The care of individuals with concerns about development, health, and wellness is often a difficult, complicated task and may rely on a team of diverse caregivers. There are many decisions that caregivers must make to help ensure that the best care and health monitoring are administered. For my dissertation work, I have explored the use of embedded capture and access to support decision-making for caregivers. Embedded capture and access integrates simple and unobtrusive capture and useful access, including trending information and rich data, into existing work practices. I hypothesized that this technology encourages more frequent access to evidence, increased collaboration amongst caregivers, and decisions made with higher confidence. I have explored this technology through real world deployments of new embedded capture and access applications in two domains. For the first domain, I have developed two applications to support decision-making for caregivers administering therapy to children with autism. The first application, Abaris, supports therapists working with a single child in a home setting, and the second application, Abaris for Schools, extends the ideas of Abaris for use in a school setting for many teachers working with multiple children. The second domain I have explored is decision-making for parents of newborn children. In particular, I developed and evaluated embedded capture and access technology to support parents, pediatricians, and secondary childcare providers in making decisions about whether a child s development is progressing normally in order to promote the earlier detection of developmental delays.

Thesis (M.S.)--University of Michigan, 2006. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 66-80).

Thesis (M.S.)--University of Michigan, 2006. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 66-80).

Informed decision making is part of any successful organization. Decision support systems help organizations to make more educated decisions by assisting decision makers in consolidating and analyzing information. Successfully introducing decision support systems is very challenging and risky because many factors need to be taken into account. In this thesis, structured and semi-structured interviews are carried out with employees at a Nordic construction company to discuss critical success factors in connection to introduction of decision support systems. The identified critical success factors are grouped into the following categories: organizational, project, technical, information quality, system quality, service quality and net benefits.

Information visualization dashboards are a widely used supportive tool in decision making. These tools can be difficult to create and utilize especially for the novice user. There is an extensive collection of company related data for decision making, resulting in a need of assistive tools. Prototypes were developed to research and identify design guidelines how to support fund managers in their decision making. This was carried out as a qualitative study involving 7 experts in fund management. The results provide insights and guidelines in decision supportive design for dashboards. The results indicate that there is a threshold in the number of displayed elements without limiting the cognitive analysis by the user. Three aspects; size, distribution and time should be included in the generating of suitable graphics. Assistive tools for connecting multiple context domains has been identified as a crucial element of decision support design. These guidelines should be investigated further in larger and more diverse studies in order to prove its full validity. / Information visualiserings dashboards är ett väl etablerat verktyg i beslutsfattning. Sådana verktyg kan vara utmanande att skapa och använda speciellt för en oerfaren användare. Idag samlas det in stora mängder av företagsrelaterad data för beslutsfattning vilket resulterar i ett behov av hjälpande verktyg. I den här studien utvecklades flera prototyper för att undersöka och tag fram designriktlinjer för hur man ska utforma och hjälpa fondförvaltare i sitt beslutsfattande. En kvalitativ studie genomfördes med 7 experter inom fond och kapitalförvaltning i framtagandet av designriktlinjer. Resultaten visar riktlinjer för beslutsstöd i utformningen av dashboards. Resultaten indikerar att det finns en gräns för hur många element man bör presentera för en användare utan att försvåra användarens kognitiva analysförmåga. Tre karaktärsdrag; storlek, distribution och tid påverkar utformningen av passande grafik. Studien har även identifierat behovet av verktyg för sammankopplingen mellan flera olika kontextdomäner i och med den kollaborativa delen av beslutsfattning. Dessa designriktlinjer ligger till grund för fortsatt undersökning i större och mer varierade studier för att styrka dess validitet.

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Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies.

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Many concrete problems may be cast in a multi-objective optimisation framework. The redundancy of existing methods for solving multi-objective programming problems susceptible to inconsistencies, coupled with the necessity for making in- herent assumptions before using a given method, make it hard for a nonspecialist to choose a method that ¯ts the situation at hand well. Moreover, using a method blindly, as suggested by the hammer principle (when you only have a hammer, you want everything in your hand to be a nail) is an awkward approach at best and a caricatural one at worst. This brings challenges to the design, development, implementation and deployment of a Decision Support System able to choose a method that is appropriate for a given problem and to apply the chosen method to solve the problem under consideration. The choice of method should be made according to the structure of the problem and the decision maker's opinion. The aim here is to embed a sample of methods representing the main multi-objective programming techniques and to help the decision maker find the most appropriate method for his problem. / Decisions Sciences / M. Sc. (Operations Research )

Approved for public release, distribution is unlimited / Current network administrators use network management software to monitor and control elements within a network. This is largely a manual process since managers must interrogate devices individually and evaluate performance statistics manually. The systems provide multiple views on network data but lack capabilities that allow operators to visualize network performance. Since personnel are required to identify problems, interpret potential solutions, and decide on appropriate corrective measures without automatic assistance, maintaining and solving problems for a network can be time-consuming and complex significantly reducing network efficiency. Since FORCENET is a heterogeneous concept that combines various C4I networks, sensors, weapon systems, and platforms, a new model must be developed for network operations. This paper researches an improved model for fleet network operations management for distributed sea-based forces using existing technologies. Combining a collaborative tool, Decision Support System (DSS), and Augmented Reality (AR) imagery transforms Naval information network management from a "minimum threshold" to an "operations fusion" perspective. Little is known about AR technologies, but the potential exists for virtual network operations centers that can remotely direct networks for sea and shore assets through collaborative efforts. The product of this paper will serve as a baseline for network operations in the network centric environment. / Lieutenant, United States Naval Reserve