Abstract

A Patient History Form that Explains Itself and Addresses Patients’ConcernsMr. Alfredo Liu-Perez, Dr. Susan McRoy, and Ms. Maria VeraDepartment of Engineering and Computer ScienceUniversity of Wisconsin - MilwaukeeMilwaukee, WI 53201AbstractThe PEAS (Patient Education and Activation System) project aims to improve medical care by using an artificial intelligence system to educate patients so that they can be more active participants in their health care.An important component of PEAS is the Layman Education and Activation Form (LEAF). LEAF analyzes patients’ medical history forms and its dialogue with them, to provide them with relevant and personalized medical information. The information to present is chosen using techniques from user modeling, discourse analysis, and cooperative problem solving. At any time during the interaction, LEAF allows patients to ask questions about the medical history form itself, health issues, and the information offered. The graphical user interface is being written in Java to allow worldwide access to PEAS. The interface incorporates hypertext, natural language dialogues, and various search techniques for fast online information retrieval. LEAF differs from other systems in that other systems rely on users knowing what to ask about, whereas this one acts as a collaborative agent by “volunteering” relevant information.IntroductionWeighing factors such as patients' life style or environment, tolerance for risk or uncertainty, or desired quality of life is a subjective process. But, to receive correct care, patients must be able to provide an accurate account of their preferences (Gustafson et al. 1993) which, in turn, requires that they understand the consequences of their choices.Modern medical care offers doctors a wide variety of options for responding to patients' ailments. Computer systems can help improve the quality and efficiency of medical care, by making patients aware of their risk tolerance and addressing their concerns. Artificial intelligence techniques can be used to provide patients with intelligent, private, and customized guidance that other computer systems do not provide.PEAS Project The PEAS (Patient Education and Activation System) project aims to improve medical care by educating patients and helping them make careful assessments of their preferences. To limit costs, usage of PEAS could occur outside patients' interaction with their doctor. PEAS would educate patients so that they can be more active participants in their health care. Since PEAS is designed to be used without medical supervision, places where medical personnel is limited might profit from such a system. PEAS integrates two projects being developed at the University of Wisconsin - Milwaukee. One of these projects is the Layman Education and Activation Form (LEAF). Its goal is to present material to patients on the basis of their medical history and subjective concerns. The goal of the other project is to assess patient preferences for different treatment options based on their subjective feelings about risk and desired quality of life (Vera In Preparation).LEAF ProjectThe LEAF (Layman Education and Activation Form) project consists of a patient history form that explains itself and addresses patients’ concerns. The aim of the project is to create more informed and "activated” patients by taking the kind of data normally input into medical history forms and using it to help patients learn about information relevant to them. LEAF uses techniques from user modeling, discourse analysis, and cooperative problem solving. It also uses current techniques in user interfaces like hypertext and natural language dialogues, and will utilize various search techniques for fast online information retrieval.The system is being written in Java to allow access to it through the World Wide Web. LEAF accesses only publicly available materials or those that have been hand coded. It lets patients fill medical forms online and initiate questions about the domain or the terminology in the form itself. The information given to patients deals with issues of prevention, testing, or treatment.After evaluating the patients’ medical history form, LEAF will direct them to educational material about the domain and suggest questions to ask their doctor. It also provides a question-answering facility to help clarify thematerial offered, and includes a graphical user interface to be used by them.At the end of a session, patients are presented with a summary of relevant and personalized private information. The summary includes a list of relevant health topics they can pursue if they choose to. Patients have the option of ending their dialogue with LEAF, or questioning LEAF on important health topics or on the summary information.ArchitectureFigure 1.0 shows the general architecture of LEAF. The arrows show the flow of information through the system. Information does not flow through all blocks all the time. For example, the medical form is not updated when the patient asks a question.The interface serves to get input from and provide output to the user. Output from the interface is sent to the information analyzer block, which contains the artificial intelligence algorithms of LEAF. The information analyzer block uses information from the dialogue history, knowledge base, medical history form, and user model in its decisions. Its output is sent to the language generator, which generates English sentences to be sent to the interface.InterfaceLEAF’s interface is being written in Java to allow access to it through the World Wide Web. Java is a new object-oriented programming language developed at Sun Microsystems (/). The creators of Java (van Hoff et al. 1995) state thatJava is simple, object-oriented, statically typed, compiled, architecture neutral, multithreaded, garbage collected, robust, secure, and extensible, and well understood.The interface is designed to be friendly, since many patients will be first-time computer users, disabled, or elderly. To achieve a friendly interface, LEAF uses metaphors that resemble actual objects as much as possible. Patients are also able to change the font size, print important material, and get help online.The interface is menu driven and has options for asking from a list of context-specific questions. A sample of questions the user can ask are “what is diabetes?,” “what is the difference between a stroke and a heart attack?,” “do soft drinks cause breast cancer?,” or “how is lung cancer treated?”User ModelUser models are used to interact with patients in an easy and natural way. The user model is used to determine the user’s capabilities, goals, plans, attitudes, and beliefs (Kass & Finin 1988).The medical history form has actual medical history form entries and entries used to create a user model. This medical form provides the user model with some of the user’s physical and mental capabilities, attitudes, and beliefs. The information obtained from the medical history form, user model, and dialogue history is analyzed and used to show patients relevant and personalized medical information. According to (Cawsey 1992), the most important information needed to update a user model are what a user says and asks, what the system replies, the user’s level of expertise, and relationships between concepts in the domain. To update the user model, LEAF takes into account what patients ask, what the system’s reply was, and the level of familiarity patients have with computers and health issues.The user model is dynamic. It starts with default information that is corrected and validated as more information is gathered. The knowledge in the user model is acquired explicitly from questions in the medical history form (e.g. are you male?), or implicitly from analyzing the form and dialogue history. For example, patients’ concerns are deduced from the dialogue history by examining which topics they ask about, whereas their health risks are discovered by analyzing their medical history form. Dialogue HistoryA dialogue history is kept of the interaction between patients and LEAF. The information kept is used to determine the patients’ interests.At any time during the process of filling the form, the patients can ask LEAF to define terms or to answer questions about the form, the answers provided, or any other type of information offered. The questions to be asked are chosen from a list of context-specific questions The terms to be defined are chosen from a static list of terms and can consist of several words (e.g. breast cancer). This list can be accessed either by typing the term or by going through the list.Knowledge BaseThe knowledge base is being written in a way that facilitates its maintenance and checking for its coherence. To take advantage of Java’s object-oriented nature, some of the knowledge base consists conceptually of objects and classes. For example, there are disease objects (e.g. breast cancer) and culprit objects (e.g. caffeine). These objects have attributes attached to them and inherit attributes from other objects.Disease objects have attributes attached to them like a definition, statistical information, symptoms, and risks. Culprit objects have attributes attached to them like definition, types, and effects. These attributes might have subattributes or links to other objects.Information AnalyzerSome of the functions of the information analyzer block shown in figure 1.0, are to analyze the input, search the knowledge base for information; update the dialogue history, knowledge base, medical history form, and user model; calculate patients’ risks of contracting several diseases; and send information from its knowledge base search to the generator. The information analyzer block uses information from the dialogue history, knowledge base, medical history form, and user model in its decisions. GenerationThe generation part of LEAF takes input from the information analyzer block and generates English sentences. Some of the input it receives are already English sentences, other input it receives is in a structure that the generator translates into English sentences.The way it generates sentences at the time is by using a series of templates to answer questions. For example, to answer the question “Does cause ?” it needs the template “ causes ,” or “ does not cause .” To determine which template to use it needs to know if the NOUN1.CAUSES attribute points to . The templates are also chosen depending on context.ExampleAn ideal interaction with LEAF is presented below. LEAF: “question 20: Have you had any previous history of breast cancer?”Patient: “No.”L: “question 21: Do you drink liquids that contain caffeine?”P: “Yes.”The patient wants to know what does caffeine have to do with breast cancer and initiates a question.P: “What is the relationship between caffeine and breast cancer?L: “Caffeine and its chemical relatives have been linked in several studies to the painful swellings of fibrocystic breast disease.”P: “What is fibrocystic breast disease?”L: “It is a benign condition associated with an increased risk of developing breast cancer.”P: “How is fibrocystic breast disease treated?”L: “This condition subsides in most women who stop consuming all sources of caffeine and its relatives.”The patient wants to know more about what was said by LEAF. In this case, LEAF gives examples of caffeine and its relatives.P: “Elaborate.”L: “Examples of coffee and its relatives are decaf, tea, chocolate, colas, and other caffeine-containing soft drinks.”The patient is satisfied with the answer and wants to continue filling the medical history form.P: “OK.”The dialogue history would encode the above interaction and the user model would reflect the patient’s interest in fibrocystic breast disease, its cause, and its treatment. This information is taken into account at the end of the session by reminding the user of this topic. The user would later have the option of pursuing the topic further.Related WorkThree related projects are CHESS, HealthDoc, and the HRT Decision Making project at McMaster University. The goal of CHESS (Comprehensive Health Enhancement Support System) is to provide people with easy access to health and human services (Gustafson 1993). It provides information, referrals, and a way of networking to experts and similar patients.CHESS does not seem to have a dynamic user model and the information presented by it is not tailored to the patient. LEAF starts with a general user model which dynamically changes as it gathers more information from the patient. Also, LEAF presents information tailored to the patient.HealthDoc offers customized information to patients and health-education materials (DiMarco et al. 1995). It differs from LEAF in that patients can not ask the system for clarification on the material presented.The HRT Decision Making project at McMaster University is a decision support tool for perimenopausal women considering hormone replacement therapy. It asks patients a series of questions to determine their risk of laterdeveloping osteoporosis, cardiovascular disease, and other ailments.The HRT Decision Making project differs from LEAF in that it presents the same information for all patients regardless of its relevance to them and can not explain the material presented.Future WorkFuture work includes a user study, and variations of the system for different classes of users (e.g. doctors or nurses). For these additional users, we may also wish to add facilities for decision support, tutoring, or for retrieving documents from MEDLINE or the ACP Journal Club. Some questions raised by LEAF at this point are how to create an interface that first-time users can use with little effort, how much information to provide in a reply, and what kind of information can LEAF forget from the dialogue history.ConclusionThe PEAS (Patient Education and Activation System) project, being developed at the University of Wisconsin -Milwaukee, integrates two other projects. One of these projects aims to assess patients’ preferences for different treatment options based on their subjective feelings about risk and desired quality of life (Vera, In Preparation). The other project is LEAF (Layman Education and Activation Form). Its goal is to present material to patients on the basis of their medical history and subjective concerns. LEAF acts as a collaborative agent by providing patients with information they might be interested in, but don’t know exactly how to ask for. This is done by studying the dialogue history, medical form, and user model to determine the patients’ interests and risk factors. The information provided is customized to the patient.Its user model is dynamic. It changes as the interaction progresses and new information is obtained from the patient. The knowledge in the user model is acquired both explicitly and implicitly.Its knowledge base classifies diseases and their cause as objects. This is done to take advantage of the object-oriented nature of Java.At any time during the interaction, LEAF allows patients to ask questions about the medical history form itself, health issues, and the information offered.Java is also used to develop the graphical interface to PEAS. This will make PEAS accessible worldwide. Its interface incorporates hypertext, natural language dialogues, and various search techniques for fast online information retrieval.LEAF can help improve the quality and efficiency of medical care, by making patients more educated and active participants in their health care and by helping them understand the consequences of their choices.AppendixUseful URLs/Contains the latest information on Java and links to other sites with material pertaining to Java.http://hiru.mcmaster.ca/cpep/This site is still under construction. It contains some information on the HRT Decision Making Project at McMaster University:ReferencesAllen, James. 1995. Natural Language Understanding. Redwood, CA: The Benjamin/Cummings Publishing Company, Inc..Cawsey, Alison. 1992. Explanation and Interaction: The Computer Generation of Explanatory Dialogues. Cambridge, Mass.: The MIT Press.Clinical Practice Enhancement Project. 1995. Department of Clinical Epidemiology & Biostatistics. McMaster University. 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