A taxonomic description of computer-based clinical decision support systems

Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis

Objectives

Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases.

Material and Methods

We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted.

Results

The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY.

Conclusion

We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies.

Registration

PROSPERO (CRD42020203716)

Reorganizing the Multidisciplinary Team Meetings in a Tertiary Centre for Gastro-Intestinal Oncology Adds Value to the Internal and Regional Care Pathways. A Mixed Method Evaluation

INTRODUCTION

The reorganisation of the structure of a Gastro-Intestinal Oncology Multidisciplinary Team Meeting (GIO-MDTM) in a tertiary centre with three care pathways is evaluated on added value.

METHODS

In a mixed method investigation, process indicators such as throughput times were analysed and stakeholders were interviewed regarding benefits and drawbacks of the reorganisation and current MDTM functioning.

RESULTS

For the hepatobiliary care pathway, the time to treatment plan increased, but the time to start treatment reduced significantly. The percentage of patients treated within the Dutch standard of 63 days increased for the three care pathways. From the interviews, three themes emerged: added value of MDTMs, focus on planning integrated care and awareness of possible improvements.

DISCUSSION

The importance of evaluating interventions in oncology care pathways is shown, including detecting unexpected drawbacks. The evaluation provides insight into complex dynamics of the care pathways and contributes with recommendations on functioning of an MDTM.

CONCLUSIONS

Throughput times are only partly determined by oncology care pathway management, but have influence on the functioning of MDTMs. Process indicator information can help to reflect on integration of care in the region, resulting in an increase of patients treated within the Dutch standard.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Towards Dynamic Uncertain Causality Graphs for the Intelligent Diagnosis and Treatment of Hepatitis B

Hepatitis B is a widespread epidemic in the world, but so far no single drug has been shown to kill or eliminate the Hepatitis B virus and heal people with chronic Hepatitis B virus infection. Based on comprehensive investigations to relevant characteristics of Hepatitis B, a diagnostic modelling and reasoning methodology using Dynamic Uncertain Causality Graph is proposed. The symptoms, physical signs, examinations results, medical histories, etiology, pathogenesis and other factors were included in the diagnosis model. In order to reduce the difficulty of building the model, a modular modeling scheme is proposed, which provides multi-perspectives and arbitrary granularity for the expression of disease causality. The chain reasoning algorithm and weighted logic operation mechanism are introduced to ensure the correctness and effectiveness of diagnostic reasoning under incomplete and uncertain information. In addition, the causal view of the potential interactions between diseases and symptoms visually shows the reasoning process in a graphical way. In the relevant model, the model of the diagnostic process and the model of the therapeutic process are symmetrical. The results show that, even with incomplete observations, the proposed methodology achieves encouraging diagnostic accuracy and effectiveness, providing a promising assistance tool for physicians in the diagnosis of Hepatitis B.

Do computerized clinical decision support systems improve the prescribing of oral anticoagulants? A systematic review

BACKGROUND

Serious adverse drug reactions have been associated with the underuse or the misuse of oral anticoagulant therapy. We systematically reviewed the impact of computerized clinical decision support systems (CDSS) on the prescribing of oral anticoagulants and we described CDSS features associated with success or failure.

METHODS

We searched Medline, Embase, CENTRAL, CINHAL, and PsycINFO for studies that compared CDSS for the initiation or monitoring of oral anticoagulants with routine care. Two reviewers performed study selection, data collection, and risk-of-bias assessment. Disagreements were resolved with a third reviewer. Potentially important CDSS features, identified from previous literature, were evaluated.

RESULTS

Sixteen studies were included in our qualitative synthesis. Most trials were performed in primary care (n = 7) or hospitals (n = 6) and included atrial fibrillation (AF) patients (n = 9). Recommendations mainly focused on anticoagulation underuse (n = 11) and warfarin-drug interactions (n = 5). Most CDSS were integrated in electronic records or prescribing and provided support automatically at the time and location of decision-making. Significant improvements in practitioner performance were found in 9 out of 16 studies, while clinical outcomes were poorly reported. CDSS features seemed slightly more common in studies that demonstrated improvement.

CONCLUSIONS

CDSS might positively impact the use of oral anticoagulants in AF patients at high risk of stroke. The scope of CDSS should now evolve to assist prescribers in selecting the most appropriate and tailored medication. Efforts should nevertheless be made to improve the relevance of notifications and to address implementation outcomes.

Usability Evaluation of Visual Representation Formats for Emergency Department Records

BACKGROUND

Integration of electronic information is a challenge for multitasking emergency providers, with implications for patient safety. Visual representations can assist sense-making of complex data sets; however, benefit and acceptability in emergency care is unproven.

OBJECTIVES

This article evaluates visually focused alternatives to lists or tabular formats, to better understand possible usability in Emergency Department Information System (EDIS).

METHODS

A counterbalanced, repeated-measures experiment, satisfaction surveys, and narrative content analysis was conducted remotely by Web platform. Participants were 37 American emergency physicians; they completed 16 clinical cases comparing 4 visual designs to the control formats from a commercially available EDIS. They then evaluated two additional chart overview representations without controls.

RESULTS

Visual designs provided benefit in several areas compared to controls. Task correctness (90% to 76%; p = 0.003) and completion time (median: 49-74 seconds; p < 0.001) were superior for a medication history timeline with class and schedule highlighting. Completion time (median: 45-60 seconds; p = 0.03) was superior for a past medical history design, using pertinent diagnosis codes in highlighting rules. Less mental effort was reported for visual allergy (p = 0.04), past medical history (p < 0.001), and medication timeline (p < 0.001) designs. Most of the participants agreed with statements of likeability, preference, and benefit for visual designs; nonetheless, contrary opinions were seen, and more complex designs were viewed less favorably.

CONCLUSION

Physician performance with visual representations of clinical data can in some cases exceed standard formats, even in absence of training. Highlighting of priority clinical categories was rated easier-to-use on average than unhighlighted controls. Perceived complexity of timeline representations can limit desirability for a subset of users, despite potential benefit.

Multidisciplinary care of breast cancer patients: a scoping review of multidisciplinary styles, processes, and outcomes

Background

Clinical practice guidelines recommend a multidisciplinary approach to cancer care that brings together all relevant disciplines to discuss optimal disease management. However, the literature is characterized by heterogeneous definitions and few reviews about the processes and outcomes of multidisciplinary care. The objective of this scoping review was to identify and classify the definitions and characteristics of multidisciplinary care, as well as outcomes and interventions for patients with breast cancer.

Methods

A systematic search for quantitative and qualitative studies about multidisciplinary care for patients with breast cancer was conducted for January 2001 to December 2017 in the following electronic databases: medline, embase, PsycInfo, and cinahl. Two reviewers independently applied our eligibility criteria at level 1 (title/abstract) and level 2 (full-text) screening. Data were extracted and synthesized descriptively.

Results

The search yielded 9537 unique results, of which 191 were included in the final analysis. Two main types of multidisciplinary care were identified: conferences and clinics. Most studies focused on outcomes of multidisciplinary care that could be variously grouped at the patient, provider, and system levels. Research into processes tended to focus on processes that facilitate implementation: team-working, meeting logistics, infrastructure, quality audit, and barriers and facilitators.

Summary

Approaches to multidisciplinary care using conferences and clinics are well described. However, studies vary by design, clinical context, patient population, and study outcome. The heterogeneity of the literature, including the patient populations studied, warrants further specification of multidisciplinary care practice and systematic reviews of the processes or contexts that make the implementation and operation of multidisciplinary care effective.

"Similar query was answered earlier": processing of patient authored text for retrieving relevant contents from health discussion forum

Online remedy finders and health-related discussion forums have become increasingly popular in recent years. Common web users write their health problems there and request suggestion from experts or other users. As a result, these forums became a huge repository of information and discussions on various health issues. An intelligent information retrieval system can help to utilize this repository in various applications. In this paper, we propose a system for the automatic identification of existing similar forum posts given a new post. The system is based on computing similarity between two patient authored texts. For computing the similarity between the current post and existing posts, the system uses a hybrid strategy based on template information, topic modelling, and latent semantic indexing. The system is tested using a set of real questions collected from a homeopathy forum namely abchomeopathy.com. The relevance of the posts retrieved by the system is evaluated by human experts. The evaluation results demonstrate that the precision of the system is 88.87%.

Computerized Systems Supporting Clinical Decision in Medicine

Statistics is the science of collection, summarizing, presentation and interpretation of data. Moreover, it yields methods used in the verification of research hypotheses. The presence of a statistician in a research group remarkably improves both the quality of design and research and the optimization of financial resources. Moreover, the involvement of a statistician in a research team helps the physician to effectively utilize the time and energy spent on diagnosing, which is an important aspect in view of limited healthcare resources. Precise, properly designed and implemented Computerized Clinical Decision Support Systems certainly lead to the improvement of healthcare and the quality of medical services, which increases patient satisfaction and reduces financial burdens on healthcare systems.

The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support

Background

Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed.

Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully.

Methods

We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist.

Results

We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy.

Conclusions

The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed.

Electronic supplementary material

The online version of this article (10.1186/s13012-018-0772-3) contains supplementary material, which is available to authorized users.

The use of electronic alerts in primary care computer systems to identify the excessive prescription of short-acting beta2-agonists for people with asthma: a systematic review

Computers are increasingly used to improve prescribing decisions in the management of long-term conditions however the effects on asthma prescribing remain unclear. We aimed to synthesise the evidence for the use of computerised alerts that identify excessive prescribing of short-acting beta₂-agonists (SABAs) to improve asthma management for people with asthma. MEDLINE, CINAHL, Embase, Cochrane and Scopus databases (1990–2016) were searched for randomised controlled trials using electronic alerts to identify excessive prescribing of SABAs for people with asthma in primary care. Inclusion eligibility, quality appraisal (Cochrane risk of bias tool) and data extraction were performed by two independent reviewers. Findings were synthesised narratively. A total of 2035 articles were screened and four trials were eligible. Three studies had low risk of bias: one reported a positive effect on our primary outcome of interest, excessive SABA prescribing; another reported positive effects on the ratio of inhaled corticosteroid (ICS)-SABA prescribing, and asthma control; a third reported no effect on outcomes of interest. One study at high risk of bias reported a reduction in exacerbations and primary care consultations. There is some evidence that electronic alerts reduce excessive prescribing of SABAs, when delivered as part of a multicomponent intervention in an integrated health care system. However due to the variation in health care systems, intervention design and outcomes measured, further research is required to establish optimal design of alerting and intervening systems.

Thinking Together: Modeling Clinical Decision-Support as a Sociotechnical System

Computerized clinical decision-support systems are members of larger sociotechnical systems, composed of human and automated actors, who send, receive, and manipulate artifacts. Sociotechnical consideration is rare in the literature. This makes it difficult to comparatively evaluate the success of CDS implementations, and it may also indicate that sociotechnical context receives inadequate consideration in practice. To facilitate sociotechnical consideration, we developed the Thinking Together model, a flexible diagrammatical means of representing CDS systems as sociotechnical systems. To develop this model, we examined the literature with the lens of Distributed Cognition (DCog) theory. We then present two case studies of vastly different CDSSs, one almost fully automated and the other with minimal automation, to illustrate the flexibility of the Thinking Together model. We show that this model, informed by DCog and the CDS literature, are capable of supporting both research, by enabling comparative evaluation, and practice, by facilitating explicit sociotechnical planning and communication.

Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review

Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O'Malley's scoping review framework which progresses through five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies for analysis; (4) charting the data; and (5) collating, summarizing and reporting results. We considered computer-based, and other available tools, such as algorithms, care pathways, rules and models. Since this research crosses multiple disciplines, we searched health care, computing science and business databases. Results Our search resulted in 4605 manuscripts. Titles and abstracts were screened for relevance. The reliability of the screening process was high with an average percentage of agreement of 92.3 %. Of the located articles, 123 were considered relevant. Within this literature, there were 43 CDS tools located. These were classified into 3 main areas: computer-based tools/questionnaires (n = 8, 19 %), treatment algorithms/models (n = 14, 33 %), and clinical prediction rules/classification systems (n = 21, 49 %). Each of these areas and the associated evidence are described. The state of evidentiary support for CDS tools is still preliminary and lacks external validation, head-to-head comparisons, or evidence of generalizability across different populations and settings. Conclusions CDS tools, especially those employing rapidly advancing computer technologies, are under development and of potential interest to health care providers, case management organizations and funders of care. Based on the results of this scoping review, we conclude that these tools, models and systems should be subjected to further validation before they can be recommended for large-scale implementation for managing patients with MSK disorders.

MACVIA clinical decision algorithm in adolescents and adults with allergic rhinitis

The selection of pharmacotherapy for patients with allergic rhinitis (AR) depends on several factors, including age, prominent symptoms, symptom severity, control of AR, patient preferences, and cost. Allergen exposure and the resulting symptoms vary, and treatment adjustment is required. Clinical decision support systems (CDSSs) might be beneficial for the assessment of disease control. CDSSs should be based on the best evidence and algorithms to aid patients and health care professionals to jointly determine treatment and its step-up or step-down strategy depending on AR control. Contre les MAladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon (MACVIA-LR [fighting chronic diseases for active and healthy ageing]), one of the reference sites of the European Innovation Partnership on Active and Healthy Ageing, has initiated an allergy sentinel network (the MACVIA-ARIA Sentinel Network). A CDSS is currently being developed to optimize AR control. An algorithm developed by consensus is presented in this article. This algorithm should be confirmed by appropriate trials.

Tailoring implementation strategies for evidence-based recommendations using computerised clinical decision support systems: protocol for the development of the GUIDES tools

BACKGROUND

A computerised clinical decision support system (CCDSS) is a technology that uses patient-specific data to provide relevant medical knowledge at the point of care. It is considered to be an important quality improvement intervention, and the implementation of CCDSS is growing substantially. However, the significant investments do not consistently result in value for money due to content, context, system and implementation issues. The Guideline Implementation with Decision Support (GUIDES) project aims to improve the impact of CCDSS through optimised implementation based on high-quality evidence-based recommendations. To achieve this, we will develop tools that address the factors that determine successful CCDSS implementation.

METHODS/DESIGN

We will develop the GUIDES tools in four steps, using the methods and results of the Tailored Implementation for Chronic Diseases (TICD) project as a starting point: (1) a review of research evidence and frameworks on the determinants of implementing recommendations using CCDSS; (2) a synthesis of a comprehensive framework for the identified determinants; (3) the development of tools for use of the framework and (4) pilot testing the utility of the tools through the development of a tailored CCDSS intervention in Norway, Belgium and Finland. We selected the conservative management of knee osteoarthritis as a prototype condition for the pilot. During the process, the authors will collaborate with an international expert group to provide input and feedback on the tools.

DISCUSSION

This project will provide guidance and tools on methods of identifying implementation determinants and selecting strategies to implement evidence-based recommendations through CCDSS. We will make the GUIDES tools available to CCDSS developers, implementers, researchers, funders, clinicians, managers, educators, and policymakers internationally. The tools and recommendations will be generic, which makes them scalable to a large spectrum of conditions. Ultimately, the better implementation of CCDSS may lead to better-informed decisions and improved care and patient outcomes for a wide range of conditions.

PROTOCOL REGISTRATION

PROSPERO, CRD42016033738.

Modeling information flows in clinical decision support: key insights for enhancing system effectiveness

A fundamental challenge in the field of clinical decision support is to determine what characteristics of systems make them effective in supporting particular types of clinical decisions. However, we lack such a theory of decision support itself and a model to describe clinical decisions and the systems to support them. This article outlines such a framework. We present a two-stream model of information flow within clinical decision-support systems (CDSSs): reasoning about the patient (the clinical stream), and reasoning about the user (the cognitive-behavioral stream). We propose that CDSS “effectiveness” be measured not only in terms of a system’s impact on clinical care, but also in terms of how (and by whom) the system is used, its effect on work processes, and whether it facilitates appropriate decisions by clinicians and patients. Future research into which factors improve the effectiveness of decision support should not regard CDSSs as a single entity, but should instead differentiate systems based on their attributes, users, and the decision being supported.

Implementation of a web based universal exchange and inference language for medicine: Sparse data, probabilities and inference in data mining of clinical data repositories

We extend Q-UEL, our universal exchange language for interoperability and inference in healthcare and biomedicine, to the more traditional fields of public health surveys. These are the type associated with screening, epidemiological and cross-sectional studies, and cohort studies in some cases similar to clinical trials. There is the challenge that there is some degree of split between frequentist notions of probability as (a) classical measures based only on the idea of counting and proportion and on classical biostatistics as used in the above conservative disciplines, and (b) more subjectivist notions of uncertainty, belief, reliability, or confidence often used in automated inference and decision support systems. Samples in the above kind of public health survey are typically small compared with our earlier "Big Data" mining efforts. An issue addressed here is how much impact on decisions should sparse data have. We describe a new Q-UEL compatible toolkit including a data analytics application DiracMiner that also delivers more standard biostatistical results, DiracBuilder that uses its output to build Hyperbolic Dirac Nets (HDN) for decision support, and HDNcoherer that ensures that probabilities are mutually consistent. Use is exemplified by participating in a real word health-screening project, and also by deployment in a industrial platform called the BioIngine, a cognitive computing platform for health management.

A Metrics Taxonomy and Reporting Strategy for Rule-Based Alerts

An action-oriented alerts taxonomy according to structure, actions, and implicit intended process outcomes using a set of 333 rule-based alerts at Kaiser Permanente Northwest (KPNW) was developed. The authors identified 9 major and 17 overall classes of alerts and developed a specific metric approach for 5 of these classes, including the 3 most numerous ones in KPNW, accounting for 224 (67%) of the alerts.

Description and pilot evaluation of the Metabolic Irregularities Narrowing down Device software: a case analysis of physician programming

Background

There is a gap between the abilities and the everyday applications of Computerized Decision Support Systems (CDSSs). This gap is further exacerbated by the different ‘worlds’ between the software designers and the clinician end-users. Software programmers often lack clinical experience whereas practicing physicians lack skills in design and engineering.

Objective

Our primary objective was to evaluate the performance of Metabolic Irregularities Narrowing down Device (MIND) intelligent medical calculator and differential diagnosis software through end-user surveys and discuss the roles of CDSS in the inpatient setting.

Setting

A tertiary care, teaching community hospital.

Study participants

Thirty-one responders answered the survey. Responders consisted of medical students, 24%; attending physicians, 16%, and residents, 60%.

Results

About 62.5% of the responders reported that MIND has the ability to potentially improve the quality of care, 20.8% were sure that MIND improves the quality of care, and only 4.2% of the responders felt that it does not improve the quality of care. Ninety-six percent of the responders felt that MIND definitely serves or has the potential to serve as a useful tool for medical students, and only 4% of the responders felt otherwise. Thirty-five percent of the responders rated the differential diagnosis list as excellent, 56% as good, 4% as fair, and 4% as poor.

Discussion

MIND is a suggesting, interpreting, alerting, and diagnosing CDSS with good performance and end-user satisfaction. In the era of the electronic medical record, the ongoing development of efficient CDSS platforms should be carefully considered by practicing physicians and institutions.

Clinical decision support system (CDSS) – effects on care quality

Purpose

– Despite their efficacy, some recommended therapies are underused. The purpose of this paper is to describe clinical decision support system (CDSS) development and its impact on clinical guideline adherence.

Design/methodology/approach

– A new CDSS was developed and introduced in a cardiac intensive care unit (CICU) in 2003, which provided physicians with patient-tailored reminders and permitted data export from electronic patient records into a national quality registry. To evaluate CDSS effects in the CICU, process indicators were compared to a control group using registry data. All CICUs were in the same region and only patients with acute coronary syndrome were included.

Findings

– CDSS introduction was associated with increases in guideline adherence, which ranged from 16 to 35 per cent, depending on the therapy. Statistically significant associations between guideline adherence and CDSS use remained over the five-year period after its introduction. During the same period, no relapses occurred in the intervention CICU.

Practical implications

– Guideline adherence and healthcare quality can be enhanced using CDSS. This study suggests that practitioners should turn to CDSS to improve healthcare quality.

Originality/value

– This paper describes and evaluates an intervention that successfully increased guideline adherence, which improved healthcare quality when the intervention CICU was compared to the control group.

Computer decision support systems for asthma: a systematic review

BACKGROUND

Increasing use of electronic health records offers the potential to incorporate computer decision support systems (CDSSs) to prompt evidence-based actions within routine consultations.

AIM

To synthesise the evidence for the use of CDSSs by professionals managing people with asthma.

MATERIALS AND METHODS

We systematically searched Medline, Embase, Health Technology Assessment, Cochrane and Inspec databases (1990 to April 2012, no language restrictions) for trials, and four online repositories for unpublished studies. We also wrote to authors. Eligible studies were randomised controlled trials of CDSSs supporting professional management of asthma. Studies were appraised (Cochrane Risk of Bias Tool) and findings synthesised narratively.

RESULTS

A total of 5787 articles were screened, and eight trials were found eligible, with six at high risk of bias. Overall, CDSSs for professionals were ineffective. Usage of the systems was generally low: in the only trial at low risk of bias the CDSS was not used at all. When a CDSS was used, compliance with the advice offered was also low. However, if actually used, CDSSs could result in closer guideline adherence (improve investigating, prescribing and issuing of action plans) and could improve some clinical outcomes. The study at moderate risk of bias showed increased prescribing of inhaled steroids.

CONCLUSIONS

The current generation of CDSSs is unlikely to result in improvements in outcomes for patients with asthma because they are rarely used and the advice is not followed. Future decision support systems need to align better with professional workflows so that pertinent and timely advice is easily accessible within the consultation.

Evaluation of stream mining classifiers for real-time clinical decision support system: a case study of blood glucose prediction in diabetes therapy

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

Diagnostic performance of electronic syndromic surveillance systems in acute care: a systematic review

CONTEXT

Healthcare Electronic Syndromic Surveillance (ESS) is the systematic collection, analysis and interpretation of ongoing clinical data with subsequent dissemination of results, which aid clinical decision-making.

OBJECTIVE

To evaluate, classify and analyze the diagnostic performance, strengths and limitations of existing acute care ESS systems.

DATA SOURCES

All available to us studies in Ovid MEDLINE, Ovid EMBASE, CINAHL and Scopus databases, from as early as January 1972 through the first week of September 2012.

STUDY SELECTION

Prospective and retrospective trials, examining the diagnostic performance of inpatient ESS and providing objective diagnostic data including sensitivity, specificity, positive and negative predictive values.

DATA EXTRACTION

Two independent reviewers extracted diagnostic performance data on ESS systems, including clinical area, number of decision points, sensitivity and specificity. Positive and negative likelihood ratios were calculated for each healthcare ESS system. A likelihood matrix summarizing the various ESS systems performance was created.

RESULTS

The described search strategy yielded 1639 articles. Of these, 1497 were excluded on abstract information. After full text review, abstraction and arbitration with a third reviewer, 33 studies met inclusion criteria, reporting 102,611 ESS decision points. The yielded I2 was high (98.8%), precluding meta-analysis. Performance was variable, with sensitivities ranging from 21% -100% and specificities ranging from 5%-100%.

CONCLUSIONS

There is significant heterogeneity in the diagnostic performance of the available ESS implements in acute care, stemming from the wide spectrum of different clinical entities and ESS systems. Based on the results, we introduce a conceptual framework using a likelihood ratio matrix for evaluation and meaningful application of future, frontline clinical decision support systems.

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department

OBJECTIVES

The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter.

METHODS

The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate.

RESULTS

The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED.

CONCLUSIONS

The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

Clinical decision support systems: potential with pitfalls

Clinical Decision Support Systems (CDSS), an important part of clinical practice, are comprised of a: knowledge base; program for integrating patient-specific information with the knowledge-base; and, user-interface to allow clinicians to interact with the system and get the right information needed to make the right decision for the right patient at the right time. We review the common approaches to CDSS, their strengths and weaknesses and how they are evaluated and developed for clinical use.

An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients

Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved. In this paper, we introduce an ontology for the care of chronically ill patients and implement two personalization processes and a decision support tool. The first personalization process adapts the contents of the ontology to the particularities observed in the health-care record of a given concrete patient, automatically providing a personalized ontology containing only the clinical information that is relevant for health-care professionals to manage that patient. The second personalization process uses the personalized ontology of a patient to automatically transform intervention plans describing health-care general treatments into individual intervention plans. For comorbid patients, this process concludes with the semi-automatic integration of several individual plans into a single personalized plan. Finally, the ontology is also used as the knowledge base of a decision support tool that helps health-care professionals to detect anomalous circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases, or preventive actions. Seven health-care centers participating in the K4CARE project, together with the group SAGESA and the Local Health System in the town of Pollenza have served as the validation platform for these two processes and tool. Health-care professionals participating in the evaluation agree about the average quality 84% (5.9/7.0) and utility 90% (6.3/7.0) of the tools and also about the correct reasoning of the decision support tool, according to clinical standards.

Informatics technology mimics ecology: dense, mutualistic collaboration networks are associated with higher publication rates

Information technology (IT) adoption enables biomedical research. Publications are an accepted measure of research output, and network models can describe the collaborative nature of publication. In particular, ecological networks can serve as analogies for publication and technology adoption. We constructed network models of adoption of bioinformatics programming languages and health IT (HIT) from the literature.

We selected seven programming languages and four types of HIT. We performed PubMed searches to identify publications since 2001. We calculated summary statistics and analyzed spatiotemporal relationships. Then, we assessed ecological models of specialization, cooperativity, competition, evolution, biodiversity, and stability associated with publications.

Adoption of HIT has been variable, while scripting languages have experienced rapid adoption. Hospital systems had the largest HIT research corpus, while Perl had the largest language corpus. Scripting languages represented the largest connected network components. The relationship between edges and nodes was linear, though Bioconductor had more edges than expected and Perl had fewer. Spatiotemporal relationships were weak. Most languages shared a bioinformatics specialization and appeared mutualistic or competitive. HIT specializations varied. Specialization was highest for Bioconductor and radiology systems. Specialization and cooperativity were positively correlated among languages but negatively correlated among HIT. Rates of language evolution were similar. Biodiversity among languages grew in the first half of the decade and stabilized, while diversity among HIT was variable but flat. Compared with publications in 2001, correlation with publications one year later was positive while correlation after ten years was weak and negative.

Adoption of new technologies can be unpredictable. Spatiotemporal relationships facilitate adoption but are not sufficient. As with ecosystems, dense, mutualistic, specialized co-habitation is associated with faster growth. There are rapidly changing trends in external technological and macroeconomic influences. We propose that a better understanding of how technologies are adopted can facilitate their development.

Cancer multidisciplinary team meetings: evidence, challenges, and the role of clinical decision support technology

Multidisciplinary team (MDT) model in cancer care was introduced and endorsed to ensure that care delivery is consistent with the best available evidence. Over the last few years, regular MDT meetings have become a standard practice in oncology and gained the status of the key decision-making forum for patient management. Despite the fact that cancer MDT meetings are well accepted by clinicians, concerns are raised over the paucity of good-quality evidence on their overall impact. There are also concerns over lack of the appropriate support for this important but overburdened decision-making platform. The growing acceptance by clinical community of the health information technology in recent years has created new opportunities and possibilities of using advanced clinical decision support (CDS) systems to realise full potential of cancer MDT meetings. In this paper, we present targeted summary of the available evidence on the impact of cancer MDT meetings, discuss the reported challenges, and explore the role that a CDS technology could play in addressing some of these challenges.

SeDeLo: using semantics and description logics to support aided clinical diagnosis

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies.

Accuracy of a computerized clinical decision-support system for asthma assessment and management

OBJECTIVE

To evaluate the accuracy of a computerized clinical decision-support system (CDSS) designed to support assessment and management of pediatric asthma in a subspecialty clinic.

DESIGN

Cohort study of all asthma visits to pediatric pulmonology from January to December, 2009.

MEASUREMENTS

CDSS and physician assessments of asthma severity, control, and treatment step.

RESULTS

Both the clinician and the computerized CDSS generated assessments of asthma control in 767/1032 (74.3%) return patients, assessments of asthma severity in 100/167 (59.9%) new patients, and recommendations for treatment step in 66/167 (39.5%) new patients. Clinicians agreed with the CDSS in 543/767 (70.8%) of control assessments, 37/100 (37%) of severity assessments, and 19/66 (29%) of step recommendations. External review classified 72% of control disagreements (21% of all control assessments), 56% of severity disagreements (37% of all severity assessments), and 76% of step disagreements (54% of all step recommendations) as CDSS errors. The remaining disagreements resulted from pulmonologist error or ambiguous guidelines. Many CDSS flaws, such as attributing all 'cough' to asthma, were easily remediable. Pediatric pulmonologists failed to follow guidelines in 8% of return visits and 18% of new visits.

LIMITATIONS

The authors relied on chart notes to determine clinical reasoning. Physicians may have changed their assessments after seeing CDSS recommendations.

CONCLUSIONS

A computerized CDSS performed relatively accurately compared to clinicians for assessment of asthma control but was inaccurate for treatment. Pediatric pulmonologists failed to follow guideline-based care in a small proportion of patients.

Comparing semi-automatic systems for recruitment of patients to clinical trials

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.

Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems

BACKGROUND

Clinical decision support (CDS) is a valuable tool for improving healthcare quality and lowering costs. However, there is no comprehensive taxonomy of types of CDS and there has been limited research on the availability of various CDS tools across current electronic health record (EHR) systems.

OBJECTIVE

To develop and validate a taxonomy of front-end CDS tools and to assess support for these tools in major commercial and internally developed EHRs.

STUDY DESIGN AND METHODS

We used a modified Delphi approach with a panel of 11 decision support experts to develop a taxonomy of 53 front-end CDS tools. Based on this taxonomy, a survey on CDS tools was sent to a purposive sample of commercial EHR vendors (n=9) and leading healthcare institutions with internally developed state-of-the-art EHRs (n=4).

RESULTS

Responses were received from all healthcare institutions and 7 of 9 EHR vendors (response rate: 85%). All 53 types of CDS tools identified in the taxonomy were found in at least one surveyed EHR system, but only 8 functions were present in all EHRs. Medication dosing support and order facilitators were the most commonly available classes of decision support, while expert systems (eg, diagnostic decision support, ventilator management suggestions) were the least common.

CONCLUSION

We developed and validated a comprehensive taxonomy of front-end CDS tools. A subsequent survey of commercial EHR vendors and leading healthcare institutions revealed a small core set of common CDS tools, but identified significant variability in the remainder of clinical decision support content.

The impact of eHealth on the quality and safety of health care: a systematic overview

BACKGROUND

There is considerable international interest in exploiting the potential of digital solutions to enhance the quality and safety of health care. Implementations of transformative eHealth technologies are underway globally, often at very considerable cost. In order to assess the impact of eHealth solutions on the quality and safety of health care, and to inform policy decisions on eHealth deployments, we undertook a systematic review of systematic reviews assessing the effectiveness and consequences of various eHealth technologies on the quality and safety of care.

METHODS AND FINDINGS

We developed novel search strategies, conceptual maps of health care quality, safety, and eHealth interventions, and then systematically identified, scrutinised, and synthesised the systematic review literature. Major biomedical databases were searched to identify systematic reviews published between 1997 and 2010. Related theoretical, methodological, and technical material was also reviewed. We identified 53 systematic reviews that focused on assessing the impact of eHealth interventions on the quality and/or safety of health care and 55 supplementary systematic reviews providing relevant supportive information. This systematic review literature was found to be generally of substandard quality with regards to methodology, reporting, and utility. We thematically categorised eHealth technologies into three main areas: (1) storing, managing, and transmission of data; (2) clinical decision support; and (3) facilitating care from a distance. We found that despite support from policymakers, there was relatively little empirical evidence to substantiate many of the claims made in relation to these technologies. Whether the success of those relatively few solutions identified to improve quality and safety would continue if these were deployed beyond the contexts in which they were originally developed, has yet to be established. Importantly, best practice guidelines in effective development and deployment strategies are lacking.

CONCLUSIONS

There is a large gap between the postulated and empirically demonstrated benefits of eHealth technologies. In addition, there is a lack of robust research on the risks of implementing these technologies and their cost-effectiveness has yet to be demonstrated, despite being frequently promoted by policymakers and "techno-enthusiasts" as if this was a given. In the light of the paucity of evidence in relation to improvements in patient outcomes, as well as the lack of evidence on their cost-effectiveness, it is vital that future eHealth technologies are evaluated against a comprehensive set of measures, ideally throughout all stages of the technology's life cycle. Such evaluation should be characterised by careful attention to socio-technical factors to maximise the likelihood of successful implementation and adoption.

The effectiveness of M-health technologies for improving health and health services: a systematic review protocol

BACKGROUND

The application of mobile computing and communication technology is rapidly expanding in the fields of health care and public health. This systematic review will summarise the evidence for the effectiveness of mobile technology interventions for improving health and health service outcomes (M-health) around the world.

FINDINGS

To be included in the review interventions must aim to improve or promote health or health service use and quality, employing any mobile computing and communication technology. This includes: (1) interventions designed to improve diagnosis, investigation, treatment, monitoring and management of disease; (2) interventions to deliver treatment or disease management programmes to patients, health promotion interventions, and interventions designed to improve treatment compliance; and (3) interventions to improve health care processes e.g. appointment attendance, result notification, vaccination reminders.A comprehensive, electronic search strategy will be used to identify controlled studies, published since 1990, and indexed in MEDLINE, EMBASE, PsycINFO, Global Health, Web of Science, the Cochrane Library, or the UK NHS Health Technology Assessment database. The search strategy will include terms (and synonyms) for the following mobile electronic devices (MEDs) and a range of compatible media: mobile phone; personal digital assistant (PDA); handheld computer (e.g. tablet PC); PDA phone (e.g. BlackBerry, Palm Pilot); Smartphone; enterprise digital assistant; portable media player (i.e. MP3 or MP4 player); handheld video game console. No terms for health or health service outcomes will be included, to ensure that all applications of mobile technology in public health and health services are identified. Bibliographies of primary studies and review articles meeting the inclusion criteria will be searched manually to identify further eligible studies. Data on objective and self-reported outcomes and study quality will be independently extracted by two review authors. Where there are sufficient numbers of similar interventions, we will calculate and report pooled risk ratios or standardised mean differences using meta-analysis.

DISCUSSION

This systematic review will provide recommendations on the use of mobile computing and communication technology in health care and public health and will guide future work on intervention development and primary research in this field.

Impact of a computerized template on antibiotic prescribing for acute respiratory infections in children and adolescents

BACKGROUND

Computerized decision support (CDS) can potentially improve patient safety and guideline adherence. The authors developed an acute respiratory illness interactive template (ARI-IT) within an electronic health record (EHR) to manage pediatric ARIs and assessed the impact on antibiotic prescribing.

METHODS

They randomized 12 practices either to receive the ARI-IT or to the control group. Antibiotic rates among all eligible ARI diagnoses were compared among control and intervention ARI visits, controlling for clustering by clinician.

RESULTS

There was no difference in total antibiotic prescriptions between control and intervention clinics. Use of the ARI-IT significantly reduced antibiotic prescriptions (31.7% vs 39.9%; P = .02) and use of macrolides (6.2% vs 9.5%; P = .02) among visits compared with those eligible visits where it was not used.

CONCLUSION

Use of the CDS reduced antibiotic prescribing and macrolide prescriptions among children with an ARI. Nonetheless, the low overall use resulted in an ineffective intervention.

Emergency Department Sickle Cell Assessment of Needs and Strengths (ED-SCANS), a focus group and decision support tool development project

OBJECTIVES

A decision support tool may guide emergency clinicians in recognizing assessment, analgesic and overall management, and health service delivery needs for patients with sickle cell disease (SCD) in the emergency department (ED). We aimed to identify data and process elements important in making decisions regarding evaluation and management of adult patients in the ED with painful episodes of SCD.

METHODS

Qualitative methods using a series of focus groups and grounded theory were used. Eligible participants included adult clients with SCD and emergency physicians and nurses with a minimum of 1 year of experience providing care to patients with SCD in the ED. Patients were recruited in conjunction with annual SCD meetings, and providers included clinicians who were and were not affiliated with sickle cell centers. Groups were conducted until saturation was reached and included a total of two patient groups, three physician groups, and two nurse groups. Focus groups were held in New York, Durham, Chicago, New Orleans, and Denver. Clinician participants were asked the following three questions to guide the discussion: 1) what information would be important to know about patients with SCD in the ED setting to effectively care for them and help you identify patient analgesic, treatment, and referral needs? 2) What treatment decisions would you make with this information? and 3) What characteristics would a decision support tool need to have to make it meaningful and useful? Client participants were asked the same questions with rewording to reflect what they believed providers should know to provide the best care and what they should do with the information. All focus groups were audiotaped and transcribed. The constant comparative method was used to analyze the data. Two coders independently coded participant responses and identified focal themes based on the key questions. An investigator and assistant independently reviewed the transcripts and met until the final coding structure was determined.

RESULTS

Forty-seven individuals participated (14 persons with SCD, 16 physicians, and 17 nurses) in a total of seven different groups. Two major themes emerged: acute management and health care utilization. Major subthemes included the following: physiologic findings, diagnostics, assessment and treatment of acute painful episodes, and disposition. The most common minor subthemes that emerged included past medical history, presence of a medical home (physician or clinic), individualized analgesic treatment plan for treatment of painful episodes, history of present illness, medical home follow-up available, patient-reported analgesic treatment that works, and availability of analgesic prescription at discharge. Additional important elements in treatment of acute pain episodes included the use of a standard analgesic protocol, need for fluids and nonpharmacologic interventions, and the assessment of typicality of pain presentation. The patients' interpretation of the need for hospital admission also ranked high.

CONCLUSIONS

Participants identified several areas that are important in the assessment, management, and disposition decisions that may help guide best practices for SCD patients in the ED setting.

Implementing an integrative multi-agent clinical decision support system with open source software

Clinical decision making is a complex multi-stage process. Decision support can play an important role at each stage of this process. At present, the majority of clinical decision support systems have been focused on supporting only certain stages. In this paper we present the design and implementation of MET3-a prototype multi-agent system providing an integrative decision support that spans over the entire decision making process. The system helps physicians with data collection, diagnosis formulation, treatment planning and finding supporting evidence. MET3 integrates with external hospital information systems via HL7 messages and runs on various computing platforms available at the point of care (e.g., tablet computers, mobile phones). Building MET3 required sophisticated and reliable software technologies. In the past decade the open source software movement has produced mature, stable, industrial strength software systems with a large user base. Therefore, one of the decisions that should be considered before developing or acquiring a decision support system is whether or not one could use open source technologies instead of proprietary ones. We believe MET3 shows that the answer to this question is positive.

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.

The technology acceptance model: its past and its future in health care

Increasing interest in end users' reactions to health information technology (IT) has elevated the importance of theories that predict and explain health IT acceptance and use. This paper reviews the application of one such theory, the Technology Acceptance Model (TAM), to health care. We reviewed 16 data sets analyzed in over 20 studies of clinicians using health IT for patient care. Studies differed greatly in samples and settings, health ITs studied, research models, relationships tested, and construct operationalization. Certain TAM relationships were consistently found to be significant, whereas others were inconsistent. Several key relationships were infrequently assessed. Findings show that TAM predicts a substantial portion of the use or acceptance of health IT, but that the theory may benefit from several additions and modifications. Aside from improved study quality, standardization, and theoretically motivated additions to the model, an important future direction for TAM is to adapt the model specifically to the health care context, using beliefs elicitation methods.

Neighborhood graph and learning discriminative distance functions for clinical decision support

There are two essential reasons for the slow progress in the acceptance of clinical case retrieval and similarity search-based decision support systems; the especial complexity of clinical data making it difficult to define a meaningful and effective distance function on them and the lack of transparency and explanation ability in many existing clinical case retrieval decision support systems. In this paper, we try to address these two problems by introducing a novel technique for visualizing inter-patient similarity based on a node-link representation with neighborhood graphs and by considering two techniques for learning discriminative distance function that help to combine the power of strong "black box" learners with the transparency of case retrieval and nearest neighbor classification.

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision support system design

OBJECTIVE

To develop a clinical decision support system (CDSS) that models the different levels of the clinician's decision-making strategies when controlling post cardiac surgery patients weaned from cardio pulmonary bypass.

METHODS

A clinical trial was conducted to define and elucidate an expert anesthetists' decision pathway utilised in controlling this patient population. This data and derived knowledge were used to elicit a decision-making model. The structural framework of the decision-making model is hierarchical, clearly defined, and dynamic. The decision levels are linked to five important components of the cardiovascular physiology in turn, i.e. the systolic blood pressure (SBP), central venous pressure (CVP), systemic vascular resistance (SVR), cardiac output (CO), and heart rate (HR). Progress down the hierarchy is dependent upon the normalisation of each physiological parameter to a value pre-selected by the clinician via fluid, chronotropes or inotropes. Since interventions at each and every level cause changes and disturbances in the other components, the proposed decision support model continuously refers back decision outcomes back to the SBP which is considered to be the overriding supervisory safety component in this hierarchical decision structure. The decision model was then translated into a computerised decision support system prototype and comprehensively tested on a physiological model of the human cardiovascular system. This model was able to reproduce conditions experienced by post-operative cardiac surgery patients including hypertension, hypovolemia, vasodilation and the systemic inflammatory response syndrome (SIRS).

RESULTS

In all the simulated patients scenarios considered the CDSS was able to initiate similar therapeutic interventions to that of the expert, and as a result, was also able to control the hemodynamic parameters to the prescribed target values.