A framework for distributed mediation of temporal-abstraction queries to clinical databases

doi:10.1016/j.artmed.2004.07.009

Artificial Intelligence in Medicine

Volume 34, Issue 1, May 2005, Pages 3-24

https://doi.org/10.1016/j.artmed.2004.07.009 Get rights and content

Summary

Objective:

The specification and creation of a distributed system that integrates medical knowledge bases with time-oriented clinical databases; the goal is to answer complex temporal queries regarding both raw data and its abstractions, such as are often required in medical applications.

Methods:

(1) Specification, design, and implementation of a generalized access method to a set of heterogeneous clinical data sources, by using a virtual medical-record interface and by mapping the local terms to a set of standardized medical vocabularies; (2) specification of a generalized interface to a set of knowledge sources; (3) specification and implementation of a service, called ALMA that computes complex time-oriented medical queries that include both raw data and abstractions derivable from it; (4) design and implementation of a mediator, called IDAN, that answers raw-data and abstract queries by integrating the appropriate clinical data with the relevant medical knowledge and uses the computation service to answer the queries; (5) an expressive language that enables definition of time-dependent medical queries, which are referred to the mediator; (6) evaluation of the effect of the system, when combined with a new visual interface, called KNAVE-II, on the speed and accuracy of answering a set of complex queries in an oncology sub domain, by a group of clinicians, compared to answering these queries using paper or an electronic spreadsheet.

Results:

We have implemented the full IDAN architecture. The IDAN/KNAVE-II combination significantly increased the accuracy and speed of answering complex queries about both the data and their abstractions, compared to the standard tools.

Conclusion:

The implemented architecture proves the feasibility of the distributed integration of medical knowledge sources with clinical data of heterogeneous sources. The results suggest that the proposed IDAN modular architecture has potential significance for supporting the automation of clinical tasks such as diagnosis, monitoring, therapy, and quality assessment.

Section snippets

Introduction: the need for integration of clinical data and knowledge

Most clinical tasks require measurement and capture of numerous patient data of multiple types, often on electronic media. Making diagnostic or therapeutic decisions requires context-sensitive interpretation of these data. Most stored data include a time stamp in which the particular datum was valid. Temporal trends and patterns in clinical data add significant insights to static analysis. Thus, it is desirable to automatically create abstractions (short, informative, and context-sensitive

IDAN: a modular distributed temporal-abstraction mediator—an overview

IDAN is a conceptual and practical architecture that fully implements the temporal-abstraction mediation approach. In this section, we will give an overview of IDAN's components and their inter-relations. In the next section, we will describe in detail each of the IDAN components. In order to clarify the following discussion, we will start by defining several useful terms.

Temporal abstraction distinguishes among three types of objects: concepts, subjects and time intervals (Fig. 1). A concept

A detailed description of the IDAN architecture's components

In the following sections, we describe in details the methods and components used in the IDAN architecture. In Section 5, we present the main advantages (and potential disadvantages) of the architecture, and the future enhancements planned for the architecture.

Evaluation of the IDAN framework

A preliminary evaluation of the functional aspects of the IDAN architecture was performed by querying a clinical database through the interface of the KNAVE-II interactive visual exploration module [26]. The evaluation included also the usability aspects of the KNAVE-II interface, and was carried out by our collaborators in the Palo Alto, CA, USA, Veterans Administration Health Care Center, and is described in more detail elsewhere [28].

In the IDAN/KNAVE-II preliminary evaluation, the IDAN

Discussion

Several previous approaches, which have answered the need for integration of clinical data from various sources, such as that of Xu et al. [2], have focused on fusion of the data from multiple heterogeneous sources, but referred the actual abstraction to the requesting application. However, such a mechanism, although quite useful for data access, still puts the specialized task of temporal abstraction squarely on each application's shoulders. In particular, it is neither specific to the

Acknowledgments

This research was supported in part by NIH award No. LM-06806 and Israeli Ministry of Defense Award No. 89357628-01. We want to thank Dr. Mira Balaban for her help in the formal definition of the TAR language; the staff of the medical-informatics laboratory in the Ben-Gurion University for their useful comments, Samson Tu and Martin O’connor for useful discussions regarding the Chronus-II and RASTA systems, and Drs. Mary Goldstein, Susana Martins, Lawrence Basso, Herbert Kaizer, Aneel Advani,

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The Picard DSS engine uses our Digital Guideline Electronic Library (DeGeL) architecture [22] to retrieve procedural GLs and specific knowledge items when applying a GL. Our IDAN temporal-abstraction mediator [23] is used to answer GL-specific queries regarding knowledge-based interpretations of the time-oriented clinical data. Currently, the Picard framework serves as the backbone of the MobiGuide European Union project [24], and is used to send alerts and personalized GL-based recommendations to chronic patients (based on data from bio sensors on the patients or at their home), through the patients’ mobile devices, or to support the decisions of the patients’ care providers at the ambulatory clinic or hospital.
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Thirty-six clinicians, including residents at different training levels and board-certified specialists at an academic OB/GYN department that handles around 15,000 deliveries annually, agreed to evaluate our continuous-care guideline-based DSS and to perform a cross-over assessment of the effects of using our guideline-based DSS. We generated electronic patient records that realistically simulated the longitudinal course of six different clinical scenarios of the preeclampsia/eclampsia/toxemia (PET) GL, encompassing 60 different decision points in total. Each clinician managed three scenarios manually without the Picard DSS engine (Non-DSS mode) and three scenarios when assisted by the Picard DSS engine (DSS mode). The main measures in both modes were correctness and completeness of actions relative to the PET GL. Correctness was further decomposed into necessary and redundant actions, relative to the guideline and the actual patient data. At the end of the assessment, a questionnaire was administered to the clinicians to assess their perceptions regarding use of the DSS.
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A continuous-care GL-based DSS, such as the Picard DSS engine, has the potential to prevent most errors of omission by ensuring uniformly high quality of clinical decision making (relative to a GL-based norm), due to the increased adherence (i.e., completeness) to the GL, and most of the errors of commission that increase therapy costs, by reducing the rate of redundant actions. However, to prevent clinical errors of commission, the DSS needs to be accompanied by additional modules, such as automated control of the quality of the physician's actual actions.

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A framework for distributed mediation of temporal-abstraction queries to clinical databases

Summary

Objective:

Methods:

Results:

Conclusion:

Section snippets

Introduction: the need for integration of clinical data and knowledge

IDAN: a modular distributed temporal-abstraction mediator—an overview

A detailed description of the IDAN architecture's components

Evaluation of the IDAN framework

Discussion

Acknowledgments

Artif Intell

Artif Intell Med

Component-based mediation services for the integration of medical applications

Artif Intell Med

Integration of temporal reasoning and temporal-data maintenance into a reusable database mediator to answer abstract, time-oriented queries: The Tzolkin System

J Intell Inform Syst

Mediators in the architecture of future information systems

IEEE Comput Mag

The conceptual basis of mediation services

IEEE Expert

Patterns of enterprise application architecture

Applying temporal abstraction in medical information systems

Ann Math Comput Teleinform

Temporal deductive databases

Advancing the state of data integration in healthcare

J Healthcare Inform Manage