Investigating the Capabilities of FHIR Search for Clinical Trial Phenotyping

Clinical trials are the foundation of evidence-based medicine and their computerized support has been a recurring theme in medical informatics. One challenging aspect is the representation of eligibility criteria in a machine-readable format to automate the identification of suitable participants. In this study, we investigate the capabilities for expressing trial eligibility criteria via the search functionality specified in HL7 FHIR, an emerging standard for exchanging healthcare information electronically which also defines a set of operations for searching for health record data. Using a randomly sampled subset of 303 eligibility criteria from ClinicalTrials.gov yielded a 34 % success rate in representing them using the FHIR search semantics. While limitations are present, the FHIR search semantics are a viable tool for supporting preliminary trial eligibility assessment.

Samply.MDR - A Metadata Repository and Its Application in Various Research Networks

Collaboration in medical research is becoming common, especially for collecting relevant cases across institutional boundaries. If the data, which is usually very heterogeneously formalized and structured, can be integrated, such a collaboration can facilitate research. An absolute prerequisite for this is an extensive description about the formalization and exact meaning of every data element contained in a dataset. This information is commonly known as metadata. Various research networking projects tackle this challenge with the development of concepts and IT tools. The Samply Metadata Repository (Samply.MDR) is a solution for managing and publishing such metadata in a standardized and reusable way. In this article we present the structure and features of the Samply.MDR as well as its flexible usability by giving an overview about its application in various projects.

Predicting Clinical Outcomes in Colorectal Cancer Using Machine Learning

Using gene markers and other patient features to predict clinical outcomes plays a vital role in enhancing clinical decision making and improving prognostic accuracy. This work uses a large set of colorectal cancer patient data to train predictive models using machine learning methods such as random forest, general linear model, and neural network for clinically relevant outcomes including disease free survival, survival, radio-chemotherapy response (RCT-R) and relapse. The most successful predictive models were created for dichotomous outcomes like relapse and RCT-R with accuracies of 0.71 and 0.70 on blinded test data respectively. The best prediction models regarding overall survival and disease-free survival had C-Index scores of 0.86 and 0.76 respectively. These models could be used in the future to aid a decision for or against chemotherapy and improve survival prognosis. We propose that future work should focus on creating reusable frameworks and infrastructure for training and delivering predictive models to physicians, so that they could be readily applied to other diseases in practice and be continuously developed integrating new data.

Implementing Pharmacogenomic Clinical Decision Support into German Hospitals

BACKGROUND

Pharmacogenomic Clinical Decision Support Systems (CDSS) are considered to be the most feasible tool for adopting pharmacogenomic testing into clinical routine.

OBJECTIVE

To discuss important factors for implementing pharmacogenomic CDSS into German hospitals.

METHODS

We analyzed two relevant studies. Furthermore, we interviewed data privacy officers of three German university hospitals and examined relevant legal regulations in literature.

RESULTS

There are three major barriers for implementing pharmacogenomic CDSS into German hospitals: (i) the legal uncertainty; (ii) the lack of machine-readable data; (iii) the remaining knowledge gap of both genetics and pharmacogenomics among physicians.

CONCLUSION

The implementation of passive clinical decision support (CDS) for somatic mutations in the form of structured pharmacogenomic reports might be the most feasible CDS feature for clinicians in German hospitals.

Clinical decision support systems in child and adolescent psychiatry: a systematic review

Psychiatric disorders are amongst the most prevalent and impairing conditions in childhood and adolescence. Unfortunately, it is well known that general practitioners (GPs) and other frontline health providers (i.e., child protection workers, public health nurses, and pediatricians) are not adequately trained to address these ubiquitous problems (Braddick et al. Child and Adolescent mental health in Europe: infrastructures, policy and programmes, European Communities, 2009; Levav et al. Eur Child Adolesc Psychiatry 13:395-401, 2004). Advances in technology may offer a solution to this problem with clinical decision support systems (CDSS) that are designed to help professionals make sound clinical decisions in real time. This paper offers a systematic review of currently available CDSS for child and adolescent mental health disorders prepared according to the PRISMA-Protocols (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols). Applying strict eligibility criteria, the identified studies (n = 5048) were screened. Ten studies, describing eight original clinical decision support systems for child and adolescent psychiatric disorders, fulfilled inclusion criteria. Based on this systematic review, there appears to be a need for a new, readily available CDSS for child neuropsychiatric disorder which promotes evidence-based, best practices, while enabling consideration of national variation in practices by leveraging data-reuse to generate predictions regarding treatment outcome, addressing a broader cluster of clinical disorders, and targeting frontline practice environments.

Quality Requirements for Electronic Health Record Systems*. A Japanese-German Information Management Perspective

BACKGROUND

For more than 30 years, there has been close cooperation between Japanese and German scientists with regard to information systems in health care. Collaboration has been formalized by an agreement between the respective scientific associations. Following this agreement, two joint workshops took place to explore the similarities and differences of electronic health record systems (EHRS) against the background of the two national healthcare systems that share many commonalities.

OBJECTIVES

To establish a framework and requirements for the quality of EHRS that may also serve as a basis for comparing different EHRS.

METHODS

Donabedian's three dimensions of quality of medical care were adapted to the outcome, process, and structural quality of EHRS and their management. These quality dimensions were proposed before the first workshop of EHRS experts and enriched during the discussions.

RESULTS

The Quality Requirements Framework of EHRS (QRF-EHRS) was defined and complemented by requirements for high quality EHRS. The framework integrates three quality dimensions (outcome, process, and structural quality), three layers of information systems (processes and data, applications, and physical tools) and three dimensions of information management (strategic, tactical, and operational information management).

CONCLUSIONS

Describing and comparing the quality of EHRS is in fact a multidimensional problem as given by the QRF-EHRS framework. This framework will be utilized to compare Japanese and German EHRS, notably those that were presented at the second workshop.

Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development

Background

Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS.

Methods

We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified.

Results

Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient’s genotype.

Conclusions

Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0480-y) contains supplementary material, which is available to authorized users.

The experience of physicians in pharmacogenomic clinical decision support within eight German university hospitals

Aim: The aim of this study was to assess the physicians’ attitude, their knowledge and their experience in pharmacogenomic clinical decision support in German hospitals. Materials & methods: We conducted an online survey to address physicians of 13 different medical specialties across eight German university hospitals. In total, 564 returned questionnaires were analyzed. Results: The remaining knowledge gap, the uncertainty of test reimbursement and the physicians’ lack of awareness of existing pharmacogenomic clinical decision support systems (CDSS) are the major barriers for implementing pharmacogenomic CDSS into German hospitals. Furthermore, pharmacogenomic CDSS are most effective in the form of real-time decision support for internists. Conclusion: Physicians in German hospitals require additional education of both genetics and pharmacogenomics. They need to be provided with access to relevant pharmacogenomic CDSS.

Electronic Support for Retrospective Analysis in the Field of Radiation Oncology: Proof of Principle Using an Example of Fractionated Stereotactic Radiotherapy of 251 Meningioma Patients

Introduction

The purpose of this study is to verify the possible benefit of a clinical data warehouse (DWH) for retrospective analysis in the field of radiation oncology.

Material and methods

We manually and electronically (using DWH) evaluated demographic, radiotherapy, and outcome data from 251 meningioma patients, who were irradiated from January 2002 to January 2015 at the Department of Radiation Oncology of the Erlangen University Hospital. Furthermore, we linked the Oncology Information System (OIS) MOSAIQ^® to the DWH in order to gain access to irradiation data. We compared the manual and electronic data retrieval method in terms of congruence of data, corresponding time, and personal requirements (physician, physicist, scientific associate).

Results

The electronically supported data retrieval (DWH) showed an average of 93.9% correct data and significantly (p = 0.009) better result compared to manual data retrieval (91.2%). Utilizing a DWH enables the user to replace large amounts of manual activities (668 h), offers the ability to significantly reduce data collection time and labor demand (35 h), while simultaneously improving data quality. In our case, work time for manually data retrieval was 637 h for the scientific assistant, 26 h for the medical physicist, and 5 h for the physician (total 668 h).

Conclusion

Our study shows that a DWH is particularly useful for retrospective analysis in the radiation oncology field. Routine clinical data for a large patient group can be provided ready for analysis to the scientist and data collection time can be significantly reduced. Furthermore, linking multiple data sources in a DWH offers the ability to improve data quality for retrospective analysis, and future research can be simplified.

Implementation of Task-Tracking Software for Clinical IT Management

Often in clinical IT departments, many different methods and IT systems are used for task-tracking and project organization. Based on managers' personal preferences and knowledge about project management methods, tools differ from team to team and even from employee to employee. This causes communication problems, especially when tasks need to be done in cooperation with different teams. Monitoring tasks and resources becomes impossible: there are no defined deliverables, which prevents reliable deadlines. Because of these problems, we implemented task-tracking software which is now in use across all seven teams at the University Hospital Erlangen. Over a period of seven months, a working group defined types of tasks (project, routine task, etc.), workflows, and views to monitor the tasks of the 7 divisions, 20 teams and 340 different IT services. The software has been in use since December 2016.

On-The-Fly Query Translation Between i2b2 and Samply in the German Biobank Node (GBN) Prototypes

Information retrieval is a major challenge in medical informatics. Various research projects have worked on this task in recent years on an institutional level by developing tools to integrate and retrieve information. However, when it comes down to querying such data across institutions, the challenge persists due to the high heterogeneity of data and differences in software systems. The German Biobank Node (GBN) project faced this challenge when trying to interconnect four biobanks to enable distributed queries for biospecimens. All biobanks had already established integrated data repositories, and some of them were already part of research networks. Instead of developing another software platform, GBN decided to form a bridge between these. This paper describes and discusses a core component from the GBN project, the OmniQuery library, which was implemented to enable on-the-fly query translation between heterogeneous research infrastructures.

Machine Learning Models of Post-Intubation Hypoxia During General Anesthesia

Fine-meshed perioperative measurements are offering enormous potential for automatically investigating clinical complications during general anesthesia. In this study, we employed multiple machine learning methods to model perioperative hypoxia and compare their respective capabilities. After exporting and visualizing 620 series of perioperative vital signs, we had ten anesthesiologists annotate the subjective presence and severity of temporary post-intubation oxygen desaturation. We then applied specific clustering and prediction methods on the acquired annotations, and evaluated their performance in comparison to the inter-rater agreement between experts. When reproducing the expert annotations, the sensitivity and specificity of multi-layer neural networks substantially outperformed clustering and simpler threshold-based methods. The achieved performance of our best automated hypoxia models thereby approximately equaled the observed agreement between different medical experts. Furthermore, we deployed our classification methods for processing unlabeled inputs to estimate the incidence of hypoxic episodes in another sizeable patient cohort, which attests to the feasibility of using the approach on a larger scale. We interpret that our machine learning models could be instrumental for computerized observational studies of the clinical determinants of post-intubation oxygen deficiency. Future research might also investigate potential benefits of more advanced preprocessing approaches such as automated feature learning.

Requirements Analysis for a Clinical Decision Support System Aiming at Improving the Artificial Nutrition of Critically Ill Patients

BACKGROUND

Nutrition support is an important aspect regarding the care of critically ill patients. Malnutrition affects the recovery process negatively. However, the impact on the clinical outcome is often underestimated in complex clinical settings due to several factors hindering optimization of nutrition.

OBJECTIVE

To identify the requirements for a clinical decision support system that enables the medical staff to improve its patients' nutritional status.

METHODS

A literature review and interviews with two senior physicians were conducted to refine the requirements for the support system as well as to determine the inclusion criteria for a subsequent intervention study.

RESULTS

The analysis resulted in: (i) the identification of 4 measurement parameters for the assessment of the nutrition status; (ii) the graphical layout in adherence to the standards-based implementation approach for the creation of multi-patient dashboards; (iii) the definition of the study group. The nutrition dashboard will be implemented and integrated based on the set requirements, followed by an intervention study evaluating the dashboard's efficacy.

Proof-of-Concept Integration of Heterogeneous Biobank IT Infrastructures into a Hybrid Biobanking Network

Cross-institutional biobank networks hold the promise of supporting medicine by enabling the exchange of associated samples for research purposes. Various initiatives, such as BBMRI-ERIC and German Biobank Node (GBN), aim to interconnect biobanks for enabling the compilation of joint biomaterial collections. However, building software platforms to facilitate such collaboration is challenging due to the heterogeneity of existing biobank IT infrastructures and the necessary efforts for installing and maintaining additional software components. As a remedy, this paper presents the concept of a hybrid network for interconnecting already existing software components commonly found in biobanks and a proof-of-concept implementation of two prototypes involving four biobanks of the German Biobank Node. Here we demonstrate the successful bridging of two IT systems found in many German biobanks - Samply and i2b2.

Standards-Based Procedural Phenotyping: The Arden Syntax on i2b2

Phenotyping, or the identification of patient cohorts, is a recurring challenge in medical informatics. While there are open source tools such as i2b2 that address this problem by providing user-friendly querying interfaces, these platforms lack semantic expressiveness to model complex phenotyping algorithms. The Arden Syntax provides procedural programming language construct, designed specifically for medical decision support and knowledge transfer. In this work, we investigate how language constructs of the Arden Syntax can be used for generic phenotyping. We implemented a prototypical tool to integrate i2b2 with an open source Arden execution environment. To demonstrate the applicability of our approach, we used the tool together with an Arden-based phenotyping algorithm to derive statistics about ICU-acquired hypernatremia. Finally, we discuss how the combination of i2b2's user-friendly cohort pre-selection and Arden's procedural expressiveness could benefit phenotyping.

From a Content Delivery Portal to a Knowledge Management System for Standardized Cancer Documentation

Heterogeneous tumor documentation and its challenges of interpretation of medical terms lead to problems in analyses of data from clinical and epidemiological cancer registries. The objective of this project was to design, implement and improve a national content delivery portal for oncological terms. Data elements of existing handbooks and documentation sources were analyzed, combined and summarized by medical experts of different comprehensive cancer centers. Informatics experts created a generic data model based on an existing metadata repository. In order to establish a national knowledge management system for standardized cancer documentation, a prototypical tumor wiki was designed and implemented. Requirements engineering techniques were applied to optimize this platform. It is targeted to user groups such as documentation officers, physicians and patients. The linkage to other information sources like PubMed and MeSH was realized.

Supporting Molecular Tumor Boards in Molecular-Guided Decision-Making - The Current Status of Five German University Hospitals

BACKGROUND

German university hospitals have started to establish molecular tumor boards in order to enable physicians to make molecular-guided decisions.

OBJECTIVE

Our aim was to describe the organizational structure and procedures which are currently supporting the molecular tumor boards of five German university hospitals.

METHODS

We conducted semi-structured interviews with experts of five university hospitals between December 2016 and February 2017.

RESULTS

We observed heterogeneity in both the organization of genetic testing and the management of the molecular tumor boards among the five hospitals. They used free-text documents in most of their support procedures rather than machine-readable documents.

CONCLUSION

There are three potentialities to support the process from genetic testing to reporting within the molecular tumor boards: (i) standardized pipeline to integrate automated variant calling and annotation; (ii) tools supporting the experts in creating their reports and presentations and (iii) implementing pharmacogenomic CDSS into clinical routine.

Two Years of tranSMART in a University Hospital for Translational Research and Education

BACKGROUND

For translational research, software platforms such as tranSMART with an integrated view of both clinical and omics data have gained more and more attention in the last years.

OBJECTIVES

We wanted to examine the success and failures of tranSMART in the fields of translational research and education by looking at the examples of six use cases at our hospital. We wanted to point out suitable scenarios and user groups as well as still existing challenges and limitations.

METHODS

We sum up the experience we made with our use cases with a focus on lessons learned.

RESULTS

tranSMART was successfully established by a bottom-up approach at our university hospital and has been running for more than two years now. It has been used in four translational research projects as well as in education in the context of lectures and bachelor/master theses.

CONCLUSION

tranSMART can be a very useful tool for translational research and education. But it should be used with both care and statistical knowledge to avoid wrong conclusions. Some technical constraints, especially for data modeling, still limit many applications. Version control and data provenance are remaining challenges.

An Abstraction Layer to Facilitate Technical Interoperability Between Medical Records and Knowledge Modules

Integrating clinical decision support (CDS) functions into an existing hospital information system (HIS) is often a tedious task. This problem area is so pervasive that the Arden Syntax, a widely used standard for CDS functions, assigned a specific designation, the so-called "curly braces problem". It derives from a pair of curly braces used to encapsulate any parameters required for the interactions with a HIS. The traditional approach is to leave the problem area of technical interoperability entirely to the specific institution, possibly entailing a considerable amount of initial programming work. This study describes a reusable and expandable solution to this problem in the form of an abstraction layer. Our study comprised an analytical phase in which we investigated the data source access capabilities of five Arden Syntax environments. Building on the results, we implemented a working prototype that is capable of querying heterogeneous data sources, which facilitates a straightforward connection of new data sources with existing and future communication protocols and standards. From our point of view, the technical aspects of the "curly braces problem" with respect to data source access have changed over the years, insofar as technical progress lead to defacto standards for data storage, data querying and inter-system communication. An agreement on such a convention, together with the supply of commonly used data source adapters could promote the further dissemination of the Arden Syntax as a standard for representing and sharing medical knowledge.

Evaluation of an Interactive Visualization Tool for the Interpretation of Pediatric Laboratory Test Results

The physiological age-related development of pediatric laboratory results interferes with pathological derangements, which can complicate the interpretation of test results. Recently proposed continuous reference intervals (RIs) promise to be beneficial, although their clinical use may depend on graphical presentations. To estimate the clinical utility of continuous RIs, we developed and evaluated an interactive visualization tool, and examined the differentiation of hemoglobinopathies that is attainable based on the underlying innovative RI model. The implemented web application allows users to easily enter laboratory test results, and displays various visualizations in conjunction with the corresponding RIs, such as charts and personalized Z-scores. To evaluate the usability of the visualization tool, we conducted concurrent think-aloud sessions with four physicians, who were prompted to solve a set of typical interpretation tasks, and acquired additional information through a questionnaire including the System Usability Scale (SUS). We used 85 de-identified clinical cases for an exemplified assessment of how well model-based interpretations of blood count parameters reproduced previously diagnosed hemoglobinopathies. Usability tests as well as questionnaire responses indicated that the developed tool was well received by the physicians. Results from the think-aloud evaluation revealed only minor problems and the tool reached an average SUS score of 86.9, suggesting good usability. Hemoglobinopathy discrimination depended on the considered subtype, although the overall performance of the novel method rivaled the one of the conventional approach. The interactive visualization of innovative continuous reference intervals demonstrated promising results, which justifies further testing on the path towards clinical routine.

Optimizing R with SparkR on a commodity cluster for biomedical research

BACKGROUND AND OBJECTIVES

Medical researchers are challenged today by the enormous amount of data collected in healthcare. Analysis methods such as genome-wide association studies (GWAS) are often computationally intensive and thus require enormous resources to be performed in a reasonable amount of time. While dedicated clusters and public clouds may deliver the desired performance, their use requires upfront financial efforts or anonymous data, which is often not possible for preliminary or occasional tasks. We explored the possibilities to build a private, flexible cluster for processing scripts in R based on commodity, non-dedicated hardware of our department.

METHODS

For this, a GWAS-calculation in R on a single desktop computer, a Message Passing Interface (MPI)-cluster, and a SparkR-cluster were compared with regards to the performance, scalability, quality, and simplicity.

RESULTS

The original script had a projected runtime of three years on a single desktop computer. Optimizing the script in R already yielded a significant reduction in computing time (2 weeks). By using R-MPI and SparkR, we were able to parallelize the computation and reduce the time to less than three hours (2.6 h) on already available, standard office computers. While MPI is a proven approach in high-performance clusters, it requires rather static, dedicated nodes. SparkR and its Hadoop siblings allow for a dynamic, elastic environment with automated failure handling. SparkR also scales better with the number of nodes in the cluster than MPI due to optimized data communication.

CONCLUSION

R is a popular environment for clinical data analysis. The new SparkR solution offers elastic resources and allows supporting big data analysis using R even on non-dedicated resources with minimal change to the original code. To unleash the full potential, additional efforts should be invested to customize and improve the algorithms, especially with regards to data distribution.

Development of a Standardized Rating Tool for Drug Alerts to Reduce Information Overload

BACKGROUND

A well-known problem in current clinical decision support systems (CDSS) is the high number of alerts, which are often medically incorrect or irrelevant. This may lead to the so-called alert fatigue, an overriding of alerts, including those that are clinically relevant, and underuse of CDSS in general.

OBJECTIVES

The aim of our study was to develop and to apply a standardized tool that allows its users to evaluate the quality of system-generated drug alerts. The users' ratings can subsequently be used to derive recommendations for developing a filter function to reduce irrelevant alerts.

METHODS

We developed a rating tool for drug alerts and performed a web-based evaluation study that also included a user review of alerts. In this study the following categories were evaluated: "data linked correctly", "medically correct", "action required", "medication change", "critical alert", "information gained" and "show again". For this purpose, 20 anonymized clinical cases were randomly selected and displayed in our customized CDSS research prototype, which used the summary of product characteristics (SPC) for alert generation. All the alerts that were provided were evaluated by 13 physicians. The users' ratings were used to derive a filtering algorithm to reduce overalerting.

RESULTS

In total, our CDSS research prototype generated 399 alerts. In 98 % of all alerts, medication data were rated as linked correctly to drug information; in 93 %, the alerts were assessed as "medically correct"; 19.5 % of all alerts were rated as "show again". The interrater-agreement was, on average, 68.4 %. After the application of our filtering algorithm, the rate of alerts that should be shown again decreased to 14.8 %.

CONCLUSIONS

The new standardized rating tool supports a standardized feedback of user-perceived clinical relevance of CDSS alerts. Overall, the results indicated that physicians may consider the majority of alerts formally correct but clinically irrelevant and override them. Filtering may help to reduce overalerting and increase the specificity of a CDSS.

Concept and implementation of a study dashboard module for a continuous monitoring of trial recruitment and documentation

BACKGROUND

The difficulty of managing patient recruitment and documentation for clinical trials prompts a demand for instruments for closely monitoring these critical but unpredictable processes. Increasingly adopted Electronic Data Capture (EDC) applications provide novel opportunities to reutilize stored information for an efficient management of traceable trial workflows. In related clinical and administrative settings, so-called digital dashboards that continuously visualize time-dependent parameters have recently met a growing acceptance. To investigate the technical feasibility of a study dashboard for monitoring the progress of patient recruitment and trial documentation, we set out to develop a propositional prototype in the form of a separate software module.

METHODS

After narrowing down functional requirements in semi-structured interviews with study coordinators, we analyzed available interfaces of a locally deployed EDC application, and designed the prototypical study dashboard based on previous findings. The module thereby leveraged a standardized export format in order to extract and import relevant trial data into a clinical data warehouse. Web-based reporting tools then facilitated the definition of diverse views, including diagrams of the progress of patient accrual and form completion at different granularity levels. To estimate the utility of the dashboard and its compatibility with current workflows, we interviewed study coordinators after a demonstration of sample outputs from ongoing trials.

RESULTS

The employed tools promoted a rapid development. Displays of the implemented dashboard are organized around an entry page that integrates key metrics for available studies, and which links to more detailed information such as study-specific enrollment per center. The interviewed experts commented that the included graphical summaries appeared suitable for detecting that something was generally amiss, although practical remedies would mostly depend on additional information such as access to the original patient-specific data. The dependency on a separate application was seen as a downside. Interestingly, the prospective users warned that in some situations knowledge of specific accrual statistics might undermine blinding in a subtle yet intricate fashion, so ignorance of certain patient features was seen as sometimes preferable for reproducibility.

DISCUSSION

Our proposed study dashboard graphically recaps key progress indicators of patient accrual and trial documentation. The modular implementation illustrates the technical feasibility of the approach. The use of a study dashboard might introduce certain technical requirements as well as subtle interpretative complexities, which may have to be weighed against potential efficiency gains.

Strategien zur Vernetzung von Biobanken

Hintergrund

Nicht selten benötigt ein medizinisches Forschungsvorhaben mehr biologisches Material, als in einer einzigen Biobank verfügbar ist. Daher unterstützt eine Vielzahl von Strategien das Auffinden potentieller Forschungspartner mit passenden Proben, auch ohne dass diese zuvor in einer zentralisierten Sammlung zusammengeführt werden müssen.

Ziel

Der vorliegende Beitrag beschreibt die Klassifizierung verschiedener Strategien zur Vernetzung von Biomaterialbanken, speziell zur Probensuche, sowie eine IT-Infrastruktur, die diese Ansätze kombiniert.

Material und Methoden

Bestehende Strategien lassen sich nach drei Kriterien klassifizieren: a) Granularität der Probendaten: grobe Daten auf Bankebene (Katalog) vs. feingranulare Daten auf Probenebene, b) Speicherort der Probendaten: zentrale (zentraler Suchdienst) vs. dezentrale Datenhaltung (föderierte Suchdienste) und c) Automatisierungsgrad: automatisch (abfragebasiert, föderierter Suchdienst) vs. halbautomatisch (anfragebasiert, dezentrale Suche). Alle genannten Suchdienste setzen eine Datenintegration voraus; dabei helfen Metadaten bei der Überwindung semantischer Heterogenität.

Ergebnisse

Der „Common Service IT“ in BBMRI-ERIC („Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium“) vereint einen Katalog, die dezentrale Suche und Metadaten in einer integrierten Plattform, um Forschern vielseitige Werkzeuge zur Suche nach passendem Probenmaterial zu geben und bei den Biobankern gleichzeitig ein hohes Maß an Datenhoheit zu bewahren.

Diskussion

Trotz ihrer Unterschiede schließen sich die vorgestellten Strategien zur Vernetzung von Biomaterialbanken gegenseitig nicht aus. Vielmehr lassen sie sich in gemeinsamen Forschungsinfrastrukturen sinnvoll ergänzen und sie können sogar voneinander profitieren.

Using Arden Syntax for the Generation of Intelligent Intensive Care Discharge Letters

Discharge letters are an important means of communication between physicians and nurses from intensive care units and their colleagues from normal wards. The patient data management system (PDMS) used at our local intensive care units provides an export tool to create discharge letters by inserting data items from electronic medical records into predefined templates. Local intensivists criticized the limitations of this tool regarding the identification and the further processing of clinically relevant data items for a flexible creation of discharge letters. As our PDMS supports Arden Syntax, and the demanded functionalities are well within the scope of this standard, we set out to investigate the suitability of Arden Syntax for the generation of discharge letters. To provide an easy-to-understand facility for integrating data items into document templates, we created an Arden Syntax interface function which replaces the names of previously defined variables with their content in a way that permits arbitrary custom formatting by clinical users. Our approach facilitates the creation of flexible text sections by conditional statements, as well as the integration of arbitrary HTML code and dynamically generated graphs. The resulting prototype enables clinical users to apply the full set of Arden Syntax language constructs to identify and process relevant data items in a way that far exceeds the capabilities of the PDMS export tool. The generation of discharge letters is an uncommon area of application for Arden Syntax, considerably differing from its original purpose. However, we found our prototype well suited for this task and plan to evaluate it in clinical production after the next major release change of our PDMS.

Algorithmic Summaries of Perioperative Blood Pressure Fluctuations

Automated perioperative measurements such as cardiovascular monitoring data are commonly compared to established upper and lower thresholds, but could also allow for more complex interpretations. Analyzing such time series in extensive electronic medical records for research purposes may itself require customized automation, so we developed a set of algorithms for quantifying different aspects of temporal fluctuations. We implemented conventional measures of dispersion, summaries of absolute gradients between successive values, and Poincaré plots. We aggregated the severity and duration of hypotensive episodes by calculating the average area under different mean arterial pressure (MAP) thresholds. We applied these methods to 30,452 de-identified MAP series, and analyzed the similarity between alternative indices via hierarchical clustering. To explore the potential utility of these propositional metrics, we computed their statistical association with presumed complications due to cardiovascular instability. We observed that hierarchical clustering reliably segregated features that had been designed to quantify dissimilar aspects. Summaries of temporary hypotension turned out to be significantly increased among patient subgroups with subsequent signs of a complicated recovery. These associations were even stronger for measures that were specifically geared to capturing short-term MAP variability. These observations suggest the potential capability of our proposed algorithms for quantifying heterogeneous aspects of short-term MAP fluctuations. Future research might also target a wider selection of outcomes and other attributes that may be subject to intraoperative variability.

Evaluation of Acceptance of Nursing Information System in a German and American Hospital

Nursing Information Systems (NIS) are not well-adopted and accepted in Germany. The evaluation of a NIS deployment in a German University Hospital supports this assumption. A second side study in the US should point out the differences regarding the technical and organizational differences. We use a questionnaire including standardized instruments like the Unified Theory of Acceptance (UTAUT). Results indicated that nurses in Germany compared to in the US do not use nursing process documentation to the same extent. The main reasons behind the low usage in comparison with the US are deficits in ease-of-use, system performance and the high expenditure of time and paper work for charting nursing plans.

Analysis and classification of oncology activities on the way to workflow based single source documentation in clinical information systems

BACKGROUND

Today, cancer documentation is still a tedious task involving many different information systems even within a single institution and it is rarely supported by appropriate documentation workflows.

METHODS

In a comprehensive 14 step analysis we compiled diagnostic and therapeutic pathways for 13 cancer entities using a mixed approach of document analysis, workflow analysis, expert interviews, workflow modelling and feedback loops. These pathways were stepwise classified and categorized to create a final set of grouped pathways and workflows including electronic documentation forms.

RESULTS

A total of 73 workflows for the 13 entities based on 82 paper documentation forms additionally to computer based documentation systems were compiled in a 724 page document comprising 130 figures, 94 tables and 23 tumour classifications as well as 12 follow-up tables. Stepwise classification made it possible to derive grouped diagnostic and therapeutic pathways for the three major classes - solid entities with surgical therapy - solid entities with surgical and additional therapeutic activities and - non-solid entities. For these classes it was possible to deduct common documentation workflows to support workflow-guided single-source documentation.

CONCLUSIONS

Clinical documentation activities within a Comprehensive Cancer Center can likely be realized in a set of three documentation workflows with conditional branching in a modern workflow supporting clinical information system.

A scoping review of cloud computing in healthcare

BACKGROUND

Cloud computing is a recent and fast growing area of development in healthcare. Ubiquitous, on-demand access to virtually endless resources in combination with a pay-per-use model allow for new ways of developing, delivering and using services. Cloud computing is often used in an "OMICS-context", e.g. for computing in genomics, proteomics and molecular medicine, while other field of application still seem to be underrepresented. Thus, the objective of this scoping review was to identify the current state and hot topics in research on cloud computing in healthcare beyond this traditional domain.

METHODS

MEDLINE was searched in July 2013 and in December 2014 for publications containing the terms "cloud computing" and "cloud-based". Each journal and conference article was categorized and summarized independently by two researchers who consolidated their findings.

RESULTS

102 publications have been analyzed and 6 main topics have been found: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Commonly used features are broad network access for sharing and accessing data and rapid elasticity to dynamically adapt to computing demands. Eight articles favor the pay-for-use characteristics of cloud-based services avoiding upfront investments. Nevertheless, while 22 articles present very general potentials of cloud computing in the medical domain and 66 articles describe conceptual or prototypic projects, only 14 articles report from successful implementations. Further, in many articles cloud computing is seen as an analogy to internet-/web-based data sharing and the characteristics of the particular cloud computing approach are unfortunately not really illustrated.

CONCLUSIONS

Even though cloud computing in healthcare is of growing interest only few successful implementations yet exist and many papers just use the term "cloud" synonymously for "using virtual machines" or "web-based" with no described benefit of the cloud paradigm. The biggest threat to the adoption in the healthcare domain is caused by involving external cloud partners: many issues of data safety and security are still to be solved. Until then, cloud computing is favored more for singular, individual features such as elasticity, pay-per-use and broad network access, rather than as cloud paradigm on its own.

Proposal for a European Public Health Research Infrastructure for Sharing of health and Medical administrative data (PHRIMA)

In Europe, health and medical administrative data is increasingly accumulating on a national level. Looking further than re-use of this data on a national level, sharing health and medical administrative data would enable large-scale analyses and European-level public health projects. There is currently no research infrastructure for this type of sharing. The PHRIMA consortium proposes to realise the Public Health Research Infrastructure for Sharing of health and Medical Administrative data (PHRIMA) which will enable and facilitate the efficient and secure sharing of healthcare data.

Comparison of electronic health record system functionalities to support the patient recruitment process in clinical trials

OBJECTIVES

Reusing data from electronic health records for clinical and translational research and especially for patient recruitment has been tackled in a broader manner since about a decade. Most projects found in the literature however focus on standalone systems and proprietary implementations at one particular institution often for only one singular trial and no generic evaluation of EHR systems for their applicability to support the patient recruitment process does yet exist. Thus we sought to assess whether the current generation of EHR systems in Germany provides modules/tools, which can readily be applied for IT-supported patient recruitment scenarios.

METHODS

We first analysed the EHR portfolio implemented at German University Hospitals and then selected 5 sites with five different EHR implementations covering all major commercial systems applied in German University Hospitals. Further, major functionalities required for patient recruitment support have been defined and the five sample EHRs and their standard tools have been compared to the major functionalities.

RESULTS

In our analysis of the site's hospital information system environments (with four commercial EHR systems and one self-developed system) we found that - even though no dedicated module for patient recruitment has been provided - most EHR products comprise generic tools such as workflow engines, querying capabilities, report generators and direct SQL-based database access which can be applied as query modules, screening lists and notification components for patient recruitment support. A major limitation of all current EHR products however is that they provide no dedicated data structures and functionalities for implementing and maintaining a local trial registry.

CONCLUSIONS

At the five sites with standard EHR tools the typical functionalities of the patient recruitment process could be mostly implemented. However, no EHR component is yet directly dedicated to support research requirements such as patient recruitment. We recommend for future developments that EHR customers and vendors focus much more on the provision of dedicated patient recruitment modules.

Employing computers for the recruitment into clinical trials: a comprehensive systematic review

BACKGROUND

Medical progress depends on the evaluation of new diagnostic and therapeutic interventions within clinical trials. Clinical trial recruitment support systems (CTRSS) aim to improve the recruitment process in terms of effectiveness and efficiency.

OBJECTIVE

The goals were to (1) create an overview of all CTRSS reported until the end of 2013, (2) find and describe similarities in design, (3) theorize on the reasons for different approaches, and (4) examine whether projects were able to illustrate the impact of CTRSS.

METHODS

We searched PubMed titles, abstracts, and keywords for terms related to CTRSS research. Query results were classified according to clinical context, workflow integration, knowledge and data sources, reasoning algorithm, and outcome.

RESULTS

A total of 101 papers on 79 different systems were found. Most lacked details in one or more categories. There were 3 different CTRSS that dominated: (1) systems for the retrospective identification of trial participants based on existing clinical data, typically through Structured Query Language (SQL) queries on relational databases, (2) systems that monitored the appearance of a key event of an existing health information technology component in which the occurrence of the event caused a comprehensive eligibility test for a patient or was directly communicated to the researcher, and (3) independent systems that required a user to enter patient data into an interface to trigger an eligibility assessment. Although the treating physician was required to act for the patient in older systems, it is now becoming increasingly popular to offer this possibility directly to the patient.

CONCLUSIONS

Many CTRSS are designed to fit the existing infrastructure of a clinical care provider or the particularities of a trial. We conclude that the success of a CTRSS depends more on its successful workflow integration than on sophisticated reasoning and data processing algorithms. Furthermore, some of the most recent literature suggest that an increase in recruited patients and improvements in recruitment efficiency can be expected, although the former will depend on the error rate of the recruitment process being replaced. Finally, to increase the quality of future CTRSS reports, we propose a checklist of items that should be included.

Medication safety and knowledge-based functions: a stepwise approach against information overload

AIMS

The aim was to improve medication safety in an emergency department (ED) by enhancing the integration and presentation of safety information for drug therapy.

METHODS

Based on an evaluation of safety of drug therapy issues in the ED and a review of computer-assisted intervention technologies we redesigned an electronic case sheet and implemented computer-assisted interventions into the routine work flow. We devised a four step system of alerts, and facilitated access to different levels of drug information. System use was analyzed over a period of 6 months. In addition, physicians answered a survey based on the technology acceptance model TAM2.

RESULTS

The new application was implemented in an informal manner to avoid work flow disruption. Log files demonstrated that step I, 'valid indication' was utilized for 3% of the recorded drugs and step II 'tooltip for well-known drug risks' for 48% of the drugs. In the questionnaire, the computer-assisted interventions were rated better than previous paper based measures (checklists, posters) with regard to usefulness, support of work and information quality.

CONCLUSION

A stepwise assisting intervention received positive user acceptance. Some intervention steps have been seldom used, others quite often. We think that we were able to avoid over-alerting and work flow intrusion in a critical ED environment.

E-pharmacovigilance: development and implementation of a computable knowledge base to identify adverse drug reactions

AIMS

Computer-assisted signal generation is an important issue for the prevention of adverse drug reactions (ADRs). However, due to poor standardization of patients' medical data and a lack of computable medical drug knowledge the specificity of computerized decision support systems for early ADR detection is too low and thus those systems are not yet implemented in daily clinical practice. We report on a method to formalize knowledge about ADRs based on the Summary of Product Characteristics (SmPCs) and linking them with structured patient data to generate safety signals automatically and with high sensitivity and specificity.

METHODS

A computable ADR knowledge base (ADR-KB) that inherently contains standardized concepts for ADRs (WHO-ART), drugs (ATC) and laboratory test results (LOINC) was built. The system was evaluated in study populations of paediatric and internal medicine inpatients.

RESULTS

A total of 262 different ADR concepts related to laboratory findings were linked to 212 LOINC terms. The ADR knowledge base was retrospectively applied to a study population of 970 admissions (474 internal and 496 paediatric patients), who underwent intensive ADR surveillance. The specificity increased from 7% without ADR-KB up to 73% in internal patients and from 19.6% up to 91% in paediatric inpatients, respectively.

CONCLUSIONS

This study shows that contextual linkage of patients' medication data with laboratory test results is a useful and reasonable instrument for computer-assisted ADR detection and a valuable step towards a systematic drug safety process. The system enables automated detection of ADRs during clinical practice with a quality close to intensive chart review.

Design and multicentric implementation of a generic software architecture for patient recruitment systems re-using existing HIS tools and routine patient data

OBJECTIVE

(1) To define features and data items of a Patient Recruitment System (PRS); (2) to design a generic software architecture of such a system covering the requirements; (3) to identify implementation options available within different Hospital Information System (HIS) environments; (4) to implement five PRS following the architecture and utilizing the implementation options as proof of concept.

METHODS

Existing PRS were reviewed and interviews with users and developers conducted. All reported PRS features were collected and prioritized according to their published success and user's request. Common feature sets were combined into software modules of a generic software architecture. Data items to process and transfer were identified for each of the modules. Each site collected implementation options available within their respective HIS environment for each module, provided a prototypical implementation based on available implementation possibilities and supported the patient recruitment of a clinical trial as a proof of concept.

RESULTS

24 commonly reported and requested features of a PRS were identified, 13 of them prioritized as being mandatory. A UML version 2 based software architecture containing 5 software modules covering these features was developed. 13 data item groups processed by the modules, thus required to be available electronically, have been identified. Several implementation options could be identified for each module, most of them being available at multiple sites. Utilizing available tools, a PRS could be implemented in each of the five participating German university hospitals.

CONCLUSION

A set of required features and data items of a PRS has been described for the first time. The software architecture covers all features in a clear, well-defined way. The variety of implementation options and the prototypes show that it is possible to implement the given architecture in different HIS environments, thus enabling more sites to successfully support patient recruitment in clinical trials.

A reference architecture for semantic interoperability and its practical application

OBJECTIVE

Reusing EPR data for secondary purposes often requires mapping to classifications and vocabularies such as ICD, LOINC or NCI thesaurus. We aimed for a common architecture which supports the use of different vocabularies and mapping tools.

METHODS

We integrated the components clinical data warehouse, vocabulary resources and mapping tools with the EPR and client applications.

RESULTS

In two projects we used this architecture to map laboratory parameters from the LIS to LOINC, and to map clinical data elements from the Soarian EPR to the cancer registry system using the NCI-Thesaurus®.

CONCLUSION

The approach was successful in both projects. The reference architecture does not resolve the mapping task, but provides reusable integration links between the different components and thus facilitates further mapping activities.

The effect object paradigm--a means to support medication safety with clinical decision support

BACKGROUND

In many countries, officially approved drug information known as summary of product characteristics (SPC) is mostly available in text form, which cannot be used for Clinical Decision Support Systems (CDSS). It may be essential however to substantiate CDSS advice with such legally binding text snippets. In an attempt to link various drug data sources including SPC towards a CDSS to support medication safety in psychiatric patients we arrived at the notion of an effect object.

METHODS

A requirements analysis revealed data items and data structure which are needed from the patient and from the drug information source for the CDSS functionality. Published drug data modelling approaches were analyzed and found unsuitable. A conceptional database modeling approach using top down and bottom up modeling was performed.

RESULTS

The schema based data model implemented within the django framework centered on SPC "effect objects" which comprise all SPC data required for the respective CDSS function such as search for contraindications in the proposed medication. Today six effect objects have been defined for contraindications and warnings, missing indications, adverse effects, drug-drug interactions, dosing and pharmacokinetics.

CONCLUSION

The transformation of SPC data to a database-driven "effect objects" structure permits decoupling between the CDSS functions and different underlying data sources and supports the design of reusable, stable and verified CDSS functions.

Prospective Assessment of an innovative test for prostate cancer screening using the VITA process model framework

Healthcare innovations are crucial for enhancing patient treatment and a high-quality healthcare system. However, bringing new technologies, methods and procedures into the healthcare market is challenging. Enormous amounts of financial, personnel and organizational resources are required with no upfront certainty for the medical and economic benefit. A new and innovative approach uses interdisciplinary medical, technical and economic expertise to forecast effects of healthcare innovations already at the early research and concept phase of an idea and before major investments are made. A process model framework was developed to operationalize this structured assessment of healthcare innovations. The Visionary Iterative Tailored Approach (VITA) is based on conceptual modeling, simulation and health economics evaluation. Its application for the prospective assessment of an innovative prostate cancer screening is presented.

Case-based visualization of a patient cohort using SEER epidemiologic data

Data from cancer registries can be used to track the epidemiology of cancer and can potentially serve to guide individual diagnostic and treatment decisions. Even though some cancer registry datasets have been made publicly available for scientific and clinical use, few applications have so far provided direct access to these data from within the patient context of an electronic patient record. The goal of this project was to implement a proof-of-concept integration of the public SEER (Surveillance, Epidemiology and End Results) cancer registry dataset with a digital breast cancer tumor board at a German university hospital and to determine its utility in the clinical settings. The integration was successfully established, using data from routine documentation to provide dynamic visualizations of cohort composition and Kaplan-Meier survival plots. Evaluation feedback was favorable regarding the concept and implementation, but highlighted that important data elements, e.g. receptor status data, were missing in the SEER dataset, limiting clinical value of the system.

Identifying barriers for implementation of computer based nursing documentation

This study was undertaken in the planning phase for the introduction of a comprehensive computer based nursing documentation system at Erlangen University Hospital. There, we expect a wide range of difficult organizational changes, because the nurses currently neither used computer based nursing documentation nor did they follow strongly the nursing process model within paper based documentation. Thus we were eager to recognize potential pitfalls early and to identify potential barriers for digital nursing documentation. In a questionnaire study we surveyed all German university hospitals for their experience with the implementation of computer based nursing documentation implementation. We received answers from 11 of the 23 hospitals. Furthermore we performed a questionnaire study about expectations and fears among the nurses of four pilot wards of our hospital. Most respondents stated a positive attitude towards the nursing process documentation, but many respondents note technical (e.g. bad performance of the software) and organizational barriers (e.g. lack of time).

Employing heat maps to mine associations in structured routine care data

OBJECTIVE

Mining the electronic medical record (EMR) has the potential to deliver new medical knowledge about causal effects, which are hidden in statistical associations between different patient attributes. It is our goal to detect such causal mechanisms within current research projects which include e.g. the detection of determinants of imminent ICU readmission. An iterative statistical approach to examine each set of considered attribute pairs delivers potential answers but is difficult to interpret. Therefore, we aimed to improve the interpretation of the resulting matrices by the use of heat maps. We propose strategies to adapt heat maps for the search for associations and causal effects within routine EMR data.

METHODS

Heat maps visualize tabulated metric datasets as grid-like choropleth maps, and thus present measures of association between numerous attribute pairs clearly arranged. Basic assumptions about plausible exposures and outcomes are used to allocate distinct attribute sets to both matrix dimensions. The image then avoids certain redundant graphical elements and provides a clearer picture of the supposed associations. Specific color schemes have been chosen to incorporate preexisting information about similarities between attributes. The use of measures of association as a clustering input has been taken as a trigger to apply transformations which ensure that distance metrics always assume finite values and treat positive and negative associations in the same way. To evaluate the general capability of the approach, we conducted analyses of simulated datasets and assessed diagnostic and procedural codes in a large routine care dataset.

RESULTS

Simulation results demonstrate that the proposed clustering procedure rearranges attributes similar to simulated statistical associations. Thus, heat maps are an excellent tool to indicate whether associations concern the same attributes or different ones, and whether affected attribute sets conform to any preexisting relationship between attributes. The dendrograms help in deciding if contiguous sequences of attributes effectively correspond to homogeneous attribute associations. The exemplary analysis of a routine care dataset revealed patterns of associations that follow plausible medical constellations for several diseases and the associated medical procedures and activities. Cases with breast cancer (ICD C50), for example, appeared to be associated with radiation therapy (8-52). In cross check, approximately 60 percent of the attribute pairs in this dataset showed a strong negative association, which can be explained by diseases treated in a medical specialty which routinely does not perform the respective procedures in these cases. The corresponding diagram clearly reflects these relationships in the shape of coherent subareas.

CONCLUSION

We could demonstrate that heat maps of measures of association are effective for the visualization of patterns in routine care EMRs. The adjustable method for the assignment of attributes to image dimensions permits a balance between the display of ample information and a favorable level of graphical complexity. The scope of the search can be adapted by the use of pre-existing assumptions about plausible effects to select exposure and outcome attributes. Thus, the proposed method promises to simplify the detection of undiscovered causal effects within routine EMR data.

Evaluating predictive modeling algorithms to assess patient eligibility for clinical trials from routine data

BACKGROUND

The necessity to translate eligibility criteria from free text into decision rules that are compatible with data from the electronic health record (EHR) constitutes the main challenge when developing and deploying clinical trial recruitment support systems. Recruitment decisions based on case-based reasoning, i.e. using past cases rather than explicit rules, could dispense with the need for translating eligibility criteria and could also be implemented largely independently from the terminology of the EHR's database. We evaluated the feasibility of predictive modeling to assess the eligibility of patients for clinical trials and report on a prototype's performance for different system configurations.

METHODS

The prototype worked by using existing basic patient data of manually assessed eligible and ineligible patients to induce prediction models. Performance was measured retrospectively for three clinical trials by plotting receiver operating characteristic curves and comparing the area under the curve (ROC-AUC) for different prediction algorithms, different sizes of the learning set and different numbers and aggregation levels of the patient attributes.

RESULTS

Random forests were generally among the best performing models with a maximum ROC-AUC of 0.81 (CI: 0.72-0.88) for trial A, 0.96 (CI: 0.95-0.97) for trial B and 0.99 (CI: 0.98-0.99) for trial C. The full potential of this algorithm was reached after learning from approximately 200 manually screened patients (eligible and ineligible). Neither block- nor category-level aggregation of diagnosis and procedure codes influenced the algorithms' performance substantially.

CONCLUSIONS

Our results indicate that predictive modeling is a feasible approach to support patient recruitment into clinical trials. Its major advantages over the commonly applied rule-based systems are its independency from the concrete representation of eligibility criteria and EHR data and its potential for automation.

[Implementation of a patient data management system. Effects on intensive care documentation]

BACKGROUND

Patient data management systems (PDMS) enable digital documentation on intensive care units (ICU). A commercial PDMS was implemented in a 25-bed ICU replacing paper-based patient charting. The ICU electronic patient record is completely managed inside the PDMS. It compiles data from vital signs monitors, ventilators and further medical devices and facilitates some drug dose and fluid balance calculations as well as data reuse for administrative purposes. Ventilation time and patient severity scoring as well as coding of diagnoses and procedures is supported. Billing data transferred via interface to the central billing system of the hospital. Such benefits should show in measurable parameters, such as documented ventilator time, number of coded diagnoses and procedures and others. These parameters influence reimbursement in the German DRG system. Therefore, measurable changes in cost and reimbursement data of the ICU were expected.

MATERIAL AND METHODS

A retrospective analysis of documentation quality parameters, cost data and mortality rate of a 25-bed surgical ICU within a German university hospital 3 years before (2004-2006) and 5 years after (2007-2011) PDMS implementation. Selected parameters were documented electronically, consistently and reproducibly for the complete time span of 8 years including those years where no electronic patient recording was available. The following parameters were included: number of cleared DRG, cleared ventilator time, case mix (CM), case mix index (CMI), length of stay, number of coded diagnoses and procedures, detailed overview of a specific procedure code based on daily Apache II and TISS Core 10 scores, mortality, total ICU costs and revenues and partial profits for specific ICU procedures, such as renal replacement therapy and blood products.

RESULTS

Systematic shifts were detected over the study period, such as increasing case numbers and decreasing length of stay as well as annual fluctuations in severity of disease seen in the CM and CMI. After PDMS introduction, the total number of coded diagnoses increased but the proportion of DRG relevant diagnoses dropped significantly. The number of procedures increased (not significantly) and the number of procedures per case did not rise significantly. The procedure 8-980 showed a significant increase after PDMS introduction whereas the DRG-relevant proportion of those procedures dropped insignificantly. The number of ventilator-associated DRG cases as well as the total ventilator time increased but not significantly. Costs and revenues increased slightly but profit varied considerably from year to year in the 5 years after system implementation. A small increase was observed per case, per nursing day and per case mix point. Additional revenues for specific ICU procedures increased in the years before and dropped after PDMS implementation. There was an insignificant increase in ICU mortality rate from 7.4 % in the year 2006 (before) to 8.5 % in 2007 (after PDMS implementation). In the following years mortality dropped below the base level.

CONCLUSION

The implementation of the PDMS showed only small effects on documentation of reimbursement-relevant parameters which were too small to set off against the total investment. The method itself, a long-term follow-up of different parameters proved successful and can be adapted by other organizations. The quality of results depends on the availability of long-term parameters in good quality. No significant influence of PDMS on mortality was found.

Does introduction of a Patient Data Management System (PDMS) improve the financial situation of an intensive care unit?

BACKGROUND

Patient Data Management Systems (PDMS) support clinical documentation at the bedside and have demonstrated effects on completeness of patient charting and the time spent on documentation. These systems are costly and raise the question if such a major investment pays off. We tried to answer the following questions: How do costs and revenues of an intensive care unit develop before and after introduction of a PDMS? Can higher revenues be obtained with improved PDMS documentation? Can we present cost savings attributable to the PDMS?

METHODS

Retrospective analysis of cost and reimbursement data of a 25 bed Intensive Care Unit at a German University Hospital, three years before (2004-2006) and three years after (2007-2009) PDMS implementation.

RESULTS

Costs and revenues increased continuously over the years. The profit of the investigated ICU was fluctuating over the years and seemingly depending on other factors as well. We found a small increase in profit in the year after the introduction of the PDMS, but not in the following years. Profit per case peaked at 1039 € in 2007, but dropped subsequently to 639 € per case. We found no clear evidence for cost savings after the PDMS introduction. Our cautious calculation did not consider additional labour costs for IT staff needed for system maintenance.

CONCLUSIONS

The introduction of a PDMS has probably minimal or no effect on reimbursement. In our case the observed increase in profit was too small to amortize the total investment for PDMS implementation.This may add some counterweight to the literature, where expectations for tools such as the PDMS can be quite unreasonable.

Comparative evaluation of different medication safety measures for the emergency department: physicians' usage and acceptance of training, poster, checklist and computerized decision support

Background

Although usage and acceptance are important factors for a successful implementation of clinical decision support systems for medication, most studies only concentrate on their design and outcome. Our objective was to comparatively investigate a set of traditional medication safety measures such as medication safety training for physicians, paper-based posters and checklists concerning potential medication problems versus the additional benefit of a computer-assisted medication check. We concentrated on usage, acceptance and suitability of such interventions in a busy emergency department (ED) of a 749 bed acute tertiary care hospital.

Methods

A retrospective, qualitative evaluation study was conducted using a field observation and a questionnaire-based survey. Six physicians were observed while treating 20 patient cases; the questionnaire, based on the Technology Acceptance Model 2 (TAM2), has been answered by nine ED physicians.

Results

During field observations, we did not observe direct use of any of the implemented interventions for medication safety (paper-based and electronic). Questionnaire results indicated that the electronic medication safety check was the most frequently used intervention, followed by checklist and posters. However, despite their positive attitude, physicians most often stated that they use the interventions in only up to ten percent for subjectively “critical” orders. Main reasons behind the low usage were deficits in ease-of-use and fit to the workflow. The intention to use the interventions was rather high after overcoming these barriers.

Conclusions

Methodologically, the study contributes to Technology Acceptance Model (TAM) research in an ED setting and confirms TAM2 as a helpful diagnostic tool in identifying barriers for a successful implementation of medication safety interventions. In our case, identified barriers explaining the low utilization of the implemented medication safety interventions - despite their positive reception - include deficits in accessibility, briefing for the physicians about the interventions, ease-of-use and compatibility to the working environment.

Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence

BACKGROUND

Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials.

METHODS

Each participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group.

RESULTS

351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were 'age' (89%), 'gender' (89%), 'addictive behaviour' (74%), 'disease, symptom and sign' (64%) and 'organ or tissue status' (61%). No data was available for 6 semantic groups.

CONCLUSIONS

There exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data.

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

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