Evolution of the Arden Syntax: Key Technical Issues from the Standards Development Organization Perspective.

BACKGROUND: The initial version of the Arden Syntax for Medical Logic Systems was created to facilitate explicit representation of medical logic in a form that could be easily composed and interpreted by clinical experts in order to facilitate clinical decision support (CDS). Because of demand from knowledge engineers and programmers to improve functionality related to complex use cases, the Arden Syntax evolved to include features typical of general programming languages but that were specialized to meet the needs of the clinical decision support environment, including integration into a clinical information system architecture. METHOD: Review of the design history and evolution of the Arden Syntax by workers who participated in this evolution from the perspective of the standards development organization (SDO). RESULTS: In order to meet user needs, a variety of features were successively incorporated in Arden Syntax. These can be grouped in several classes of change, including control flow, data structures, operators and external links. These changes included expansion of operators to manipulate lists and strings; a formalism for structured output; iteration constructs; user-defined objects and operators to manipulate them; features to support international use and output in different natural languages; additional control features; fuzzy logic formalisms; and mapping of the entire syntax to XML. The history and rationale of this evolution are summarized. CONCLUSION: In response to user demand and to reflect its growing role in clinical decision support, the Arden Syntax has evolved to include a number of powerful features. These depart somewhat from the original vision of the syntax as simple and easily understandable but from the SDO perspective increase the utility of this standard for implementation of CDS. Backwards compatibility has been maintained, allowing continued support of the earlier, simpler decision support models.

Clinical Decision Support for the Classification of Diabetic Retinopathy: A Comparison of Manual and Automated Results.

The management of diabetic retinopathy, a frequent ophthalmological manifestation of diabetes mellitus, consists of regular examinations and a standardized, manual classification of disease severity, which is used to recommend re-examination intervals. To evaluate the feasibility and safety of implementing automated, guideline-based diabetic retinopathy (DR) grading into clinical routine by applying established clinical decision support (CDS) technology. We compared manual with automated classification that was generated using medical documentation and an Arden server with a specific medical logic module. Of 7169 included eyes, 47% (n=3373) showed inter-method classification agreement, specifically 29.4% in mild DR, 38.3% in moderate DR, 27.6% in severe DR, and 65.7% in proliferative DR. We demonstrate that the implementation of a CDS system for automated disease severity classification in diabetic retinopathy is feasible but also that, due to the highly individual nature of medical documentation, certain important criteria for the used electronic health record system need to be met in order to achieve reliable results.

Fuzzy-Arden-Syntax-based, Vendor-agnostic, Scalable Clinical Decision Support and Monitoring Platform.

This study's objective is to develop and use a scalable genuine technology platform for clinical decision support based on Arden Syntax, which was extended by fuzzy set theory and fuzzy logic. Arden Syntax is a widely recognized formal language for representing clinical and scientific knowledge in an executable format, and is maintained by Health Level Seven (HL7) International and approved by the American National Standards Institute (ANSI). Fuzzy set theory and logic permit the representation of knowledge and automated reasoning under linguistic and propositional uncertainty. These forms of uncertainty are a common feature of patients' medical data, the body of medical knowledge, and deductive clinical reasoning.

Detecting, Monitoring, and Reporting Possible Adverse Drug Events Using an Arden-Syntax-based Rule Engine.

The detection of adverse drug events (ADEs) is an important aspect of improving patient safety. The iMedication system employs predefined triggers associated with significant events in a patient's clinical data to automatically detect possible ADEs. We defined four clinically relevant conditions: hyperkalemia, hyponatremia, renal failure, and over-anticoagulation. These are some of the most relevant ADEs in internal medical and geriatric wards. For each patient, ADE risk scores for all four situations are calculated, compared against a threshold, and judged to be monitored, or reported. A ward-based cockpit view summarizes the results.

Mobile Display of Information about Aggregated Antibiotic Resistance in the Hospital Setting Supported by Near Field Communication.

Antibiotic resistance is a heterogeneous phenomenon. It does not only differ between countries or states, but also between wards of hospitals, where different resistance patterns have been found. To support clinicians in administering empiric antibiotic therapy, we developed software to present information about antibiotic resistance using a mobile concept. A pre-existing infrastructure was deployed as the server component. The systems analyze and aggregate data from laboratory information systems, generating statistical data on antibiotic resistance. The information is presented to the Android client using a Representational State Transfer (REST) interface. Geographical localization is performed using near field communication (NFC) tags. The prototype provides tabulated data concerning antibiotic resistance patterns in the wards of a hospital. Using Android, NFC, and data caching, the usability of the system is estimated to be high. We hypothesize that antibiotic stewardship in hospitals can be supported by this software, thus improving medical monitoring of antibiotic resistance. Future studies in a productive environment are needed to measure the impact of the system on the outcome of patient care.

Framework for near-field-communication-based geo-localization and personalization for Android-based smartphones--application in hospital environments.

Various applications using near field communication (NFC) have been developed for the medical sector. As a method of short-range wireless contact-driven data transfer, NFC is a useful tool in medicine. It can be used to transfer data such as blood pressure, control adherence to medication, or transmit in vivo data. The first proposed general framework uses NFC as a mechanism for indoor geo-localization in hospitals. NFC geo-localization is economical compared to classical concepts using indoor GPS or WLAN triangulation, and the granularity of location retrieval can be defined at a tag level. Using this framework, we facilitate the development of medical applications that require exact indoor geo-localization. Multi-user Android systems are addressed in the second framework. Using private NFC tags, users are able to carry on their personal settings for enabled applications. This eliminates the need for multiple user accounts on common Android devices, improves usability, and eases technical administration. Based on the prototypes presented here, we show a novel concept of using NFC-enabled Android devices in hospital environments.

An Arden-Syntax-based clinical decision support framework for medical guidelines--Lyme borreliosis as an example.

Medicine is evolving at a very fast pace. The overwhelming quantity of new data compels the practician to be consistently informed about the most recent scientific advances. While medical guidelines have proven to be an acceptable tool for bringing new medical knowledge into clinical practice and also support medical personnel, reading them may be rather time-consuming. Clinical decision support systems have been developed to simplify this process. However, the implementation or adaptation of such systems for individual guidelines involves substantial effort. This paper introduces a clinical decision support platform that uses Arden Syntax to implement medical guidelines using client-server architecture. It provides a means of implementing different guidelines without the need for adapting the system's source code. To implement a prototype, three Lyme borreliosis guidelines were aggregated and a knowledge base created. The prototype employs transfer objects to represent any text-based medical guideline. As part of the implementation, we show how Fuzzy Arden Syntax can improve the overall usability of a clinical decision support system.

Delivering antibiotic resistance information specifically tailored to location and time.

Antibiotic resistance poses a significant threat to humanity. Hundred years since the beginning of the era of antibacterial drugs, we are facing increasing numbers of infections with multi-resistant pathogens. The current approach of distributing information on antibiotic resistance in printed form in the clinics has disadvantages with respect to the actuality of the data and the regional heterogeneity of resistance patterns. We developed an application named qRe using representational state transfer as a communication standard to deliver antibiotic resistance percentage information to the end user. The data is selected specifically for his/her geographic location. The user can display the information using either the application for Android smart phones or the web application. With the presented software we show the technical feasibility of delivering antibiotic resistance information specifically tailored to location and time. A short evaluation of the software showed an overall positive response from physicians. Based on recommendations of previous investigations, we expect a measurable clinical impact.

Effectiveness of an automated surveillance system for intensive care unit-acquired infections.

This study assessed the effectiveness of a fully automated surveillance system for the detection of healthcare-associated infections (HCAIs) in intensive care units. Manual ward surveillance (MS) and electronic surveillance (ES) were performed for two intensive care units of the Vienna General Hospital. All patients admitted for a period longer than 48 h between 13 November 2006 and 7 February 2007 were evaluated according to HELICS-defined rules for HCAI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and personnel time spent per surveillance type were calculated. Ninety-three patient admissions were observed, whereby 30 HCAI episodes were taken as a reference standard. Results with MS versus ES were: sensitivity 40% versus 87%, specificity 94% versus 99%, PPV 71% versus 96%, NPV 80% versus 95%, and time spent per surveillance type 82.5 h versus 12.5 h. In conclusion, ES was found to be more effective than MS while consuming fewer personnel resources.

The Arden Syntax standard for clinical decision support: experiences and directions.

Arden Syntax is a widely recognized standard for representing clinical and scientific knowledge in an executable format. It has a history that reaches back until 1989 and is currently maintained by the Health Level 7 (HL7) organization. We created a production-ready development environment, compiler, rule engine and application server for Arden Syntax. Over the course of several years, we have applied this Arden - Syntax - based CDS system in a wide variety of clinical problem domains, such as hepatitis serology interpretation, monitoring of nosocomial infections or the prediction of metastatic events in melanoma patients. We found the Arden Syntax standard to be very suitable for the practical implementation of CDS systems. Among the advantages of Arden Syntax are its status as an actively developed HL7 standard, the readability of the syntax, and various syntactic features such as flexible list handling. A major challenge we encountered was the technical integration of our CDS systems in existing, heterogeneous health information systems. To address this issue, we are currently working on incorporating the HL7 standard GELLO, which provides a standardized interface and query language for accessing data in health information systems. We hope that these planned extensions of the Arden Syntax might eventually help in realizing the vision of a global, interoperable and shared library of clinical decision support knowledge.

Clinical information system services and capabilities desired for scalable, standards-based, service-oriented decision support: consensus assessment of the Health Level 7 clinical decision support Work Group.

A standards-based, service-oriented architecture for clinical decision support (CDS) has the potential to significantly enhance CDS scalability and robustness. To enable such a CDS architecture, the Health Level 7 CDS Work Group reviewed the literature, hosted multi-stakeholder discussions, and consulted domain experts to identify and prioritize the services and capabilities required from clinical information systems (CISs) to enable service-oriented CDS. In addition, relevant available standards were identified. Through this process, ten CIS services and eight CIS capabilities were identified as being important for enabling scalable, service-oriented CDS. In particular, through a survey of 46 domain experts, five services and capabilities were identified as being especially critical: 1) the use of standard information models and terminologies; 2) the ability to leverage a Decision Support Service (DSS); 3) support for a clinical data query service; 4) support for an event subscription and notification service; and 5) support for a user communication service.