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Concept: Decision support system


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


The way hemodynamic therapies are delivered today in anesthesia and critical care is suboptimal. Hemodynamic variables are not always understood correctly and used properly. The adoption of hemodynamic goal-directed strategies, known to be clinically useful, is poor. Ensuring therapies are delivered effectively is the goal of decision support tools and closed loop systems. Graphical displays (metaphor screens) may help clinicians to better capture and integrate the multivariable hemodynamic information. This may result in faster and more accurate diagnosis and therapeutic decisions. Graphical displays (target screens) have the potential to increase adherence to goal-directed strategies and ultimately improve patients' outcomes, but this remains to be confirmed by prospective studies. Closed loop systems are the ultimate solution to ensure therapies are delivered. However, most therapeutic decisions cannot be based on a limited number of output variables. Therefore, one should focus on the development of systems designed to relieve clinicians from very simple and repetitive tasks. Whether intraoperative goal-directed fluid therapy may be one of these tasks remains to be evaluated.

Concepts: Medicine, Therapy, Classification of Pharmaco-Therapeutic Referrals, Decision theory, Control theory, Decision support system, Graphic design, Medical error


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


Healthcare Information Systems should capture clinical data in a structured and preferably coded format. This is crucial for data exchange between health information systems, epidemiological analysis, quality and research, clinical decision support systems, administrative functions, among others. Structured data entry is an obstacle for the usability of electronic health record (EHR) applications and their acceptance by physicians who prefer to document patient EHRs using “free text”. Natural language allows for rich expressiveness but at the same time is ambiguous; it has great dependence on context and uses jargon and acronyms. Although much progress has been made in knowledge and natural language processing techniques, the result is not yet satisfactory enough for the use of free text in all dimensions of clinical documentation. In order to address the trade-off between capturing data with free text and at the same time coding data for computer processing, numerous terminological systems for the systematic recording of clinical data have been developed. The purpose of terminology services consists of representing facts that happen in the real world through database management in order to allow for semantic interoperability and computerized applications. These systems interrelate concepts of a particular domain and provide references to related terms with standards codes. In this way, standard terminologies allow the creation of a controlled medical vocabulary, making terminology services a fundamental component for health data management in the healthcare environment. The Hospital Italiano de Buenos Aires has been working in the development of its own terminology server. This work describes its experience in the field.

Concepts: Health care, Decision theory, Linguistics, Controlled vocabulary, Electronic health record, Decision support system, Health informatics, Medical informatics


With the increasing ability to routinely and rapidly digitize whole slide images with slide scanners, there has been interest in developing computerized image analysis algorithms for automated detection of disease extent from digital pathology images. The manual identification of presence and extent of breast cancer by a pathologist is critical for patient management for tumor staging and assessing treatment response. However, this process is tedious and subject to inter- and intra-reader variability. For computerized methods to be useful as decision support tools, they need to be resilient to data acquired from different sources, different staining and cutting protocols and different scanners. The objective of this study was to evaluate the accuracy and robustness of a deep learning-based method to automatically identify the extent of invasive tumor on digitized images. Here, we present a new method that employs a convolutional neural network for detecting presence of invasive tumor on whole slide images. Our approach involves training the classifier on nearly 400 exemplars from multiple different sites, and scanners, and then independently validating on almost 200 cases from The Cancer Genome Atlas. Our approach yielded a Dice coefficient of 75.86%, a positive predictive value of 71.62% and a negative predictive value of 96.77% in terms of pixel-by-pixel evaluation compared to manually annotated regions of invasive ductal carcinoma.

Concepts: Cancer, Breast cancer, Cancer staging, Mammary ductal carcinoma, Decision support system


Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall.

Concepts: Health care, Health care provider, Medicine, Decision theory, Decision support system, Clinical decision support system, Information systems, Medical error


. 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


OBJECTIVE:To determine the self-reported practices and attitudes surrounding concussion diagnosis and management in a single, large pediatric care network.METHODS:A cross-sectional survey was distributed to pediatric primary care and emergency medicine providers in a single, large pediatric care network. For all survey participants, practices and attitudes about concussion diagnosis and treatment were queried.RESULTS:There were 145 responses from 276 eligible providers, resulting in a 53% response rate, of which 91% (95% confidence interval [CI]: 86%-95%) had cared for at least 1 concussion patient in the previous 3 months. A Likert scale from 1 “not a barrier” to 5 “significant barrier” was used to assess providers' barriers to educating families about the diagnosis of concussion. Providers selected 4 or 5 on the scale for the following barriers and frequencies: inadequate training to educate 16% (95% CI: 11%-23%), inadequate time to educate 15% (95% CI: 12%-24%), and not my role to educate 1% (95% CI: 0.4%-5%). Ninety-six percent (95% CI: 91%-98%) of providers without a provider decision support tool (such as a clinical pathway or protocol) specific to concussion, and 100% (95% CI: 94%-100%) of providers without discharge instructions specific to concussion believed these resources would be helpful.CONCLUSIONS:Although pediatric primary care and emergency medicine providers regularly care for concussion patients, they may not have adequate training or infrastructure to systematically diagnose and manage these patients. Specific provider education, decision support tools, and patient information could help enhance and standardize concussion management.

Concepts: Medical terms, Education, Physician, Educational psychology, Normal distribution, Scale, Decision support system, Likert scale


Medication errors can lead to significant morbidity and mortality for patients. Children are particularly vulnerable to medication errors. A strategy for reducing medication errors and the harm resulting from these errors is use of computerized provider order entry (CPOE). This article examines the frequency and nature of prescribing errors for pediatric patients. Also discussed are the proposed benefits from CPOE use, including elimination of eligibility errors, ensuring completeness in prescribing fields, reduction in transcription errors, and improved prescribing practices through the use of clinical decision support. The literature on the effect of CPOE in actual use is explored, as are policy implications and directions for future research.

Concepts: Medicine, Medical terms, Physician, Decision theory, Decision support system, Patient safety, Medical informatics, Computer physician order entry


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