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Single source information system, hospital information system, nuclear medicine, myocardialscintigraphy (SPECT/CT-data) SummaryObjective: Data for clinical documentation and medical research are usually managed in separate systems. We developed, implemented and assessed a documentation system for myocardial scintigraphy (SPECT/CT-data) in order to integrate clinical and research documentation. This paper presents concept, implementation and evaluation of this single source system including methods to improve data quality by plausibility checks. Methods: We analyzed the documentation process for myocardial scintigraphy, especially for collecting medical history, symptoms and medication as well as stress and rest injection protocols. Corresponding electronic forms were implemented in our hospital information system (HIS) including plausibility checks to support correctness and completeness of data entry. Research data can be extracted from routine data by dedicated HIS reports. Results: A single source system based on HIS-electronic documentation merges clinical and scientific documentation and thus avoids multiple documentation. Within nine months 495 patients were documented with our system by 8 physicians and 6 radiographers (466 medical history protocols, 466 stress and 414 rest injection protocols). Documentation consists of 295 attributes, three quarters are conditional items. Data quality improved substantially compared to previous paper-based documentation. Conclusion: A single source system to collect routine and research data for myocardial scintigraphy is feasible in a real-world setting and can generate high-quality data through online plausibility checks. Citation: Herzberg S, Rahbar K, Stegger L, Schäfers M, Dugas M. Concept and implementation of a single source information system in nuclear medicine for myocardial scintigraphy (SPECT-CT data).

SummaryObjective: Follow-up data must be collected according to the protocol of each clinical study, i.e. at certain time points. Missing follow-up information is a critical problem and may impede or bias the analysis of study data and result in delays. Moreover, additional patient recruitment may be necessary due to incomplete follow-up data. Current electronic data capture (EDC) systems in clinical studies are usually separated from hospital information systems (HIS) and therefore can provide limited functionality to support clinical workflow. In two case studies, we assessed the feasibility of HIS-based support of follow-up documentation. Methods: We have developed a data model and a HIS-based workflow to provide follow-up forms according to clinical study protocols. If a follow-up form was due, a database procedure created a followup event which was translated by a communication server into an HL7 message and transferred to the import interface of the clinical information system (CIS). This procedure generated the required followup form and enqueued a link to it in a work list of the relating study nurses and study physicians, respectively. Results: A HIS-based follow-up system automatically generated follow-up forms as defined by a clinical study protocol. These forms were scheduled into work lists of study nurses and study physicians. This system was integrated into the clinical workflow of two clinical studies. In a study from nuclear medicine, each scenario from the test concept according to the protocol of the single photon emission computer tomography/computer tomography (SPECT/CT) study was simulated and each scenario passed the test. For a study in psychiatry, 128 follow-up forms were automatically generated within 27 weeks, on average five forms per week (maximum 12, minimum 1 form per week). Conclusion: HIS-based support of follow-up documentation in clinical studies is technically feasible and can support compliance with study protocols. BackgroundClinical studies typically consist of several visits, and after an initial assessment, several follow-up visits need to be organized and documented. Therefore, follow-up data needs to be collected according to each study protocol at certain time points. According to Chan et al., "data completeness varied substantially across studies" [1] which may be caused by the huge documentation workload of physicians in routine care [2]. Forster et al. have reported that the median rate of loss to followup in a 15-country study was 8.5% [3]. Consequently, data completeness in studies is a critical and unsolved problem. There are different causes of patients being lost to follow-up. First, patients do not return to the physicians, and second, there are organizational issues in hospitals, for example regarding scheduling. Commonly, electronic data capture (EDC) systems are used for research documentation which includes follow-up documents. El Emam et al. state that "Electronic data capture (EDC) tools provide automated support for data collection, reporting, query re...