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
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:73598 |
| Date | 28 January 2021 |
| Creators | Spangenberg, Norman |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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