Improving the Accuracy and Timeliness
of the Medical Problem List
Peter Haug,
1,
2
M.D. ,
University of
Introduction
In recent years, events have focused
national attention on the quality of medical
care. Not only are there continuing efforts to
define and promote quality medicine, but
also the causes of medical errors are
receiving intense scrutiny. A substantial
subset of the errors seen in modern care
settings relate to failings in the diagnostic
process. These failings represent not only
errors in assigning the proper diagnosis to
patients but also reflect a failure to
communicate known diagnoses among the
multiple healthcare professionals who
participate in the delivery of care to an
individual patient.
Stephane Meystre, M.D.,
Kathryn Gibb Kuttler,
2
1,
2
Ph.D. , Jauhuei Lin, M.D., Ph.D.
1
Utah
and Intermountain
2
Healthcare ,
Salt Lake City, Utah
Results
Three Approaches to Support the Medical Problem List
Figure 1
The use of natural language processing (NLP) to support
the completeness of the Problem List (Figure 1). In
analyzing electronic Problem Lists, we observed a low
level of completeness and timeliness in this document. In
response, we developed a NLP system to automatically
search for a group of common diagnoses in narrative
documents from the medical record. When a diagnosis
was identified, it was compared with existing diagnoses
recorded in the problem list. If absent, it was proposed as
an addition to this list the next time a clinician accessed
the Problem List Management Application (PLMA).
In a clinical environment where patient data
is managed electronically, the problemoriented approach to medical documentation
can contribute to the delivery of consistent,
high quality care. The key to this model is
the basic medical Problem List.
The
Problem List serves the dual function of
providing a succinct summary of the patient’s
aggregate medical condition as well as a
focus around which the continuing process
of documenting medical data, decisions, and
the plan of care can evolve.
•NLP System: A significant improvement in
the completeness of the problem list was
noted in a randomized trial of the technology.
•Data Preparation Framework: The DPF
appears capable of dramatically reducing the
amount of time necessary to assemble the
data and apply the training algorithms
required to build disease-detection modules
•Structured Daily Note: Incorporating
reporting of new problems into the automated
intensive care note increased the frequency
with which these problems are appropriately
documented.
Deriving suggested problems using machine learning
techniques (Figure 2). In the past, we have used machine
learning techniques to develop diagnostic systems for single
diseases of interest. Developing these models has proven to
be labor intensive. Recently, we have tested a Data
Preparation Framework (DPF) that greatly reduces the effort
required to train individual diagnostic modules and that
optimizes the data for machine learning. We anticipate that
this approach will allow us to generate hundreds of modules
These modules will inspect each patient's clinical data and,
upon concluding that an undocumented disease is present,
will add it to the list of proposed problems for adjudication by
the physicians in the PLMA.
Problem Statement
Maintaining the medical problem list as a
component of the EHR resolves a key
challenge by providing this resource in
multiple care settings simultaneously.
However, in order to guarantee the accuracy
and timeliness of the Problem List,
approaches unique to the electronic
healthcare environment are required. Here
we describe three efforts which we believe
will enhance the value of the Problem List
and help maximize the ability of this
document to influence care.
1
Ph.D. ,
Conclusion
The three approaches described will provide
us a starting point in our efforts to improve
the completeness and timeliness of the
electronic medical Problem List. Our goal is
to first guarantee that the list is complete and
timely. Second, we would like to use the
information captured there to support key
clinical processes including ordering, clinical
documentation,
and
the
delivery
of
interventions in a way consistent with each
patient's overall clinical status.
Medical
Information
Bus (MIB)
Figure 2
HELP System
RN Patient Care
Embedding problem documentation in a
structured daily note (Figure 3). In an effort
to incorporate problem list management into
the daily documentation effort, we developed
an electronic, intensive care note. This
application expedites documentation-byorgan-system
while
encouraging
the
recording of those problems associated with
each system reviewed.
TACX
IP/RP
MD Examines
Patient
Acknowledgements
Data
Automatically
HELP2 CDR
Inserted into
Stored as
Note
Webform’s
components
Data Abstracted
Incomplete
Progress Note
taken to Rounds
Data Abstracted
MD & RN
Patient
Update
XML Blob
Critical Care
Progress Note
started as a
HELP2 webform
(Watermark printed
preliminary page)
via Cocoon; XSLT
Figure 3
•Deseret Foundation Research Grant: BNs in
Problems, 2002 (Co-PI’s: Peter Haug, MD and
Jauhuei Lin, MD)
•Deseret Foundation Research Grant: Problem List
Management
Using
Natural
Language
Understanding, 2003 (Co-PI’s: Peter Haug, MD
and Stephane Meystre, MD)
Contact Information
Peter Haug MD
Peter.Haug@imail.org