Clinical Decision Support System

Clinical decision support systems (CDSSs) have been hailed for their potential to reduce medical errors 1 and increase health care quality and efficiency. 2 At the same time, evidence-based medicine has been widely promoted as a means of improving clinical outcomes, where evidence-based medicine refers to the practice of medicine based on the best available scientific evidence. The use of CDSSs to facilitate evidence-based medicine therefore promises to substantially improve health care quality.

Computerized clinical decision support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. natural language processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.

Computer-based clinical decision support systems (CDSSs) have been promoted for their potential to improve quality of health care. However, given the limited range of clinical settings in which they have been tested, such systems must be evaluated rigorously before widespread introduction into clinical practice.

Context: Physicians in the United States are only slowly adopting information technology (IT) tools, despite studies demonstrating their clinical benefits. More is known about IT use within institutional settings than by individual physicians. Objectives: This study investigates physicians' current use of, future plans for, and perceived barriers to adopting electronic medical records (EMRs), computerized prescribing and order entry, clinical decision support systems, and electronic communication (email) with other physicians and with patients. Design: Self-administered mail surveys were completed between March and May 2003 among a national random sample of physicians involved in direct patient care of adults. A total of 1837 surveys were returned for a response rate of 52.8%. Results: Physicians most commonly use IT for billing. For clinical management, the most common tool is computerized access to laboratory results (59%). Other tools are less prevalent: Twenty-seven percent of respondents use EMRs routinely or occasionally; 27% prescribe or order tests electronically; and 12% receive electronic alerts about potential drug-prescribing problems. Only 24% of surveyed physicians practice in a "high-tech" office setting. Physicians in groups of 50 or more are significantly more likely to use any IT tools and to practice in a high-tech office, as compared with physicians in solo practice (odds ratio = 7.7). The top 3 barriers to adoption of IT are start-up costs (56%), lack of uniform standards (44%), and lack of time (39%). Conclusion: Most physicians do not use EMRs and related technologies. Adoption is uneven, and a technologic divide exists between physicians depending on their practice environment and mode of compensation. Cost remains the most important barrier to adoption. Attention needs to be focused on policies and business models that will make IT tools accessible and affordable to all physicians.

Artifact detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in critical care units (CCU) by assessing quality of data prior to clinical event detection (CED) and parameter derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: 1) CCU; 2) physiologic data source; 3) harvested data; 4) data analysis; 5) clinical evaluation; and 6) clinical implementation. Review results show that most published algorithms: a) are designed for one specific type of CCU; b) are validated on data harvested only from one OEM monitor; c) generate signal quality indicators (SQI) that are not yet formalized for useful integration in clinical workflows; d) operate either in standalone mode or coupled with CED or PD applications; e) are rarely evaluated in real-time; and f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: 1) type; 2) frequency; 3) length; and 4) SQIs. This shall promote: a) reusability of algorithms across different CCU domains; b) evaluation on different OEM monitor data; c) fair comparison through formalized SQIs; d) meaningful integration with other AD, CED and PD algorithms; and e) real-time implementation in clinical workflows.

Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation and evaluation. We studied eleven types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow.

Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer's Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer's Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.

The need for flexible and well understood knowledge representations which are capable of capturing clinical guidelines and protocols for decision support systems is widely recognised. The PROforma method for specifying clinical guidelines and protocols comprises a graphical notation for their design, and a formal knowledge representation language to enable them to be executed by a computer to support the management of medical procedures and clinical decision making. PROforma technology consists of a graphical knowledge editor for the creation of guidelines, and an enactment engine for testing and executing them. This paper provides an overview of the motivation and structure of PROforma, and illustrates its use in the development of clinical applications.

We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.

Background: Clinical practice guidelines have been developed aiming to improve the quality of care. The implementation of the computerized clinical guidelines (CCG) has been supported by the development of computerized clinical decision support systems. This systematic review assesses the impact of CCG on the process of care compared with non-computerized clinical guidelines. Methods: Specific features of CCG were studied through an extensive search of scientific literature, querying electronic databases: Pubmed/Medline, Embase and Cochrane Controlled Trials Register. A multivariable logistic regression was carried out to evaluate the association of CCG's features with positive effect on the process of care. Results: Forty-five articles were selected. The logistic model showed that Automatic provision of recommendation in electronic version as part of clinician workflow (Odds Ratio [OR]= 17.5; 95% confidence interval [CI]: 1.6-193.7) and Publication Year (OR = 6.7; 95%CI: 1.3-34.3) were statistically significant predictors. Conclusions: From the research that has been carried out, we can conclude that after implementation of CCG significant improvements in process of care are shown. Our findings also suggest clinicians, managers and other health care decision makers which features of CCG might improve the structure of computerized system.

Rationale Information retrieval technology tends to become nothing less than crucial in physician daily practice, notably in family medicine. Nevertheless, few studies examine impacts of this technology and their results appear controversial. Aims and objectives Our article aims to explore these impacts using the medical literature, an organizational case study and the literature on organizations. Methods The case study was embedded in an evaluation of the implementation of medical and pharmaceutical databases on handheld computers in a Canadian family medicine centre. Six physicians were interviewed on specific events relative to the use of these databases and on their general perception of impacts of this use on clinical decision making and the doctor-patient relationship. A thematic data analysis was performed concomitantly by both authors. Results and conclusion Findings indicate six types of impact: practice improvement, reassurance, learning, confirmation, recall and frustration. These findings are interpreted in accordance with both a medical and organizational perspective. The fit with the literature on inter-organizational memory supports the transferability of the findings. In turn, this fit suggests how information retrieval technology may change physician routine. This study suggests a new basis for evaluating the impact of information retrieval technology in daily clinical practice. In conclusion, our paper encourages policy-makers to develop, and physicians to use, this technology.

Objective Pharmacy clinical decision-support (CDS) software that contains drugedrug interaction (DDI) information may augment pharmacists' ability to detect clinically significant interactions. However, studies indicate these systems may miss some important interactions. The purpose of this study was to assess the performance of pharmacy CDS programs to detect clinically important DDIs. Design Researchers made on-site visits to 64 participating Arizona pharmacies between December 2008 and November 2009 to analyze the ability of pharmacy information systems and associated CDS to detect DDIs. Software evaluation was conducted to determine whether DDI alerts arose from prescription orders entered into the pharmacy computer systems for a standardized fictitious patient. The fictitious patient's orders consisted of 18 different medications including 19 drug pairsd13 of which were clinically significant DDIs, and six were non-interacting drug pairs. Measurements The sensitivity, specificity, positive predictive value, negative predictive value, and percentage of correct responses were measured for each of the pharmacy CDS systems. Results Only 18 (28%) of the 64 pharmacies correctly identified eligible interactions and non-interactions. The median percentage of correct DDI responses was 89% (range 47e100%) for participating pharmacies. The median sensitivity to detect well-established interactions was 0.85 (range 0.23e1.0); median specificity was 1.0 (range 0.83e1.0); median positive predictive value was 1.0 (range 0.88e1.0); and median negative predictive value was 0.75 (range 0.38e1.0). Conclusions These study results indicate that many pharmacy clinical decision-support systems perform less than optimally with respect to identifying well-known, clinically relevant interactions. Comprehensive system improvements regarding the manner in which pharmacy information systems identify potential DDIs are warranted.

Objective: To measure critical order check override rates in VA Puget Sound Health Care System's computerized practitioner order entry (CPOE) system and to compare 2006 results to a similar 2001 study. Design: Analysis of ordering and order check data gathered by a post-hoc logging program. Use of Pearson's chisquare contingency table test comparing results from this study and the earlier study. Measurements: Factors measured were total number of orders, frequency of order check types, frequency of order check overrides by order check type and comparisons of these results with previous results. Results: A total of 37,040 orders generated 908 (2.5%) critical order checks. Drug-drug critical alert override rate was 74/85 (87%) in 2006 compared to 95/108 (88%) in 2001 (X 2 ϭ0.04, dfϭ1, pϭ0.85). The drug-allergy override rate was 341/420 (81%) compared to 72/105 (69%) in 2001 (X 2 ϭ7.97, dfϭ1, pϭ0.005). In 2001, 0.25% (105/42,621) orders generated a drug-allergy order check compared to 1.13% (420/37,040) in 2006 (X 2 ϭ238.45, dfϭ1, pϽ0.0001).

Clinical decision support is a powerful tool for improving healthcare quality and patient safety. However, developing a comprehensive package of decision support interventions is costly and difficult. If used well, Web 2.0 methods may make it easier and less costly to develop decision support. Web 2.0 is characterized by online communities, open sharing, interactivity and collaboration. Although most previous attempts at sharing clinical decision support content have worked outside of the Web 2.0 framework, several initiatives are beginning to use Web 2.0 to share and collaborate on decision support content. We present case studies of three efforts: the Clinfowiki, a world-accessible wiki for developing decision support content; Partners HealthCare eRooms, web-based tools for developing decision support within a single organization; and Epic Systems Corporation's Community Library, a repository for sharing decision support content for customers of a single clinical system vendor. We evaluate the potential of Web 2.0 technologies to enable collaborative development and sharing of clinical decision support systems through the lens of three case studies; analyzing technical, legal and organizational issues for developers, consumers and organizers of clinical decision support content in Web 2.0. We believe the case for Web 2.0 as a tool for collaborating on clinical decision support content appears strong, particularly for collaborative content development within an organization.

Many hospitals utilize antimicrobial management teams (AMTs) to improve patient care. However, most function with minimal computer support. We evaluated the effectiveness and costeffectiveness of a computerized clinical decision support system for the management of antimicrobial utilization. Design: A randomized controlled trial in adult inpatients between May 10 and August 3, 2004. Antimicrobial utilization was managed by an existing AMT using the system in the intervention arm and without the system in the control arm. The system was developed to alert the AMT of potentially inadequate antimicrobial therapy. Measurements: Outcomes assessed were hospital antimicrobial expenditures, mortality, length of hospitalization, and time spent managing antimicrobial utilization. Results: The AMT intervened on 359 (16%) of 2,237 patients in the intervention arm and 180 (8%) of 2,270 in the control arm, while spending approximately one hour less each day on the intervention arm.

OBJECTIVES: To evaluate the efficacy of computerized provider order entry with clinical decision support for preventing adverse drug events in long-term care. DESIGN: Cluster-randomized controlled trial. SETTING: Two large long-term care facilities. PATIENTS: One thousand one hundred eighteen longterm care residents of 29 resident care units.

Decision support systems in the medical field have to be easily modified by medical experts themselves. We have designed a knowledge acquisition tool to facilitate the creation and the maintenance of a knowledge base by the domain expert and its sharing and reuse by other institutions. The Unified Medical Language System (UMLS) constitutes the domain entities and relations repository from which the expert builds, through a specific browser, the explicit domain ontology. The expert is then guided in creating the knowledge base according to the preestablished domain ontology and condition-action rule templates that are well adapted to several clinical decision making processes. Corresponding Medical Logic Modules (MLMs) are eventually generated. The application of this knowledge acquisition tool to the construction of a decision support system in blood transfusion demonstrates the value of such a pragmatic methodology for the design of rule-based clinical systems that rely on highly progressive knowledge embedded in hospital information systems.

Clinical notes, typically written in natural language, often contain substructure that divides them into sections, such as "History of Present Illness" or "Family Medical History." The authors designed and evaluated an algorithm ("SecTag") to identify both labeled and unlabeled (implied) note section headers in "history and physical examination" documents ("H&P notes"). The SecTag algorithm uses a combination of natural language processing techniques, word variant recognition with spelling correction, terminology-based rules, and naive Bayesian scoring methods to identify note section headers. Eleven physicians evaluated SecTag's performance on 319 randomly chosen H&P notes. The primary outcomes were the algorithm's recall and precision in identifying all document sections and a predefined list of twenty-nine major sections. A secondary outcome was to evaluate the algorithm's ability to recognize the correct start and end boundaries of identified sections. The SecTag algorithm identified 16,036 total sections and 7,858 major sections. Physician evaluators classified 15,329 as true positives and identified 160 sections omitted by SecTag. The recall and precision of the SecTag algorithm were 99.0 and 95.6% for all sections, 98.6 and 96.2% for major sections, and 96.6 and 86.8% for unlabeled sections. The algorithm determined the correct starting and ending text boundaries for 94.8% of labeled sections and 85.9% of unlabeled sections. The SecTag algorithm accurately identified both labeled and unlabeled sections in history and physical documents. This type of algorithm may assist in natural language processing applications, such as clinical decision support systems or competency assessment for medical trainees.

Effective electromyographic (EMG) signal characterization is critical in the diagnosis of neuromuscular disorders. Machine-learning based pattern classification algorithms are commonly used to produce such characterizations. Several classifiers have been investigated to develop accurate and computationally efficient strategies for EMG signal characterization. This paper provides a critical review of some of the classification methodologies used in EMG characterization, and presents the state-of-the-art accomplishments in this field, emphasizing neuromuscular pathology. The techniques studied are grouped by their methodology, and a summary of the salient findings associated with each method is presented.

The inability of many clinical decision-support applications to integrate with existing databases limits the wide-scale deployment of such systems. To overcome this obstacle, we have designed a data-interpretation module that can be embedded in a general architecture for protocol-based reasoning and that can support the fundamental task of detecting temporal abstractions. We have developed this software module by coupling two existing systems--RESUME and Chronus--that provide complementary temporal-abstraction techniques at the application and the database levels, respectively. Their encapsulation into a single module thus can resolve the temporal queries of protocol planners with the domain-specific knowledge needed for the temporal-abstraction task and with primary time-stamped data stored in autonomous clinical databases. We show that other computer methods for the detection of temporal abstractions do not scale up to the data- and knowledge-intensive environments of protocol-bas...

Objectives: : (i) To review contributions and limitations of decision support systems for automatic recruitment of patients to clinical trials (Clinical Trial Recruitment Support Systems, CTRSS). (ii) To characterize the important features of this domain, the main classes of approach that have been used, and their advantages and disadvantages. (iii) To assess the effectiveness and potential of such systems in improving trial recruitment rates. Data sources: : A systematic MESH keyword-based search of Pubmed, Embase, and Scholar Google for relevant CTRSS publications from January 1st 1998 to August 31st 2009 yielded 73 references, from which 33 relevant papers describing 28 distinct studies were chosen for review, based on their report of a novel decision support system for trial recruitment which reused already available patient data. Method: The reviewed papers were classified using a modified version of an existing taxonomy for clinical decision support systems, using 10 axes relevant to the trial recruitment domain. Results: It proved possible and useful to characterize CTRSS on a relatively small number of dimensions and a number of clear trends emerge from the study. Only nine papers reported a useful evaluation of the effectiveness of the system in terms of trial pre-inclusion or enrolment rate. While all the systems reviewed re-use structured and coded patient data none attempts the more difficult task of using unstructured patient notes to pre-screen for trial inclusion. Few studies address acceptance of systems by clinicians, or integration into clinical workflow, and there is little evidence of use of interoperability standards. Conclusions: System design, scope, and assessment methodology vary significantly between papers, making it difficult to establish the impact of different approaches on recruitment rate. It is clear, however, that the pre-screening phase of trial recruitment is the most effective part of the process to address with CTRSS, that clinical workflow integration and clinician acceptance are critical for this class of decision support, and that the current trends in this field are towards generalization and scalability.

Due to increasing percentage of graying population and patients with chronic diseases, the world is facing serious problems for serving high quality healthcare services to citizens at a reasonable costs. In this paper, we are providing a Clininical Desicion Support system for remote monitoring of patients at their homes, and at the hospital to decrease the load of medical practitioners and also healthcare costs. As the expert knowledge required to build the clinical decision support system, Clinical Guidelines are exploited. Examining the reasons of failure for adoption of clinical guidelines by healthcare institutes, we have realized that necessary measures should be taken in order to establish a semantic interoperability environment to be able to communicate with various heteregenous clinical systems. In this paper these requirements are detailed and a semantic infrastructure to enable easy deployment and execution of clinical guidelines in heteregenous healtcare enviroments is presented. Due to the nature of the problem which necessitates having many autonomous entities dealing with heteregenous distributed resources, we have built the system as a Multi Agent System. The architecture described in this paper is realized within the scope of IST-27074 SAPHIRE project.

In this paper, the design and evaluation of decision support systems, including those incorporating a telematic component, are considered. It is argued that effective design and evaluation are dependent upon the adoption of appropriate methodology set firmly within a systemic framework. Systems modeling is proposed as an approach to system design, with evaluation adopting an approach incorporating evaluability analysis and formative and summative evaluation, including the use of stakeholder matrix analysis. The relevance of such systemic methodology is demonstrated in the context of diabetes and endstage renal disease as examples of the generic clinical problem of the management of chronic disease.

After having an introduction to the essential topics, the previous chapters have all provided effective use of deep learning for diagnosis of important diseases, as they are base for the medical decision support systems. There are of course many more research aspects to be discussed but if is also a good approach to focus on some insights regarding future of medical decision support systems.

Computerized physician order entry with clinical decision support has been shown to improve medication safety in adult inpatients, but few data are available regarding its usefulness in the longterm care setting. The objective of this study was to examine opportunities for improving medication safety in that clinical setting by determining the proportion of medication orders that would generate a warning message to the prescriber via a computerized clinical decision support system and assessing the extent to which these alerts would affect prescribers' actions.

Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the 'Historical Decision' bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.

Objective: We investigated the effect of written drug information for senior clinicians on the incidence of drug-drug interactions (DDIs) and DDI-related adverse events in intensive care patients. Design and methods: A prospective controlled intervention cohort study was conducted in a medical intensive and intermediate care unit in a university hospital. From 1,062 consecutive intensive care patients, those 265 (control: 136, intervention: 129) with C8 concurrently prescribed drugs were included in the study (to include high-risk patients with polypharmacy). The DDI information for senior clinicians during an intervention period of 3 months was based on a computerised clinical decision support system (CDSS) containing information on risk and management of 9,453 drug combinations. Results: The number of patients with at least one DDI at the end of the respective study phase decreased by 18% (relative risk reduction) from 90 (66%) patients in controls to 70 (54%) in the intervention group (p = 0.02). The relative risk of a patient suffering from at least one DDI-related adverse event decreased by 43% from 60 (44%) patients in controls to 32 (25%) in the intervention group (p \ 0.01). Among these events, the incidence of QT C prolongation was reduced by 64% from 15 (11%) patients in the control group to 5 (4%) in the intervention group (p = 0.04), and the incidence of hypokalemia by 80% from 14 (10%) to 2 (2%, p \ 0.01). Conclusion: Written drug information based on a CDSS considerably decreased DDIs and DDI-related adverse events in routine practice.

Background: Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson's disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty.

In a recent study, artificial neural networks were trained to detect coronary artery disease using scintigraphic data as input. The performance of the networks was better than that of human experts using coronary angiography as a gold standard. In clinical practice, this type of neural networks will not take over the decision-making process from the physician but will assist by proposing an interpretation of the scintigram. The purpose of this study was to assess the influence of such decision support on the interpretations of the physicians. A population of 135 patients who had undergone both myocardial 99mTc-sestamibi rest/stress scintigraphy and coronary angiography within a 3-mo period was studied. An image set consisting of the bull's-eye rest, stress, difference and quote images was constructed for each patient. Three experienced physicians independently classified all image sets regarding the presence and/or absence of coronary artery disease in two vascular territories u...

Early diagnosis of dementia benefits both patient and caregiver. Nevertheless, dementia in primary care is currently under-diagnosed. Some educational interventions developed to improve dementia diagnosis and management were successful in increasing the number of dementia diagnoses and in changing attitudes and knowledge of health care staff. However, none of these interventions focussed on collaboration between GPs and nurses in dementia care. We developed an EASYcare-based Dementia Training Program (DTP) aimed at stimulating collaboration in dementia primary care. We expect this program to increase the number of cognitive assessments and dementia diagnoses and to improve attitudes and knowledge of GPs and nurses. The DTP is a complex educational intervention that consists of two workshops, a coaching program, access to an internet forum, and a Computerized Clinical Decision Support System on dementia diagnostics. One hundred duos of GPs and nurses will be recruited, from which 2/3...

The purpose of this study was to determine if race/ethnicity, payer type, or nursing specialty affected depression screening rates in primary care settings in which nurses received a reminder to screen. The sample comprised 4,160 encounters in which nurses enrolled in advanced practice training were prompted to screen for depression using the Patient Health Questionnaire (PHQ)-2/PHQ-9 integrated into a personal digital assistant-based clinical decision support system for depression screening and management. Nurses chose to screen in response to 52.5% of reminders. Adjusted odds ratios showed that payer type and nurse specialty, but not race/ethnicity, significantly predicted proportion of patients screened.

The purpose of this study was to develop automatic classifiers to simplify the clinical use and increase the accuracy of the forced oscillation technique (FOT) in the categorisation of airway obstruction level in patients with chronic obstructive pulmonary disease (COPD). The data consisted of FOT parameters obtained from 168 volunteers (42 healthy and 126 COPD subjects with four different levels of obstruction). The first part of this study showed that FOT parameters do not provide adequate accuracy in identifying COPD subjects in the first levels of obstruction, as well as in discriminating between close levels of obstruction. In the second part of this study, different supervised machine learning (ML) techniques were investigated, including k-nearest neighbour (KNN), random forest (RF) and support vector machines with linear (SVML) and radial basis function kernels (SVMR). These algorithms were applied only in situations where high categorisation accuracy [area under the Receiver...

Background Patients receiving mechanical ventilation are at high risk for pneumonia due to aspiration. Published guidelines recommend elevating the head of the bed 30° to 45°, if not contraindicated, to reduce risk, but this intervention is underused.Objectives To facilitate incorporating evidence-based practice by improving positioning of patients receiving mechanical ventilation and to identify patient and nurse characteristics that predict use of the guideline.Methods A modified interrupted time-series design was used. Data were collected on 43 patients and 33 nurses 3 separate times in a 12-bed intensive care unit at a medium-sized hospital. A total of 105 observations were recorded for analysis each time.Results Mean elevations of the head of the bed increased significantly from phase 1 (27.7°) to phase 2 (31.7°) and from phase 1 to phase 3 (31.1°). Elevations were higher for tube-fed patients than for patients not given enteral tube feedings. Elevations were higher for patient...

Breast cancer follow-up care can be provided by family physicians after specialists complete the primary treatment. Cancer Care Nova Scotia has developed a breast cancer followup Clinical Practice Guideline (CPG) targeting family physicians. In this paper we present a project to computerize and deploy the said CPG in a Breast Cancer Follow-up Decision Support System (BCF-DSS) for use by family physicians in a primary care setting. We present a semantic web approach to model the CPG knowledge and employ a logic-based proof engine to execute the CPG in order to infer patient-specific recommendations. We present the three stages of the development of BCF-DSS-i.e. (a) Computerization of the paperbased CPG for Breast Cancer follow-up; (b) Development of three ontologies-i.e. the Breast Cancer Ontology, the CPG ontology based on the Guideline Element Model (GEM) and a Patient Ontology; and (c) Execution of the Breast Cancer follow-up CPG through a logic-based CPG execution engine.

Background: Frail older people admitted to nursing homes are at risk of a range of adverse outcomes, including pressure ulcers. Clinical decision support systems are believed to have the potential to improve care and to change the behaviour of healthcare professionals. Objectives: To determine whether a multi-faceted tailored strategy to implement an electronic clinical decision support system for pressure ulcer prevention improves adherence to recommendations for pressure ulcer prevention in nursing homes. Design: Two-armed randomized controlled trial in a nursing home setting in Belgium. The trial consisted of a 16-week implementation intervention between February and June 2010, including one baseline, four intermediate, and one post-testing measurement. Primary outcome was the adherence to guideline-based care recommendations (in terms of allocating adequate pressure ulcer prevention in residents at risk). Secondary outcomes were the change in resident outcomes (pressure ulcer prevalence) and intermediate outcomes (knowledge and attitudes of healthcare professionals). Setting: Random sample of 11 wards (6 experimental; 5 control) in a convenience sample of 4 nursing homes in Belgium. Participants: In total, 464 nursing home residents and 118 healthcare professionals participated. Methods: The experimental arm was involved in a multi-faceted tailored implementation intervention of a clinical decision support system, including interactive education, reminders, monitoring, feedback and leadership. The control arm received a hard-copy of the pressure ulcer prevention protocol, supported by standardized 30 min group lecture. Results: Patients in the intervention arm were significantly more likely to receive fully adequate pressure ulcer prevention when seated in a chair (F = 16.4, P = 0.003). No significant improvement was observed on pressure ulcer prevalence and knowledge of the professionals. While baseline attitude scores were comparable between both groups [exp. 74.3% vs. contr. 74.5% (P = 0.92)], the mean score after the intervention was 83.5% in the experimental group vs. 72.1% in the control group (F = 15.12, P < 0.001).

Clinical databases typically contain a significant amount of temporal information. This information is often crucial in medical decision-support systems. Although temporal queries are common in clinical systems, the medical informatics field has no standard means for representing or querying temporal data. Over the past decade, the temporal database community has made a significant amount of progress in temporal systems. Much of this research can be applied to clinical database systems. This paper outlines a temporal database mediator called Chronus II. Chronus II extends the standard relational model and the SQL query language to support temporal queries. It provides an expressive general-purpose temporal query language that is tuned to the querying requirements of clinical decision support systems. This paper describes how we have used Chronus II to tackle a variety of clinical problems in decision support systems developed by our group.