Skeletal plans are a powerful way to reuse existing domain-specific procedural knowledge. They are

this paper, we adopt Newell's perspective [51] of a "knowledge level"' analysis rather than addressing this topic at the "symbol level". Practical plan management requires a "knowledge rich" model [69] that facilitates efficient reasoning given the demands of the surrounding environment. According to our medical interest, approaches dealing with time handling, context, and incomplete information about the world's states and the effects of actions (dynamically changing environments) are most important.

Skeletal plans are a powerful way to reuse existing domain-specific procedural knowledge. The main drawbacks are that the compositions and the interdependencies of different skeletal plans and their components are not lucid. The aim of this paper is to overcome these limitations and to present the visualization of time-oriented, skeletal plans. Within the Asgaard project, we have developed a time-oriented and intention-based language, called Asbru, to represent such skeletal plans. The Asbru syntax is defined in Backus-Naur form (BNF). Reading BNF or similar forms are next to impossible even for domain experts. We explored different representations and automated knowledge-acquisition tools. However, the domain experts did not accept any of these representations. Consequently, we investigated different metaphor graphics and ended up with a plan visualization utilizing the metaphors of "tracks" and "traffic", called AsbruView. We formatively evaluated different approaches ...

The Patient Advocate is designed to be an intelligent assistant for patient-centered health care. Residing on a home computer or special-purpose device and operating within an extended health-care information network, the Patient Advocate will extend medical expertise into the outpatient setting. It will have remote access to the patient’s medical record, an understanding of the patient’s health status and history, and a model of the patient’s interest in health-related issues, preferences for modes and contents of interaction, etc. The Patient Advocate is being designed to provide three kinds of functions. First, it will assist the patient in managing continuing ambulatory conditions, for example chronic problems such as diabetes, special normal conditions such as prenatal care, and wellness issues such as diet, exercise, and stress. Second, it will provide health-related information by allowing the patient to interact with the online health-care information network and scan media resources to suggest information of interest. Third, it will act as a remote triage point for clinical services by coordinating patient-relevant information such as reminding the patient when a visit to the clinic is indicated. We describe a prototype of the Patient Advocate which is designed to support obstetrics patients at risk of gestational diabetes. It is important that the Patient Advocate is platform-independent, runs on widely available hosts, and has access to various Internet resources. Therefore, it is implemented in Java and is Internet accessible. 1. Patient-Centered Care is Needed In recent years, several knowledge-based systems have been introduced to support health care providers (mainly clinicians) with the monitoring of critical care patients and to assist them with diagnostic decisions and therapy planning (Uckun 1994b). These systems range from simple intelligent alarms to sophisticated systems for anesthesia monitoring or ventilator management (e.g.,

Organizational simulations have been used in business, manufacturing, and engineering design tasks to gain insight into organizational process bottlenecks, and to improve the quality and efficiency of processes within these industries. As market pressures demand increased efficiencies within the health care industry, organizational simulation techniques could provide similar insight into the design of better medical care processes, or protocols, in medical organizations. To simulate the process of medical care within a specific organization however, requires models that can represent 1) unpredictable patient responses to care, 2) the flexibility needed to adapt to different patients, and 3) different preferences of health care professionals and the implicit preferences contained within the protocol. Using previous work on simulation in the Virtual Design Team (VDT), and an example protocol drawn from an existing protocol in bone marrow transplantation, we describe extensions to the VDT information processing representation that will allow us to simulate the performance characteristics of a medical protocol used within a medical organization. Our representational extensions capture the uncertainty of medical care for patients, the activity flexibility within the organization, and the preferences of health care professionals that will make information processing organizational simulations in the medical domain possible. We believe our representation will provide a robust simulation "toolbox" that can be used to investigate the performance of specific medical protocols within different hospital settings, and explore organizational theory within the health care industry.

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Abstract—Healthcare service providers, including those involved in renal disease management, are concerned about the planning of their patients ’ treatments. With efforts to automate the planning process, shortcomings are apparent due to the following reasons: (1) current plan representations or ontologies are too fine grained, and (2) current planning systems are often static. To address these issues, we introduce a planning system called Dynamic Personalized Planner (DP Planner) which consists of: (1) a suitably light-weight and generic plan representation, and (2) a constraint-based dynamic planning engine. The plan representation is based on existing plan ontologies, and developed in XML. With the available plans, the planning engine focuses on personalizing pre-existing (or generic) plans that can be dynamically changed as the condition of the patient changes over time. To illustrate our dynamic personalized planning approach, we present an example in renal disease management. In a comparative study, we observed that the resulting DP Planner possesses features that rival that of other planning systems, in particular that of Asgaard and O-Plan. Keywords-patient care planning; treatment protocols; dynamic treatment planning; personal health services. I.