Data Capture and Navigation
- Ian Foster itf@mcs.anl.gov
- Mark Frisse Mark_Frisse@msnotes.wustl.edu
- Andy Grimshaw grimshaw@virginia.edu
- Carl Kesselman carl@vlsi.cs.caltech.edu
- Melanie Loots mloots@nlm.nih.gov
- Peter Steenkiste peter.steenkiste@cs.cmu.edu
- Joel Saltz saltz@cs.umd.edu
Introduction
This working group focused on the ways in which pervasive access to data
could impact the care provided to individual patients. and the
computer science issues that, if successfully addressed, can be
expected to increase the utility and reduce the cost of this data
access.
At present, there are many structural impediments to the effective
use of computing technologies in health care, such as limited
computerization of databases, a lack of standard database formats,
limited networking, and a reluctance to share data. However, it
appears likely that within a five to ten year timeframe, these
structural impediments will be largely removed. In part because of a
demand for closer accounting for health care costs, and in part
because of the increasing trend to consolidation of hospitals into
larger administrative units, we expect that the health care system of
the future will be characterized by:
- Widespread integration of databases
- Use of universal patient identifiers to provide unambiguous
access to patient information
- Development of high speed ATM networks, and digitization of
most health care data modalities (X-rays, MRI, etc.)
We also assume the widespread deployment of settop boxes in the home,
allowing interactive consultations with remote physicians; it appears
quite likely that these will form a significant part of a future
health care infrastructure.
Health Care Application
Listed below are a
few specific applications that have implications for computer science
research:
- Teleconsultation with patients in their homes
- This class of applications will require
sophisticated computer-aided diagnosis support to aid in diagnosis
from limited data.
- Identification of "similar" case histories.
- This is a complex problem
that requires the ability to encode "process" (care plans and
diseases) and to detect similarities between process encodings
- Software Integration
- For example, the integration of both computer simulations and image processing
modules into diagnosis systems requires developments in parallel
algorithms and distributed systems
- Expert assistants
- The development of "expert assistants" for diagnosis, alerts for
unusual events during treatment, and public health warnings requires
significant developments in A.I.; it may also involve difficult
High Peformance Computing Issues
Making effective use of the database, networking, and computing
infrastructure that will be deployed in hospitals in the next five to
ten years is first and foremost a very complex distributed computing
problem. To a large extent, the research challenges that must be
addressed to make this infrastructure truly usable will be the same
as those faced in commerce, manufacturing, and entertainment.
However, health care also introduces some unique concerns. In the
following, we examine a range of both generic and health
care-specific issues.
The most fundamental challenge, in our view, is to develop system
architectures will provide the flexibility, scalability, and
extensibility required to support a wide range of health care
applications. The danger is that the health care information systems
developed in the next five years will become incompatible,
hard-to-extend legacy systems of tomorrow. Computer scientists can
help informaticients avoid this problem by working with them to
develop appropriate system architectures. Particular challenges
include:
- Software Architectures
- Developing architectures that support the interoperation of diverse
information and computational resources. The integration of
computational resources into a data-intensive distributed computing
infrastructure.
- Resource naming, discovery, and management
- Applications must be
able to locate patient records quickly in an emergency, from a large
distributed database system. Expert assistants and image processing
systems must be able to locate computational resources, or database
resources containing (for example) representative case histories for
comparative purposes. The soft real time character of many medical
applications makes the efficiency of these mechanisms, and the
algorithms used to schedule resources, particularly important.
- Encoding process
- One important research area which seems to
underly many aspects of future health care systems is the development
of techniques for encoding "processes," such as care plans and
diseases. These techniques need to support the development of
process representations, the automatic detection of processes from
database records, and identification of "similar" process
representations. The research problems here seem to be primarily
concerned with knowledge representation and A-I., although HPCC
issues may arise if the fundamental algorithms are computationally
demanding.
- Security
- Problems particular to health care include maintaining
patient privacy when sharing medical information, and guarding
against denial of service. Privacy is problematic because of
numerous database applications will require the sharing of data, yet
it is remarkably difficult to "sanitize" patient records to prevent
detection of a patient's identity. Denial of service issues arise
because inevitably computational and network resources used for
life-critical applications (such as image processing or expert
assistance during surgery) will also be used for other applications.
- Reliability
- The health-care infrastructure must clearly be highly
reliable and fault tolerant. As demands on this infrastructure can
be expected to be particularly heavy following disasters, the
infrastructure must be designed to cope significant damage.
Tradeoffs between security, reliability, and performance must be
carefully evaluated.
- Parallel algorithms
- Applications developed using the
infrastructure are likely to incorporate computer simulations, impage
processing modules, large-scale statistical analysis, and expert
assistants. Many of these system components must execute in soft
real time. Research in parallel algorithms will be required to allow
their efficient execution.