Can Health
Care Information Technology
Adapt?
Prepared for
Veterans Health
Administration
Department of Veterans
Affairs
Tom Munnecke
Science Applications
International Corporation
(858) 756 4218
Version 1.0
Available at http://www.munnecke.com/papers/D23.doc
Complexity, Information, and our Ways of Understanding
How Adaptive Have Our
Systems Been?
Lessons Learned from Y2K
Issue
The Transition to Genomics
and Proteomics
Transition from Biological
to the Genetic Era of Medicine
Science and Biological
Medicine
Imagine a scientist
trying to understand a symphony played by an orchestra. The scientist might start by putting a
microphone in the audience, recording the sound waves as they come from the
orchestra. These would be digitalized
into bit streams to be analyzed by a computer to look for patterns. When the first attempt failed, the scientist
might increase the sensitivity of the microphone and increase the sampling
rate, generating even more data. When
this attempt failed, the scientist might add 15 more microphones in different
sampling positions, hoping to finally get enough data to understand the music.
If the scientist
wandered up on stage, however, the score used by the conductor would be
obvious. The notes of the symphony could be represented with
kilobytes of information. The gigabytes of data
collected by the array of microphones made it difficult to understand what was
obvious and simply represented on the musical notation.
The conductor’s score
represented a language which was interpreted to become the symphony. The technique of recording
all the emanations of the instruments as discrete events and digital
“snapshots” lead to an ever-increasing labyrinth of complexity. More data created more complexity. Having a musical language, however, creates a
simpler way of representing what otherwise would be an enormously complex
undertaking.
Our current situation in health care
can be likened to that scientist in the auditorium. We are already
receiving an overwhelming array of data and
information, and we know that with the advent of
genetically based medicine this flow will increase dramatically,
perhaps by orders of magnitude. This information may
be of a fundamentally different nature than what we are receiving today. Our current models of
understanding health and medicine may undergo fundamental
revisions.
Perhaps this new technology will
appear gradually, merely being minor additions to the formulary and some
additional lab tests. Current physicians
will be able to study papers and take some CME courses to understand it. Perhaps some new specialties will arise
within the existing framework of health care delivery.
On the other hand, these changes may
have far greater scope than is currently imagined. Issues of privacy, politics, fear of the
unknown, and media frenzies may swamp scientific evidence and
clinical research. New knowledge may
emerge from the lab and be driven by direct to consumer
marketing activities faster than our current knowledge system, to the extent
that it exists, can assimilate them.
Efforts to automate the medical
record go back at least 30 years, yet there is no
wide-spread success.
Clinical knowledge can take 17
years to disseminate for general use. In today’s world of “Internet time” these
numbers are amazingly long. Medicine and our health care system stands on
the brink of waves of rapid change, yet its information and knowledge
infrastructure stands as one of the longest running failures in the information
technology industry.
Like our symphony scientists getting
overwhelmed by the data generated by their array of microphones, medicine and
health care are being overwhelmed by inappropriate
information and knowledge structures. Our way out of the exploding complexity we
face is through smarter information
structures, and perhaps wandering out of the audience to discover a
higher level language – a “score” which simplifies our quest.
Waves of Accelerating Change
There
is a huge gap between technology and our ability to apply it in health care,
much of which reduces to our ability to handle information:
“Health
care today is characterized by more to know, more to manage, more to watch,
more to do, and more people involved in doing it than at any time in the
nation’s history. Our current methods of
organizing and delivering care are unable to meet the expectations of patients
and their families because the science and technologies involved in health care
– the knowledge, skills, care interventions, devices, and drugs – have advanced
more rapidly than our ability to deliver them safely, effectively, and
efficiently.[1]
We
can expect these technological changes to continue at an increasing rate from
many different directions. Technology in
general is accelerating:
“[We are approaching] the "perfect
storm" of the converging exponentials of bio-X, nanotech, and information
technologies/telecommunications. They will cause more change in less time than
anything humankind has ever witnessed.”[2]
Specific advances in
proteomics will have dramatic effects on clinical systems:
“The
next technological leap will be the application of proteomic technologies to
the bedside…This will directly
change clinical practice by affecting critical elements of care and management. Outcomes may include early detection of
disease using proteomic patterns of body fluid samples, diagnosis based on
proteomic signatures as a complement to histopathology, individualized
selection of therapeutic combinations that best target the entire
disease-specific protein network, real-time assessment of
therapeutic efficacy and toxicity, and rational modulation of therapy based on
changes in the diseased protein network.”[3]
Our understanding of
interactions between drugs and genotype-specific activities will also trigger
tremendous changes in health care:
Pharmacogenomics requires the integration and
analysis of genomic, molecular, cellular, and clinical data, and thus offers a remarkable set
of challenges to biomedical informatics. These include infrastructural
challenges such as the creation of data models and data bases for storing this data, the
integration of these data with external databases, the extraction of information from natural language
text, and the protection of databases with sensitive information. There are also
scientific challenge in creating tools to support gene expression analysis, three-dimensional
structural analysis, and comparative genomic analysis.[4]
Given these dramatic
and accelerating forces on our health care system, it is instructive to look at
how well the current system adapts to change.
Past history does not indicate a particularly adaptive
response to even simple issues:
·
An average of 17 years
is required for new knowledge generated by randomized controlled trials to be
incorporated into clinical practice.[5]
·
Changing our computer systems to deal with the Year 2000 (Y2K) problem cost the
·
The feedback loop between treatment and its
effectiveness has not always worked well:
“By
the time Moniz and Hess shared the Nobel Prize in 1949, [for inventing the
frontal lobotomy] thousands of lobotomies were being performed every year. Yet by the end of the 1950s, careful studies
revealed what had somehow escaped the notice of many practicing physicians for
two decades: the procedure severely damaged the mental and emotional lives of
the men and women who underwent it.
“Lobotomized” became a popular synonym for “zombie,” and the number of
lobotomies being performed dropped to near zero.”[7]
·
Despite 30 years of
aggressive attempts to create an electronic medical
record, this goal is still elusive. For example, in 1991, the Institute of
Medicine’s Committee on Improving the Patient Record set a goal of making the
computer-based patient record a standard technology in health care by 2001.[8] Given
the pressures of cost cutting, continuous changes in the industry, and
increasingly complex issues relating to privacy, liability, bioterrorism, and
genetic information security, it is likely
that our ability to achieve this
goal is diminishing, rather than increasing. One reason for this continued failure is the
brittleness of the technology we are attempting to use. It is simply not adaptive enough for the
task. A Critical Time to Act
The calendar
change
to the new millennium triggered a Y2K problem of immense magnitude. Some pPredictions
ofed a global recession as
computer systems, electronic funds transfers, and transportation systems shut
down did not occur. The fact
that the world
could bewas brought to the brink
of such catastropheYet the Y2K problem
was a relatively minor change to the system:s is
remarkable due tofor the following reasonsissues:
1.
1.
The root problem was trivial – expanding a date field from two
to four digits was something that could be
accomplished by even the most inexperiencednovice programmers. The problem was easily stated and recognized
2.
2.
We had perfect foreknowledge of the problem. The fact
that there would be a year 2000 was always known.
The arrival of
3.
3.
The problem was reversible. With certain
exceptions (for example, the safety of a factory control system), problems
which may have been encountered during the changeover would have triggered
delays in operation. For example, Eeven if an airline reservation system
failed, for
example, service could be eventually restored and the system could returned
to normal.
4.
It
illustrated the network effect. The problem did not only exist in isolated
computer systems, but also in all of the interconnections between them. Electronic funds transfer systems, for
example, connected the world’s banking systems
together, and a failure in a critical component could have cascaded into other
systems. What started out as isolated,
enterprise-only applications had become globally connected.
Nevertheless, avoiding
the this
problemY2K problem cost the United States an
estimated $100 billion and the federal government $8.5 billion to avoid
the Y2K problem.
The world is
facingnow faces another mega-issue, based on our rapidly
increasing understanding
knowledge of DNAgenomicsAs w. We are just beginning to unraveling the complex mysteries of the gene, . O our understanding of genomics and
proteomics could
will
have
dramatic effects on our