NSF Workshop on High Performance Computing and Communications and Health Care
April 15, 1995
- Larry S. Davis , University of Maryland Institute for Advanced Computer Studies
- Joel Saltz , University of Maryland Institute for Advanced Computer Studies
- Jerry Feldman, International Computer Science Institute
During the summer of 1994 the National Science Foundation provided a grant to the
University of Maryland Institute for Advanced Computer Studies (UMIACS) (in
conjunction with the University of California Berkeley and the Center for
Research in Parallel Computation at Rice) to organize
a workshop on Computer Science and Health Care. The goal of the workshop
was to identify fundamental research problems in computer science,
especially high performance computing and communications, that
are critical enabling technologies for addressing issues related to
improving the quality and cost of health care in the United States.
The workshop was held in Washington D.C. on December 8-10 and brought together
over 50 scientists from academia, industry and government representing
the computer science, the medical informatics and medical science
research communities. In order to focus the
workshop, four health care application areas were identified,
each considered in depth by a working group consisting of computer and medical
scientists. The four working groups were:
Working group discussions were focused through the presentation of a sequence of
"vignettes" by medical researchers, each representing a hypothetical situation
in which an automated health care system assists in developing a solution to a
health care application. These vignettes were designed to stress the current
state of computing and lead subsequent discussion to the identification of those
basic computer science problems which must be addressed for the hypothetical health
care systems to become realizable.
- Robotics, Telemedicine and Image Processing , chaired by Prof.
Eric Grimson of M.I.T. and Dr. Anthony DiGioia of the University of Pittsburgh.
This working group focused on fundamental problems in vision and
visualization and planning that arise in robot surgery and telemedicine.
It considered how the computational
demands of these applications will drive them towards high performance
- Languages and Tools for Medical Computing , chaired by Prof. Kathy
Yelick of University of California Berkeley and Dr. Larry Kingsland of the
National Library of Medicine.
Different data abstractions are useful at different times; this working group
considered what HPC/NIE languages and methods would make it possible for users
to specify how data should be abstracted and apply these abstractions
to massive distributed databases. This will be particularly
important when providers need to browse libraries which combine
medical literature, patient data and care plans.
- Database and multi-database design , chaired by Prof. Geoffrey Fox of
Syracuse University and Dr. Bill Braithwaite of the University of
Colorado Medical School.
This group covered health services research and the use of clinical
and financial information in the management of health care organizations.
The group envisaged a set of
longitudinal patient records recording the care and health of every patient.
It addressed issues such as how one should
design multi-database architectures able to manage
patient records as well as to manipulate large quantities of
aggregate patient data, the roles HPC/NIE will play in supporting
these multidatabases, and new HPC/NIE methods that might be employed to
explore databases in order to find cost effective clinical strategies
that lead to the best possible outcomes.
- Data capture and navigation, chaired by Dr. Ian Foster of Argonne National Laboratories
and Dr. Mark Frisse of Washington University.
The data capture and navigation group was dedicated to
characterizing how medical databases and their interfaces should
be structured so that the usefulness of these databases to patients and
providers can be optimized.
The need here is to provide support
to make it possible for providers to pose clinical questions and
view the results from those queries in a timely and intuitive nature.
It will be necessary for providers to be able to
access information in
any clinical database that might contain data about a given patient.
The detailed recommendations of the individual working groups, along with the
driving medical and health care applications,
are presented in sections 2-5 of this report. Here we
summarize those computer science research topics
which are relevant to
the health care applications addressed by some or all of the working groups.
These topics were
identified by workshop participants as research areas that
the Foundation should emphasize in its high performance computing and communications research programs.
The current NSF program in High Performance Computing and Communications is
already addressing many of these problems, but some redirection of research priorities
is needed in parallel computing. The traditional NSF focus on parallel
computing as computational science needs to be expanded to meet the challenges
of advanced health care systems and other national challenges. The languages, systems, and
tools that have been used in physics and chemistry are not sufficient for the
applications considered by the workshop. Some of the additional technology areas
identified by the workshop as being critical to addressing these national challenges,
such as heterogeneous database systems, are being addressed by both other
Government research agencies and by industry, but there is a great deal of fundamental
research in systems, languages, integration tools and human/computer interaction
that only the National Science Foundation is in a position to support.
- Architectures for interoperability of information and computational
Making effective use of the database, networking, and computing
infrastructure that will be deployed in hospitals in the next five to
ten years is a very complex distributed computing
problem. Applications will have to be
able to locate patient records quickly in an emergency, from a large
distributed database system.
In addition, medical researchers
and health care system administrators will want to
link multiple patient databases to one another and to auxiliary
databases used to define such items as hospital facilities and procedures.
There is a fundamental challenge to develop system architectures
that will provide the flexibility, scalability and extensibility required to
support the wide range of software services needed by large integrated health
service providers. A key challenge is to develop extensible
systems software architectures so that the health care information systems
developed in the next five years will not become incompatible,
hard-to-extend legacy systems of tomorrow.
- Security . The extensive expansion of computing through high speed networking
has focused attention on security issues throughout the computing community.
There are security problems critical to the development of health care information
systems whose solution would also contribute to providing security in many commercial
systems. These include maintaining privacy while sharing medical information, and
guarding against denial of service, guarantees that an individual's
medical records cannot be lost or tampered with, etc. (the analogs in other domains, such as financial
ones, being obvious).
- Reliability and robustness . Reliability and robustness
are issues that arise in the design and development of any system whose
performance will effect people's safety or livelihood.
These issues become paramount in health care applications
where patient safety is an issue, such as in robotic surgical interventions, or in automated
diagnostic systems. In addition to the problems posed by the design of mechanisms
and architectures that promote reliability and robustness, issues related to
metrics for system reliability and robustness and experimental methods for
evaluating such metrics will be very important for health care applications.
- Real time high performance computing . Time critical problems arise in
robot surgery, telepresence applications and crisis management. The
fundamental research issues that will allow us to develop real time systems
that can assemble heterogeneous computational resources across networks to effectively
address such problems need to be identified and addressed.
- Knowledge and process based systems . In addition to the traditional
issues in knowledge representation that must be addressed to encode high level
semantic information about domains for health care information systems, there
are also challenging new problems related to the representation of medical "processes"
(diseases, care plans) that need to be formalized and studied by the AI community.
Methods for inference of process models from databases of case histories
and validation of process models across heterogeneous databases of patient
records are especially important problems.
- Image processing and computer vision . At the core of many advanced
health care applications are the requirements to manage, communicate and interpret
massive amounts of multi-modal, multi-temporal imagery. Fundamental problems that
need to be addressed include registration of imagery across time and modality,
segmentation of imagery into natural categories and visualization of multimodal
imagery in diagnostic and surgical domains.
- Algorithms for Analyzing and Exploring Large Datasets.
Historically, challenges posed by medical problems have motivated many
advances in the fields of Statistics and Artificial Intelligence.
Traditionally, researchers in both fields have had to make do with
relatively small medical datasets that typically consisted of no
more than a few thousand patient records.
This situation will dramatically change over the next decade by which time
that most health care organizations will have adopted computerized
patient record systems. A decade from now, we can expect that there will be
some 100 million and eventually many more patient records
with, for example, a full database size of 10 terabytes corresponding to 100
text pages of information for each of 100 million patients.
Functionalities needed in the use of and analysis of
distributed medical databases will include
segmentation of medical data into typical models or templates (e.g.
characterization of disease states),
and comparison of individual patients
with templates (to aid diagnosis and to establish canonical care maps).
The need to explore these large datasets will drive research projects
in statistics, optimization and artificial intelligence.
- Fast Exploration of Large Datasets .
Care providers and managers will want to be able to rapidly analyze data
extracted from large
distributed and parallel databases that contain both text and image data.
We anticipate that there will be significant performance issues that
will arise because of the demand to interactively analyze large
(multi-terabyte) datasets. Users will want to minimize waste of time and
funds due to searches that reveal little or no relevant information
in response to a query, or retrieval of irrelevant, incorrect or
corrupted data sets.
The workshop also identified a need for the Foundation to identify new (co-) funding
mechanisms with the more mission-oriented agencies that address problems in
health care and medicine so that computer scientists and their colleagues
from health care and medicine can both design and prototype the computer systems
that will demonstrate the feasibility of advanced applications. While the Grand
Challenge and National Challenge programs within the Foundation represent the
appropriate program structures, these programs all represent alliances between
Directorates within the Foundation. Similar, cross agency programs in health
care need to be developed, with the Foundation taking the lead in identifying the
intellectual content of these programs, working closely with agencies like the
National Institutes of Health to identify appropriate funding and administrative
Working group reports