RFID technology in outpatient logistics: MSc defense Burak Onurlu

Burak OnurluBurak Onurlu has defended successfully his M.Sc. thesis titled “RFID Technology in Outpatient Logistics: an analysis of its potential and acceptance ”. As student of Innovation Management, Burak studied the potential of RFID technology in outpatient logistics and the acceptance of the technology by healthcare practitioners.

The dramatic and continuous increase in health care spending threatens the economic stability and safety of the countries. Therefore, the governments and the health care organizations are forced to rethink and redesign their strategies in order to provide timely and cost effective healthcare services. Within this perspective, today’s technological developments have drawn attention of decision makers in health care industry due to their potential benefits in terms of efficiency and cost reduction. In this research, we focus on the main outpatient logistics problems and the recent use of a relatively new technology, Radio Frequency Identification (RFID), in order to solve those problems. In addition, the main concentration of the research is to explore the potential and the adoption of RFID in outpatient logistics. 109 Dutch health care professionals with diverse backgrounds, from doctors and nurses to managers and IT specialists, participated in a web-survey which was based on one of the most popular technology adoption theories, the Technology Acceptance Model. The proposed research model and the collected data were analyzed by using Structural Equation Modeling. The results of the research shows that perceived ease of use and perceived usefulness are the significant determinants of behavioral intention to use RFID technology in outpatient logistics. They can also explain/predict 67% of the variance in health care professionals’ behavioral intention to use RFID technology in outpatient logistics. Moreover, based on the Method Evaluation Model, the results suggest that RFID technology will likely be adopted in practice by health care professionals.

Performance measurement for clinical pathways: MSc defense Parvathy Meenakshy

Today, Parvathy Meenakshy defended successfully her M.Sc. thesis titled “A Performance Measurement Framework for Clinical Pathways Monitoring”. Parvathy studied Business Information Systems and her project was a collaboration with Catharina Hospital Eindhoven.

Healthcare domain around the world is facing challenges due to the skyrocketing cost, chronic illness, ever increasing aging population, sedentary life style and changing demographics. There is an intense pressure to balance cost and quality of care. Several researches are conducted to investigate the benefits of adopting process orientation and process oriented information systems in healthcare, which ascertain the fact that process orientation can improve the quality of care, efficiency and cost effectiveness. Clinical pathway is an example of process management tool in healthcare to streamline process and standardize care. The actual execution and continuous monitoring of the pathway is of utmost importance for improvement. The monitoring of the pathway is essentially the measurement of indicators and its reporting to analyze the performance of the pathway. In this thesis, we develop a framework for performance measurement system for clinical pathway monitoring. The system uses an ontology of pathway indicators with a formal method to define them. A proof of concpet system has also been implemented.

Machine learning for medical prediction: MSc defense Luis De Luna Orozco

Luis De LunaLuis Jorge De Luna Orozco has defended successfully his M.Sc. thesis titled “Performance of Machine Learning Algorithms to Predict Anastomotic Failure in Bariatric Surgery”. Luis studied Business Information Systems and his project was a collaboration with Catharina Hospital Eindhoven.

The main purpose of this report is to perform a machine learning study to find out to which degree these algorithms can predict occurrence of anastomotic failure in bariatric surgery. The information used in this study was obtained from two databases provided by the Catharina Hospital in Eindhoven which contain preoperative information about patients (Sleeves) and perioperative blood pressure (4KP) recorded for 1116 surgeries since 2006. First part of this study covers an analysis to find out which features or variables can be obtained from this set of data and to which degree they have predictive power to identify complications after bariatric surgery, more specifically intestinal leakage when using them in classification predictive models.

Second, investigation and performance analysis of three different machine learning classification algorithms is carried using techniques to compensate imbalanced sets of data due to the fact that only 3% of the patients present anastomotic failure after surgery. Finally relation between hypotension episodes an anastomotic failure is studied and explained.

Main results of this study show that Random Forest classifying technique can offer prediction accuracy up to 90% when taking into consideration perioperative and preoperative bariatric surgery information like smoking assessment, surgery duration, medication assessment, surgery technique and occurrence of hypotension episodes under sedation. At the end all these results are discussed and a Random Forest model in WEKA is provided, moreover possible future work is suggested to continue with the creation of a full clinical decision support system.

PhD defense Viorel Milea

Viorel MileaOn 7 February 2013, Viorel Milea has successfully defended his PhD thesis at Erasmus University Rotterdam. The title of his thesis was “News Analytics for Financial Decision Support”. Viorel has investigated the incorporation of news into (automated) trading algorithms. This relates to three main tasks: i) the extraction of the information contained in news, ii) the representation of the information contained in news, and iii) the aggregation of this information into actionable knowledge. The approach was validated by designing and implementing three semantic systems: a system for the computational content analysis of European Central Bank statements, a system for incorporating news in stock trading strategies, and a time-aware system for trading based on analyst recommendations. Computational intelligence techniques form an integral part of Viorel’s approach, which led to publications in multiple scientific journals. Congratulations Viorel!

PhD defense André Fialho

André Fialho has successfully defended his thesis today, at Instituto Superior Técnico of TU Lisbon. Like his colleague Federico, who graduated two days ago, André is one of the first bio-engineering graduates of the MIT-Portugal program. He received his degree with distinction. The title of André’s thesis was “Knowledge Discovery in Intensive Care Unit Shock Patients”. André studied advanced modeling and decision support techniques for the prognosis and the improvement of therapeutic interventions for shock patients in intensive care units (ICU), based on computational intelligence techniques. He has developed the very first of probabilistic fuzzy system models for decision support in the medical domain. These models were co-developed when André visited our group for six months in the past year. I and the whole healthcare cluster thank André for his companionship and wish him all the best in his future career.

Portuguese hospitals prefer to be paperless

20121118-110945.jpgI visited two hospitals in the neighborhood of Lisbon. I understand that Hospital da Luz is the largest private hospital in Portugal. It resides in a large health complex consisting of an acute care hospital and a residential hospital. Hospital Beatriz Ángelo is a public-private partnership and is larger in size. Both hospitals have the strategy to be a “paperless” health centre. It was interesting to see the level of information systems integration they had achieved, which opens new avenues for operational excellence. I think many hospitals in the Netherlands have a lot to learn from their experience.

Process mining with clinical data

Today, I have given an invited seminar at Instituto Superior Técnico on “process mining with clinial data: what is needed?” Process mining is a promising application of data mining. However, process mining from clinical data is not trivial. In my presentation, I discussed our healthcare cluster’s experience with process mining using data from an operation room and pointed out new research directions for increasing the applicability of process mining techniques when dealing with such data.

PhD defense Federico Cismondi

Today, Federico Cismondi has successfully defended his PhD thesis at Instituto Superior Técnico of TU Lisbon. Federico is one of the first bio-engineering graduates of the MIT-Portugal program and he received his degree with distinction. The title of his thesis was “Pre-Processing and Misclassifying Issues in Clinical Data Sets for Prediction and Intervention”. Federico has investigated how data pre-processing can be made effectively with clinical data. He proposed a novel classification scheme for the missing values in the data and has applied multi-objective model validation techniques to determine improved clinical decision making models. Federico shows that by using more advanced prediction models, many laboratory tests at intensive care units could be avoided, hence improving the quality of care for the patients and increasing the efficiency of the care processes. It is a nice example illustrating how intelligent decision support can change and improve care processes. It was a pleasure to have the discussion with you during your defense, Federico. Congratulations!

Comparison of workflow support and clinical decision support systems: MSc defense Xuchen Wang

Xuchen ThesisXuchen Wang has successfully defended her M.Sc. thesis titled “A Comparison of Workflow Management Systems and Clinical Decision Support Systems in Supporting Clinical Processes”. Xuchen studied Operations Management and Logistics.

Healthcare institutions are facing the challenge to deliver safe care against affordable costs. Both workflow management systems and clinical decision support systems are powerful tools in enhance clinical services but from two different fields. This projects aims to investigate the commonalities and differences of applying workflow management systems (WFMSs) and clinical decision support systems (CDSSs) in clinical environment. By conducting theoretical analyses and practical implementations, the functionalities and the advantage and disadvantages of both systems will be discussed. In the end of the project, the proposal of combining the two types of systems to achieve better clinical care is put forward.