System Dynamics is a powerful approach to understanding the behaviour of complex systems. This thesis is focused on a simulation model, that goes from principles of System Dynamics and the model will solve the problem of insufficient number of applicants that meet a specific set of project requirements, which are defined by the government agency, CzechInvest. The project, CzechEkoSystem, is mainly focused on efficient development of small and medium sized enterprises based on their pioneering business model. The thesis begins by describing the basic theories of System Dynamics that are introduced and elaborated on, within the particular tools of the simulation model problem statement. Then key terms about the CzechEkoSystem project are introduced. In the conclusion, results and recommendations for increasing the portfolio of pioneering small and medium sized enterprises are presented.

Medication is an essential part of health care and enables the prevention andtreatment of many conditions. However, medication errors and drug-relatedproblems (DRP) are frequent and cause suffering for patients and substantial costsfor society. eMedication, defined as information technology (IT) in themedication management process, has the potential to increase quality, efficiencyand safety but can also cause new problems and risks.In this thesis, we have studied the employment of IT in different steps of themedication management process with a focus on the user's perspective. Sweden isone of the leading countries when it comes to ePrescribing, i.e. prescriptionstransferred and stored electronically. We found that ePrescribing is well acceptedand appreciated by pharmacists (Study I) and patients (Study II), but that therewas a need for improvement in several aspects. When the pharmacy market inSweden was re-regulated, four new dispensing systems were developed andimplemented. Soon after the implementation, we found weaknesses related toreliability, functionality, and usability, which could affect patient safety (StudyIII). In the last decade, several county councils in Sweden have implementedshared medication lists within the respective region. We found that physiciansperceived that a regionally shared medication list generally was more complete butoften not accurate (Study IV). Electronic expert support (EES) is a decisionsupport system which analyses patients´ electronically-stored prescriptions in orderto detect potential DRP, i.e. drug-drug interactions, therapy duplication, highdose, and inappropriate drugs for geriatric or pediatric patients. We found thatEES detected potential DRP in most patients with multi-dose drug dispensing inSweden (Study V), and that the majority of alerts were regarded as clinicallyrelevant (Study VI).For an improved eMedication, we need a holistic approach that combinestechnology, users, and organization in implementation and evaluation. The thesissuggests a need for improved sharing of information and support for decisionmaking, coordination, and education, as well as clarification of responsibilitiesamong involved actors in order to employ appropriate IT. We suggestcollaborative strategic work and that the relevant authorities establish guidelinesand requirements for IT in the medication management process. / Läkemedel förbättrar och förlänger livet för många och utgör en väsentlig del av dagens hälso- och sjukvård men om läkemedel tas i fel dos eller kombineras felaktigt med varandra kan behandlingen leda till en försämrad livskvalitet, sjukhusinläggningar och dödsfall. En del av dessa problem skulle kunna förebyggas med rätt information till rätt person vid rätt tidpunkt och i rätt form. Informationsteknik i läkemedelsprocessen har potentialen att öka kvalitet, effektivitet och säkerhet genom att göra information tillgänglig och användbar men kan också innebära problem och risker. Det är dock en stor utmaning att i läkemedelsprocessen föra in effektiva och användbara IT-system som stödjer och inte stör personalen inom sjukvård och på apotek, skyddar den känsliga informationen för obehöriga och dessutom fungerar tillsammans med andra system. Dagens IT-stöd i läkemedelsprocessen är otillräckliga. Till exempel saknar läkare, farmaceuter och patienter ofta tillgång på fullständig och korrekt information om en patients aktuella läkemedel; det händer att fel läkemedel blir utskrivet eller expedierat på apotek; och bristande eller långsamma system skapar frustration hos användarna. Dessutom är det flera delar av läkemedelsprocessen som fortfarande är pappersbaserade. Därför är det viktigt att utvärdera IT-system i läkemedelsprocessen. Vi har studerat IT i olika delar av läkemedelsprocessen, före eller efter införandet, framför allt utifrån användarnas perspektiv. Sverige har lång erfarenhet och tillhör de ledande länderna i världen när det gäller eRecept, det vill säga recept som skickas och lagras elektroniskt. I två studier fann vi att eRecept är väl accepterat och uppskattat av farmaceuter (Studie I) och patienter (Studie II), men att det finns behov av förbättringar. När apoteksmarknaden omreglerades 2009 infördes fyra nya receptexpeditionssystem på apoteken. Vi fann att det efter införandet uppstod problem med användbarhet, tillförlitlighet och funktionalitet som kan ha inneburit en risk för patientsäkerheten (Studie III). I Sverige har man inom flera sjukvårdsregioner infört gemensamma elektroniska läkemedelslistor. I en av studierna kunde vi visa att detta har inneburit en ökad tillgänglighet av information, men att en gemensam lista inte alltid blir mer korrekt och kan innebära en ökad risk att känslig information nås av obehöriga (Studie IV). I två av studierna undersöktes beslutsstödssystemet elektroniskt expertstöd (EES):s potential som stöd för läkare att upptäcka läkemedelsrelaterade problem till exempel om en patient har två olika läkemedel som inte passar ihop, eller ett läkemedel som kanske är olämpligt för en äldre person. Studierna visade att EES gav signaler för potentiella problem hos de flesta patienter med dosdispenserade läkemedel i Sverige (Studie V), och läkarna ansåg att majoriteten av signalerna är kliniskt relevanta och att några av signalerna kan leda till förändringar i läkemedelsbehandlingen (Studie VI). Sammantaget visar avhandlingen att IT-stöd har blivit en naturlig och nödvändig del i läkemedelsprocessen i Sverige men att flera problem är olösta. Vi fann svagheter med användbarhet, tillförlitlighet och funktionalitet i de använda IT-systemen. Patienterna är inte tillräckligt informerade och delaktiga i sin läkemedelsbehandling. Läkare och farmaceuter saknar fullständig och korrekt information om patienters läkemedel, och de har i dagsläget inte tillräckliga beslutsstöd för att förebygga läkemedelsrelaterade problem. Eftersom läkemedelsprocessen är komplex med många aspekter som påverkar utfall behöver vi ett helhetstänkande när vi planerar, utvecklar, implementerar och utvärderar IT-lösningar där vi väger in både tekniska, sociala och organisatoriska aspekter. Avhandlingens resultat visar på ett behov av ökad koordination och utbildning samt förtydligande av ansvaret för inblandade aktörer. Vi föreslår gemensamt strategiskt arbete och att inblandade myndigheter tar fram vägledning och krav för IT i läkemedelsprocessen.

This work has its focus on decision support in operational business situations and especially on the very short-term decisions in Online Surgery Scheduling, which has the goal of efficient and structured operations in the Operating Room area at minimal costs. This use case includes all intra-day decisions needed to ensure the execution of all planned and unplanned surgeries of the surgery schedule, with all of the concomitant uncertainties like unexpected events, delays, cancellations and emergency patients. This so far barely considered problem needs research for decision support, since few approaches are available that relieve the OR manager through tool support and reduce the informational, communicational and cognitive workloads needed to ensure efficient and seamless operations. With the strong growth of generated data and the digitization of business processes that make previously unobtrusive business elements become more visible, and their combination with large-scale data processing technologies and intelligent methods of the fields of AI or Analytics, new opportunities for data-driven real-time Decision Support Systems become evident. The objective of this research is the development of an approach that supports the operational decision processes in Operating Room Management and Online Surgery Scheduling, like facilitating the information collection and reducing the cognitive effort for decision-making by providing predictive information or alternative actions. In order to achieve this goal, a decision support approach is developed that utilizes streaming data of medical and surgical devices in a Situation Detection Subsystem, a Prediction Subsystem and a Rescheduling Subsystem. These components combine intelligent methods and scalable data processing technologies, consequently contributing a data-driven Decision Support System for Online Surgery Scheduling. The scientific contribution relates to the field of Business and Decision Analytics with its main challenges of increasing complexity and dynamics of today’s business decisions. This work provides a novel DSS approach, innovative models and concepts which consider exactly these problems with regards to the characteristics of OSS.:Table of Contents ................................ I List of Figures .................................. III List of Tables ................................... IV List of Abbrevations ............................. V 1 Introduction.................................... 1 1.1 Motivation ................................... 1 1.2 Research Objective and Questions ............. 2 1.3 Research Methodology ......................... 4 1.4 Outline ...................................... 7 2 Background ..................................... 9 2.1 Operational Decisions and Decision Support Systems .................................. 9 2.1.1 Decisions and Decision-making .............. 9 2.1.2 Operational Decision-making ................ 11 2.1.3 Decision Support Systems ................... 13 2.1.4 Business Value and Benefits ................ 18 2.2 Business Analytics ........................... 19 2.2.1 Characterization and Definition ............ 19 2.2.2 Delimitation of Areas ...................... 20 2.2.3 Types of Business Analytics ................ 21 2.2.4 Methods and Technologies ................... 23 3 Use-Case: Operational Decisions in Operating Room Management .................................. 29 3.1 Preliminary Considerations ................... 29 3.2 Online Surgery Scheduling .................... 31 3.2.1 Mapping of Decision Theory and Online Surgery Scheduling ............................... 32 3.2.2 Information Demands ........................ 33 3.2.3 State of the Art in Decision Support Systems 36 4 Motivation and Requirements .................... 38 4.1 Development of an Information System Architecture for Online Surgery Scheduling ....... 38 4.2 Summary ...................................... 49 5 Evaluation of Big Data Processing Frameworks.... 51 5.1 Evaluating new Approaches of Big Data Analytics Frameworks ............................. 51 5.2 Summary ...................................... 63 6 Stream Processing for Intra-surgical Phase Detection ........................................ 64 6.1 Method for Intra-surgical Phase Detection by Using Real-time Medical Device Data .............. 64 6.2 Summary ...................................... 71 7 Real-time Predictive Analytics in Operating Room Management ....................................... 72 7.1 A Big Data Architecture for Intra-surgical Remaining Time Predictions ....................... 72 7.2 Summary ...................................... 81 8 Data-driven Online Surgery Rescheduling ........ 83 8.1 Online Surgery Rescheduling - A Data-driven Approach for Real-time Decision Support .......... 83 8.2 Summary ...................................... 92 9 Prototypical Implementation: Decision Support in Online Surgery Scheduling ........................ 93 9.1 Implementation of a Situation Aware and Real-time Approach for Decision Support in Online Surgery Scheduling ............................... 93 9.2 Summary ...................................... 99 10 Conclusion .................................... 100 10.1 Summary and Contributions ................... 100 10.2 Limitations and Future Work ................. 103 Bibliography ..................................... VII Appendix ......................................... XXV Wissenschaftlicher Werdegang ..................... XXXI Selbständigkeitserklärung ........................ XXXII

In today’s constantly evolving technological environment, businesses have more tools to support decision-making and these can be categorized as Decision Support Systems (DSS). One of the tools is Business Intelligence (BI), which is regarded as a high-priority investment in organizations nowadays. Even though there exists a vast amount of research in the DSS area, most of the influential work is conducted in time incomparable to today’s technological environment. In addition, most of the research focuses on profit-seeking organizations, as BI has been regarded as a tool to increase profits. However, non-profit organizations also use BI, but are not portrayed in the BI research area. The aim with this study is to explore how BI is used in relation to decision-making in a non-profit organization and to investigate the crucial factors in the usage of BI in relation to decision-making. A qualitative case study approach is applied where the Swedish non-profit organization Systembolaget AB is the case company. The main findings indicate that interaction between the two decision-making types is needed when using BI in a non-profit context. Moreover, having data literacy, data reliability, and data accessibility is found crucial in order to achieve BI success in relation to decision-making, especially when more and more decisions are made at the operational level. Finally, the results of this study amplify the need for an update in the DSS framework.

With the help of appropriate IT support, health care services can be executed in a more effective and secure way. In Sweden, the NPÖ (National Patients’ Översikt) stands for National Patients’ Overview. It is a platform where authorized health care providers can access comprehensive and continuous information about health care and patients’ situation, based on which care providers can offer safe and qualified services. The NPÖ project is focusing on the information sharing phase. In order to improve the efficiency and correctness of care services, the next step is that health care systems can offer clinical suggestions and warnings with the existing patients’ data and medication information. Clinical Decision Support Systems (CDSSs) are aimed to offer such assistance and are necessary to be integrated. But by now, there is no explicit architecture to guide Swedish government to implement the integration. Although some architectures have been proposed for integrating CDSSs in health information systems, those architectures are developed for certain use cases and cannot be adopted directly in NPÖ. An integration architecture which takes full consideration of NPÖ-adopting data types, message structures and interface types is needed. This thesis adopts constructive research method, which contains three main phases. First, related backgrounds about national electronic health care system, clinical decision supports system and integration techniques are introduced. Second, the integration architecture is constructed following service-oriented principles. Third, theoretical valuation work is finished by assessing system features and making interviews. This thesis takes advantage of service-oriented architecture to design an architecture with Clinical Decision Support (CDS) middleware for health care information system integration. With this structure, national electronic health care systems, such as NPÖ, can have interaction with various types of CDSSs to provide more efficient and secure health care. It offers united interfaces which enable different CDSSs with different developing platforms to communicate without obstacles. Unlike the existing CDSS integration architectures, the new one with CDS Middleware can provide maximized scalability. Evaluation work has been done from two aspects. Feature criteria and interviews with national health care system developers indicate that the architecture can contribute to the development of NPÖ, and future works such as involving security agents can be continued to optimize the results.

Treatment decision-making in head and neck oncology is gaining complexity by the increasing evidence pointing towards more individualized and selective treatment options. Therefore, decision making in multidisciplinary teams is becoming the key point in the clinical pathways. Clinical decision-support systems based on Bayesian networks can support complex decision-making processes by providing mathematically correct and transparent advises. In the last three decades, different clinical applications of Bayesian networks have been proposed. Because appropriate data for model learning and testing is often unobtainable, expert modeling is required. To decrease the modeling and validation effort, networks usually represent small or highly simplified decision structures. However, especially systems for supporting multidisciplinary treatment decisions may only gain a user’s confidence if the systems’ results are comprehensive and comprehensible. Challenges in developing such systems relate to knowledge engineering, model validation, system interaction, clinical implementation and standardization. These challenges are well-known, however, they are not or only partially addressed by the developers. The thesis presented a methodology for the development of Bayesian network-based clinical treatment decision support systems. For this purpose, a concept introduced interactions between actors and systems. The proposed concept emphasizes model development with an exemplary use case of model interaction. A graph model design was presented that allows integrating all relevant variables of multidisciplinary treatment decisions. At the current stage, we developed TreLynCa: A graph model representing the treatment decisions of laryngeal cancer. From TreLynCa, a subnetwork that represents the TNM staging is completed by the required probabilistic parameters, and finally validated. The model validation required the development of a validation cycle in combination with existing data- and expert-based validation methods. Furthermore, modeling methods were developed that enable domain experts to model autonomously without Bayesian network expertise. Specifically, a novel graph modeling method was developed, and an existing method for modeling probabilistic parameters was extended. Both methods transform Bayesian network modeling tasks into a natural language form and provide a regulated modeling environment. A method for graph modeling is based on the presented graph model design with a regulated and restricted modeling procedure. This modeling procedure is supposed to enable collaborative modeling of compatible models. The method is currently under development. A method for probabilistic modeling is extended to reduce the modeling effort to a linear time. The method has been implemented as a web tool and was tested and evaluated in two studies. Finally, for clinical application of the TNM model, requirements were collected and constructed in a visual framework. In collaboration with visual scientists, the framework has been implemented and evaluated.

Product quality is an often-overlooked factor in the financial analysis of video games. Quality measurements have been proven to work as a reliable predictor of sales while also directly influencing performance in financial markets. If markets are efficient in reflecting new information, perception of video game quality will lead to a rational response. This thesis examines the market reaction to this information set. The release structure in the video game industry allows for a direct observation of the isolated quality effect through third-party reviews. These reviews form an objective measurement of game quality without having other revealing characteristics, as all other information is released prior to these reviews. The possibility to exploit this unique case motivates the analysis through multiple empirical designs. Results from a multivariate regression model show a statistically significant positive effect of higher quality on short-term returns over all models. The release of a lower quality game reduces returns only for high-profile games. Both of these results are confirmed by the results from a rules-based trading strategy. These effects subside in the face of longer holding periods and higher exposure. This thesis finds sufficient evidence that video game quality should be an important factor in the analysis of video game companies. At the same time, these effects are only persistent in the short-time validating an efficient response to new information by financial investors.

Natural and man-made disasters, such as earthquakes, floods, plane crashes, high-rise building collapses, or major nuclear facility malfunctions, pose an ever-present challenge to public emergency services. Disasters may result in a large volume of responders arriving on-scene to provide assistance to victims. Coordination of responding resources is a major problem in disasters. The main motivation for the work is that disaster response and recovery efforts require timely interaction and coordination of public emergency services in order to save lives and property. In the present research effort, we are primarily concerned with assisting the Emergency medical agencies that deal with emergency situations by developing a decision-support system that can help them respond quickly and efficiently to a given situation. The overall goal of this project develop a practical solution for the resource allocation problem which can be integrated with the DIORAMA system that we have developed in our lab. The DIORAMA system collects information like victim’s location and condition in disaster site. Based on the information collected by the DIORAMA system, we developed an algorithm that can find the nearest resources from the disaster site to mitigate the risk. This problem can be solved in two phases, allocation and dispatching. The Emergency manager will provide the system Priority ratings of the cluster with respect to the emergency response resources and also the demands at each cluster. In the first phase allocation, we determine the number of emergency resources that can be allocated at each cluster which minimizes the overall risk. We define risk as the fraction of the unsatisfied demand. The output of this phase is the optimal resource allocation table. In the second phase, we find the nearest resource warehouse that can cater the demands of the cluster and dispatch the resources accordingly to the disaster site. This is also an integer programming problem. The final output of this phase is the dispatch table from which we can determine from where should the resources has to be sent to the clusters for an efficient and timely response. This is also rendered on Google Maps.

The world's water resources face unsustainable pressure from population growth, changes in consumption patterns, pollution, and overexploitation. Water resources managers have developed holistic approaches such as IWRM (Integrated Water Resources Management) and, more recently, the WEEF (Water-Energy-Environment-Food) nexus to address the situation. However, their application in day-to-day water resources management is still challenging due to the of little knowledge, data, and tools. One area where that challenge needs practical solutions is reservoir operation. The current study aims to improve the reservoir module in the Soil and Water Assessment Tool (SWAT) so that operation rules that aim to meet various water, food, and electricity objectives can be simulated. The improved SWAT model is used to simulate the management of the Sélingué reservoir in Mali, West Africa. The reservoir operation was simulated under three different operation rules: 1) priority to monthly hydropower production (HPP) target (rule 1); 2) respect of predefined monthly target storage (rule 2); 3) priority to downstream environmental flow, irrigation, and municipal water demands (rule 3). Results show that when priority is given to the HPP target (rule 1), 98.3% of the electricity demand is met. At the same time, the dam can supply 81.72% of the water demand to maintain environmental flow and sustain irrigation and municipal water consumption. It also ensures water availability with an annual target storage deviation estimated at 1.8%. When rule 2 is implemented, a gap of 8.5% between electricity production and electricity demand is observed. Rule 2 also failed to sustain environmental flow and supply flow for irrigation and municipal consumption as a gap of 15.39% between the supply and the demand was observed. Similarly to rule 1, It ensures water availability with an annual target storage deviation estimated at 1.25%. When rule 3 is enforced (i.e., the priority is given to environmental flow, irrigation, and municipal water demands) the reservoir can maintain the environmental flow and maintain irrigation, and municipal water requirements with a gap of 17.7% between the supply and the demands. However, HPP production decreases with a gap of 12.56% between the electricity supply and demand. Its capacity to supply water in the long term is low as it has the highest target storage deviation with a value of 18%. These results indicate that rule 1 offers more guarantees considering the food and electricity security and environmental challenges. Note that the simulations are done assuming that these rules are systematically followed. In practice, decision-makers can deviate from a rule in exceptional circumstances to maximize benefits or avert unwanted consequences. Finally, a decision support system (DSS) was developed to assist decision-makers in selecting efficient reservoir operation policies for multipurpose reservoirs combining HPP and irrigation.

Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers