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International Journal of Medical Informatics
... Other clinical information standard used as data model for CDS systems is the HL7 Clinical Document Architecture (CDA). CDA is earning momentum as standard for clinical documents consumed by CDS systems as a consequence of the Meaningful Use initiatives [8][9][10][11]15,21,22,26,46]. An example of the use of CDA was found in Bouhaddou et al. [46]. ...
... One of the pioneer works that proposed to take advantage of the Service Oriented Architecture (SOA) for CDS is the presented by Kawamoto et al. [43]. Recently, Dixon et al. [8] and Wright et al. [26] performed a pilot to study the challenges in offering a CDS system in the cloud to several independent health organizations. Among the lessons learned they reported that the main challenges were the difficulties in the negotiation of the legal framework, concerns of clinicians about lack of control over the CDS rules hosted in other organization and the high cost in implementing SIOp. ...
... The mechanisms presented have effectively helped to decouple CDSS from the EHR and advanced in their interoperability capabilities. Nevertheless, challenges implementing SIOp to share CDS across organizational boundaries are still present [8,26]. ...
The interoperability of Clinical Decision Support (CDS) systems is an important obstacle for their adoption. The lack of appropriate mechanisms to specify the semantics of their interfaces is a common barrier in their implementation. In this systematic review we aim to provide a clear insight into current approaches for the integration and semantic interoperability of CDS systems. Published conference papers, book chapters and journal papers from Pubmed, IEEE Xplore and Science Direct databases were searched from January
2007 until January 2016. Inclusion criteria was based on the approaches to enhance semantic interoperability of CDS systems. We selected 41 papers to include in the systematic re-
view. Five main complementary mechanisms to enable CDS systems interoperability were found. 22% of the studies covered the application of medical logic and guidelines represen-
tation formalisms; 63% presented the use of clinical information standards; 32% made use of semantic web technologies such as ontologies; 46% covered the use of standard ter-
minologies; and 32% proposed the use of web services for CDS encapsulation or new techniques for the discovery of systems. Information model standards, terminologies, ontolo-
gies, medical logic specification formalisms and web services are the main areas of work for semantic interoperability in CDS. Main barriers in the interoperability of CDS systems are
related to the effort of standardization, the variety of terminologies available, vagueness of concepts in clinical guidelines, terminological expressions computation and definitions
of reusable models.
... As a consequence, barriers to enable client-service semantic interoperability (SIOp) have been detected related to difficulties understanding the semantics of the CDS service interfaces. Dixon et al. [12] and Wright et al. [20] detected major challenges to enable client-service SIOp related to difficulties in understanding the semantics of the CDS service interfaces when sharing CDS services among 4 organizations. When it comes to large health networks, such as those in European public health systems, SIOp becomes much more complex, and yet reusing such artifacts becomes even more appealing. ...
... Currently, SOA architectural principles are recommended for wide implementations of CDS systems [17,19]. Several works have covered the definition of SOA architectures to leverage the use of information standards and terminologies [17,18,20,37]. Recently, the HL7 CDS group published the HL7 Decision Support Service Implementation Guideline (HL7 DSS IG) [19], based on the experiences gathered in the last decade. ...
Background
The high costs involved in the development of Clinical Decision Support Systems (CDSS) make it necessary to share their functionality across different systems and organizations. Service Oriented Architectures (SOA) have been proposed to allow reusing CDSS by encapsulating them in a Web service. However, strong barriers in sharing CDS functionality are still present as a consequence of lack of expressiveness of services’ interfaces. Linked Services are the evolution of the Semantic Web Services paradigm to process Linked Data. They aim to provide semantic descriptions over SOA implementations to overcome the limitations derived from the syntactic nature of Web services technologies.
Objective
To facilitate the publication, discovery and interoperability of CDS services by evolving them into Linked Services that expose their interfaces as Linked Data.
Materials and Methods
We developed methods and models to enhance CDS SOA as Linked Services that define a rich semantic layer based on machine interpretable ontologies that powers their interoperability and reuse. These ontologies provided unambiguous descriptions of CDS services properties to expose them to the Web of Data.
Results
We developed models compliant with Linked Data principles to create a semantic representation of the components that compose CDS services. To evaluate our approach we implemented a set of CDS Linked Services using a Web service definition ontology. The definitions of Web services were linked to the models developed in order to attach unambiguous semantics to the service components. All models were bound to SNOMED-CT and public ontologies (e.g. Dublin Core) in order to count on a lingua franca to explore them. Discovery and analysis of CDS services based on machine interpretable models was performed reasoning over the ontologies built.
Discussion
Linked Services can be used effectively to expose CDS services to the Web of Data by building on current CDS standards. This allows building shared Linked Knowledge Bases to provide machine interpretable semantics to the CDS service description alleviating the challenges on interoperability and reuse. Linked Services allow for building ‘digital libraries’ of distributed CDS services that can be hosted and maintained in different organizations.
... Good care is dependent on the flexible access to previous LHRs; which must be a feature of future health systems. Rather than being episodic or fragmented, healthcare must take the patient's entire health history into consideration in order to provide a long-term outlook [13]. Nevertheless, electronic information sharing among hospitals remains limited, as reported by [5] the Director of National Centre for Management Development and Information Technology of the Ministry of Planning in Iraq. ...
The purpose of this paper is to develop a framework which can sustain local conditions of developing countries. Design and implementation of sustainable telemedicine information systems is still a big
challenge for most developing countries, despite of its wide usage in the developed countries. While various
frameworks exist, not much of them have adequately addressed the issue of design for sustainability. This paper proposes an appropriate framework, which will guide telemedicine information systems designers on designing telemedicine systems that are sustainable in local conditions of developing countries. The proposed paper mainly works in the area of information systems design for sustainability, from a developing country perspective. The main factor for designing sustainable telemedicine information systems in developing countries were identified as the speed, ease to use and affordable.
Objective
Conduct a scoping review of research studies that describe rule-based clinical decision support (CDS) malfunctions.
Materials and Methods
In April 2022, we searched three bibliographic databases (MEDLINE, CINAHL, and Embase) for literature referencing CDS malfunctions. We coded the identified malfunctions according to an existing CDS malfunction taxonomy and added new categories for factors not already captured. We also extracted and summarized information related to the CDS system, such as architecture, data source, and data format.
Results
Twenty-eight articles met inclusion criteria, capturing 130 malfunctions. Architectures used included stand-alone systems (eg, web-based calculator), integrated systems (eg, best practices alerts), and service-oriented architectures (eg, distributed systems like SMART or CDS Hooks). No standards-based CDS malfunctions were identified. The “Cause” category of the original taxonomy includes three new types (organizational policy, hardware error, and data source) and two existing causes were expanded to include additional layers. Only 29 malfunctions (22%) described the potential impact of the malfunction on patient care.
Discussion
While a substantial amount of research on CDS exists, our review indicates there is a limited focus on CDS malfunctions, with even less attention on malfunctions associated with modern delivery architectures such as SMART and CDS Hooks.
Conclusion
CDS malfunctions can and do occur across several different care delivery architectures. To account for advances in health information technology, existing taxonomies of CDS malfunctions must be continually updated. This will be especially important for service-oriented architectures, which connect several disparate systems, and are increasing in use.
Objective:
Clinical decision support systems (CDSS) were implemented in community pharmacies over 40 years ago. However, unlike CDSS studies in other health settings, few studies have been undertaken to evaluate and improve their use in community pharmacies, where billions of prescriptions are filled every year. The aim of this scoping review is to summarize what research has been done surrounding CDSS in community pharmacies and call for rigorous research in this area.
Materials and methods:
Six databases were searched using a combination of controlled vocabulary and keywords relating to community pharmacy and CDSS. After deduplicating the initial search results, 2 independent reviewers conducted title/abstract screening and full-text review. Then, the selected studies were synthesized in terms of investigational/clinical focuses.
Results:
The selected 21 studies investigated the perception of and response to CDSS alerts (n = 7), the impact of CDSS alerts (n = 7), and drug-drug interaction (DDI) alerts (n = 8). Three causes of the failures to prevent DDIs of clinical importance have been noted: the perception of and response to a high volume of DDI alerts, a suboptimal performance of CDSS, and a dearth of sociotechnical considerations for managing workload and workflow. Additionally, 7 studies emphasized the importance of utilizing CDSS for a specific clinical focus, ie, antibiotics, diabetes, opioids, and vaccinations.
Conclusion:
Despite the range of topics dealt in the last 30 years, this scoping review confirms that research on CDSS in community pharmacies is limited and disjointed, lacking a comprehensive approach to highlight areas for improvement and ways to optimize CDSS utilization.
Objective
The COVID-19 pandemic increased the demand for rapid evaluation of innovation in health and social care. Assessment of rapid methodologies is lacking although challenges in ensuring rigour and effective use of resources are known. We mapped reports of rapid evaluations of health and social care innovations, categorised different approaches to rapid evaluation, explored comparative benefits of rapid evaluation, and identified knowledge gaps.
Design
Scoping review.
Data sources
MEDLINE, EMBASE and Health Management Information Consortium (HMIC) databases were searched through 13 September 2022.
Eligibility criteria for selecting studies
We included publications reporting primary research or methods for rapid evaluation of interventions or services in health and social care in high-income countries.
Data extraction and synthesis
Two reviewers developed and piloted a data extraction form. One reviewer extracted data, a second reviewer checked 10% of the studies; disagreements and uncertainty were resolved through consensus. We used narrative synthesis to map different approaches to conducting rapid evaluation.
Results
We identified 16 759 records and included 162 which met inclusion criteria.
We identified four main approaches for rapid evaluation: (1) Using methodology designed specifically for rapid evaluation; (2) Increasing rapidity by doing less or using less time-intensive methodology; (3) Using alternative technologies and/or data to increase speed of existing evaluation method; (4) Adapting part of non-rapid evaluation.
The COVID-19 pandemic resulted in an increase in publications and some limited changes in identified methods. We found little research comparing rapid and non-rapid evaluation.
Conclusions
We found a lack of clarity about what ‘rapid evaluation’ means but identified some useful preliminary categories. There is a need for clarity and consistency about what constitutes rapid evaluation; consistent terminology in reporting evaluations as rapid; development of specific methodologies for making evaluation more rapid; and assessment of advantages and disadvantages of rapid methodology in terms of rigour, cost and impact.
Objectives To improve clinical decision support (CDS) by allowing users to provide real-time feedback when they interact with CDS tools and by creating processes for responding to and acting on this feedback.
Methods Two organizations implemented similar real-time feedback tools and processes in their electronic health record and gathered data over a 30-month period. At both sites, users could provide feedback by using Likert feedback links embedded in all end-user facing alerts, with results stored outside the electronic health record, and provide feedback as a comment when they overrode an alert. Both systems are monitored daily by clinical informatics teams.
Results The two sites received 2,639 Likert feedback comments and 623,270 override comments over a 30-month period. Through four case studies, we describe our use of end-user feedback to rapidly respond to build errors, as well as identifying inaccurate knowledge management, user-interface issues, and unique workflows.
Conclusion Feedback on CDS tools can be solicited in multiple ways, and it contains valuable and actionable suggestions to improve CDS alerts. Additionally, end users appreciate knowing their feedback is being received and may also make other suggestions to improve the electronic health record. Incorporation of end-user feedback into CDS monitoring, evaluation, and remediation is a way to improve CDS.
Objective:
Electronic prescribing systems offer considerable opportunities to enhance the safety, effectiveness and efficiency of prescribing and medicines management decisions but, despite considerable investments in health IT infrastructure and healthcare professional training, realising these benefits continues to prove challenging. How systems are customised and configured to achieve optimal functionality is an increasing focus for policymakers. We sought to develop an overview of the policy landscape currently supporting optimisation of hospital ePrescribing systems in economically developed countries with a view to deriving lessons for the United Kingdom (UK).
Methods:
We conducted a review of research literature and policy documents pertaining to optimisation of ePrescribing within hospitals across Organisation for Economic Co-operation and Development (OECD) countries on Embase, Medline, National Institute for Health (NIH), Google Scholar databases from 2010 to 2020 and the websites of organisations with international and national health policy interests in digital health and ePrescribing. We designed a typology of policies targeting optimisation of ePrescribing systems that provides an overview of evidence relating to the level at which policy is set, the aims and the barriers encountered in enacting these policies.
Results:
Our database searches retrieved 11 relevant articles and other web resources mainly from North America and Western Europe. We identified very few countries with a national level strategy for optimisation of ePrescribing in hospitals. There were hotspots of digital maturity in relation to ePrescribing at institutional, specialisation, regional and national levels in the US and Europe. We noted that such countries with digital maturity fostered innovations such as patient involvement.
Conclusions:
We found that, whilst helpful to achieve certain aims, coordinated strategies within and across countries for optimisation of ePrescribing systems are rare, even in countries with well-established ePrescribing and digital health infrastructures. There is at present little policy focus on maximising the utility of ePrescribing systems.
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.
Supporting healthcare decision-making that is patient-centered and evidence-based requires investments in the development of tools and techniques for dissemination of patient-centered outcomes research findings via methods such as clinical decision support (CDS). This article explores the technical landscape for patient-centered CDS (PC CDS) and the gaps in making PC CDS more shareable, standards-based, and publicly available, with the goal of improving patient care and clinical outcomes. This landscape assessment used: (1) a technical expert panel; (2) a literature review; and (3) interviews with 18 CDS stakeholders. We identified 7 salient technical considerations that span 5 phases of PC CDS development. While progress has been made in the technical landscape, the field must advance standards for translating clinical guidelines into PC CDS, the standardization of CDS insertion points into the clinical workflow, and processes to capture, standardize, and integrate patient-generated health data.
Introduction
The exponential growth in health information technology (HIT) presents an immense opportunity for facilitating the data‐to‐knowledge‐to‐performance loop which supports learning health systems. This scoping review addresses the gap in knowledge around HIT implementation contextual factors such as organizational culture and provides a current state assessment.
Methods
A search of 13 databases guided by Arskey and O'Malley's framework identified content on HIT implementations and organizational culture. The Consolidated Framework for Implementation Research (CFIR) was used to assess culture and to develop review criteria. Culture stress, culture effort, implementation climate, learning climate, readiness for implementation, leadership engagement, and available resources were the constructs examined. Rayyan and Qualtrics were used for screening and data extraction.
Results
Fifty two studies included were mainly conducted in Academic Health Centers (n = 18, 35%) and at urban locations (n = 50, 96%). Interviews frequently used for data collection (n = 26, 50%) and guided by multiple frameworks (n = 34). Studies mostly focused on EHR implementations (n = 23, 44%) followed by clinical decision support (n = 9, 17%). About two‐thirds (n = 34, 65%) reflected culture stress theme and 62% (21 of 34) acknowledged it as a barrier. Culture effort identified in 27 studies and was a facilitator in most (78%, 21 of 27). Leadership engagement theme in majority studies (71%, n = 37), with 35% (n = 13) noting it as a facilitator. Eighty percent (42 studies) noted available resources, 12 of which identified this as barrier to successful implementation.
Conclusions
It is vital to determine the culture and other CFIR inner setting constructs that are significant to HIT implementation as facilitators or barriers. This scoping review presents a limited number of empirical studies in this topic highlighting the need for additional research to quantify the effects of culture. This will help build evidence and best practices that facilitate HIT implementations and hence serve as a platform to support robust learning health systems.
Background
The relationship between ethnography and healthcare improvement has been the subject of methodological concern. We conducted a scoping review of ethnographic literature on healthcare improvement topics, with two aims: (1) to describe current ethnographic methods and practices in healthcare improvement research and (2) to consider how these may affect habit and skill formation in the service of healthcare improvement.
Methods
We used a scoping review methodology drawing on Arksey and O’Malley’s methods and more recent guidance. We systematically searched electronic databases including Medline, PsychINFO, EMBASE and CINAHL for papers published between April 2013 – April 2018, with an update in September 2019. Information about study aims, methodology and recommendations for improvement were extracted. We used a theoretical framework outlining the habits and skills required for healthcare improvement to consider how ethnographic research may foster improvement skills.
Results
We included 283 studies covering a wide range of healthcare topics and methods. Ethnography was commonly used for healthcare improvement research about vulnerable populations, e.g. elderly, psychiatry. Focussed ethnography was a prominent method, using a rapid feedback loop into improvement through focus and insider status. Ethnographic approaches such as the use of theory and focus on every day practices can foster improvement skills and habits such as creativity, learning and systems thinking.
Conclusions
We have identified that a variety of ethnographic approaches can be relevant to improvement. The skills and habits we identified may help ethnographers reflect on their approaches in planning healthcare improvement studies and guide peer-review in this field. An important area of future research will be to understand how ethnographic findings are received by decision-makers.
This chapter introduces evaluation as an important aspect of the field of biomedical informatics. The text emphasizes how one goes about studying the need for, design of, performance of, and impact of the information resources that support individuals and groups in the pursuit of better health. The chapter begins by introducing the rationale for undertaking these studies, and continues by describing a general structure that all evaluation studies share and the importance of asking good questions as a prerequisite to obtaining useful answers. The chapter then introduces a nine-level classification of evaluation studies and describes the purpose served by each type of study in relation to the lifecycle of an information resource. This discussion sets the stage for the introduction of specific study methods: objectivist/quantitative studies, and subjectivist/qualitative studies. We describe important considerations in the design of quantitative and qualitative studies, along with the collection and analysis of study data. These discussions emphasize the special challenges that arise when health information resources are the focus of study. The chapter concludes with a discussion of the importance of employing effective methods to report study results.
Rapid ethnographies are used in a wide range of fields to speed up research quickly and effectively. This book is the first practical guide to rapid ethnographies, helping readers to improve skills in the design, implementation, dissemination and use of findings generated through rapid ethnographic research. It gives advice and guidelines for carrying out rapid and rigorous research and provides details of tools used in the field. Vignettes reflecting on the author's research are included throughout, including observations on research carried out during the COVID-19 pandemic, to highlight how challenges of conducting rapid ethnographies can be overcome. Case studies across a range of subjects are also included, to demonstrate how rapid ethnographies can be applied in practice. With its useful tools and easy-to-read format, it will be used by teachers and students, as well as researchers wanting to successfully implement rapid ethnographies in their own work.
Rapid ethnographies are used in a wide range of fields to speed up research quickly and effectively. This book is the first practical guide to rapid ethnographies, helping readers to improve skills in the design, implementation, dissemination and use of findings generated through rapid ethnographic research. It gives advice and guidelines for carrying out rapid and rigorous research and provides details of tools used in the field. Vignettes reflecting on the author's research are included throughout, including observations on research carried out during the COVID-19 pandemic, to highlight how challenges of conducting rapid ethnographies can be overcome. Case studies across a range of subjects are also included, to demonstrate how rapid ethnographies can be applied in practice. With its useful tools and easy-to-read format, it will be used by teachers and students, as well as researchers wanting to successfully implement rapid ethnographies in their own work.
Background Computerised Physician Order Entry (CPOE) is considered to enhance the safety of prescribing. However, it can have unintended consequences and new forms of prescribing error have been reported. Objective The aim of this study was to explore the causes and contributing factors associated with prescribing errors reported by multidisciplinary prescribers working within a CPOE system. Main Outcome Measure Multidisciplinary prescribers experience of prescribing errors in an CPOE system. Method This qualitative study was conducted in a hospital with a well-established CPOE system. Semi-structured qualitative interviews were conducted with prescribers from the professions of pharmacy, nursing, and medicine. Interviews analysed using a mixed inductive and deductive approach to develop a framework for the causes of error. Results Twenty-three prescribers were interviewed. Six main themes influencing prescribing were found: the system, the prescriber, the patient, the team, the task of prescribing and the work environment. Prominent issues related to CPOE included, incorrect drug name picking, default auto-population of dosages, alert fatigue and remote prescribing. These interacted within a complex prescribing environment. No substantial differences in the experience of CPOE were found between the professions. Conclusion Medical and non-medical prescribers have similar experiences of prescribing errors when using CPOE, aligned with existing published literature about medical prescribing. Causes of electronic prescribing errors are multifactorial in nature and prescribers describe how factors interact to create the conditions errors. While interventions should focus on direct CPOE issues, such as training and design, socio-technical, and environmental aspects of practice remain important.
Computerized clinical decision support (CDS) faces challenges to interoperability and scalability. Centralized, web-based solutions offer a mechanism to share the cost of CDS development, maintenance, and implementation across practices. Data standards have emerged to facilitate interoperability and rapid integration of such third-party CDS. This case report describes the challenges to implementation and scalability of an integrated, web-based CDS intervention for EMergency department-initiated BuprenorphinE for opioid use Disorder which will soon be evaluated in a trial across 20 sites in five healthcare systems. Due to limitations of current standards, security concerns, and the need for resource-intensive local customization, barriers persist related to centralized CDS at this scale. These challenges demonstrate the need and importance for future standards to support two-way messaging (read and write) between electronic health records and web applications, thus allowing for more robust sharing across health systems and decreasing redundant, resource-intensive CDS development at individual sites.
Clinical decision support (CDS) systems are prevalent in electronic health records and drive many safety advantages. However, CDS systems can also cause unintended consequences. Monitoring programs focused on alert firing rates are important to detect anomalies and ensure systems are working as intended. Monitoring efforts do not generally include system load and time to generate decision support, which is becoming increasingly important as more CDS systems rely on external, web-based content and algorithms. We report a case in which a web-based service caused significant increase in the time to generate decision support, in turn leading to marked delays in electronic health record system responsiveness, which could have led to patient safety events. Given this, it is critical to consider adding decision support-time generation to ongoing CDS system monitoring programs.
Background and Objectives Clinical decision support (CDS) and computerized provider order entry have been shown to improve health care quality and safety, but may also generate previously unanticipated errors. We identified multiple CDS tools for platelet transfusion orders. In this study, we sought to evaluate and improve the effectiveness of those CDS tools while creating and testing a framework for future evaluation of other CDS tools.
Methods Using a query of an enterprise data warehouse at a tertiary care pediatric hospital, we conducted a retrospective analysis to assess baseline use and performance of existing CDS for platelet transfusion orders. Our outcome measure was the percentage of platelet undertransfusion ordering errors. Errors were defined as platelet transfusion volumes ordered which were less than the amount recommended by the order set used. We then redesigned our CDS and measured the impact of our intervention prospectively using statistical process control methodology.
Results We identified that 62% of all platelet transfusion orders were placed with one of two order sets (Inpatient Service 1 and Inpatient Service 2). The Inpatient Service 1 order set had a significantly higher occurrence of ordering errors (3.10% compared with 1.20%). After our interventions, platelet transfusion order error occurrence on Inpatient Service 1 decreased from 3.10 to 0.33%.
Conclusion We successfully reduced platelet transfusion ordering errors by redesigning our CDS tools. We suggest that the use of collections of clinical data may help identify patterns in erroneous ordering, which could otherwise go undetected. We have created a framework which can be used to evaluate the effectiveness of other similar CDS tools.
Mixed methods research - i.e., research that draws on both qualitative and quantitative methods in varying configurations - is well suited to address the increasing complexity of public health problems and their solutions. This review focuses specifically on innovations in mixed methods evaluations of intervention, program or policy (i.e., practice) effectiveness, and implementation. The article begins with an overview of the structure, function, and process of different mixed methods designs and then provides illustrations of their use in effectiveness studies, implementation studies, and combined effectiveness-implementation hybrid studies. The article then examines four specific innovations: procedures for transforming (or "quantitizing") qualitative data, application of rapid assessment and analysis procedures in the context of mixed methods studies, development of measures to assess implementation outcomes, and strategies for conducting both random and purposive sampling, particularly in implementation-focused evaluation research. The article concludes with an assessment of challenges to integrating qualitative and quantitative data in evaluation research.
Pragmatic clinical trials of mental health services are increasingly being developed to establish comparative effectiveness, influence sustainable implementation, and address real world policy decisions. However, use of time and resource intensive qualitative methods in pragmatic trials may be inconsistent with the aims of efficiency and cost minimization. This paper introduces a qualitative method known as Rapid Assessment Procedure-Informed Clinical Ethnography (RAPICE) that combines the techniques of Rapid Assessment Procedures with clinical ethnography. A case study is presented to illustrate how RAPICE can be used to efficiently understand pragmatic trial implementation processes and associated real world policy implications.
Background:
Recent clinical practice guidelines from major national organizations, including a joint United States Department of Veterans Affairs (VA) and Department of Defense (DoD) committee, have substantially changed recommendations for the use of the cholesterol-lowering statin medications after years of relative stability. Because statin medications are among the most commonly prescribed treatments in the United States, any change in their use may have significant implications for patients and providers alike. Prior research has shown that effective implementation interventions should be both user centered and specifically chosen to address identified barriers.
Objective:
The objectives of this study were to identify potential determinants of provider uptake of the new statin guidelines and to use that information to tailor a coordinated and streamlined local quality improvement intervention focused on prescribing appropriate statins.
Methods:
We employed user-centered design principles to guide the development and testing of a multicomponent guideline implementation intervention to improve statin prescribing. This paper describes the intervention development process whereby semistructured qualitative interviews with providers were conducted to (1) illuminate the knowledge, attitudes, and behaviors of providers and (2) elicit feedback on intervention prototypes developed to align with and support the use of the VA/DoD guidelines. Our aim was to use this information to design a local quality improvement intervention focused on statin prescribing that was tailored to the needs of primary care providers at our facility. Cabana's Clinical Practice Guidelines Framework for Improvement and Nielsen's Usability Heuristics were used to guide the analysis of data obtained in the intervention development process.
Results:
Semistructured qualitative interviews were conducted with 15 primary care Patient Aligned Care Team professionals (13 physicians and 2 clinical pharmacists) at a single VA medical center. Findings highlight that providers were generally comfortable with the paradigm shift to risk-based guidelines but less clear on the need for the VA/DoD guidelines in specific. Providers preferred a clinical decision support tool that helped them calculate patient risk and guide their care without limiting autonomy. They were less comfortable with risk communication and performance measurement systems that do not account for shared decision making. When possible, we incorporated their recommendations into the intervention.
Conclusions:
By combining qualitative methods and user-centered design principles, we could inform the design of a multicomponent guideline implementation intervention to better address the needs and preferences of providers, including clear and direct language, logical decision prompts with an option to dismiss a clinical decision support tool, and logical ordering of feedback information. Additionally, this process allowed us to identify future design considerations for quality improvement interventions.
Background Clinical decision support (CDS) embedded into the electronic health record (EHR), is a potentially powerful tool for institution of antimicrobial stewardship programs (ASPs) in emergency departments (EDs). However, design and implementation of CDS systems should be informed by the existing workflow to ensure its congruence with ED practice, which is characterized by erratic workflow, intermittent computer interactions, and variable timing of antibiotic prescription.
Objective This article aims to characterize ED workflow for four provider types, to guide future design and implementation of an ED-based ASP using the EHR.
Methods Workflow was systematically examined in a single, tertiary-care academic children's hospital ED. Clinicians with four roles (attending, nurse practitioner, physician assistant, resident) were observed over a 3-month period using a tablet computer-based data collection tool. Structural observations were recorded by investigators, and classified using a predetermined set of activities. Clinicians were queried regarding timing of diagnosis and disposition decision points.
Results A total of 23 providers were observed for 90 hours. Sixty-four different activities were captured for a total of 6,060 times. Among these activities, nine were conducted at different frequency or time allocation across four roles. Moreover, we identified differences in sequential patterns across roles. Decision points, whereby clinicians then proceeded with treatment, were identified 127 times. The most common decision points identified were: (1) after/during examining or talking to patient or relative; (2) after talking to a specialist; and (3) after diagnostic test/image was resulted and discussed with patient/family.
Conclusion The design and implementation of CDS for ASP should support clinicians in various provider roles, despite having different workflow patterns. The clinicians make their decisions about treatment at different points of overall care delivery practice; likewise, the CDS should also support decisions at different points of care.
Medical problems during flight have become an important issue as the number of passengers and miles flown continues to increase. The case of an incident in the plane falls within the scope of the healthcare management in the context of scarce resources associated with isolation of medical actors working in very complex conditions, both in terms of human and material resources. Telemedicine uses information and communication technologies to provide remote and flexible medical services, especially for geographically isolated people. Therefore, telemedicine can generate interesting solutions to the medical problems during flight. Our aim is to build a knowledge-based system able to help health professionals or staff members addressing an urgent situation by given them relevant information, some knowledge, and some judicious advice. In this context, knowledge representation and reasoning can be correctly realized using an ontology that is a representation of concepts, their attributes, and the relationships between them in a particular domain. Particularly, a medical ontology is a formal representation of a vocabulary related to a specific health domain. We propose a new approach to explain the arrangement of different ontological models (task ontology, inference ontology, and domain ontology), which are useful for monitoring remote medical activities and generating required information. These layers of ontologies facilitate the semantic modeling and structuring of health information. The incorporation of existing ontologies [for instance, Systematic Nomenclature Medical Clinical Terms (SNOMED CT)] guarantees improved health concept coverage with experienced knowledge. The proposal comprises conceptual means to generate substantial reasoning and relevant knowledge supporting telemedicine activities during the management of a medical incident and its characterization in the context of air travel. The considered modeling framework is sufficiently generic to cover complex medical situations for isolated and vulnerable populations needing some care and support services.
Background:
The ability to capture the complexities of healthcare practices and the quick turnaround of findings make rapid ethnographies appealing to the healthcare sector, where changing organisational climates and priorities require actionable findings at strategic time points. Despite methodological advancement, there continue to be challenges in the implementation of rapid ethnographies concerning sampling, the interpretation of findings and management of field research. The purpose of this review was to explore the benefits and challenges of using rapid ethnographies to inform healthcare organisation and delivery and identify areas that require improvement.
Methods:
This was a systematic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We used the Mixed Methods Appraisal Tool to assess the quality of the articles. We developed the search strategy using the 'Population, Intervention, Comparison, Outcomes, Setting' framework and searched for peer-reviewed articles in MEDLINE, CINAHL PLUS, Web of Science and ProQuest Central. We included articles that reported findings from rapid ethnographies in healthcare contexts or addressing issues related to health service use.
Results:
26 articles were included in the review. We found an increase in the use of rapid ethnographies in the last 2 years. We found variability in terminology and developed a typology to clarify conceptual differences. The studies generated findings that could be used to inform policy and practice. The main limitations of the studies were: the poor quality of reporting of study designs, mainly data analysis methods, and lack of reflexivity.
Conclusions:
Rapid ethnographies have the potential to generate findings that can inform changes in healthcare practices in a timely manner, but greater attention needs to be paid to the reflexive interpretation of findings and the description of research methods.
Trial registration number:
CRD42017065874.
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.
Introduction
Can aging models be used to develop a model of pharmaceutical practice that improves patient care in a context of increasing workload and budgetary constraints?
Material and method
A before–after observational study was performed in nine healthcare institutions to assess the impact of a context-aware computerized decision-support system designed with available software (SAP Business Object®) to automatically compare prescriptions recorded in computerized patient files (Cariatide®) against the main consensual guidelines for medical management of elderly subjects and the French Health Authority's Drug Prescription in Elderly Subjects (PMSA) program. The system generated alerts to improve medication management and displayed a list of clinical actions to be taken for individual patients. The name and localization of these patients were identified so that the pharmacist could analyze the alerts and take any necessary action to adjust potentially inappropriate prescriptions as part of a systematic individualized update of good therapeutic practice in elderly subjects. Drug interactions were not taken into consideration. During the study period, data were collected from all patients over 65 years of age receiving daily care (n = 369) with no change in the size of the pharmacy team.
Results
Over a 10.5-month period from 27 October 2015 to 16 September 2016, this context-aware pharmaceutical analysis tool was implemented for all patients aged more than 65 years: n = 184; 118 F, 66 M; mean age: 73.9 ± 7.1 years. Over the corresponding period of the previous year (27 October 2014 to 16 September 2015), 185 elderly patients (116 F, 69 M; mean age, 75.4 ± 7.4 years) were managed before the new computerized system had been implemented. The new tool took a mean 45 s to display the good drug management analysis, highlighting discrepancies with respect to guidelines for the treatment of elderly subjects and listing potentially inappropriate drugs per patient and per care unit. The tool generated a table displaying PMSA health-quality indicators per care unit. Another screen displayed potentially inappropriate prescriptions for the elderly. Mean hospital stay for elderly patients was comparable for the two periods: 33 and 37 days. Between the two periods, prescription of short-acting (< 20 h) benzodiazepines increased by 46% (6706 vs 9827 doses) and prescription of anticholinergics decreased (6538 vs 4696 doses). Considering the focus of PMSA health-quality indicators, and the time interval necessary to perform a readmission study using National Health Insurance data, a preliminary efficacy assessment was performed based on the number of in-hospital falls sustained by patients: 57 for the period 2014–2015 period versus 34 for the 2015–2016 period of computer-assisted analysis. One patient's care pathway provided an illustration of how this tool can assist in prevention.
Discussion and conclusion
The literature shows that implementation of good clinical practice guidelines is improved when they are communicated via decision-support instruments, preferably context-aware tools linked to patient files. Despite possible limitations, the project was found to comply with practice objectives and models based on recognized factors contributing to the development of clinical pharmacy departments. A study of context-aware computer-assisted drug management in nursing homes or rehabilitation units prior to hospital (re)admission is proposed, with the aim of reducing admission rates for the elderly, and with the added benefit of providing extended assessment of this model of pharmaceutical practice.
Résumé
Introduction
En choisissant le vieillissement comme modèle, peut-on concilier un modèle de pratique pharmaceutique améliorant la prise en charge du patient, une augmentation de volume de travail et des moyens contraints ?
Matériel et méthode
Une étude observationnelle avant–après a été menée pour tous les patients âgés de plus de 65 ans hospitalisés parmi les 369 patients pris en charge quotidiennement sur nos 9 sites départementaux. Pour respecter un effectif pharmacien constant, un outil informatique constitué par un ensemble de requêtes adossées aux principales recommandations consensuelles concernant la prise en charge médicamenteuse du sujet âgé a été développé sous SAP Business Object®. L’outil interroge et analyse les informations du dossier patient informatisé Cariatide®. Puis génère un bilan des alertes pour une bonne prise en charge médicamenteuse. À l’écran du pharmacien apparaît le tableau des actions de pratique clinique de prise en charge médicamenteuse du sujet âgé (PMSA) à mener dans le service de soin, le nom et la localisation des patients avec des médicaments à adapter car potentiellement inappropriés aux personnes âgées ou dépendant de l’état rénal. Le pharmacien analyse les alertes extraites et exporte celles nécessaires pour rédiger le bilan nominatif systématique de la bonne prise en charge thérapeutique du sujet âgé. Ce n’est pas un processus de conciliation médicamenteuse.
Résultats
Pendant 10,5 mois, du 27/10/2015 au 16/09/2016, tous les patients âgés de plus de 65 ans ont bénéficié de cette analyse pharmaceutique renforcée (n = 184, 118 F et 66 H d’âge moyen 73,9 ± 7,1 ans). Sur la même période de l’année précédente du 27/10/2014 au 16/09/15, 185 patients âgés 116 F et 69 H d’âge moyen 75,4 ± 7,4 ans ont été pris en charge sans cette analyse automatisée. À chaque utilisation, l’outil informatique a extrait et généré en 45 secondes en moyenne le bilan de bonne prise en charge médicamenteuse pour tous les patients présents parmi les 369 patients hospitalisés quotidiennement. Ce bilan met en évidence les écarts aux recommandations de prise en charge thérapeutique des patients âgés et liste les médicaments potentiellement inappropriés par patients dans chaque site de soin. Un écran affiche le tableau par service des indicateurs qualité en santé de la HAS par unité de soin (IPC PMSA HAS). Un autre écran affiche le pourcentage d’ordonnance contenant un médicament potentiellement inapproprié à la personne âgée. Avec des durées moyennes d’hospitalisation des patients de plus de 65 ans proche pour les deux périodes (33 et 37 jours respectivement), la prescription des benzodiazépines à demie vie < 20 h a augmenté de 46 % (6706 vs. 9827 prises) et la prescription des médicaments à effet anticholinergique a diminué (6538 vs. 4696 prises). Étant donné l’orientation des indicateurs PMSA, et le délai d’un an nécessaire à l’étude des taux de réhospitalisation avec la Caisse nationale de l’assurance maladie des travailleurs salariés, une première évaluation de l’efficacité de l’action a été faite en observant le nombre de chutes. Le nombre de chutes observé a été de 57 pour la période allant du 27/10/2014 au 16/09/15 contre 34 pour la période avec analyse renforcée allant du 27/10/2015 au 16/09/2016. L’exemple d’un parcours patient permet aussi d’illustrer l’utilité de l’outil en prévention.
Discussion conclusion
La littérature montre que l’utilisation des recommandations de bon usage dans la pratique clinique est améliorée si les recommandations sont communiquées par l’intermédiaire d’outils d’aide à la décision, de préférence reliés au dossier patient. Les limites éventuelles du projet ont été envisagées mais le projet est conforme aux objectifs et aux modèles de pratique correspondant aux facilitateurs reconnus du développement des services de pharmacie clinique. Une étude de prise en charge médicamenteuse renforcée en amont de l’hospitalisation (Ehpad et SSR) afin de diminuer le nombre d’hospitalisation des sujets âgés est demandée pour évaluer plus avant ce modèle de pratique pharmaceutique.
Although the health information technology industry has made considerable progress in the design, development, implementation, and use of electronic health records (EHRs), the lofty expectations of the early pioneers have not been met. In 2006, the Provider Order Entry Team at Oregon Health & Science University described a set of unintended adverse consequences (UACs), or unpredictable, emergent problems associated with computer-based provider order entry implementation, use, and maintenance. Many of these originally identified UACs have not been completely addressed or alleviated, some have evolved over time, and some new ones have emerged as EHRs became more widely available. The rapid increase in the adoption of EHRs, coupled with the changes in the types and attitudes of clinical users, has led to several new UACs, specifically: complete clinical information unavailable at the point of care; lack of innovations to improve system usability leading to frustrating user experiences; inadvertent disclosure of large amounts of patient-specific information; increased focus on computer-based quality measurement negatively affecting clinical workflows and patient-provider interactions; information overload from marginally useful computer-generated data; and a decline in the development and use of internally-developed EHRs. While each of these new UACs poses significant challenges to EHR developers and users alike, they also offer many opportunities. The challenge for clinical informatics researchers is to continue to refine our current systems while exploring new methods of overcoming these challenges and developing innovations to improve EHR interoperability, usability, security, functionality, clinical quality measurement, and information summarization and display.
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.
Multimorbidity is increasing in aging populations with a corresponding increase in polypharmacy as well as inappropriate prescribing. Depending on definitions, 25-50 % of patients aged 75 years or older are exposed to at least five drugs. Evidence is increasing that polypharmacy, even when guidelines advise the prescribing of each drug individually, can potentially cause more harm than benefit to older patients, due to factors such as drug-drug and drug-disease interactions. Several approaches reducing polypharmacy and inappropriate prescribing have been proposed, but evidence showing a benefit of these measures regarding clinically relevant endpoints is scarce. There is an urgent need to implement more effective strategies. We therefore set out to develop an evidence-based electronic decision support (eDS) tool to aid physicians in reducing inappropriate prescribing and test its effectiveness in a large-scale cluster-randomized controlled trial.
The “Polypharmacy in chronic diseases–Reduction of Inappropriate Medication and Adverse drug events in older populations” (PRIMA)-eDS tool is a tool comprising an indication check and recommendations for the reduction of polypharmacy and inappropriate prescribing based on systematic reviews and guidelines, the European list of inappropriate medications for older people, the SFINX-database of interactions, the PHARAO-database on adverse effects, and the RENBASE-database on renal dosing. The tool will be evaluated in a cluster-randomized controlled trial involving 325 general practitioners (GPs) and around 3500 patients across five study centres in the United Kingdom, Germany, Austria and Italy. GP practices will be asked to recruit 11 patients aged 75 years or older who are taking at least eight medications and will be cluster-randomized after completion of patient recruitment. Intervention GPs will have access to the PRIMA-eDS tool, while control GPs will treat their patients according to current guidelines (usual care) without access to the PRIMA-eDS tool. After an observation time of 2 years, intervention and control groups will be compared regarding the primary composite endpoint of first non-elective hospitalization or death.
The principal hypothesis is that reduction of polypharmacy and inappropriate prescribing can improve the clinical composite outcome of hospitalization or death. A positive result of the trial will contribute substantially to the improvement of care in multimorbidity. The trial is necessary to investigate not only whether the reduction of polypharmacy improves outcome, but also whether GPs and patients are willing to follow the recommendations of the PRIMA-eDS tool.
Trial registration
This trial has been registered with Current Controlled Trials Ltd. on 31 July 2014 (ISRCTN10137559).
Objective:
To evaluate the architecture, integration requirements, and execution characteristics of a remote clinical decision support (CDS) service used in a multicenter clinical trial. The trial tested the efficacy of implementing brain injury prediction rules for children with minor blunt head trauma.
Materials and methods:
We integrated the Epic(®) electronic health record (EHR) with the Enterprise Clinical Rules Service (ECRS), a web-based CDS service, at two emergency departments. Patterns of CDS review included either a delayed, near-real-time review, where the physician viewed CDS recommendations generated by the nursing assessment, or a real-time review, where the physician viewed recommendations generated by their own documentation. A backstopping, vendor-based CDS triggered with zero delay when no recommendation was available in the EHR from the web-service. We assessed the execution characteristics of the integrated system and the source of the generated recommendations viewed by physicians.
Results:
The ECRS mean execution time was 0.74 ±0.72 s. Overall execution time was substantially different at the two sites, with mean total transaction times of 19.67 and 3.99 s. Of 1930 analyzed transactions from the two sites, 60% (310/521) of all physician documentation-initiated recommendations and 99% (1390/1409) of all nurse documentation-initiated recommendations originated from the remote web service.
Discussion:
The remote CDS system was the source of recommendations in more than half of the real-time cases and virtually all the near-real-time cases. Comparisons are limited by allowable variation in user workflow and resolution of the EHR clock.
Conclusion:
With maturation and adoption of standards for CDS services, remote CDS shows promise to decrease time-to-trial for multicenter evaluations of candidate decision support interventions.
This work aims at uncovering challenges in biomedical knowledge representation research by providing an understanding of what was historically called "medical concept representation" and used as the name for a working group of the International Medical Informatics Association.
Bibliometrics, text mining, and a social media survey compare the research done in this area between two periods, before and after 2000.
Both the opinion of socially active groups of researchers and the interpretation of bibliometric data since 1988 suggest that the focus of research has moved from "medical concept representation" to "medical ontologies".
It remains debatable whether the observed change amounts to a paradigm shift or whether it simply reflects changes in naming, following the natural evolution of ontology research and engineering activities in the 1990s. The availability of powerful tools to handle ontologies devoted to certain areas of biomedicine has not resulted in a large-scale breakthrough beyond advances in basic research.
Background and objective
Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors.
Materials and methods
We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements.
Results
We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents.
Conclusions
We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.
A cloud-based clinical decision support system (CDSS) was implemented to remotely provide evidence-based guideline reminders in support of preventative health. Following implementation, we measured the agreement between preventive care reminders generated by an existing, local CDSS and the new, cloud-based CDSS operating on the same patient visit data.
Electronic health record data for the same set of patients seen in primary care were sent to both the cloud-based web service and local CDSS. The clinical reminders returned by both services were captured for analysis. Cohen's Kappa coefficient was calculated to compare the two sets of reminders. Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients.
The cloud-based CDSS generated 965 clinical reminders for 405 patient visits over 3 months. The local CDSS returned 889 reminders for the same patient visit data. When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 - 0.42) to 0.99 (95% CI 0.97 - 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders.
Preventive care reminders delivered by two disparate CDS systems show substantial agreement. Subtle differences in rule logic and terminology mapping appear to account for much of the discordance. Cloud-based CDSS therefore show promise, opening the door for future development and implementation in support of health care providers with limited resources for knowledge management of complex logic and rules.
To describe the activities performed by people involved in clinical decision support (CDS) at leading sites.
We conducted ethnographic observations at seven diverse sites with a history of excellence in CDS using the Rapid Assessment Process and analyzed the data using a series of card sorts, informed by Linstone's Multiple Perspectives Model.
We identified 18 activities and grouped them into four areas. Area 1: Fostering relationships across the organization, with activities (a) training and support, (b) visibility/presence on the floor, (c) liaising between people, (d) administration and leadership, (e) project management, (f) cheerleading/buy-in/sponsorship, (g) preparing for CDS implementation. Area 2: Assembling the system with activities (a) providing technical support, (b) CDS content development, (c) purchasing products from vendors (d) knowledge management, (e) system integration. Area 3: Using CDS to achieve the organization's goals with activities (a) reporting, (b) requirements-gathering/specifications, (c) monitoring CDS, (d) linking CDS to goals, (e) managing data. Area 4: Participation in external policy and standards activities (this area consists of only a single activity). We also identified a set of recommendations associated with these 18 activities.
All 18 activities we identified were performed at all sites, although the way they were organized into roles differed substantially. We consider these activities critical to the success of a CDS program.
A series of activities are performed by sites strong in CDS, and sites adopting CDS should ensure they incorporate these activities into their efforts.
To create a clinical decision support (CDS) system that is shareable across healthcare delivery systems and settings over large geographic regions.
The enterprise clinical rules service (ECRS) realizes nine design principles through a series of enterprise java beans and leverages off-the-shelf rules management systems in order to provide consistent, maintainable, and scalable decision support in a variety of settings.
The ECRS is deployed at Partners HealthCare System (PHS) and is in use for a series of trials by members of the CDS consortium, including internally developed systems at PHS, the Regenstrief Institute, and vendor-based systems deployed at locations in Oregon and New Jersey. Performance measures indicate that the ECRS provides sub-second response time when measured apart from services required to retrieve data and assemble the continuity of care document used as input.
We consider related work, design decisions, comparisons with emerging national standards, and discuss uses and limitations of the ECRS.
ECRS design, implementation, and use in CDS consortium trials indicate that it provides the flexibility and modularity needed for broad use and performs adequately. Future work will investigate additional CDS patterns, alternative methods of data passing, and further optimizations in ECRS performance.
To catalogue study designs used to assess the clinical effectiveness of CDSSs and KMSs, to identify features that impact the success of CDSSs/KMSs, to document the impact of CDSSs/KMSs on outcomes, and to identify knowledge types that can be integrated into CDSSs/KMSs.
MEDLINE®, CINAHL®, PsycINFO®, and Web of Science®.
We included studies published in English from January 1976 through December 2010. After screening titles and abstracts, full-text versions of articles were reviewed by two independent reviewers. Included articles were abstracted to evidence tables by two reviewers. Meta-analyses were performed for seven domains in which sufficient studies with common outcomes were included.
We identified 15,176 articles, from which 323 articles describing 311 unique studies including 160 reports on 148 randomized control trials (RCTs) were selected for inclusion. RCTs comprised 47.5 percent of the comparative studies on CDSSs/KMSs. Both commercially and locally developed CDSSs effectively improved health care process measures related to performing preventive services (n = 25; OR 1.42, 95% confidence interval [CI] 1.27 to 1.58), ordering clinical studies (n = 20; OR 1.72, 95% CI 1.47 to 2.00), and prescribing therapies (n = 46; OR 1.57, 95% CI 1.35 to 1.82). Fourteen CDSS/KMS features were assessed for correlation with success of CDSSs/KMSs across all endpoints. Meta-analyses identified six new success features: Integration with charting or order entry system. Promotion of action rather than inaction. No need for additional clinician data entry. Justification of decision support via research evidence. Local user involvement. Provision of decision support results to patients as well as providers. Three previously identified success features were confirmed: Automatic provision of decision support as part of clinician workflow. Provision of decision support at time and location of decisionmaking. Provision of a recommendation, not just an assessment. Only 29 (19.6%) RCTs assessed the impact of CDSSs on clinical outcomes, 22 (14.9%) assessed costs, and 3 assessed KMSs on any outcomes. The primary source of knowledge used in CDSSs was derived from structured care protocols.
Strong evidence shows that CDSSs/KMSs are effective in improving health care process measures across diverse settings using both commercially and locally developed systems. Evidence for the effectiveness of CDSSs on clinical outcomes and costs and KMSs on any outcomes is minimal. Nine features of CDSSs/KMSs that correlate with a successful impact of clinical decision support have been newly identified or confirmed.
Objective:
Recent legislation in the United States provides strong incentives for implementation of electronic health records (EHRs). The ensuing transformation in U.S. health care will increase demand for new methods to evaluate clinical informatics interventions. Timeline constraints and a rapidly changing environment will make traditional evaluation techniques burdensome. This paper describes an anthropological approach that provides a fast and flexible way to evaluate clinical information systems.
Methods:
Adapting mixed-method evaluation approaches from anthropology, we describe a rapid assessment process (RAP) for assessing clinical informatics interventions in health care that we developed and used during seven site visits to diverse community hospitals and primary care settings in the U.S.
Setting:
Our multidisciplinary team used RAP to evaluate factors that either encouraged people to use clinical decision support (CDS) systems or interfered with use of these systems in settings ranging from large urban hospitals to single-practitioner, private family practices in small towns.
Results:
Critical elements of the method include: 1) developing a fieldwork guide; 2) carefully selecting observation sites and participants; 3) thoroughly preparing for site visits; 4) partnering with local collaborators; 5) collecting robust data by using multiple researchers and methods; and 6) analyzing and reporting data in a structured manner helpful to the organizations being evaluated.
Conclusions:
RAP, iteratively developed over the course of visits to seven clinical sites across the U.S., has succeeded in allowing a multidisciplinary team of informatics researchers to plan, gather and analyze data, and report results in a maximally efficient manner.
The purpose of this study was to identify recommended practices for computerized clinical decision support (CDS) development and implementation and for knowledge management (KM) processes in ambulatory clinics and community hospitals using commercial or locally developed systems in the U.S.
Guided by the Multiple Perspectives Framework, the authors conducted ethnographic field studies at two community hospitals and five ambulatory clinic organizations across the U.S. Using a Rapid Assessment Process, a multidisciplinary research team: gathered preliminary assessment data; conducted on-site interviews, observations, and field surveys; analyzed data using both template and grounded methods; and developed universal themes. A panel of experts produced recommended practices.
The team identified ten themes related to CDS and KM. These include: 1) workflow; 2) knowledge management; 3) data as a foundation for CDS; 4) user computer interaction; 5) measurement and metrics; 6) governance; 7) translation for collaboration; 8) the meaning of CDS; 9) roles of special, essential people; and 10) communication, training, and support. Experts developed recommendations about each theme. The original Multiple Perspectives framework was modified to make explicit a new theoretical construct, that of Translational Interaction.
These ten themes represent areas that need attention if a clinic or community hospital plans to implement and successfully utilize CDS. In addition, they have implications for workforce education, research, and national-level policy development. The Translational Interaction construct could guide future applied informatics research endeavors.
The goal of the CDS Consortium (CDSC) is to assess, define, demonstrate, and evaluate best practices for knowledge management and clinical decision support in healthcare information technology at scale - across multiple ambulatory care settings and Electronic Health Record technology platforms. In the course of the CDSC research effort, it became evident that a sound legal foundation was required for knowledge sharing and clinical decision support services in order to address data sharing, intellectual property, accountability, and liability concerns. This paper outlines the framework utilized for developing agreements in support of sharing, accessing, and publishing content via the CDSC Knowledge Management Portal as well as an agreement in support of deployment and consumption of CDSC developed web services in the context of a research project under IRB oversight.
Interoperability is a requirement of recent electronic health record (EHR) adoption incentive programs in the United States. One approved structure for clinical data exchange is the continuity of care document (CCD). While primarily designed to promote communication between providers during care transitions, coded data in the CCD can be re-used to aggregate data from different EHRs. This provides an opportunity for provider networks to measure quality and improve population health from a consolidated database. To evaluate such potential, this research collected CCDs from 14 organizations and developed a computer program to parse and aggregate them. In total, 139 CCDs were parsed yielding 680 data in the core content modules of problems, medications, allergies and results. Challenges to interoperability were catalogued and potential quality metrics evaluated based on available content. This research highlights the promise of CCDs for population health and recommends changes for future interoperability standards.
There are several challenges in encoding guideline knowledge in a form that is portable to different clinical sites, including the heterogeneity of clinical decision support (CDS) tools, of patient data representations, and of workflows.
We have developed a multi-layered knowledge representation framework for structuring guideline recommendations for implementation in a variety of CDS contexts. In this framework, guideline recommendations are increasingly structured through four layers, successively transforming a narrative text recommendation into input for a CDS system. We have used this framework to implement rules for a CDS service based on three guidelines. We also conducted a preliminary evaluation, where we asked CDS experts at four institutions to rate the implementability of six recommendations from the three guidelines.
The experience in using the framework and the preliminary evaluation indicate that this approach has promise in creating structured knowledge, to implement in CDS systems, that is usable across organizations.
The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations).
We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes.
Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported.
A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.
Using an eight-dimensional model for studying socio-technical systems, a multidisciplinary team of investigators identified barriers and facilitators to clinical decision support (CDS) implementation in a community setting, the Mid-Valley Independent Physicians Association in the Salem, Oregon area. The team used the Rapid Assessment Process, which included nine formal interviews with CDS stakeholders, and observation of 27 clinicians. The research team, which has studied 21 healthcare sites of various sizes over the past 12 years, believes this site is an excellent example of an organization which is using a commercially available electronic-health-record system with CDS well. The eight-dimensional model proved useful as an organizing structure for the evaluation.
Conceptual models have been developed to address challenges inherent in studying health information technology (HIT).
This manuscript introduces an eight-dimensional model specifically designed to address the sociotechnical challenges involved in design, development, implementation, use and evaluation of HIT within complex adaptive healthcare systems.
The eight dimensions are not independent, sequential or hierarchical, but rather are interdependent and inter-related concepts similar to compositions of other complex adaptive systems. Hardware and software computing infrastructure refers to equipment and software used to power, support and operate clinical applications and devices. Clinical content refers to textual or numeric data and images that constitute the 'language' of clinical applications. The human--computer interface includes all aspects of the computer that users can see, touch or hear as they interact with it. People refers to everyone who interacts in some way with the system, from developer to end user, including potential patient-users. Workflow and communication are the processes or steps involved in ensuring that patient care tasks are carried out effectively. Two additional dimensions of the model are internal organisational features (eg, policies, procedures and culture) and external rules and regulations, both of which may facilitate or constrain many aspects of the preceding dimensions. The final dimension is measurement and monitoring, which refers to the process of measuring and evaluating both intended and unintended consequences of HIT implementation and use. We illustrate how our model has been successfully applied in real-world complex adaptive settings to understand and improve HIT applications at various stages of development and implementation.
To investigate best practices for implementing and managing clinical decision support (CDS) in community hospitals and ambulatory settings, we carried out a series of ethnographic studies to gather information from nine diverse organizations. Using the Rapid Assessment Process methodology, we conducted surveys, interviews, and observations over a period of two years in eight different geographic regions of the U.S.A. We first utilized a template organizing method for an expedited analysis of the data, followed by a deeper and more time consuming interpretive approach. We identified five major categories of best practices that require careful consideration while carrying out the planning, implementation, and knowledge management processes related to CDS. As more health care organizations implement clinical systems such as computerized provider order entry with CDS, descriptions of lessons learned by CDS pioneers can provide valuable guidance so that CDS can have optimal impact on health care quality.
In 2005, the American Medical Informatics Association undertook a set of activities relating to clinical decision support (CDS), with support from the office of the national coordinator and the Agency for Healthcare Research and Quality. They culminated in the release of the roadmap for national action on CDS in 2006. This article assesses progress toward the short-term goals within the roadmap, and recommends activities to continue to improve CDS adoption throughout the United States. The report finds that considerable progress has been made in the past four years, although significant work remains. Healthcare quality organizations are increasingly recognizing the role of health information technology in improving care, multi-site CDS demonstration projects are under way, and there are growing incentives for adoption. Specific recommendations include: (1) designating a national entity to coordinate CDS work and collaboration; (2) developing approaches to monitor and track CDS adoption and use; (3) defining and funding a CDS research agenda; and (4) updating the CDS 'critical path'.
Clinical decision support (CDS) can impact the outcomes of care when used at the point of care in electronic medical records (EMR). CDS has been shown to increase quality and patient safety, improve adherence to guidelines for prevention and treatment, and avoid medication errors. Systematic reviews have shown that CDS can be useful across a variety of clinical purposes and topics. Despite broad national policy objectives to increase EMR adoption in the US, current adoption of advanced clinical decision support is limited due to a variety of reasons, including: limited implementation of EMR, CPOE, PHR, etc., difficulty developing clinical practice guidelines ready for implementation in EMR, lack of standards, absence of a central repository or knowledge resource, poor support for CDS in commercial EMRs, challenges in integrating CDS into the clinical workflow, and limited understanding of organizational and cultural issues relating to clinical decision support. To better understand and overcome these barriers, and accelerate the translation of clinical practice guideline knowledge into CDS in EMRs, the CDS Consortium is established to assess, define, demonstrate, and evaluate best practices for knowledge management and clinical decision support in healthcare information technology at scale - across multiple ambulatory care settings and EHR technology platforms.
Objective: To allow exchange of clinical practice guidelines among institutions and computer-based applications.
Design: The GuideLine Interchange Format (GLIF) specification consists of the GLIF model and the GLIF syntax. The GLIF model is an object-oriented representation that consists of a set of classes for guideline entities, attributes for those classes, and data types for the attribute values. The GLIF syntax specifies the format of the test file that contains the encoding.
Methods: Researchers from the InterMed Collaboratory at Columbia University, Harvard University (Brigham and Women's Hospital and Massachusetts General Hospital), and Stanford University analyzed four existing guideline systems to derive a set of requirements for guideline representation. The GLIF specification is a consensus representation developed through a brainstorming process. Four clinical guidelines were encoded in GLIF to assess its expressivity and to study the variability that occurs when two people from different sites encode the same guideline.
Results: The encoders reported that GLIF was adequately expressive. A comparison of the encodings revealed substantial variability.
Conclusion: GLIF was sufficient to model the guidelines for the four conditions that were examined. GLIF needs improvement in standard representation of medical concepts, criterion logic, temporal information, and uncertainty.
A new architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support) is introduced and its performance evaluated. The architecture provides a method for performing clinical decision support across a network, as in a health information exchange. Using the prototype we demonstrated that, first, a number of useful types of decision support can be carried out using our architecture; and, second, that the architecture exhibits desirable reliability and performance characteristics.
Developing automated tools for clinical information management requires an appreciation of user needs and capabilities. To address the reality of practice style variation, and the varying degree of comfort with computers in clinical users, we developed the KnowledgeBank concept. The KnowledgeBank concept includes an end-user authoring tool for clinical content in the EMR, and a web-based repository of content for sharing clinical content. We describe the early experiences of end-users using the KnowledgeBank.
Implementation of practice guidelines refers to the creation of strategies and systems to operationalize the knowledge and recommendations set forth by guideline developers. We describe an approach to guideline implementation that makes direct use of the guideline document as a knowledge base. The Guideline Elements Model (GEM) provides an XML-based guideline document model that facilitates implementation of guidelines. Knowledge extraction using GEM requires document markup rather than programming and can promote authenticity and consistent knowledge encoding. Knowledge customization for the local enterprise requires addition of meta-information to pertinent components of the GEM hierarchy in a design database. GEM provides an audit trail to track local adaptation. Knowledge integration with patient data can be promoted using information management services. A design goal is to devise a system that can be applied by local clinical domain experts, quality assurance experts, and information systems programmers without requiring trained informaticians and knowledge engineers to serve as intermediaries
The use of clinical decision support systems to facilitate the practice of evidence-based medicine promises to substantially improve health care quality.
To describe, on the basis of the proceedings of the Evidence and Decision Support track at the 2000 AMIA Spring Symposium, the research and policy challenges for capturing research and practice-based evidence in machine-interpretable repositories, and to present recommendations for accelerating the development and adoption of clinical decision support systems for evidence-based medicine.
The recommendations fall into five broad areas--capture literature-based and practice-based evidence in machine--interpretable knowledge bases; develop maintainable technical and methodological foundations for computer-based decision support; evaluate the clinical effects and costs of clinical decision support systems and the ways clinical decision support systems affect and are affected by professional and organizational practices; identify and disseminate best practices for work flow-sensitive implementations of clinical decision support systems; and establish public policies that provide incentives for implementing clinical decision support systems to improve health care quality.
Although the promise of clinical decision support system-facilitated evidence-based medicine is strong, substantial work remains to be done to realize the potential benefits.
Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials.
To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit.
We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information.
We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes.
Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes.
One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001).
Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.
To identify features of clinical decision support systems critical for improving clinical practice.
Systematic review of randomised controlled trials.
Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews.
Studies had to evaluate the ability of decision support systems to improve clinical practice.
Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature.
Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P < 0.00001), provision of recommendations rather than just assessments (P = 0.0187), provision of decision support at the time and location of decision making (P = 0.0263), and computer based decision support (P = 0.0294). Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations.
Several features were closely correlated with decision support systems' ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.
Experts consider health information technology key to improving efficiency and quality of health care.
To systematically review evidence on the effect of health information technology on quality, efficiency, and costs of health care.
The authors systematically searched the English-language literature indexed in MEDLINE (1995 to January 2004), the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database. We also added studies identified by experts up to April 2005.
Descriptive and comparative studies and systematic reviews of health information technology.
Two reviewers independently extracted information on system capabilities, design, effects on quality, system acquisition, implementation context, and costs.
257 studies met the inclusion criteria. Most studies addressed decision support systems or electronic health records. Approximately 25% of the studies were from 4 academic institutions that implemented internally developed systems; only 9 studies evaluated multifunctional, commercially developed systems. Three major benefits on quality were demonstrated: increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors. The primary domain of improvement was preventive health. The major efficiency benefit shown was decreased utilization of care. Data on another efficiency measure, time utilization, were mixed. Empirical cost data were limited.
Available quantitative research was limited and was done by a small number of institutions. Systems were heterogeneous and sometimes incompletely described. Available financial and contextual data were limited.
Four benchmark institutions have demonstrated the efficacy of health information technologies in improving quality and efficiency. Whether and how other institutions can achieve similar benefits, and at what costs, are unclear.
There is a pressing need for high-quality, effective means of designing, developing, presenting, implementing, evaluating, and maintaining all types of clinical decision support capabilities for clinicians, patients and consumers. Using an iterative, consensus-building process we identified a rank-ordered list of the top 10 grand challenges in clinical decision support. This list was created to educate and inspire researchers, developers, funders, and policy-makers. The list of challenges in order of importance that they be solved if patients and organizations are to begin realizing the fullest benefits possible of these systems consists of: improve the human-computer interface; disseminate best practices in CDS design, development, and implementation; summarize patient-level information; prioritize and filter recommendations to the user; create an architecture for sharing executable CDS modules and services; combine recommendations for patients with co-morbidities; prioritize CDS content development and implementation; create internet-accessible clinical decision support repositories; use freetext information to drive clinical decision support; mine large clinical databases to create new CDS. Identification of solutions to these challenges is critical if clinical decision support is to achieve its potential and improve the quality, safety and efficiency of healthcare.
The Arden Syntax for sharing medical knowledge bases is described. Its current focus is on knowledge that is represented as a set of independent modules that can provide therapeutic suggestions, alerts, diagnosis scores, etc. The syntax is based largely upon HELP and the Regenstrief Medical Record System. Each module, called a Medical Logic Module or MLM, is made of slots grouped into maintenance, library, and knowledge categories. The syntax has provisions for querying a clinical database and representing time. Several clinical information systems were analyzed and appear to be compatible with the syntax. The syntax has been tested for syntactic ambiguities using the tools lex and yacc. Seventeen institutions are currently in the process of adopting the Arden Syntax for their decision-support systems. A subcommittee of ASTM has been formed to develop standards for sharing medical knowledge bases. The Arden Syntax has been published by ASTM as a initial standard for sharing medical knowledge.
The Arden Syntax for sharing medical knowledge bases is described. Its current focus is on knowledge that is represented as a set of independent modules that can provide therapeutic suggestions, alerts, diagnosis scores, etc. The syntax is based largely upon HELP and the Regenstrief Medical Record System. Each module, called a Medical Logic Module or MLM, is made of slots grouped into maintenance, library, and knowledge categories. The syntax has provisions for querying a clinical database and representing time. Several clinical information systems were analyzed and appear to be compatible with the syntax. The syntax has been tested for syntactic ambiguities using the tools lex and yacc. Seventeen institutions are currently in the process of adopting the Arden Syntax for their decision-support systems. A subcommittee of ASTM has been formed to develop standards for sharing medical knowledge bases. The Arden Syntax has been published by ASTM as a initial standard for sharing medical knowledge.
The Clinical Decision Support Consortium has completed two demonstration trials involving a web service for the execution of clinical decision support (CDS) rules in one or more electronic health record (EHR) systems. The initial trial ran in a local EHR at Partners HealthCare. A second EHR site, associated with Wishard Memorial Hospital, Indianapolis, IN, was added in the second trial. Data were gathered during each 6 month period and analyzed to assess performance, reliability, and response time in the form of means and standard deviations for all technical components of the service, including assembling and preparation of input data. The mean service call time for each period was just over 2 seconds. In this paper we report on the findings and analysis to date while describing the areas for further analysis and optimization as we continue to expand our use of a Services Oriented Architecture approach for CDS across multiple institutions.
To provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms.
To develop a conceptual model for describing and understanding clinical summarization in both computer-independent and computer-supported clinical tasks.
Based on extensive literature review and clinical input, we developed a conceptual model of clinical summarization to lay the foundation for future research on clinician workflow and automated summarization using electronic health records (EHRs).
Our model identifies five distinct stages of clinical summarization: (1) Aggregation, (2) Organization, (3) Reduction and/or Transformation, (4) Interpretation and (5) Synthesis (AORTIS). The AORTIS model describes the creation of complex, task-specific clinical summaries and provides a framework for clinical workflow analysis and directed research on test results review, clinical documentation and medical decision-making. We describe a hypothetical case study to illustrate the application of this model in the primary care setting.
Both practicing physicians and clinical informaticians need a structured method of developing, studying and evaluating clinical summaries in support of a wide range of clinical tasks. Our proposed model of clinical summarization provides a potential pathway to advance knowledge in this area and highlights directions for further research.
The widespread use of electronic health records (EHRs) in the United States is inevitable. EHRs will improve caregivers' decisions and patients' outcomes. Once patients experience the benefits of this technology, they will demand nothing less from their providers. Hundreds of thousands of physicians have already seen these benefits in their clinical practice. But inevitability does not mean easy transition. We have years of professional agreement and bipartisan consensus regarding the potential value of EHRs. Yet we have not moved significantly to extend the availability of EHRs from a few large institutions to the smaller clinics and practices where most Americans . . .
To determine whether clinical errors can be reduced by prospective computer suggestions about the management of simple clinical events, I studied the responses of nine physicians to computer suggestions generated by 390 protocols in a controlled crossover design. These protocols dealt primarily with conditions managed (e.g., elevated blood pressure) or caused (e.g., liver toxicity) by drugs. Physicians responded to 51 per cent of 327 events when given, and 22 per cent of 385 events when not given computer suggestions. Neither level of postgraduate training (first-year postgraduate or third-year post-graduate) nor the order in which physicians served as study and control subjects had statistically significant overall effect on the results. It appears that the prospective reminders do reduce errors, and that many of these errors are probably due to man's limitations as a data processor rather than to correctable human deficiencies.
Arden Syntax for Medical Logic Module (MLM)1 was designed for writing and sharing task-specific health knowledge in 1989. Several researchers have developed frameworks to improve the sharability and adaptability of Arden Syntax MLMs, an issue known as "curly braces" problem. Karadimas et al proposed an Arden Syntax MLM-based decision support system that uses an object oriented model and the dynamic linking features of the Java platform.2 Peleg et al proposed creating a Guideline Expression Language (GEL) based on Arden Syntax's logic grammar.3 The New York Presbyterian Hospital (NYPH) has a collection of about 200 MLMs. In a process of adapting the current MLMs for an object-oriented event monitor, we identified two problems that may influence the "curly braces" one: (1) the query expressions within the curly braces of Arden Syntax used in our institution are cryptic to the physicians, institutional dependent and written ineffectively (unpublished results), and (2) the events are coded individually within a curly braces, resulting sometimes in a large number of events - up to 200.
Despite their demonstrated effectiveness, clinical decision support (CDS) systems are not widely used within the U.S. The Roadmap for National Action on Clinical Decision Support, published in June 2006 by the American Medical Informatics Association, identifies six strategic objectives for achieving widespread adoption of effective CDS capabilities. In this manuscript, we propose a Service-Oriented Architecture (SOA) for CDS that facilitates achievement of these six objectives. Within the proposed framework, CDS capabilities are implemented through the orchestration of independent software services whose interfaces are being standardized by Health Level 7 and the Object Management Group through their joint Healthcare Services Specification Project (HSSP). Core services within this framework include the HSSP Decision Support Service, the HSSP Common Terminology Service, and the HSSP Retrieve, Locate, and Update Service. Our experiences, and those of others, indicate that the proposed SOA approach to CDS could enable the widespread adoption of effective CDS within the U.S. health care system.
This document comprises an AMIA Board of Directors approved White Paper that presents a roadmap for national action on clinical decision support. It is published in JAMIA for archival and dissemination purposes. The full text of this material has been previously published on the AMIA Web site (www.amia.org/inside/initiatives/cds). AMIA is the copyright holder.
In the current issue of JAMIA , Kawamoto and Lobach1 propose a software framework intended to facilitate widespread, effective, clinical decision support. The proposed framework embraces a service-oriented-architecture (SOA) approach. Service-oriented-architecture is a philosophy of design described as “the software equivalent of Lego bricks,”2 where a toolset of mix-and-match units (“services”), each performing a well-defined task, can reside on different machines (including geographically separated ones), ready to be used when needed. The most widespread implementations of SOA involve the use of Web services, where a given computational resource/service can be invoked by a remote machine via messages composed in XML and sent over HTTP, so that they can operate across firewalls. Think of a Web service, in its simplest form, as a subroutine that can be called over the Internet.
Thanks to success stories such as Amazon.com,3 and at software giants such as SAP,4,5 the status of SOA in the business information technology (IT) domain has risen meteorically. The Kawamoto and Lobach paper1 reiterates the following potential benefits of SOA:
Some IT articles, however, present a “caveat emptor” skepticism about SOA.6,7 One must carefully inspect the claimed benefits of SOA closely, examine its potential drawbacks, and take a balanced approach to any proposed new SOA application while taking into consideration past lessons from business IT.
In software design, simplification through problem decomposition into semi-independent units (subroutines, classes) is an important, incontrovertible principle. In SOA, where the units (services) lead a relatively autonomous existence (e.g., units are free to reside on physically separate hardware), …
This study sought to develop a functional taxonomy of rule-based clinical decision support.
The rule-based clinical decision support content of a large integrated delivery network with a long history of computer-based point-of-care decision support was reviewed and analyzed along four functional dimensions: trigger, input data elements, interventions, and offered choices.
A total of 181 rule types were reviewed, comprising 7,120 different instances of rule usage. A total of 42 taxa were identified across the four categories. Many rules fell into multiple taxa in a given category. Entered order and stored laboratory result were the most common triggers; laboratory result, drug list, and hospital unit were the most frequent data elements used. Notify and log were the most common interventions, and write order, defer warning, and override rule were the most common offered choices.
A relatively small number of taxa successfully described a large body of clinical knowledge. These taxa can be directly mapped to functions of clinical systems and decision support systems, providing feature guidance for developers, implementers, and certifiers of clinical information systems.
A large body of evidence over many years suggests that clinical decision support systems can be helpful in improving both clinical outcomes and adherence to evidence-based guidelines. However, to this day, clinical decision support systems are not widely used outside of a small number of sites. One reason why decision support systems are not widely used is the relative difficulty of integrating such systems into clinical workflows and computer systems.
To review and synthesize the history of clinical decision support systems, and to propose a model of various architectures for integrating clinical decision support systems with clinical systems.
The authors conducted an extensive review of the clinical decision support literature since 1959, sequenced the systems and developed a model.
The model developed consists of four phases: standalone decision support systems, decision support integrated into clinical systems, standards for sharing clinical decision support content and service models for decision support. These four phases have not heretofore been identified, but they track remarkably well with the chronological history of clinical decision support, and show evolving and increasingly sophisticated attempts to ease integrating decision support systems into clinical workflows and other clinical systems.
Each of the four evolutionary approaches to decision support architecture has unique advantages and disadvantages. A key lesson was that there were common limitations that almost all the approaches faced, and no single approach has been able to entirely surmount: (1) fixed knowledge representation systems inherently circumscribe the type of knowledge that can be represented in them, (2) there are serious terminological issues, (3) patient data may be spread across several sources with no single source having a complete view of the patient, and (4) major difficulties exist in transferring successful interventions from one site to another.
In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are:
Stage 2 Eligible Professional (EP) Meaningful Use Core and Menu Measures Grand challenges in clinical decision support
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