[Development of competencies in the Medical Informatics Initiative (MII)-educational offers for a competent and secure handling of medical data]. / Kompetenzentwicklung in der Medizininformatik-Initiative (MII) ­ Lehrangebote für einen souveränen und sicheren Umgang mit medizinischen Daten.

In order to achieve the goals of the Medical Informatics Initiative (MII), staff with skills in the field of medical informatics and data science are required. Each consortium has established training activities. Further, cross-consortium activities have emerged. This article describes the concepts, implemented programs, and experiences in the consortia. Fifty-one new professorships have been established and 10 new study programs have been created: 1 bachelor's degree and 6 consecutive and 3 part-time master's degree programs. Further, learning and training opportunities can be used by all MII partners. Certification and recognition opportunities have been created.The educational offers are aimed at target groups with a background in computer science, medicine, nursing, bioinformatics, biology, natural science, and data science. Additional qualifications for physicians in computer science and computer scientists in medicine seem to be particularly important. They can lead to higher quality in software development and better support for treatment processes by application systems.Digital learning methods were important in all consortia. They offer flexibility for cross-location and interprofessional training. This enables learning at an individual pace and an exchange between professional groups.The success of the MII depends largely on society's acceptance of the multiple use of medical data in both healthcare and research. The information required for this is provided by the MII's public relations work. There is also an enormous need in society for medical and digital literacy.

How to design effective educational videos for teaching evidence-based medicine to undergraduate learners - systematic review with complementing qualitative research to develop a practicable guide.

BACKGROUND: eLearning can be an effective tool to achieve learning objectives. It facilitates asynchronous distance learning, increasing flexibility for learners and instructors. In this context, the high educational value of videos provides an invaluable primary component for longitudinal digital curricula, especially for maintaining knowledge on otherwise rarely taught subjects. Although literature concerning eLearning evaluation exists, research comprehensively describing how to design effective educational videos is lacking. In particular, studies on the requirements and design goals of educational videos need to be complemented by qualitative research using grounded theory methodology. METHODS: Due to the paucity of randomized controlled trials in this area, there is an urgent need to generate recommendations based on a broader fundament than a literature search alone. Thus, the authors have employed grounded theory as a guiding framework, augmented by Mayring's qualitative content analysis and commonly used standards. An adaptive approach was conducted based on a literature search and qualitative semi-structured interviews. Drawing on these results, the authors elaborated a guide for creating effective educational videos. RESULTS: The authors identified 40 effective or presumedly effective factors fostering the success of video-based eLearning in teaching evidence-based medicine, providing a ready-to-use checklist. The information collected via the interviews supported and enriched much of the advice found in the literature. DISCUSSION: To the authors' knowledge, this type of comprehensive guide for video-based eLearning needs has not previously been published. The interviews considerably contributed to the results. Due to the grounded theory-based approach, in particular, consensus was achieved without the presence of a formal expert panel. Although the guide was created with a focus on teaching evidence-based medicine, due to the general study selection process and research approach, the recommendations are applicable to a wide range of subjects in medical education where the teaching aim is to impart conceptual knowledge.

Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept.

BACKGROUND: Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training. OBJECTIVES: The focus is the development and presentation of a data management approach specifically tailored for clinical researchers involved in active patient care, especially in the neuroscientific environment of German university hospitals. Our design considers the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the secure handling of sensitive data in compliance with the General Data Protection Regulation. METHODS: We introduce a streamlined database concept, featuring an intuitive graphical interface built on Hypertext Markup Language revision 5 (HTML5)/Cascading Style Sheets (CSS) technology. The system can be effortlessly deployed within local networks, that is, in Microsoft Windows 10 environments. Our design incorporates FAIR principles for effective data management. Moreover, we have streamlined data interchange through established standards like HL7 Clinical Document Architecture (CDA). To ensure data integrity, we have integrated real-time validation mechanisms that cover data type, plausibility, and Clinical Quality Language logic during data import and entry. RESULTS: We have developed and evaluated our concept with clinicians using a sample dataset of subjects who visited our memory clinic over a 3-year period and collected several multimodal clinical parameters. A notable advantage is the unified data matrix, which simplifies data aggregation, anonymization, and export. THIS STREAMLINES DATA EXCHANGE AND ENHANCES DATABASE INTEGRATION WITH PLATFORMS LIKE KONSTANZ INFORMATION MINER (KNIME): . CONCLUSION: Our approach offers a significant advancement for capturing and managing clinical research data, specifically tailored for small-scale initiatives operating within limited information technology (IT) infrastructures. It is designed for immediate, hassle-free deployment by clinicians and researchers.The database template and precompiled versions of the user interface are available at: https://github.com/stebro01/research_database_sqlite_i2b2.git.

TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research.

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary. This allows for secure and controlled data exchange, while offering flexible datasharing options, considering trustworthiness, risk tolerance, and healthcare interoperability. Empirical studies and participatory research will be conducted to develop a trustworthy and effective data trust model.

On the Effective Dissemination and Use of Learning Objectives Catalogs for Health Information Curricula Development.

Catalogs of competency-based learning objectives (CLO) were introduced and promoted as a prerequisite for high-quality, systematic curriculum development. While this is common in medicine, the consistent use of CLO is not yet well established in epidemiology, biometry, medical informatics, biomedical informatics, and nursing informatics especially in Germany. This paper aims to identify underlying obstacles and give recommendations in order to promote the dissemination of CLO for curricular development in health data and information sciences. To determine these obstacles and recommendations a public online expert workshop was organized. This paper summarizes the findings.

Caution with competitive gamification in medical education: unexpected results of a randomised cross-over study.

BACKGROUND: To intrinsically motivate students in the long term, longitudinal e-learning systems combined with repeated testing and competitive gamification seem promising. The effects of this approach have never been closely examined in the field of evidence-based medicine. The authors investigated if a simple, competitive learning application enhances students' risk competence and intrinsic motivation. METHODS: Participants were 5.-9. semester medical students (n = 48), recruited in an elective evidence-based medicine subject and randomly distributed to two groups (group 1: n = 23; group 2: n = 25). Both accessed a competitive evidence-based medicine quiz game. Following a cross-over design, each group practiced with one of two thematically different questionnaires A or B, before the allocation switched after one month. To analyse whether there was a measurable learning effect in the practiced topics, a paired t-test was performed with quantitative data from 3 e-tests. Students further reported their experience in evaluation surveys. RESULTS: Students' improvements in e-test scores after training with the corresponding topics in the learning application can be attributed to chance. Even though the majority enjoyed playing and felt motivated to study, they invested a minimum of time and rejected competition. CONCLUSION: The authors found no evidence for benefits of the investigated learning programme on students' risk competence or on their internal motivation. The majority disapproved the competitive concept, indicating adverse side effects of the applied gamification element. To intrinsically motivate more students, prospective learning programmes should favour complex and collaborative programmes over simple and competitive ones.

Medical multivariate time series imputation and forecasting based on a recurrent conditional Wasserstein GAN and attention.

OBJECTIVE: In the fields of medical care and research as well as hospital management, time series are an important part of the overall data basis. To ensure high quality standards and enable suitable decisions, tools for precise and generic imputations and forecasts that integrate the temporal dynamics are of great importance. Since forecasting and imputation tasks involve an inherent uncertainty, the focus of our work lay on a probabilistic multivariate generative approach that samples infillings or forecasts from an analysable distribution rather than producing deterministic results. MATERIALS AND METHODS: For this task, we developed a system based on generative adversarial networks that consist of recurrent encoders and decoders with attention mechanisms and can learn the distribution of intervals from multivariate time series conditioned on the periods before and, if available, periods after the values that are to be predicted. For training, validation and testing, a data set of jointly measured blood pressure series (ABP) and electrocardiograms (ECG) (length: 1,250=ˆ10s) was generated. For the imputation tasks, one interval of fixed length was masked randomly and independently in both channels of every sample. For the forecasting task, all masks were positioned at the end. RESULTS: The models were trained on around 65,000 bivariate samples and tested against 14,000 series of different persons. For the evaluation, 50 samples were produced for every masked interval to estimate the range of the generated infillings or forecasts. The element-wise arithmetic average of these samples served as an estimator for the mean of the learned conditional distribution. The approach showed better results than a state-of-the-art probabilistic multivariate forecasting mechanism based on Gaussian copula transformation and recurrent neural networks. On the imputation task, the proposed method reached a mean squared error (MSE) of 0.057 on the ECG channel and an MSE of 28.30 on the ABP channel, while the baseline approach reached MSEs of 0.095 (ECG) and 229.1 (ABP). Moreover, on the forecasting task, the presented system achieved MSEs of 0.069 (ECG) and 33.73 (ABP), outperforming the recurrent copula approach, which reached MSEs of 0.082 (ECG) and 196.53 (ABP). CONCLUSION: The presented generative probabilistic system for the imputation and forecasting of (medical) time series features the flexibility to handle masks of different sizes and positions, the ability to quantify uncertainty due to its probabilistic predictions, and an adjustable trade-off between the goals of minimising errors in individual predictions and minimising the distance between the learned and the real conditional distribution of the infillings or forecasts.

Federated electronic data capture (fEDC): Architecture and prototype.

In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR). However, when researchers from different institutions collaborate in collecting data, they often use a single joint eCRF system on the Internet. In this case, integration with EHR systems is not possible in most cases due to information security and data protection restrictions. To overcome this shortcoming, we propose a novel architecture for a federated electronic data capture system (fEDC). Four key requirements were identified for fEDC: Definitions of forms have to be available in a reliable and controlled fashion, integration with electronic health record systems must be possible, patient data should be under full local control until they are explicitly transferred for joint analysis, and the system must support data sharing principles accepted by the scientific community for both data model and data captured. With our approach, sites participating in a joint study can run their own instance of an fEDC system that complies with local standards (such as being behind a network firewall) while also being able to benefit from using identical form definitions by sharing metadata in the Operational Data Model (ODM) format published by the Clinical Data Interchange Standards Consortium (CDISC) throughout the collaboration. The fEDC architecture was validated with a working open-source prototype at five German university hospitals. The fEDC architecture provides a novel approach with the potential to significantly improve collaborative data capture: Efforts for data entry are reduced and at the same time, data quality is increased since barriers for integrating with local electronic health record systems are lowered. Further, metadata are shared and patient privacy is ensured at a high level.

System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software Implementation.

BACKGROUND: Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model. OBJECTIVE: Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient's current treatment context. METHODS: We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine. RESULTS: We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians. CONCLUSIONS: The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.

Generative adversarial networks for biomedical time series forecasting and imputation.

In the present systematic review we identified and summarised current research activities in the field of time series forecasting and imputation with the help of generative adversarial networks (GANs). We differentiate between imputation which describes the filling of missing values at intermediate steps and forecasting defining the prediction of future values. Especially the utilisation of such methods in the biomedical domain was to be investigated. To this end, 1057 publications were identified with the help of PubMed, Web of Science and Scopus. All studies that describe the use of GANs for the imputation/forecasting of time series were included irrespective of the application domain. Finally, 33 records were identified as eligible and grouped according to the topologies, losses, inputs and outputs of the presented GANs. In combination with a summary of all described application domains, this grouping served as a basis for analysing the peculiarities of the method in the biomedical context. Due to the broad spectrum of biomedical research, nearly all recognised methodologies are also applied in this domain. We could not identify any approach that proved itself superior in the biomedical area. Although GANs were initially designed to work in the image domain, many publications show that they are capable of imputing/forecasting non-visual time series.

Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural Networks: Modeling Study.

BACKGROUND: Platelets are a valuable and perishable blood product. Managing platelet inventory is a demanding task because of short shelf lives and high variation in daily platelet use patterns. Predicting platelet demand is a promising step toward avoiding obsolescence and shortages and ensuring optimal care. OBJECTIVE: The aim of this study is to forecast platelet demand for a given hospital using both a statistical model and a deep neural network. In addition, we aim to calculate the possible reduction in waste and shortage of platelets using said predictions in a retrospective simulation of the platelet inventory. METHODS: Predictions of daily platelet demand were made by a least absolute shrinkage and selection operator (LASSO) model and a recurrent neural network (RNN) with long short-term memory (LSTM). Both models used the same set of 81 clinical features. Predictions were passed to a simulation of the blood inventory to calculate the possible reduction in waste and shortage as compared with historical data. RESULTS: From January 1, 2008, to December 31, 2018, the waste and shortage rates for platelets were 10.1% and 6.5%, respectively. In simulations of platelet inventory, waste could be lowered to 4.9% with the LASSO and 5% with the RNN, whereas shortages were 2.1% and 1.7% with the LASSO and RNN, respectively. Daily predictions of platelet demand for the next 2 days had mean absolute percent errors of 25.5% (95% CI 24.6%-26.6%) with the LASSO and 26.3% (95% CI 25.3%-27.4%) with the LSTM (P=.01). Predictions for the next 4 days had mean absolute percent errors of 18.1% (95% CI 17.6%-18.6%) with the LASSO and 19.2% (95% CI 18.6%-19.8%) with the LSTM (P<.001). CONCLUSIONS: Both models allow for predictions of platelet demand with similar and sufficient accuracy to significantly reduce waste and shortage in a retrospective simulation study. The possible improvements in platelet inventory management are roughly equivalent to US $250,000 per year.

Using machine learning to estimate the calendar age based on autonomic cardiovascular function.

Introduction: Aging is accompanied by physiological changes in cardiovascular regulation that can be evaluated using a variety of metrics. In this study, we employ machine learning on autonomic cardiovascular indices in order to estimate participants' age. Methods: We analyzed a database including resting state electrocardiogram and continuous blood pressure recordings of healthy volunteers. A total of 884 data sets met the inclusion criteria. Data of 72 other participants with an BMI indicating obesity (>30 kg/m²) were withheld as an evaluation sample. For all participants, 29 different cardiovascular indices were calculated including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics. Based on cardiovascular indices, sex and device, four different approaches were applied in order to estimate the calendar age of healthy subjects, i.e., relevance vector regression (RVR), Gaussian process regression (GPR), support vector regression (SVR), and linear regression (LR). To estimate age in the obese group, we drew normal-weight controls from the large sample to build a training set and a validation set that had an age distribution similar to the obesity test sample. Results: In a five-fold cross validation scheme, we found the GPR model to be suited best to estimate calendar age, with a correlation of r=0.81 and a mean absolute error of MAE=5.6 years. In men, the error (MAE=5.4 years) seemed to be lower than that in women (MAE=6.0 years). In comparison to normal-weight subjects, GPR and SVR significantly overestimated the age of obese participants compared with controls. The highest age gap indicated advanced cardiovascular aging by 5.7 years in obese participants. Discussion: In conclusion, machine learning can be used to estimate age on cardiovascular function in a healthy population when considering previous models of biological aging. The estimated age might serve as a comprehensive and readily interpretable marker of cardiovascular function. Whether it is a useful risk predictor should be investigated in future studies.

Effects of mobile learning on writing panoramic radiograph reports: a quasi-experimental trial in dental education.

BACKGROUND: In dentistry, the reporting of panoramic radiographs is particularly challenging, as many structures are depicted in one image and pathologies need to be identified completely. To enhance the learning process for these interpretations, the advantages of the increasingly popular education method of mobile learning could be used. Therefore, this study aimed to determine the effectiveness of learning to report panoramic radiographs using an application (app) on a mobile device. METHODS: The existing e-learning programme 'PantoDict' was further developed into a mobile app with a new training section. Participants of a dental radiology course were divided into two groups, one of which additionally had the chance to practise reporting panoramic radiographs using the app. A test to assess the knowledge gained was conducted at the end of the semester; the course and the app were also evaluated. RESULTS: The group that used the app showed significantly better results in the test than the control group (p < 0.05). Although the app group approved a high satisfaction using the app as an additional supplement to the course, this did not result in a higher overall satisfaction with the course. Further, these students observed that the traditional face-to-face seminar could not be replaced by the app. CONCLUSION: By using the PantoDict app, students were offered better training options for writing reports on panoramic radiographs, which resulted in significantly better test results than the results of the control group. Therefore, the mobile app is a useful supplement to classical education formats within the context of a blended learning approach.

GAN-Based Prediction of Time Series.

The study aims at generating initial and directional insights in the applicability of conditional recurrent generative adversarial nets for the imputation and forecasting of medical time series data. Our experiment with blood pressure series showed that a generative recurrent autoencoder exhibits significant individual learning progress but needs further tuning to benefit from joint training.

Health Informatics Learning Objectives on an Interoperable, Collaborative Platform.

Catalogues of learning objectives for Biomedical and Health Informatics are relevant prerequisites for systematic and effective qualification. Catalogue management needs to integrate different catalogues and support collaborative revisioning. The Health Informatics Learning Objectives Navigator (HI-LONa) offers an open, interoperable platform based on Semantic Web Technology. At present HI-LONa contains 983 learning objectives of three relevant catalogues. HI-LONa successfully supported a multiprofessional consensus process.

Semantic Anomaly Detection in Medical Time Series.

The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity of the heart (ECG). Denoising autoencoders based on recurrent neural networks with gated recurrent units were used for the semantic encoding of such time frames. A subsequent cluster analysis conducted in the code space served as the decision mechanism labelling samples as anomalies or normal intervals, respectively. The cluster ensemble method called cluster-based similarity partitioning proved itself well suited for this task when used in combination with density-based spatial clustering of applications with noise. The best performing system reached an adjusted Rand index of 0.11 on real-world ECG signals labelled by medical experts. This corresponds to a precision and recall regarding the detection task of around 0.72. The new general approach outperformed several state-of-the-art outlier recognition methods and can be applied to all kinds of (medical) time series data. It can serve as a basis for more specific detectors that work in an unsupervised fashion or that are partially guided by medical experts.

An innovative PantoDict program for reporting panoramic radiographs using automatic speech recognition in dental education: a randomized observer-blinded study.

OBJECTIVE: The objective of this study was to assess the effect of an e-learning program including automatic speech recognition on outcomes assessment in interpreting panoramic radiographs at a dental school. STUDY DESIGN: For instruction in reporting findings on panoramic radiographs, 36 participants were divided randomly into 3 seminar groups. Group A used the new PantoDict digital e-learning program for training. Group B used both PantoDict and conventional face-to-face classroom instruction. Group C used conventional instruction only. After attending 3 seminars, all students completed an examination on reporting a panoramic radiograph and evaluated the course. RESULTS: Both groups using PantoDict (groups A and B) had significantly higher examination scores than the conventional group (P ≤ .002). However, students in group C were more likely than those in group A to agree that their knowledge and confidence improved following the seminars. Students in group A would have preferred an instructor for the first seminar. The evaluation confirmed that students were satisfied with the e-module regarding usability and didactics. Most students indicated that they would like to use PantoDict all the time. CONCLUSIONS: The e-learning program with automatic speech recognition is a useful device for completing radiology reports and can be used as a complementary tool in face-to-face teaching.

360-degree Delphi: addressing sociotechnical challenges of healthcare IT.

BACKGROUND: IT systems in the healthcare field can have a marked sociotechnical impact: they modify communication habits, alter clinical processes and may have serious ethical implications. The introduction of such systems involves very different groups of stakeholders because of the inherent multi-professionalism in medicine and the role of patients and their relatives that are often underrepresented. Each group contributes distinct perspectives and particular needs, which create specific requirements for IT systems and may strongly influence their acceptance and success. In the past, needs analysis, challenges and requirements for medical IT systems have often been addressed using consensus techniques such as the Delphi technique. Facing the heterogeneous spectrum of stakeholders there is a need to develop these techniques further to control the (strong) influence of the composition of the expert panel on the outcome and to deal systematically with potentially incompatible needs of stakeholder groups. This approach uses the strong advantages a Delphi study has, identifies the disadvantages of traditional Delphi techniques and aims to introduce and evaluate a modified approach called 360-Degree Delphi. Key aspects of 360-Degree Delphi are tested by applying the approach to the needs and requirements analysis of a system for managing patients' advance directives and living wills. METHODS: 360-Degree Delphi (short 360°D), as a modified Delphi process, is specified as a structured workflow with the optional use of stakeholder groups. The approach redefines the composition of the expert panel by setting up groups of different stakeholders. Consensus is created within individual stakeholder groups, but is also communicated between groups, while the iterative structure of the Delphi process remains unchanged. We hypothesize that (1) 360-Degree Delphi yields complementary statements from different stakeholders, which would be lost in classical Delphi; while (2) the variation of statements within individual stakeholder groups is lower than within the total collective. A user study is performed that addresses five stakeholder groups (patients, relatives, medical doctors, nurses and software developers) on the topic of living will communication in an emergency context. Qualitative open questions are used in a Delphi round 0. Answer texts are coded by independent raters who carry out systematic bottom-up qualitative text analysis. Inter-rater reliability is calculated and the resulting codes are used to test the hypotheses. Qualitative results are transferred into quantitative questions and then surveyed in round 1. The study took place in Germany. RESULTS: About 25% of the invited experts (stakeholders) agreed to take part in the Delphi round 0 (three patients, two relatives, three medical doctors, two qualified nurses and three developers), forming a structured panel of the five stakeholder groups. Two raters created a bottom-up coding, and 238 thematic codes were identified by the qualitative text analysis. The inter-rater reliability showed that 44.95% of the codes were semantically similar and coded for the same parts of the raw textual replies. Based on a consented coding list, a quantitative online-questionnaire was developed and send to different stakeholder groups. With respect to the hypotheses, Delphi round 0 had the following results: (1) doctors had a completely different focus from all the other stakeholder groups on possible channels of communications with the patient; (2) the dispersion of codes within individual stakeholder groups and within the total collective - visualized by box plots - was approximately 28% higher in the total collective than in the sub-collectives, but without a marked effect size. With respect to the hypotheses, Delphi round 1 had the following results: different stakeholder groups had highly diverging opinions with respect to central questions on IT-development. For example, when asked to rate the importance of access control against high availability of data (likert scale, 1 meaning restrictive data access, 6 easy access to all data), patients (mean 4.862, Stdev +/- 1.866) and caregivers (mean 5.667, Stdev: +/- 0.816) highly favored data availability, while relatives would restrict data access (mean 2.778, stdev +/- 1.093). In comparison, the total group would not be representative of either of these individual stakeholder needs (mean 4.344, stdev +/- 1.870). CONCLUSION: 360-Degree Delphi is feasible and allows different stakeholder groups within an expert panel to reach agreement individually. Thus, it generates a more detailed consensus which pays more tribute to individual stakeholders needs. This has the potential to improve the time to consensus as well as to produce a more representative and precise needs and requirements analysis. However, the method may create new challenges for the IT development process, which will have to deal with complementary or even contradictory statements from different stakeholder groups.