denotes premium content | May 25 2013 Feature
posted 1 Jun 1998 in Volume 1 Issue 6
The
Evolving Role of Knowledge Management in Medicine
Bryan Bergeron, MD at MGH Critical
Care examines how knowledge management has a key role to play in the practice
and management of modern medicine.
In medicine, knowledge management
issues have long been relegated to the academic community, which was primarily
focused on supporting the more theoretical, clinical aspects of medical care.
Because of social, political and technical barriers to acceptance by practising
clinicians, there have been few obvious commercial successes in the application
of knowledge management in clinical practice. The majority of the clinical
knowledge management systems that have been developed remain as mere academic
curiosities, and do not influence the daily practice of most clinicians.
However, contemporary pressures of economics, legal and expense accountability,
as well as recent advances in information technology, have brought knowledge
management techniques to centre stage for the vast majority of clinicians.
Knowledge management is coming to be viewed by the entire medical community as a
necessary and crucial component of any modern technology-enabled healthcare
initiative.
Introduction
The practice of medicine is at a
crossroads. Clinicians, like physician assistants, nurses and other medical
knowledge workers, are finding their time compressed, their responsibilities
expanded, and their control dissipated by regulatory agencies, insurance
companies, and other third party payers. In this time of turmoil, technology is
often viewed as a lifeboat in a sea of uncertainty.1 Paradoxically, the same technologies
that many regulatory agencies use to critique and control medical practices on a
population and healthcare enterprise level make it possible for clinicians to
control their world on a patient level.2 For example, clinicians are today
saddled with the responsibility of exactly documenting the procedures and
diagnostic studies that are used to diagnose their patients. In addition, they
are expected to follow best practices - typically expressed as clinical
guidelines that have been developed by any number of agencies, from local
hospitals to national groups - in diagnosing and treating their patients. In
private practice, clinicians are additionally faced with competition from the
larger health maintenance organizations. Many of these smaller practice
clinicians view applied knowledge management technologies as one means of
competing effectively with these larger entities.
Clinicians are knowledge workers.
Traditionally, their tasks include gathering patient data, analysing the data
for patterns that they recognise, and then formulating a differential diagnosis,
which in turn leads to a treatment plan. The differential diagnosis, a list of
potential maladies that are associated with the patient presentation, represents
the potential problem landscape that clinicians must traverse. Much of their
work is involved with either refuting or affirming each of the potential
maladies listed in the differential diagnosis. One of the traits of a skilled
clinician is the ability to quickly create a short list of differential
diagnoses that presumably contains the true diagnosis, while not having so many
potential diagnoses as to create an economic and time requirements disaster for
the patient. Unfortunately, much of the traditional clinical work - work that
clinicians have trained for - is being quickly superannuated by the need to
index and code every activity, procedure, and diagnosis for billing and
administrative purposes. It is not uncommon for some clinicians to devote 40
percent of their time in these non-clinical activities.3
Technologic innovations aimed at
curbing cost and time expenditures have fuelled considerable interest in
knowledge management tools. For example, the electronic medical record (EMR),
the repository for all patient information at a given hospital or health care
enterprise, represents the new core knowledge management vehicle in clinical
practice.4 The EMR, sometimes
referred to as the longitudinal medical record, contains the complete medical
history for a given patient, including not only information on the patients
current complaints, but on the past medical history and treatment as well. Once
a medical enterprise makes an EMR system available to clinicians, it becomes
relatively straightforward to develop ancillary programs that feed off of the
patient data.5 For example,
decision support tools, including expert systems, can use the EMR as the basis
for assisting in clinical decision support, including formulating a differential
diagnosis. However, the attraction for most clinicians is the ability to
identify potential avenues for increased reimbursement, and to quantify clinical
outcomes for administration and planning purposes. In contrast, the records of
paper-base practices, which include the majority of US medical practices, must
be analysed by hand. This slow, tedious process is usually based on samples of
patient records because of the sheer number of records involved. As such, the
data are not as timely, are more open to variation in interpretation, and are
more error-prone due to errors in transcription.
A Historical
Perspective
As noted earlier, knowledge management techniques have been used in
medicine, with varying degrees of success, for decades. The more notable
projects have been primarily associated with medical expert system development,
critiquing systems, in the creation of decision support tools, as well as
visualisation techniques used to make information more understandable.6,7 In these and other areas, knowledge
acquisition and management have been the weak links in development. Also,
because clinicians have traditionally placed little emphasis on clinical
decision support assistance, there has been relatively little commercial
activity in this area. Most of the research and development has been limited to
academic research centres.
One of the areas that knowledge
management techniques have been used with some degree of success is medical
education.8 For example, keeping
track of multimedia teaching files for a radiology course and for a resident
teaching file is no mean task. There are the rather easily addressed issues of
identifying the most appropriate storage media and database structure, but these
issues aside, there is an even more critical issue of indexing images so that
they can be quickly be identified and, most importantly, repurposed in different
projects. For example, consider the difficulty of indexing several thousand
histology slides. The images could be indexed by cell type, gross location,
anatomic origin, stain type, pathogen or disease state. Because creating a
multimedia library is so time intensive, failure to be able to quickly locate
any media element in a knowledge base translates to lost productivity, added
expense, and, at best, faculty frustration.
The same knowledge management
techniques that have been applied to medical education, especially in multimedia
indexing, retrieval, and repurposing, have been applied to clinical information
management as well. For example, the issue of how to best archive and later
retrieved radiographic images has received considerable attention. There are the
technical issues of care and marking of the films, together with indexing,
transport from one laceration in the enterprise to another, and tracking. With
the move to digital imaging, the relatively large (10MB or more per image) image
size is a technical challenge, especially when it’s desirable to have tens of
thousands of digital radiographic images on hand within a few minutes notice.
Hardware issues aside, there are knowledge management issues of how to best
store images for rapid access.
Contemporary knowledge
management issues
Knowledge management has a prominent
place in the modern medical practice. While research continues in how to best
apply knowledge management tools and techniques to clinical decision support and
medical education, most of the effort in the field is focused on data gathering,
in defining best practices for maximum quality of care, in maximising
reimbursement, in managing electronic medical records, in communicating with
colleagues, and in predicting individual and a population outcomes. Today, the
pressures of outcomes measures, determining best practices, maximising
reimbursement, and quality controls, all require timely, accurate information.
Clinician interest is firmly affixed on the economic survival of their practice.
For hospitals, reimbursement from the government and other third party payers is
keyed to diagnostic impressions and physician work. As such, there is
significant motivation to understand, catalogue, and have readily available
timely local patient demographics, disease prevalent figures, and other
populations specific problems.
A prominent example of how knowledge
management is influencing medicine is in the development and use of a standard
medical vocabulary.9 A standard
vocabulary for describing patient findings, clinician orders, and patient
progress notes not only allows disparate electronic record systems in the same
hospital or clinic to share information, but it also allows regional and
national comparison of patient data. For example, clinicians in one department
might use the term “high blood pressure”, whereas another group of clinicians
might use the term “hypertension” to describe the same concept. Since this
preference varies from physician to physician and from hospital to hospital, a
virtual tower of Babel often results.
There have been numerous projects that
have attempted to create either a universal medical language, or to provide a
translation medium between systems. For example, there is the Unified Medical
Language System (UMLS) project, and the older Medical Subject Heading (MESH)
vocabulary, both funded by the National Library of Medicine.10,11 Both attempt to address the tower
of Babel issue by using concepts that map onto a variety of naming schemes. For
example, hypertension and high blood pressure map to the same meaning. The
importance of this mapping is that it allows free, unencumbered access to
thousands of medical record documents that would have otherwise been
unexaminable. For example, when searching a knowledge base for “high blood
pressure”, the user generally wants references to equivalent terms as well. The
alternative, of course, would be to have the researcher perform the translation,
supplying all possible related terms. However, such an approach would be time
and resource intensive, and prone to errors of omission.
A related knowledge management issue
that has recently become significant in the medical community is how to handle
the myriad of medical resources now available through the World Wide Web.12 While some well-known,
high-quality resources, such as MEDLINE, from the National Library of Medicine13, are relatively easy to locate,
other resources are not.14 Even
so, many clinicians and their patients are making use of medical resources on
the Web at a surprising rate. There are resources for continuing medical
education, for decision support, and for creating electronic medical records.
Although there are issues of security and confidentiality, Web-based electronic
medical records is certainly the wave of the future. Given the richness and
variable quality of the medical resources available through the Web, there is
considerable pressure to develop a workable knowledge management scheme that
provides easy-to-use, efficient access to a variety of medical sites.
Another example of how
knowledge management technologies are being applied to modern medicine is in the
areas of medical voice recognition.15 With the increasing cost and
delays associated with traditional transcription services, where clinicians and
hospitals pay typists to transcribe their recorded transcripts of patient
findings, there is renewed interest in alternative recording technologies. One
of the most promising, voice recognition, requires that context-specific
language models be created for each area of practice. For example, there are
typically different language models for radiology, oncology, pathology, and
primary care. Creating a language model, which describes both the vocabulary and
probability of word phrases, requires processing thousands of typical medical
records for a given speciality. These prototypical records must be selected and
analysed carefully for the best results.
A third example of how knowledge
management techniques are being applied in modern medicine is in maximising the
reimbursement to clinicians from third-party payers. Because the rules of
reimbursement are so convoluted and difficult to understand, many clinicians
simply accept lower pay for more work. Others are looking actively to utilities
that can help them decide how to best manage their time and their patient care
practices to receive appropriate compensation. For example, there are systems
that interactively process the EMR and inform the clinician, during the patient
encounter, that how to increase reimbursement without compromising the quality
of patient care or subjecting the patient to any unnecessary procedures. These
knowledge-based systems suggest, for example, that the clinician might want to
examine the ears in order to move from one payment level to another (which can
amount to approximately $45 for an additional 30 seconds of work). The danger,
of course, is that clinicians can use these programs to “game” the healthcare
reimbursement system, increasing the cost of healthcare in the long term.
Summary
Knowledge management has a key role in
the practice and management of modern medicine. Due to economic pressures, the
clinical role is greatly overshadowed by systems and techniques that have a
direct, immediate impact on the bottom line. Medicine has become big business,
and knowledge management has become another tool in the healthcare
administrator’s arsenal against the bottom line.
Bryan Bergeron is MD at MGH
Critical Care. He can be contacted at:
bergeron@hstbme.mit.edu
