An Early Warning System for Hospital Acquired Pneumonia

Pneumonia is a dangerous, often fatal secondary disease acquired by patients during their stay at Intensive Care Units. ICU patients have scores of data collected on a real time basis. Based on two years of data for a large ICU, we develop an early warning system for the onset of pneumonia that is based on Alternating Decision Trees for supervised learning, Sequential Pattern Mining, and the stacking paradigm to combine the two. Mainly due to decreased stay, the system will save € 180000 in this hospital alone while at the same time increasing the quality and consistent standard of health care. The ultimate system relies on a rather small numeric data base alone and is thus amenable to integration in a treatment protocol and a newly conceived ICU workflow system

Adopting Proactive Knowledge Use as an Innovation: The Case of a Knowledge Management System in Rheumatology

The aim of the study is to present a tentative framework to explore and investigate the drivers and barriers of adoption of the innovation of proactive knowledge use in connection to a knowledge management system (KMS) in health care. Semi-structured interviews were performed with champion implementers and physicians using the KMS along with a document analysis depicting significant events of the implementation process. The findings from the study suggested that drivers of the innovation were the characteristics of change agents, quality improvement, budget control and knowledge brought to the physician-patient dialogue by the KMS. In particular, there were indications of the KMS facilitating the process of making tacit knowledge explicit in the physician-patient dialogue. Identified barriers towards the innovation were resistance from clinical management, lack of motivation to share knowledge, lack of time and perceived flaws in the interface and compilation of data in the KMS

Science Through the “Golden Security Triangle”: Information Security and Data Journeys in Data-intensive Biomedicine

This paper talks about ways in which infrastructure for biomedical data-intensive discovery is operationalized. Specifically, it is interested in information security solutions and how the processes of scientific research through data-intensive infrastructures are shaped by them. The implications of information security for big data biomedical research have not been discussed in depth by the extant IS literature. Yet, information security might exert a strong influence on the processes and outcomes of data sharing efforts. In this research-in-progress paper I present a developing, in-depth study of a leading information linkage infrastructure that is representative of the kind of opportunities that big data technologies are occasioning in the medical field. This research calls for IS to extend the discussion to consider, building on the empirical detail of intensive case studies, a whole range of relations between provisions for information security and the processes of scientific research and data work

Empowering Healthcare Professionals by IS Education: Enhancing Reflective Empowerment

The paper presents findings from a research study of an information systems master program in Sweden, targeting healthcare professionals. The aim of the study was to explore if and how mechanisms of empowerment and reflective practice can be evoked in healthcare professionals by participating in a master program in information systems. A mixed research methodology was applied, including participant observation, document analysis and a learning style inventory. The findings of the study showed signs of the students achieving a higher degree of empowerment in their professional roles, as well as beginning to actively use reflective practice as a means of professional development. The findings are summarised in a tentative framework of reflective empowerment. The findings call for further research on how IT-centred master programs targeting healthcare professional could enhance professional development

A Continuance Model for a Mobile/Web Based Self-Management System for Adolescent Diabetics: The Role of Loyalty Incentive

It is estimated that 200 children per day worldwide develop Juvenile Diabetes (JD). There is no cure for JD, therefore treatment protocols focus on controlling the disease. Several information systems (IS) have been developed to help patients manage their chronic diseases, but often these systems suffer from reduced use over time or complete abandonment. Limited research has been conducted that examines continued usage in this domain. Through this study, our purpose is to build and evaluate a mobile/web based JD monitoring system combined with a rewards program designed to increase continued system use. We propose a comprehensive continuance intention model by combining the IS Continuance Model proposed by Bhattacherjee with DeLone and McLean’s IS Success Model. We also explore the role of the context specific constructs of Interaction Quality and Perceived Disease Management Effort and the moderating role of several individual factors on relations in the proposed model. We propose a longitudinal study utilizing a survey methodology to empirically validate the proposed model. Data analysis will utilize structural equation modeling using partial least squares. Participants in this survey consist of adolescent JD patients and their parents, allowing us to understand the factors which are most relevant to each stakeholder group

Post-Acceptance of Electronic Medical Records: Evidence from a Longitudinal Field Study

Many studies investigating post-acceptance of electronic medical records (EMR) assume that healthcare professionals exclusively base their continuance behavior on reasoned actions. While rational considerations certainly affect the intention to use an EMR, it does not fully explain the definitive user continuance behavior. Evidence exists that also subliminal effects such as habits and emotions play an important role. Consequently, we propose to investigate post-acceptance of EMR applying three different, but complementary views: (i) continuance behavior as result of reasoned actions, (ii) continuance behavior as result of emotional responses, and (iii) continuance behavior as result of habitual responses. The results from a longitudinal field study showed that automatic behavior, enabled by sufficient facilitating conditions and a good task-technology-fit, as well as positive emotions considerably affected healthcare professionals EMR continuance behavior. It also showed that a user’s computer literacy level didn’t play a significant role regarding the post-acceptance behavior

Marrying Work and the Technical Artifact Within the Healthcare Organization: A Narrative Network Perspective on IT Innovation-Mediated Organizational Change

Despite the implicit belief that IT innovations brings beneficial change, medical practitioners and healthcare professionals constantly struggle to realize the innovation potential of electronic medical records (EMR) system in revolutionizing clinical practices. To understand this conundrum, this paper uses an in-depth case study of an EMR implementation to develop a grounded theory of why, when, and how IT-innovation mediated change occur. We propose the Narrative Network Perspective that combines the analysis of the processes of configuration, implementation and use of the system. This combined view allows researchers to understand how “production narrative network”, infrastructure and the macrostructure in healthcare environment co-evolve with the idealized production narrative network inscribed in the EMR system within and across the three phases. By tracing and taking into account all these elements time, this perspective provides plausible answers to when and why organizational innovation occur with the introduction of IT innovations. (147

Cognitive workload reduction in hospital information systems

Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians’ physical and cognitive workload and improve patient safety. Order sets represent time intervalclustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians’ cognitive workload associated with prescribing order sets. Furthermore, we provide structural insights into the problem which lead us to a valid lower bound on the order set size. In a case study using order data on Asthma patients with moderate complexity from a major pediatric hospital, we compare the hospital’s current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results confirm our lower bound and reveal that using a time interval decomposition approach substantially reduces computation times for the mathematical program, as does a K−means clustering based decomposition approach which, however, does not guarantee optimality because it violates the lower bound. The results of comparing the mathematical program with the current order set configuration in the hospital indicates that cognitive workload can be reduced by about 20.2% by allowing 1 to 5 order sets, respectively. The comparison of the K−means based decomposition with the hospital’s current configuration reveals a cognitive workload reduction of about 19.5%, also by allowing 1 to 5 order sets, respectively. We finally provide a decision support system to help practitioners analyse the current order set configuration, the results of the mathematical program and the heuristic approach

Reducing clinical workload in the care prescription process: optimization of order sets

Order sets are a critical component in hospital information systems, designed to substantially reduce clinician workload and improve patient safety and health outcomes. Order sets represent clusters of order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In prior research, we constructed order sets for defined time intervals during inpatient stay based on historical data on items ordered by clinicians across a large number of patients. In this study, we build on our prior work to formulate a mathematical program for optimizing order sets that are applicable across the entire duration of inpatient stay and are independent of the time intervals. Furthermore, due to the intractability of the problem, we develop a Greedy algorithm to tackle real-world test instances. We extract data sets for three clinical scenarios and conduct both cognitive and physical workload analyses. Finally, we extend a software application to facilitate the comparison of order sets by practitioners. Our computational results reveal that the optimization-based physical and cognitive workload models can solve small test instances to optimality. However, for real-world instances, the Greedy heuristic is more competitive, in particular when physical workload instead of cognitive workload is the optimization objective. Overall, the Greedy heuristic can solve the test instances within one minute and outperforms the mathematical program in 2/3 of the test instances within a time limit of ten minutes, demonstrating a feasible and promising approach to develop inpatient order sets that can subsequently be validated by clinical experts