A Comparative Analysis of the Australian and German eHealth System - Eigner, Isabella; Hamper, Andreas; Wickramasinghe, Nilmini; Bodendorf, Freimut (Proceedings of the 29th Bled eConference, 2016)

A Literature Review on Predicting Unplanned Patient Readmissions - Eigner, Isabella; Cooney, Andrew (Delivering Superior Health and Wellness Management with IoT and Analytics, 2020)
Research on predicting unplanned readmissions in hospitals is becoming more popular with a larger amount of hospital data becoming available. To gain an in-depth observation of recent insights in the field, a literature review analysing contributions between the years of 2005 and 2017 is conducted. The aggregated results show the most important risk factors included in prediction models so far, evaluation metrics of both all-cause and diagnosis-specific prediction models as well as the most prominent classification methods used in this context. Furthermore, the development of research on predicting unplanned patient readmissions over time is shown, and current gaps are identified.<br
Keywords: Risk prediction; Predictive analytics; Healthcare; Data mining; Readmissions

An Intelligent Clinical Decision Support System to Determine the Optimal Time of Patient Discharge in Hospitals - Eigner, Isabella; Bodendorf, Freimut (ICIS 2020 Proceedings, 2020)
Unplanned readmissions are a popular factor to determine the quality of healthcare services that can lead to negative financial and reputational ramifications for hospitals. Identifying patients at high-risk of readmission is key to allow for early interventions and proper discharge management. This paper proposes an intelligent clinical decision support system (ICDSS) that incorporates multiple risk prediction models according to the eight surgical groups defined by the Australian Institute of Health and Welfare (AIHW). The goal of this ICDSS is to enable the identification and visualisation of individual patients at high risk of readmission combined with economic factors to support decision-making in hospital discharge. This paper presents the design, prototypical implementation, and evaluation of an ICDSS that offers relevant insights to healthcare providers based on procedure-specific readmission risk prediction models. The results of the evaluation confirm the suitability and effectiveness of the system to support the decision process in patient discharge.<br
Keywords: Machine Learning; Decision Support; Intelligent Decision Support System; Clinical Decision Support; Healthcare; Hospital Management; Patient Discharge

An intelligent decision support system for readmission prediction in healthcare - Eigner, Isabella; Bodendorf, Freimut (it - Information Technology, 2018)
Readmission prediction in hospitals is a highly complex task involving multiple risk factors that can vary among different disease groups. We address this issue by implementing multiple cross-validated classification models within an intelligent CDSS to enhance patient discharge management. Depending on the diagnosis, the system selects and applies the appropriate model and visualises the prediction results. In addition, the cost and reimbursement development for each episode are determined. The architecture of the CDSS and the integration of the prediction models are presented in this paper.<br
Keywords: Decision support; IDSS; CDSS; readmissions; risk prediction; machine learning

Assessing the Influences of Trip Similarity and User Relationships on Individual Travel Recommendations - Schlick, Sabine; Eigner, Isabella; Ensslen, Sabine (2015)
People often ask others for advice before making a decision. Especially in the context of travel planning, this leads to an increasing importance of communities. By conducting a study with members of an online travel community, this paper analyzes human recommendation behavior within a travel context. First, the concept of “similarity of trips”, a measure used by content-based recommender systems, is evaluated. Second, the role of friendships within a recommendation network is examined. Findings show that people tend to rate trips better if they have a high similarity to trips they did in the past. Additionally, people prefer trips recommended by their friends, even more so if a friend did this trip in the past. Finally, the results show that automatically recommended trips of the participants’ friends are accepted at least as well as user-generated recommendations for trips of friends and better than user-generated recommendations for trips of non-friend
Keywords: Travel recommendations; content-based filtering; user-generated recommendations

A theoretical framework for research on readmission risk prediction - Eigner, Isabella; Bodendorf, Freimut; Wickramasinghe, Nilmini (32ND Bled eConference Humanizing Technology for a Sustainable Society, 2019)
On the one hand, predictive analytics is an important field of research in Information Systems (IS); however, research on predictive analytics in healthcare is still scarce in IS literature. One area where predictive analytics can be of great benefit is with regard to unplanned readmissions. While a number of studies on readmission prediction already exists in related research areas, there are few guidelines to date on how to conduct such analytics projects. To address this gap the paper presents the general process to develop empirical models by Shmueli and Koppius (2011) and extends this to the specific requirements of readmission risk prediction. Based on a systematic literature review, the resulting process defines important aspects of readmission prediction. It also structures relevant questions and tasks that need to be taken care of in this context. This extension of the guidelines by Shmueli and Koppius (2011) provides a best practice as well as a template that can be used in future studies on readmission risk prediction, thus allowing for more comparable results across various research fields.<br
Keywords: readmissions; predictive analytics; prediction process; healthcare

Case-based reasoning for complexity management in Industry 4.0 - Schott, Peter; Lederer, Matthias; Eigner, Isabella; Bodendorf, Freimut (Journal of Manufacturing Technology Management, 2020)
Purpose: Increasingly, dynamic market environments lead to growing complexity in manufacturing and pose a severe threat for the competitiveness of manufacturing companies. Systematic guidance to manage this complexity, especially in the context of Industry 4.0 and the therewith rising trends such as digitalization and data-driven production optimization, is lacking. To address this deficit a case-based reasoning (CBR) system for providing knowledge about managing complexity in Industry 4.0 is presented. Design/methodology/approach: First, the explicit knowledge representation for managing complexity in IT-based manufacturing is introduced. Second, the CBR process step to retrieve knowledge from an artificially composed case base with in total 70 cases of data-based complexity management in the context of Industry 4.0 is set out. Third, knowledge transfer alongside several maturity levels of information technology capabilities of manufacturing systems for reuse in new problem scenarios is introduced. Findings: The paper comprises the conceptual approach for designing a CBR system to support data-based complexity management in manufacturing systems. Furthermore, the appropriateness of the CBR system to provide applicable knowledge for reducing and managing complexity in corporate practice is shown. Research limitations/implications: The presented research results are evaluated in the course of an embedded single case study and may therefore lack generalizability. Future research to test and enhance the appropriateness of the developed CBR system will strengthen the research contribution. Originality/value: The paper provides a novel approach to systematically support knowledge transfer for data-based complexity management by transferring the well-known and established methodology of CBR to the rising application domain of manufacturing systems in the context of Industry 4.0.
CRIS-Team Scopus Importer:2020-08-14
Keywords: Complexity; Knowledge based systems; Manufacturing systems

Decision makers and criteria for patient discharge - a qualitative study - Eigner, Isabella; Hamper, Andreas; Wickramasinghe, Nilmini; Bodendorf, Freimut (30TH BLED ECONFERENCE: DIGITAL TRANSFORMATION – FROM CONNECTING THINGS TO TRANSFORMING OUR LIVES, 2017)

Decision support for patient discharge in hospitals – analyzing the relationship between length of stay and readmission risk, cost, and profit - Eigner, Isabella; Bodendorf, Freimut (Services – SERVICES 2020, 2020)

Decision Support for Patient Discharge in Hospitals – Analyzing the Relationship Between Length of Stay and Readmission Risk, Cost, and Profit - Eigner, Isabella; Bodendorf, Freimut (Services – SERVICES 2020, 2020)
Determining the optimal time for patient discharge is a challenging and complex task that involves multiple opposing decision perspectives. On the one hand, patient safety and the quality of healthcare service delivery and on the other hand, economic factors and resource availability need to be considered by hospital personnel. By using state-of-the-art machine learning methods, this paper presents a novel approach to determine the optimal time of patient discharge from different viewpoints, including a cost-centered, an outcome-centered, and a balanced perspective. The proposed approach has been developed and tested as part of a case study in an Australian private hospital group. For this purpose, unplanned readmissions and associated costs for episodes of admitted patient care are analyzed with regards to the respective time of discharge. The results of the analyses show that increasing the length of stay for certain procedure groups can lead to reduced costs. The developed approach can aid physicians and hospital management to make more evidence-based decisions to ensure both sufficient healthcare quality and cost-effective resource allocation in hospitals.<br
Keywords: Machine learning; Length of stay; Unplanned readmissions; Patient discharge

Dementia Monitoring with Artificial Intelligence - Hamper, Andreas; Eigner, Isabella (Contemporary Consumer Health Informatics, 2016)

Development and evaluation of ensemble-based classification models for predicting unplanned hospital readmissions after hysterectomy - Eigner, Isabella; Reischl, Daniel; Bodendorf, Freimut (ACIS 2018 Proceedings, 2018)
Unplanned hospital readmissions are a key indicator of quality in healthcare and can lead to high unnecessary costs for the hospital due to additional required resources or reduced payments by insurers or governments. Predictive analytics can support the identification of patients at high-risk for readmission early on to enable timely interventions. In Australia, hysterectomies present the 2nd highest observed readmission rates of all surgical procedures in public hospitals. Prior research so far only focuses on developing explanatory models to identify associated risk factors for past patients. In this study, we develop and compare 24 prediction models using state-of-the-art sampling and ensemble methods to counter common problems in readmission prediction, such as imbalanced data and poor performance of individual classifiers. The application and evaluation of these models are presented, resulting in an excellent predictive power with under- and oversampling and an additional slight increase in performance when combined with ensemble methods.<br

Mining customer satisfaction on b2b online platforms using service quality and web usage metrics - Figalist, Iris; Dieffenbacher, Marco; Eigner, Isabella; Bosch, Jan; Olsson, Helena Holmstrom; Elsner, Christoph (Proceedings - Asia-Pacific Software Engineering Conference, APSEC, 2020)
In order to distinguish themselves from their competitors, software service providers constantly try to assess and improve customer satisfaction. However, measuring customer satisfaction in a continuous way is often time and cost intensive, or requires effort on the customer side. Especially in B2B contexts, a continuous assessment of customer satisfaction is difficult to achieve due to potential restrictions and complex provider-customer-end user setups. While concepts such as web usage mining enable software providers to get a deep understanding of how their products are used, its application to quantitatively measure customer satisfaction has not yet been studied in greater detail. For that reason, our study aims at combining existing knowledge on customer satisfaction, web usage mining, and B2B service characteristics to derive a model that enables an automated calculation of quantitative customer satisfaction scores. We apply web usage mining to validate these scores and to compare the usage behavior of satisfied and dissatisfied customers. This approach is based on domain-specific service quality and web usage metrics and is, therefore, suitable for continuous measurements without requiring active customer participation. The applicability of the model is validated by instantiating it in a real-world B2B online platform.
CRIS-Team Scopus Importer:2021-04-19
Keywords: b2b; customer satisfaction; data analytics; web usage mining

Predicting high-cost patients by Machine Learning: A case study in an Australian private hospital group - Eigner, Isabella; Bodendorf, Freimut; Wickramasinghe, Nilmini (Proceedings of 11th International Conference on Bioinformatics and Computational Biology, 2019)
Healthcare is considered a data-intensive industry, offering large data volumes that can, for example, be used as the basis for data-driven decisions in hospital resource planning. A significant aspect in that context is the prediction of cost-intensive patients. The presented paper introduces prediction models to identify patients at risk of causing extensive costs to the hospital. Based on a data set from a private Australian hospital group, four logistic regression models designed and evaluated to predict cost-intensive patients. Each model utilizes different feature sets including attributes gradually available throughout a patient episode. The results show that in particular variables reflecting hospital resources have a high influence on the probability to become a cost-intensive patient. The corresponding prediction model that incorporates attributes describing resource utilization achieves a sensitivity of 94.32% and thus enables an effective prediction of cost-intensive patients.<br
Keywords: cost-intensive; Healthcare; machine learning; prediction; Predictive Analytics

Predictive Analytics in Health Care: Methods and Approaches to Identify the Risk of Readmission - Eigner, Isabella; Hamper, Andreas (Theories to Inform Superior Health Informatics Research and Practice, 2018)
The increasing focus on evidence-based healthcare services as well as rising health expenditures for inpatient treatment forces hospitals to introduce new approaches to allow for a more efficient delivery of said services. As a new measure of healthcare quality, readmission rates are increasingly used to determine the quality of care, benchmark hospital performance and determine funding rates or even issue penalties. It is therefore key to determine patients at high risk of readmission. This can be done by using predictive risk models that are able to predict the risk of readmission to the hospital for individual patients using various data mining techniques and algorithms. Based on these models and with the increasing amount of data collected in hospitals, clinicians and hospital management can be supported in their daily decision-making to reduce readmission rates. Ultimately, the implementation of such prediction models can help avoid unnecessary costs as well as improve the quality of healthcare services. This work aims at identifying and analysing state-of-the-art risk prediction models in healthcare with regard to their specific application areas, applied algorithms and resulting accuracy to determine the suitability of different methods in different healthcare contexts.<br
Keywords: Risk prediction; Predictive analytics; Healthcare; Data mining; Readmissions

Principles of Agile Case Modeling and Tool Support - Huber, Sebastian; Schott, Peter; Eigner, Isabella (Proceedings of the 2nd International Workshop on Process Modelling Support Systems, 2015)
WiSo2-Import:2015-10-02:655

Readmission risk prediction for patients after total hip or knee arthroplasty - Eigner, Isabella; Tajak, Lukas; Bodendorf, Freimut; Wickramasinghe, Nilmini (Proceedings of the Australasian Conference on Information Systems 2017, 2017)
Keywords: predictive analytics; readmissions; total hip arthroplasty; total knee arthroplasty; risk management

Rehabilitation Risk Management: Enabling Data Analytics with Quantified Self and Smart Home Data - Hamper, Andreas; Eigner, Isabella; Wickramasinghe, Nilmini; Bodendorf, Freimut (Health Informatics Meets eHealth, 2017)
Keywords: Data Analytics; Integrated Care; Personal Health; Quantified Self; Rehabilitation; Wearable Technology; Wellbeing

Success factors for national eHealth strategies: a comparative analysis of the Australian and German eHealth system - Eigner, Isabella; Hamper, Andreas; Wickramasinghe, Nilmini; Bodendorf, Freimut (International Journal of Networking and Virtual Organisations, 2019)
The Australian and German healthcare systems share similar traits in insurance and healthcare management. Both nations have also embarked upon national eHealth strategies to connect stakeholders and enable collaboration in healthcare services throughout their respective countries. While Australia has focused on a platform-based approach initially termed 'personally controlled electronic health record', Germany introduced the 'electronic health card', mandatory for every publicly insured person. This paper presents a comparative analysis of the Australian and German eHealth systems to determine commonalities, barriers and success factors of both strategies. A combined PESTLE/SWOT-analysis is used. Results show the key advantages and obstacles of both approaches and identify relevant areas for successful eHealth implementation.<br
Keywords: eHealth; Australia; Germany; SWOT analysis; PESTLE analysis; PCEHR; MyHealthRecord; electronic health card; EHC.

Towards a Medical Tricorder: Defining medical conditions for consumer self-care with focus on non-invasive technologies - Hamper, Andreas; Eigner, Isabella; Neitzel, Lucas; Wickramasinghe, Nilmini; Bodendorf, Freimut (AMCIS 2017 Proceedings, 2017)

Using Collective Intelligence to Generate Trend-based Travel Recommendations - Schlick, Sabine; Eigner, Isabella; Fechner, Alex (Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015)
Trips are multifaceted, complex products which cannot be tested in advance due to their geographical distance. Hence, making a travel decision people often ask others for advice. This leads to an increasing importance of communities. Within communities people share their experiences, which results in new, more extensive knowledge beyond the individual knowledge of each member. The objective of this paper is to use this knowledge by developing an algorithm that automatically generates trend-based travel recommendations. Based on the travel experiences of the community members, interesting travel areas are identified. Five key figures to evaluate these areas according to general criteria and the users' individual preferences are developed. The algorithm allows to generate recommendations for the whole community and not only for highly active members, resulting in a high coverage. A study conducted within an online travel community shows that automatically generated, trend-based trip recommendations are rated better than user-generated recommendations.<br