Patient Advocate:

Cooperative Agents to Support Patient-Centered Needs and Demands


Project Description
Project Members
Research Funding
Publications


The Patient Advocate project is part of the Adaptive Intelligent Systems project at the Knowledge Systems Laboratory, which in turn is part of the Computer Science Department at Stanford University

Project Description

Knowledge-based monitoring and therapy planning systems were mainly built for the convenience of health care providers. They neglect the consumers of health care, namely, the patients. 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.

Clinical treatment protocols are represented in an intention-based temporal representation language to overcome the drawbacks of vague or ill-structured problem definitions (e.g., missing functional dependencies). These representations are used to guide the patient, to provide necessary explanations, and to observe and critique whether the patient obeys the instructions of the health-care provider.

Patient Advocate's Functionality

In general, the Patient Advocate project supports the following functionality :
  • Monitoring and consultation regarding the patient's health condition:
  • The improvement of technical equipment facilitates more frequent and accurate monitoring of patient's health condition. There are several kinds of data about the patient's health condition available from different clinical analyses and devices. All this information is time-stamped and time-varying as a result of changing therapeutic actions and reactions of the human body. The health care provider has to analyze and interpret as much data as possible to derive diagnoses and to recommend appropriate therapeutic actions. In particular the patients have to comply with health care providers' instructions and advice, to exercise health-related common sense, and to monitor themselves for condition-specific danger signs over a period of time. Medical consulting time is expensive. Some patients cannot afford the expense or the time for adequate and regular medical consulting meetings.

    Therefore, the central aim of the Patient Advocate project is to assist in clinical practice by helping patients to get a closer insight into their health conditions. Task-adequate methods give a global, comprehensive picture of all information available (e.g., observed parameters available at home, physicians'/nurses' instructions and advice) including explanations about the patients' needs, preferences, and experiences as well as the degree of severity of a situation. Such a comprehensive picture can be achieved by context-sensitive interweaving of different knowledge-based approaches to classify input data and by adequate visualization techniques. Currently, our attention is directed at this interweaving process, even though it is a very complex and partially domain-specific task.

    The monitoring and consultation tasks consist of data selection, data validation, data abstraction, data visualization, recommendations, and explanations of data and vision.

  • Facilitating access to web resources:
  • Hundreds of medical information resources around the world are available on the World Wide Web (WWW), including information from federal agencies (e.g., NIH, FDA), clinical guidelines and protocols, literature and library services, medical encyclopedias, continuing medical education resources and, of course, many other non-medical services. Exploring the WWW is a time-consuming and unreliable task because there are too much data available. The variety of candidate sites and paths results in failing to retrieve necessary and accurate information in a reasonable time.

    The Patient Advocate will facilitate a context-sensitive access to these various resources, providing the patient with additional explanation or teaching utilities on request. The guiding principle is that only those sites and resources are activated which are meaningful for the current patient's health condition. These sites and resources are annotated with an importance ranking to simplify the search process.

  • Coordinating patient-relevant information:
  • The patient often requires, or benefits from, interaction with different health care professionals, other patients, or support groups. Therefore, the patient is usually confronted with several coordination issues. She/He has to schedule a new appointment with the health care provider, remember scheduled or unscheduled therapy, find other patients with similar clinical conditions or support groups, and more. The Patient Advocate will provide the patients with necessary tools to assist these coordination tasks, such as an appointment scheduler, email connection to the medical staff or other patients, or access to supporting newsgroups.

    In the current phase of the Patient Advocate project we are working on a prototype to implement an intelligent assistant performing the monitoring and consulting of the patient's health condition. Figure 1 shows a mock-up of a prototype which monitors and consults women with gestational diabetes mellitus.

     
    Figure 1: Part of Patient Advocate's user interface (scenario GDM Type-II)


    Project Members


    Research Funding

    Patient Advocate is part of the HIIP project (Health Information Infrastructure Program) supported by DARPA Grant N66001-94-D-6055. Silvia Miksch was supported by "Erwin Schrödinger Auslandstipendium, Fonds zur Förderung der wissenschaftlichen Forschung", J01042-MAT.


    Publications

    These are recent papers relating to the Patient Advocate project. Other relevant papers are listed under Adaptive Intelligent Systems.
    Last update July 31, 1996 by Silvia Miksch, miksch@hpp.stanford.edu