. The introduction of pain management decision support systems (PM-DSS) is considered a potential solution for addressing the complex problems encountered in pain management. This study aims to investigate factors affecting acceptance of PM-DSS from a nurse anesthetist perspective. METHODS: A questionnaire survey was conducted to collect data from nurse anesthetists in a case hospital. A total of 113 questionnaires were distributed, and 101 complete copies were returned, indicating a valid response rate of 89.3 %. Collected data were analyzed by structure equation modeling using the partial least square tool. RESULTS: The results show that perceived information quality (gamma=.451, p<.001), computer self-efficacy (gamma=.315, p<.01), and organizational structure (gamma=.210, p<.05), both significantly impact nurse anesthetists' perceived usefulness of PM-DSS. Information quality (gamma=.267, p<.05) significantly impacts nurse anesthetists' perceptions of PM-DSS ease of use. Furthermore, both perceived ease of use (beta=.436, p<.001, R2=.487) and perceived usefulness (beta=.443, p<.001, R2=.646) significantly affected nurse anesthetists' PM-DSS acceptance (R2=.640). Thus, the critical role of information quality in the development of clinical decision support system is demonstrated. CONCLUSIONS: The findings of this study enable hospital managers to understand the important considerations for nurse anesthetists in accepting PM-DSS, particularly for the issues related to the improvement of information quality, perceived usefulness and perceived ease of use of the system. In addition, the results also provide useful suggestions for designers and implementers of PM-DSS in improving system development.

Concepts: Decision theory, Anesthesia, Pain, Decision support system, Clinical decision support system, Decision engineering, Information systems, Data warehouse

151

We created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and deliver highly sensitive and specific decision support to the point of care using a mobile application. The investigators hypothesized that this system would result in a reduction in sepsis mortality.

Concepts: Decision theory, Decision support system, Decision engineering, Data warehouse, Halting problem, Discrete mathematics, Knowledge engineering, Self service software

121

The patient’s role in medical decision making is often not matched to the clinical circumstances: rather than making strong recommendations when there’s greater certainty and allowing patients to decide when there’s greater uncertainty, we should do the opposite.

Concepts: Decision making, Patient, Risk, Physician, Cognition, Decision theory, Decision making software, Decision engineering

50

Early resolution of uncertainty during an epidemic outbreak can lead to rapid and efficient decision making, provided that the uncertainty affects prioritization of actions. The wide range in caseload projections for the 2014 Ebola outbreak caused great concern and debate about the utility of models. By coding and running 37 published Ebola models with five candidate interventions, we found that, despite this large variation in caseload projection, the ranking of management options was relatively consistent. Reducing funeral transmission and reducing community transmission were generally ranked as the two best options. Value of information (VoI) analyses show that caseloads could be reduced by 11% by resolving all model-specific uncertainties, with information about model structure accounting for 82% of this reduction and uncertainty about caseload only accounting for 12%. Our study shows that the uncertainty that is of most interest epidemiologically may not be the same as the uncertainty that is most relevant for management. If the goal is to improve management outcomes, then the focus of study should be to identify and resolve those uncertainties that most hinder the choice of an optimal intervention. Our study further shows that simplifying multiple alternative models into a smaller number of relevant groups (here, with shared structure) could streamline the decision-making process and may allow for a better integration of epidemiological modeling and decision making for policy.

Concepts: Epidemiology, Decision making, Risk, Cognition, Decision theory, Decision making software, Decision engineering, Business Decision Mapping

31

Numerous studies have shown that diagnostic failure depends upon a variety of factors. Psychological factors are fundamental in influencing the cognitive performance of the decision maker. In this first of two papers, we discuss the basics of reasoning and the Dual Process Theory (DPT) of decision making. The general properties of the DPT model, as it applies to diagnostic reasoning, are reviewed. A variety of cognitive and affective biases are known to compromise the decision-making process. They mostly appear to originate in the fast intuitive processes of Type 1 that dominate (or drive) decision making. Type 1 processes work well most of the time but they may open the door for biases. Removing or at least mitigating these biases would appear to be an important goal. We will also review the origins of biases. The consensus is that there are two major sources: innate, hard-wired biases that developed in our evolutionary past, and acquired biases established in the course of development and within our working environments. Both are associated with abbreviated decision making in the form of heuristics. Other work suggests that ambient and contextual factors may create high risk situations that dispose decision makers to particular biases. Fatigue, sleep deprivation and cognitive overload appear to be important determinants. The theoretical basis of several approaches towards debiasing is then discussed. All share a common feature that involves a deliberate decoupling from Type 1 intuitive processing and moving to Type 2 analytical processing so that eventually unexamined intuitive judgments can be submitted to verification. This decoupling step appears to be the critical feature of cognitive and affective debiasing.

Concepts: Decision making, Critical thinking, Risk, Cognition, Decision theory, Decision making software, Unsolved problems in neuroscience, Decision engineering

30

. To better understand 1) why patients have a negative perception of the use of computerized clinical decision support systems (CDSSs) and 2) what contributes to the documented heterogeneity in the evaluations of physicians who use a CDSS.

Concepts: Decision theory, Decision support system, Clinical decision support system, Decision engineering, Information systems, Data warehouse, Knowledge engineering, DXplain

27

Clinical decision support systems have the potential to improve patient care in a multitude of ways. Clinical decision support systems can aid in the reduction of medical errors and reduction in adverse drug events, ensure comprehensive treatment of patient illnesses and conditions, encourage the adherence to guidelines, shorten patient length of stay, and decrease expenses over time. A clinical decision support system is one of the key components for reaching compliance for Meaningful Use. In this article, the advantages, potential drawbacks, and clinical decision support system adoption barriers are discussed, followed by an in-depth review of the characteristics that make a clinical decision support system successful. The legal and ethical issues that come with the implementation of a clinical decision support system within an organization and the future expectations of clinical decision support system are reviewed.

Concepts: Illness, Decision theory, Decision support system, Clinical decision support system, Decision engineering, Information systems, Data warehouse, Knowledge engineering

27

Municipal solid waste management (MSWM) is an important, practical and challenging environmental subject. The processes of a MSWM system include household collection, transportation, treatment, material recycling, compost and disposal. A regional program of MSWM is more complicated owing to the involvement of multi-municipality and multi-facility issues. Therefore, an effective decision support system capable of solving regional MSWM problems is necessary for decision-makers. This article employs linear programming techniques to establish a MSWM decision support system (MSWM-DSS) that is able to determine the least costs of regional MSWM strategies. The results of investigating a real-world case in central Taiwan indicate that a regional program is more economical and efficient. For the redeployment of MSW streams, the relatively least cost of operation for the MSWM system can still be achieved through the re-estimation of the MSWM-DSS. This tool and results are useful for MSWM policy-making in central Taiwan.

Concepts: Decision theory, Waste management, Decision support system, Recycling, Waste-to-energy, Waste, Decision engineering, Compost

27

ViĊĦekriterijumsko kompromisno rangiranje (VIKOR) method is one of the commonly used multi criteria decision making (MCDM) methods for improving the quality of decision making. VIKOR has an advantage in providing a ranking procedure for positive attributes and negative attributes when it is used and examined in decision support. However, we noticed that this method may failed to support an objective result inmedical field because most medical data have normal reference ranges (e.g., for normally distributed data: [Formula: see text]), this limitation shows a negative effect on the acceptance of it as an effective decision supporting method in medical decision making. This paper proposes an improved VIKOR method with enhanced accuracy (ea-VIKOR) to make it suitable for such data in medical field by introducing a new data normalization method taking the original distance to the normal reference range (ODNRR) into account. In addition, an experimental example was presented to demonstrate efficiency and feasibility of the ea-VIKOR method, the results demonstrate the abilityof ea-VIKOR to deal with moderate data and support the decision making in healthcare care management. Forthis reason, the ea-VIKOR should be considered for use as a decision support tool for future study.

Concepts: Decision making, Risk, Cognition, Decision theory, Decision making software, Normal distribution, Decision engineering, Multi-criteria decision analysis

27

This study aims to determine what the initial disposition of physicians towards the use of Clinical Decision Support Systems (CDSS) based on Computerised Clinical Guidelines and Protocols (CCGP) is; and whether their prolonged utilisation has a positive effect on their intention to adopt them in the future. For a period of 3 months, 8 volunteer paediatricians monitored each up to 10 asthmatic patients using two CCGPs deployed in thee-GuidesMed CDSS. A Technology Acceptance Model (TAM) questionnaire was supplied to them before and after using the system. Results from both questionnaires are analysed searching for significant improvements in opinion between them. An additional survey was performed to analyse the usability of the system. It was found that initial disposition of physicians towards e-Guidesmed is good. Improvement between the pre and post iterationsof the TAM questionnaire has been found to be statistically significant. Nonetheless, slightly lower values in the Compatibility and Habit variables show that participants perceive possible difficulties to integrate e-GuidesMed into their daily routine. The variable Facilitators shows the highest correlation with the Intention to Use. Usabilityof the system has also been rated very high and, in this regard, no fundamental flaw has been detected. Initial views towards e-GuidesMed are positive, and become reinforced after continued utilisation of the system. In order to achieve an effective implementation, it becomes essential to facilitate conditions to integrate the system intothe physician’s daily routine.

Concepts: Decision theory, Decision support system, Clinical decision support system, Decision engineering, Information systems, Data warehouse, Knowledge engineering, Self service software