Assessing the feasibility of a mobile health-supported clinical decision support system for nutritional triage in oncology outpatients using Arden Syntax.

BACKGROUND: Nutritional screening procedures followed by regular nutrition monitoring for oncological outpatients are no standard practice in many European hospital wards and outpatient settings. As a result, early signs of malnutrition are missed and nutritional treatment is initiated when patients have already experienced severe weight loss. OBJECTIVE: We report on a novel clinical decision support system (CDSS) for the global assessment and nutritional triage of the nutritional condition of oncology outpatients. The system combines clinical and laboratory data collected in the clinical setting with patient-generated data from a smartphone application for monitoring the patients' nutritional status. Our objective is to assess the feasibility of a CDSS that combines the aforementioned data sources and describe its integration into a hospital information system. Furthermore, we collected patients' opinions on the value of the system, and whether they would regard the system as a useful aid in coping with their condition. MATERIALS AND METHODS: The system implements the Patient-Generated Subjective Global Assessment (PG-SGA) to monitor nutritional status in the outpatient setting. A smartphone application is used to collect patient-generated data by performing weekly mini-surveys on patients concerning their eating habits, weight, and overall well-being. Data are uploaded on completion of each mini-survey and stored on a secure server at the Medical University of Vienna (MUV). The data are then combined with relevant clinical information from the Vienna General Hospital (VGH) information system. The knowledge base for the CDSS is implemented in medical logic modules (MLMs) using Arden Syntax. A three-month pilot clinical trial was performed to test the feasibility of the system. Qualitative questionnaires were used to obtain the patients' opinions on the usability and personal value of the system during the four-week test period. RESULTS: We used the existing separation between the scientific and clinical data domains in the secured network environment (SNE) at the MUV and VGH to our advantage by importing, storing, and processing both patient-generated and routine data in the scientific data domain. To limit exposure to the SNE, patient-generated data stored outside the SNE were imported to the scientific domain once a day. The CDSS created for nutritional assessment and triage comprised ten MLMs, each including either a sub-assessment or the final results of the PG-SGA. Finally, an interface created for the hospital information system showed the results directly in clinical routine. In all 22 patients completed the clinical study. The results of the questionnaires showed that 91% of the patients were generally happy with the usability of the system, 91% believed that the application was of additional value in detecting cancer-related malnutrition, and 82% found it helpful as a long-term monitoring tool. DISCUSSION AND CONCLUSION: Despite strict protection of the clinical data domain, a CDSS employing patient-generated data can be integrated into clinical routine. The CDSS discussed in this report combined the information entered into a smartphone application with clinical data in order to inform the physician of a patient's nutritional status and thus permit suitable and timely intervention. The initial results show that the smartphone application was well accepted by patients, who considered it useful, but not many oncological outpatients were willing to participate in the clinical study because they did not possess an Android phone or lacked smartphone expertise. Furthermore, the results indicate that patient-generated data could be employed to augment clinical data and calculate metrics such as the PG-SGA without excessive effort by using a secure intermediate location as the locus of data storage and processing.

Clinical decision support systems at the Vienna General Hospital using Arden Syntax: Design, implementation, and integration.

INTRODUCTION: The Allgemeines Krankenhaus Informations Management (AKIM) project was started at the Vienna General Hospital (VGH) several years ago. This led to the introduction of a new hospital information system (HIS), and the installation of the expert system platform (EXP) for the integration of Arden-Syntax-based clinical decision support systems (CDSSs). In this report we take a look at the milestones achieved and the challenges faced in the creation and modification of CDSSs, and their integration into the HIS over the last three years. MATERIALS AND METHODS: We introduce a three-stage development method, which is followed in nearly all CDSS projects at the Medical University of Vienna and the VGH. Stage one comprises requirements engineering and system conception. Stage two focuses on the implementation and testing of the system. Finally, stage three describes the deployment and integration of the system in the VGH HIS. The HIS provides a clinical work environment for healthcare specialists using customizable graphical interfaces known as parametric medical documents. Multiple Arden Syntax servers are employed to host and execute the CDSS knowledge bases: two embedded in the EXP for production and development, and a further three in clinical routine for production, development, and quality assurance. RESULTS: Three systems are discussed; the systems serve different purposes in different clinical areas, but are all implemented with Arden Syntax. MONI-ICU is an automated surveillance system for monitoring healthcare-associated infections in the intensive care setting. TSM-CDS is a CDSS used for risk prediction in the formation of cutaneous melanoma metastases. Finally, TacroDS is a CDSS for the manipulation of dosages for tacrolimus, an immunosuppressive agent used after kidney transplantation. Problems in development and integration were related to data quality or availability, although organizational difficulties also caused delays in development and integration. DISCUSSION AND CONCLUSION: Since the inception of the AKIM project at the VGH and its ability to support standards such as Arden Syntax and integrate CDSSs into clinical routine, the clinicians' interest in, and demand for, decision support has increased substantially. The use of Arden Syntax as a standard for CDSSs played a substantial role in the ability to rapidly create high-quality CDSS systems, whereas the ability to integrate these systems into the HIS made CDSSs more popular among physicians. Despite these successes, challenges such as lack of (consistent and high-quality) electronic data, social acceptance among healthcare personnel, and legislative issues remain. These have to be addressed effectively before CDSSs can be more widely accepted and adopted.

Knowledge-based immunosuppressive therapy for kidney transplant patients--from theoretical model to clinical integration.

Immunosuppressive therapy is a risky necessity after a patient received a kidney transplant. To reduce risks, a knowledge-based system was developed that determines the right dosage of the immunosuppresive agent Tacrolimus. A theoretical model, to classify medication blood levels as well as medication adaptions, was created using data from almost 500 patients, and over 13.000 examinations. This model was then translated into an Arden Syntax knowledge base, and integrated directly into the hospital information system of the Vienna General Hospital. In this paper we give an overview of the construction and integration of such a system.

Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review.

OBJECTIVE: As more electronic health records have become available during the last decade, we aimed to uncover recent trends in use of electronically available patient data by electronic surveillance systems for healthcare associated infections (HAIs) and identify consequences for system effectiveness. METHODS: A systematic review of published literature evaluating electronic HAI surveillance systems was performed. The PubMed service was used to retrieve publications between January 2001 and December 2011. Studies were included in the review if they accurately described what electronic data were used and if system effectiveness was evaluated using sensitivity, specificity, positive predictive value, or negative predictive value. Trends were identified by analyzing changes in the number and types of electronic data sources used. RESULTS: 26 publications comprising discussions on 27 electronic systems met the eligibility criteria. Trend analysis showed that systems use an increasing number of data sources which are either medico-administrative or clinical and laboratory-based data. Trends on the use of individual types of electronic data confirmed the paramount role of microbiology data in HAI detection, but also showed increased use of biochemistry and pharmacy data, and the limited adoption of clinical data and physician narratives. System effectiveness assessments indicate that the use of heterogeneous data sources results in higher system sensitivity at the expense of specificity. CONCLUSIONS: Driven by the increased availability of electronic patient data, electronic HAI surveillance systems use more data, making systems more sensitive yet less specific, but also allow systems to be tailored to the needs of healthcare institutes' surveillance programs.

Knowledge-based tacrolimus therapy for kidney transplant patients.

Immunosuppressive therapy is necessary when patients with chronic kidney disease receive a kidney transplant. Certain immunosuppressive agents need therapeutic drug monitoring. The goal of this paper was to identify adaptation rules for Tacrolimus therapy from a clinical data set. For knowledge acquisition, patient data from 1995 to 2008 from the Department of Nephrology and Dialysis of the Vienna General Hospital were used, including patient demographics, laboratory parameters, time since kidney transplantation and other immuno-suppressive drugs administered. Tacrolimus was chosen from the available immunosuppressive drugs. By applying a regression tree, we create homogeneous groups of data. Models were generated for these groups that can predict the level of the drug concentration for the next ward round. A knowledge base was developed on the basis of the determined models, which is used within a clinical decision support system for Tacrolimus therapy planning, which was integrated into clinical routine.