A four-phase model of the evolution of clinical decision support architectures

Data Representation Structure to Support Clinical Decision-Making in the Pediatric Intensive Care Unit: Interview Study and Preliminary Decision Support Interface Design

BACKGROUND

Clinical decision-making is a complex cognitive process that relies on the interpretation of a large variety of data from different sources and involves the use of knowledge bases and scientific recommendations. The representation of clinical data plays a key role in the speed and efficiency of its interpretation. In addition, the increasing use of clinical decision support systems (CDSSs) provides assistance to clinicians in their practice, allowing them to improve patient outcomes. In the pediatric intensive care unit (PICU), clinicians must process high volumes of data and deal with ever-growing workloads. As they use multiple systems daily to assess patients' status and to adjust the health care plan, including electronic health records (EHR), clinical systems (eg, laboratory, imaging and pharmacy), and connected devices (eg, bedside monitors, mechanical ventilators, intravenous pumps, and syringes), clinicians rely mostly on their judgment and ability to trace relevant data for decision-making. In these circumstances, the lack of optimal data structure and adapted visual representation hinder clinician's cognitive processes and clinical decision-making skills.

OBJECTIVE

In this study, we designed a prototype to optimize the representation of clinical data collected from existing sources (eg, EHR, clinical systems, and devices) via a structure that supports the integration of a home-developed CDSS in the PICU. This study was based on analyzing end user needs and their clinical workflow.

METHODS

First, we observed clinical activities in a PICU to secure a better understanding of the workflow in terms of staff tasks and their use of EHR on a typical work shift. Second, we conducted interviews with 11 clinicians from different staff categories (eg, intensivists, fellows, nurses, and nurse practitioners) to compile their needs for decision support. Third, we structured the data to design a prototype that illustrates the proposed representation. We used a brain injury care scenario to validate the relevance of integrated data and the utility of main functionalities in a clinical context. Fourth, we held design meetings with 5 clinicians to present, revise, and adapt the prototype to meet their needs.

RESULTS

We created a structure with 3 levels of abstraction-unit level, patient level, and system level-to optimize clinical data representation and display for efficient patient assessment and to provide a flexible platform to host the internally developed CDSS. Subsequently, we designed a preliminary prototype based on this structure.

CONCLUSIONS

The data representation structure allows prioritizing patients via criticality indicators, assessing their conditions using a personalized dashboard, and monitoring their courses based on the evolution of clinical values. Further research is required to define and model the concepts of criticality, problem recognition, and evolution. Furthermore, feasibility tests will be conducted to ensure user satisfaction.

The Case Manager: An Agent Controlling the Activation of Knowledge Sources in a FHIR-Based Distributed Reasoning Environment

BACKGROUND

Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines.

OBJECTIVES

As in many multi-agent systems we needed to coordinate the activities of all agents involved. Moreover, since the agents share a common blackboard where all patients' data are stored, we also needed to implement a mechanism for the prompt notification of each agent upon addition of new information potentially triggering its activation.

METHODS

The communication needs have been investigated and modeled using the HL7-FHIR (Health Level 7-Fast Healthcare Interoperability Resources) standard to ensure proper semantic interoperability among agents. Then a syntax rooted in the FHIR search framework has been defined for representing the conditions to be monitored on the system blackboard for activating each agent.

RESULTS

The Case Manager (CM) has been implemented as a dedicated component playing the role of an orchestrator directing the behavior of all agents involved. Agents dynamically inform the CM about the conditions to be monitored on the blackboard, using the syntax we developed. The CM then notifies each agent whenever any condition of interest occurs. The functionalities of the CM and other actors have been validated using simulated scenarios mimicking the ones that will be faced during pilot studies and in production.

CONCLUSION

The CM proved to be a key facilitator for properly achieving the required behavior of our multi-agent system. The proposed architecture may also be leveraged in many clinical contexts for integrating separate legacy services, turning them into a consistent telemedicine framework and enabling application reusability.

Transitioning Cognitive Aids into Decision Support Platforms: Requirements and Design Guidelines

Digital cognitive aids have the potential to serve as clinical decision support platforms, triggering alerts about process delays and recommending interventions. In this mixed-methods study, we examined how a digital checklist for pediatric trauma resuscitation could trigger decision support alerts and recommendations. We identified two criteria that cognitive aids must satisfy to support these alerts: (1) context information must be entered in a timely, accurate, and standardized manner, and (2) task status must be accurately documented. Using co-design sessions and near-live simulations, we created two checklist features to satisfy these criteria: a form for entering the pre-hospital information and a progress slider for documenting the progression of a multi-step task. We evaluated these two features in the wild, contributing guidelines for designing these features on cognitive aids to support alerts and recommendations in time- and safety-critical scenarios.

System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software Implementation

Background

Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model.

Objective

Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient’s current treatment context.

Methods

We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine.

Results

We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians.

Conclusions

The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Background

Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability.

Aim

In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance.

Results

A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well‐trained end‐users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved.

Conclusion

We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success.

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

BACKGROUND

Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care.

OBJECTIVE

The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data.

METHODS

The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors.

RESULTS

Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters.

CONCLUSIONS

MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments.

Clinical decision support alert malfunctions: analysis and empirically derived taxonomy

Objective

To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions.

Materials and Methods

We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions.

Results

We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common.

Discussion

Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS.

Conclusion

CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

Multi-model-based interactive authoring environment for creating shareable medical knowledge

OBJECTIVE

Technologically integrated healthcare environments can be realized if physicians are encouraged to use smart systems for the creation and sharing of knowledge used in clinical decision support systems (CDSS). While CDSSs are heading toward smart environments, they lack support for abstraction of technology-oriented knowledge from physicians. Therefore, abstraction in the form of a user-friendly and flexible authoring environment is required in order for physicians to create shareable and interoperable knowledge for CDSS workflows. Our proposed system provides a user-friendly authoring environment to create Arden Syntax MLM (Medical Logic Module) as shareable knowledge rules for intelligent decision-making by CDSS.

METHODS AND MATERIALS

Existing systems are not physician friendly and lack interoperability and shareability of knowledge. In this paper, we proposed Intelligent-Knowledge Authoring Tool (I-KAT), a knowledge authoring environment that overcomes the above mentioned limitations. Shareability is achieved by creating a knowledge base from MLMs using Arden Syntax. Interoperability is enhanced using standard data models and terminologies. However, creation of shareable and interoperable knowledge using Arden Syntax without abstraction increases complexity, which ultimately makes it difficult for physicians to use the authoring environment. Therefore, physician friendliness is provided by abstraction at the application layer to reduce complexity. This abstraction is regulated by mappings created between legacy system concepts, which are modeled as domain clinical model (DCM) and decision support standards such as virtual medical record (vMR) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We represent these mappings with a semantic reconciliation model (SRM).

RESULTS

The objective of the study is the creation of shareable and interoperable knowledge using a user-friendly and flexible I-KAT. Therefore we evaluated our system using completeness and user satisfaction criteria, which we assessed through the system- and user-centric evaluation processes. For system-centric evaluation, we compared the implementation of clinical information modelling system requirements in our proposed system and in existing systems. The results suggested that 82.05% of the requirements were fully supported, 7.69% were partially supported, and 10.25% were not supported by our system. In the existing systems, 35.89% of requirements were fully supported, 28.20% were partially supported, and 35.89% were not supported. For user-centric evaluation, the assessment criterion was 'ease of use'. Our proposed system showed 15 times better results with respect to MLM creation time than the existing systems. Moreover, on average, the participants made only one error in MLM creation using our proposed system, but 13 errors per MLM using the existing systems.

CONCLUSION

We provide a user-friendly authoring environment for creation of shareable and interoperable knowledge for CDSS to overcome knowledge acquisition complexity. The authoring environment uses state-of-the-art decision support-related clinical standards with increased ease of use.

Reconciliation of SNOMED CT and domain clinical model for interoperable medical knowledge creation

Use of heterogeneous data models in hospital information systems (HIS), obstructs the integration of clinical decision support system (CDSS) with clinical workflows. The diverse concepts diminish the interoperability level among the CDSS knowledge bases and data models of HIS. Standard terminology utilization in knowledge acquisition and its reconciliation with HIS data models are the candidate solution to overcome the interoperability barrier. We propose a reconciliation model to map concepts of diverse domain clinical models (DCM) with the standard terminology. In the proposed model, the implicit and explicit semantics are complemented to the word set of the targeted DCM concepts. The inclusion of semantics, mapped the DCM concepts to the SNOMED CT concepts with high accuracy. The results showed that the system correctly mapped 95% of concepts of DCM with standard terminology SNOMED CT concepts.

Evaluating Terminologies to Enable Imaging-Related Decision Rule Sharing

Purpose: Clinical decision support tools provide recommendations based on decision rules. A fundamental challenge regarding decision rule-sharing involves inadequate expression using standard terminology. We aimed to evaluate the coverage of three standard terminologies for mapping imaging-related decision rules. Methods: 50 decision rules, randomly selected from an existing library, were mapped to Systemized Nomenclature of Medicine (SNOMED CT), Radiology Lexicon (RadLex) and International Classification of Disease (ICD-10-CM). Decision rule attributes and values were mapped to unique concepts, obtaining the best possible coverage with the fewest concepts. Manual and automated mapping using Clinical Text Analysis and Knowledge Extraction System (cTAKES) were performed. Results: Using manual mapping, SNOMED CT provided the greatest concept coverage (83%), compared to RadLex (36%) and ICD-10-CM (8%) (p<0.0001). Combined mapping had 86% concept coverage. Automated mapping achieved 85% mapping coverage vs. 94% with manual mapping (p<0.001). Conclusion: Although some gaps remain, standard terminologies provide ample coverage for mapping imaging- related evidence.

Templates as a method for implementing data provenance in decision support systems

Decision support systems are used as a method of promoting consistent guideline-based diagnosis supporting clinical reasoning at point of care. However, despite the availability of numerous commercial products, the wider acceptance of these systems has been hampered by concerns about diagnostic performance and a perceived lack of transparency in the process of generating clinical recommendations. This resonates with the Learning Health System paradigm that promotes data-driven medicine relying on routine data capture and transformation, which also stresses the need for trust in an evidence-based system. Data provenance is a way of automatically capturing the trace of a research task and its resulting data, thereby facilitating trust and the principles of reproducible research. While computational domains have started to embrace this technology through provenance-enabled execution middlewares, traditionally non-computational disciplines, such as medical research, that do not rely on a single software platform, are still struggling with its adoption. In order to address these issues, we introduce provenance templates - abstract provenance fragments representing meaningful domain actions. Templates can be used to generate a model-driven service interface for domain software tools to routinely capture the provenance of their data and tasks. This paper specifies the requirements for a Decision Support tool based on the Learning Health System, introduces the theoretical model for provenance templates and demonstrates the resulting architecture. Our methods were tested and validated on the provenance infrastructure for a Diagnostic Decision Support System that was developed as part of the EU FP7 TRANSFoRm project.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

Supporting Healthcare Executive Managers’ Decisions Through Dashboards

Information visualisation plays an important role for executives in order to drive business effectively and efficiently. Dashboard design, which is one of the best tools of visualization to meet the information needs at adequate levels, is becoming more important parallel to advances in information processing technologies. This study aims to provide a conceptual framework for developing a dashboard for executives. Healthcare management was chosen to demonstrate the methodology of research design, data gathering and visualisation examples. Results show that the study is applicable to other areas as well to meet the information requirements of top and mid-level managers.

Using Arden Syntax for the creation of a multi-patient surveillance dashboard

OBJECTIVE

Most practically deployed Arden-Syntax-based clinical decision support (CDS) modules process data from individual patients. The specification of Arden Syntax, however, would in principle also support multi-patient CDS. The patient data management system (PDMS) at our local intensive care units does not natively support patient overviews from customizable CDS routines, but local physicians indicated a demand for multi-patient tabular overviews of important clinical parameters such as key laboratory measurements. As our PDMS installation provides Arden Syntax support, we set out to explore the capability of Arden Syntax for multi-patient CDS by implementing a prototypical dashboard for visualizing laboratory findings from patient sets.

METHODS AND MATERIAL

Our implementation leveraged the object data type, supported by later versions of Arden, which turned out to be serviceable for representing complex input data from several patients. For our prototype, we designed a modularized architecture that separates the definition of technical operations, in particular the control of the patient context, from the actual clinical knowledge. Individual Medical Logic Modules (MLMs) for processing single patient attributes could then be developed according to well-tried Arden Syntax conventions.

RESULTS

We successfully implemented a working dashboard prototype entirely in Arden Syntax. The architecture consists of a controller MLM to handle the patient context, a presenter MLM to generate a dashboard view, and a set of traditional MLMs containing the clinical decision logic. Our prototype could be integrated into the graphical user interface of the local PDMS. We observed that with realistic input data the average execution time of about 200ms for generating dashboard views attained applicable performance.

CONCLUSION

Our study demonstrated the general feasibility of creating multi-patient CDS routines in Arden Syntax. We believe that our prototypical dashboard also suggests that such implementations can be relatively easy, and may simultaneously hold promise for sharing dashboards between institutions and reusing elementary components for additional dashboards.

Using Arden Syntax Medical Logic Modules to reduce overutilization of laboratory tests for detection of bacterial infections-Success or failure?

OBJECTIVE

Bacterial infections frequently cause prolonged intensive care unit (ICU) stays. Repeated measurements of the procalcitonin (PCT) biomarker are typically used for early detection and follow up of bacterial infections and sepsis, but those PCT measurements are costly. To avoid overutilization, we developed and evaluated a clinical decision support system (CDSS) in Arden Syntax which computes necessary and preventable PCT orders.

METHODS

The CDSS implements a rule set based on the latest PCT value, the time period since this measurement, and the PCT trend scenario. We assessed the CDSS effects on the daily rate of ordered PCT tests within a prospective study having two ON and two OFF phases in a surgical ICU. In addition, we performed interviews with the participating physicians to investigate their experience with the CDSS advice.

RESULTS

Prior to the deployment of the CDSS, 22% of the performed PCT tests were potentially preventable according to the rule set. During the first ON phase the daily rate of ordered PCT tests per patient decreased significantly from 0.807 to 0.662. In subsequent OFF, ON and OFF phases, however, PCT utilization reached again daily rates of 0.733, 0.803, and 0.792, respectively. The interviews demonstrated that the physicians were aware of the problem of PCT overutilization, which they primarily attributed to acute time constraints. The responders assumed that the majority of preventable measurements are indiscriminately ordered for patients during longer ICU stays.

CONCLUSION

We observed an 18% reduction of PCT tests within the first four weeks of CDSS support in the investigated ICU. This reduction may have been influenced by raised awareness of the overutilization problem; the extent of this influence cannot be determined in our study design. No reduction of PCT tests could be observed during the second ON phase. The physician interviews indicated that time critical ICU situations can prevent extensive reflection about the necessity of individual tests. In order to achieve an enduring effect on PCT utilization, we will have to proceed to electronic order entry.

Lessons learned from implementing service-oriented clinical decision support at four sites: A qualitative study

OBJECTIVE

To identify challenges, lessons learned and best practices for service-oriented clinical decision support, based on the results of the Clinical Decision Support Consortium, a multi-site study which developed, implemented and evaluated clinical decision support services in a diverse range of electronic health records.

METHODS

Ethnographic investigation using the rapid assessment process, a procedure for agile qualitative data collection and analysis, including clinical observation, system demonstrations and analysis and 91 interviews.

RESULTS

We identified challenges and lessons learned in eight dimensions: (1) hardware and software computing infrastructure, (2) clinical content, (3) human-computer interface, (4) people, (5) workflow and communication, (6) internal organizational policies, procedures, environment and culture, (7) external rules, regulations, and pressures and (8) system measurement and monitoring. Key challenges included performance issues (particularly related to data retrieval), differences in terminologies used across sites, workflow variability and the need for a legal framework.

DISCUSSION

Based on the challenges and lessons learned, we identified eight best practices for developers and implementers of service-oriented clinical decision support: (1) optimize performance, or make asynchronous calls, (2) be liberal in what you accept (particularly for terminology), (3) foster clinical transparency, (4) develop a legal framework, (5) support a flexible front-end, (6) dedicate human resources, (7) support peer-to-peer communication, (8) improve standards.

CONCLUSION

The Clinical Decision Support Consortium successfully developed a clinical decision support service and implemented it in four different electronic health records and four diverse clinical sites; however, the process was arduous. The lessons identified by the Consortium may be useful for other developers and implementers of clinical decision support services.

A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial

INTRODUCTION

The promise of Clinical Decision Support (CDS) has always been to transform patient care and improve patient outcomes through the delivery of timely and appropriate recommendations that are patient specific and, more often than not, are appropriately actionable. However, the users of CDS-providers-are frequently bombarded with inappropriate and inapplicable CDS that often are not informational, not integrated into the workflow, not patient specific, and that may present out of date and irrelevant recommendations.

METHODS

The integrated clinical prediction rule (iCPR) project was a randomized clinical trial (RCT) conducted to determine if a novel form of CDS, i.e., clinical prediction rules (CPRs), could be efficiently integrated into workflow and result in changes in outcomes (e.g., antibiotic ordering) when embedded within a commercial electronic health record (EHR). We use the lessons learned from the iCPR project to illustrate a framework for constructing usable, useful, and effective actionable CDS while employing off-the-shelf functionality in a production system. Innovations that make up the framework combine the following: (1) active and actionable decision support, (2) multiple rounds of usability testing with iterative development for user acceptance, (3) numerous context sensitive triggers, (4) dedicated training and support for users of the CDS tool for user adoption, and (5) support from clinical and administrative leadership. We define "context sensitive triggers" as being workflow events (i.e., context) that result in a CDS intervention.

DISCUSSION

Success of the framework can be measured by CDS adoption (i.e., intervention is being used), acceptance (compliance with recommendations), and clinical outcomes (where appropriate). This framework may have broader implications for the deployment of Health Information Technology (HIT).

RESULTS AND CONCLUSION

iCPR was well adopted(57.4% of users) and accepted (42.7% of users). Usability testing identified and fixed many issues before the iCPR RCT. The level of leadership support and clinical guidance for iCPR was key in establishing a culture of acceptance for both the tool and its recommendations contributing to adoption and acceptance. The dedicated training and support lead to the majority of the residents reporting a high level of comfort with both iCPR tools strep pharyngitis (64.4 percent) and pneumonia (62.7 percent) as well as a high likelihood of using the tools in the future. A surprising framework addition resulted from usability testing: context sensitive triggers.

An electronic medical record system with treatment recommendations based on patient similarity

As the core of health information technology (HIT), electronic medical record (EMR) systems have been changing to meet health care demands. To construct a new-generation EMR system framework with the capability of self-learning and real-time feedback, thus adding intelligence to the EMR system itself, this paper proposed a novel EMR system framework by constructing a direct pathway between the EMR workflow and EMR data. A prototype of this framework was implemented based on patient similarity learning. Patient diagnoses, demographic data, vital signs and structured lab test results were considered for similarity calculations. Real hospitalization data from 12,818 patients were substituted, and Precision @ Position measurements were used to validate self-learning performance. Our EMR system changed the way in which orders are placed by establishing recommendation order menu and shortcut applications. Two learning modes (EASY MODE and COMPLEX MODE) were provided, and the precision values @ position 5 of both modes were 0.7458 and 0.8792, respectively. The precision performance of COMPLEX MODE was better than that of EASY MODE (tested using a paired Wilcoxon-Mann-Whitney test, p < 0.001). Applying the proposed framework, the EMR data value was directly demonstrated in the clinical workflow, and intelligence was added to the EMR system, which could improve system usability, reliability and the physician's work efficiency. This self-learning mechanism is based on dynamic learning models and is not limited to a specific disease or clinical scenario, thus decreasing maintenance costs in real world applications and increasing its adaptability.

Information technology and precision medicine

OBJECTIVES

To provide oncology nurses with an overview of clinical decision support (CDS) and explore opportunities for genomic CDS interventions. The nation's first personalized cancer decision support tool, My Cancer Genome, is presented as an exemplar of a novel CDS tool.

DATA SOURCES

Published nursing and medical literature and the internet for an exemplar.

CONCLUSION

CDS is a sophisticated health information technology that can translate and integrate genomic knowledge with patient information, providing recommendations at the point of care.

IMPLICATIONS FOR NURSING PRACTICE

Nurses, as key stakeholders, must have an understanding of CDS interventions and their application to fully participate in all stages of CDS development and implementation.

Healthcare decision support system for administration of chronic diseases

Objectives

A healthcare decision-making support model and rule management system is proposed based on a personalized rule-based intelligent concept, to effectively manage chronic diseases.

Methods

A Web service was built using a standard message transfer protocol for interoperability of personal health records among healthcare institutions. An intelligent decision service is provided that analyzes data using a service-oriented healthcare rule inference function and machine-learning platform; the rules are extensively compiled by physicians through a developmental user interface that enables knowledge base construction, modification, and integration. Further, screening results are visualized for the self-intuitive understanding of personal health status by patients.

Results

A recommendation message is output through the Web service by receiving patient information from the hospital information recording system and object attribute values as input factors. The proposed system can verify patient behavior by acting as an intellectualized backbone of chronic diseases management; further, it supports self-management and scheduling of screening.

Conclusions

Chronic patients can continuously receive active recommendations related to their healthcare through the rule management system, and they can model the system by acting as decision makers in diseases management; secondary diseases can be prevented and health management can be performed by reference to patient-specific lifestyle guidelines.

A method for inferring medical diagnoses from patient similarities

BACKGROUND

Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools.

METHODS

Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses.

RESULTS

Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses.

CONCLUSIONS

Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.

Beyond the threshold: real-time use of evidence in practice

In two landmark reports on Quality and Information Technology, the Institute of Medicine described a 21st century healthcare delivery system that would improve the quality of care while reducing its costs. To achieve the improvements envisioned in these reports, it is necessary to increase the efficiency and effectiveness of the clinical decision support that is delivered to clinicians through electronic health records at the point of care. To make these dramatic improvements will require significant changes to the way in which clinical practice guidelines are developed, incorporated into existing electronic health records (EHR), and integrated into clinicians' workflow at the point of care. In this paper, we: 1) discuss the challenges associated with translating evidence to practice; 2) consider what it will take to bridge the gap between the current limits to use of CPGs and expectations for their meaningful use at the point of care in practices with EHRs; 3) describe a framework that underlies CDS systems which, if incorporated in the development of CPGs, can be a means to bridge this gap, 4) review the general types and adoption of current CDS systems, and 5) describe how the adoption of EHRs and related technologies will directly influence the content and form of CPGs. Achieving these objectives should result in improvements in the quality and reductions in the cost of healthcare, both of which are necessary to ensure a 21st century delivery system that consistently provides safe and effective care to all patients.

Integrating Arden-Syntax-based clinical decision support with extended presentation formats into a commercial patient data management system

The purpose of this study was to introduce clinical decision support (CDS) that exceeds conventional alerting at tertiary care intensive care units. We investigated physicians' functional CDS requirements in periodic interviews, and analyzed technical interfaces of the existing commercial patient data management system (PDMS). Building on these assessments, we adapted a platform that processes Arden Syntax medical logic modules (MLMs). Clinicians demanded data-driven, user-driven and time-driven execution of MLMs, as well as multiple presentation formats such as tables and graphics. The used PDMS represented a black box insofar as it did not provide standardized interfaces for event notification and external access to patient data; enabling CDS thus required periodically exporting datasets for making them accessible to the invoked Arden engine. A client-server-architecture with a simple browser-based viewer allows users to activate MLM execution and to access CDS results, while an MLM library generates hypertext for diverse presentation targets. The workaround that involves a periodic data replication entails a trade-off between the necessary computational resources and a delay of generated alert messages. Web technologies proved serviceable for reconciling Arden-based CDS functions with alternative presentation formats, including tables, text formatting, graphical outputs, as well as list-based overviews of data from several patients that the native PDMS did not support.

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.

Using smart pumps to reduce medication errors in the NICU

Infants in the neonatal intensive care unit (NICU) are considered one of the most vulnerable patient populations, and medication errors in this population can result in devastating, life-threatening consequences. The use of "smart pump" technology has the potential to minimize risk of error by providing safety measures before medication administration. Successful integration of smart pumps requires a clear communication plan to facilitate staff education and acceptance of advanced technology systems. Unit adoption of smart pumps can enhance patient safety while supporting the implementation of evidenced-based practices in nursing care.

Integration of heterogeneous clinical decision support systems and their knowledge sets: feasibility study with Drug-Drug Interaction alerts

There exist limitations in both commercial and in-house clinical decision support systems (CDSSs) and issues related to the integration of different knowledge sources and CDSSs. We chose Standard-based Shareable Active Guideline Environment (SAGE) as a new architecture with knowledge integration and a centralized knowledge base which includes authoring/management functions and independent CDSS, and applied it to Drug-Drug Interaction (DDI) CDSS. The aim of this study was to evaluate the feasibility of the newly integrated DDI alerting CDSS into a real world hospital information system involving construction of an integrated CDSS derived from two heterogeneous systems and their knowledge sets. The proposed CDSS was successfully implemented and compensated for the weaknesses of the old CDSS from knowledge integration and management, and its applicability in actual situations was verified. Although the DDI CDSS was constructed as an example case, the new CDS architecture might prove applicable to areas of CDSSs.

Comparison of Computer-based Clinical Decision Support Systems and Content for Diabetes Mellitus

BACKGROUND

Computer-based clinical decision support (CDS) systems have been shown to improve quality of care and workflow efficiency, and health care reform legislation relies on electronic health records and CDS systems to improve the cost and quality of health care in the United States; however, the heterogeneity of CDS content and infrastructure of CDS systems across sites is not well known.

OBJECTIVE

We aimed to determine the scope of CDS content in diabetes care at six sites, assess the capabilities of CDS in use at these sites, characterize the scope of CDS infrastructure at these sites, and determine how the sites use CDS beyond individual patient care in order to identify characteristics of CDS systems and content that have been successfully implemented in diabetes care.

METHODS

We compared CDS systems in six collaborating sites of the Clinical Decision Support Consortium. We gathered CDS content on care for patients with diabetes mellitus and surveyed institutions on characteristics of their site, the infrastructure of CDS at these sites, and the capabilities of CDS at these sites.

RESULTS

The approach to CDS and the characteristics of CDS content varied among sites. Some commonalities included providing customizability by role or user, applying sophisticated exclusion criteria, and using CDS automatically at the time of decision-making. Many messages were actionable recommendations. Most sites had monitoring rules (e.g. assessing hemoglobin A1c), but few had rules to diagnose diabetes or suggest specific treatments. All sites had numerous prevention rules including reminders for providing eye examinations, influenza vaccines, lipid screenings, nephropathy screenings, and pneumococcal vaccines.

CONCLUSION

Computer-based CDS systems vary widely across sites in content and scope, but both institution-created and purchased systems had many similar features and functionality, such as integration of alerts and reminders into the decision-making workflow of the provider and providing messages that are actionable recommendations.

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.

The Use of Computerized Provider Order Entry to Improve the Effectiveness and Efficiency of Coagulation Testing

Effective pathology services require timely communication of patient-related information between the laboratory and clinicians. The aim of this study was to measure the effect of a computerized provider order entry (CPOE) system on the frequency with which clinicians notify the Hematology Laboratory of details on heparin or warfarin treatments when ordering activated partial thromboplastin time (aPTT) or the prothrombin time (PT) and international normalized ratio (INR). Although information about the total number of patients on warfarin or heparin was unavailable, it was possible to ascertain that the percentage of abnormal results for each year ranged from 39% in 2005 to 45%, 40%, and 38% in the years 2006 to 2008. The proportion of order requests that reported whether patients were on warfarin or heparin increased from 3% of the aPTT tests (253 of 8307) and 1.9% of the PT and INR requests (161 of 8433) in August through September 2005 (before the CPOE was implemented) to 3.9% (393 of 9990; P &lt; .001) and 2.6% (282 of 10814; P  =  .009), respectively, in August through September 2008 (after CPOE implementation). During that period (2005–2008), the median turnaround time for the laboratory decreased from 28 to 21 minutes for the PT and INR test results (P &lt; .001) and from 34 to 23 minutes for the aPTT test results (P &lt; .001). The results show that CPOE and decision-support systems can enhance laboratory efficiency and improve its contribution to effective patient care.

Development of a real-time clinical decision support system upon the Web MVC-based architecture for prostate cancer treatment

BACKGROUND

A real-time clinical decision support system (RTCDSS) with interactive diagrams enables clinicians to instantly and efficiently track patients' clinical records (PCRs) and improve their quality of clinical care. We propose a RTCDSS to process online clinical informatics from multiple databases for clinical decision making in the treatment of prostate cancer based on Web Model-View-Controller (MVC) architecture, by which the system can easily be adapted to different diseases and applications.

METHODS

We designed a framework upon the Web MVC-based architecture in which the reusable and extractable models can be conveniently adapted to other hospital information systems and which allows for efficient database integration. Then, we determined the clinical variables of the prostate cancer treatment based on participating clinicians' opinions and developed a computational model to determine the pretreatment parameters. Furthermore, the components of the RTCDSS integrated PCRs and decision factors for real-time analysis to provide evidence-based diagrams upon the clinician-oriented interface for visualization of treatment guidance and health risk assessment.

RESULTS

The resulting system can improve quality of clinical treatment by allowing clinicians to concurrently analyze and evaluate the clinical markers of prostate cancer patients with instantaneous clinical data and evidence-based diagrams which can automatically identify pretreatment parameters. Moreover, the proposed RTCDSS can aid interactions between patients and clinicians.

CONCLUSIONS

Our proposed framework supports online clinical informatics, evaluates treatment risks, offers interactive guidance, and provides real-time reference for decision making in the treatment of prostate cancer. The developed clinician-oriented interface can assist clinicians in conveniently presenting evidence-based information to patients and can be readily adapted to an existing hospital information system and be easily applied in other chronic diseases.

Standards for scalable clinical decision support: need, current and emerging standards, gaps, and proposal for progress

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS.

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.

Clinical decision support capabilities of commercially-available clinical information systems

BACKGROUND

The most effective decision support systems are integrated with clinical information systems, such as inpatient and outpatient electronic health records (EHRs) and computerized provider order entry (CPOE) systems. Purpose The goal of this project was to describe and quantify the results of a study of decision support capabilities in Certification Commission for Health Information Technology (CCHIT) certified electronic health record systems.

METHODS

The authors conducted a series of interviews with representatives of nine commercially available clinical information systems, evaluating their capabilities against 42 different clinical decision support features.

RESULTS

Six of the nine reviewed systems offered all the applicable event-driven, action-oriented, real-time clinical decision support triggers required for initiating clinical decision support interventions. Five of the nine systems could access all the patient-specific data items identified as necessary. Six of the nine systems supported all the intervention types identified as necessary to allow clinical information systems to tailor their interventions based on the severity of the clinical situation and the user's workflow. Only one system supported all the offered choices identified as key to allowing physicians to take action directly from within the alert. Discussion The principal finding relates to system-by-system variability. The best system in our analysis had only a single missing feature (from 42 total) while the worst had eighteen.This dramatic variability in CDS capability among commercially available systems was unexpected and is a cause for concern.

CONCLUSIONS

These findings have implications for four distinct constituencies: purchasers of clinical information systems, developers of clinical decision support, vendors of clinical information systems and certification bodies.

A framework and model for evaluating clinical decision support architectures

In this paper, we develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures. We apply this framework to several well-known decision support architectures, including Arden Syntax, GLIF, SEBASTIAN, and SAGE.