first that the patient had hypertension, diabetes type 2,

first case study was done on a 76-year-old
male who was admitted to the hospital with ammonia.  The physician who was creating the physical
and history on this patient noticed that the patient was admitted to the
hospital about six months ago with the same issue. So, when creating the
History and Physical (H&P) for this visit the provider copied some
information from the prior visit and pasted it into the new note.  One of the lines that were captured was that
the patient had hypertension, diabetes type 2, and a urinary tract infection
(UTI).  As the patient was treated
throughout the visit and was eventually discharged the coding departed received
the documentation for this visit with the new diagnosis of pneumonia,
hypertension, diabetes type 2 and a urinary tract infection (UTI).  The coding outcome was of this account was a
DRG of 194 and a reimbursement of $4,069 dollars. The issue was that the
urinary tract infection (UTI) from the prior visit was not an issue on the
former visit and coding for the diagnosis resulted in a complications or comorbidity
(CC) however removal of the UTI changes the DRG to 195 simple pneumonia and
pleurisy without a complication (CC) and the reimbursement dropped by over a
thousand dollars to $2, 959.  This is an
example where leaving in information in this case of a prior diagnosis isn’t
implacable to the current visit had an impact on the coding and resulted in
overpayment.

 

CASE STUDY 2

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             The second case study was done on a 63 year
old woman admitted in the morning to a hospital for congestive heart failure (CHF.  Upon admission some additional lab test were
done one of which showed that the patient had a potassium level of 5.1 and the
normal range for potassium is from 3.7 to 5.2. 
In creating the documentation for this visit specifically the history
and physical note the admitting physician notices that the patient had some
prior admissions due to her congestive heart failure (CHF), copied some context
from the prior visit notes, pasted it into the current history and physical
(H) in order to speed up the creation of the note.  In doing so one of the items that was
captured was the patient’s potassium level in admission for a prior visit.  On the prior visit the patient’s potassium level
was  to 2.9, which is below the normal
range and in copying that information forward and not editing it made it appear
as if the patients potassium level in the current visit was 2.9.  Thus, moving forward to the evening shift new
hospital staff arrive to cover this patient. 
The evening shift hospitalist reads the history & physical
(H), for the new admission and notes that the potassium level is low at
2.9.  This hospitalist then ordered
potassium supplements and a recheck for the potassium levels for the next day.  The issue in this case study is the mix match
potassium levels because the patient was actually within the normal range
however the documentation made it appear as if the patients potassium level was
low.  Two issues arise from these
encounters and the most important one is an adverse event risk because
administering potassium supplements to a patient with normal potassium levels
can have a devastating impact on the patient. 
The second is avoidable cost ordering a re-check of the potassium level
is an additional cost that could have been prevented.  So, here there is a second instance of
copy-paste being used in the electronic health record (EHR) and in many
instances no issues would have occurred but now we have just one bit of
information that proves that copy forward can have some adverse event risks for
this patients encounter.

 

SPELLING ERRORS IN CLINICAL
DOCUMENTATION

 

            Accurate medical
documentation is critical for safe patient care and effective inter-provider
communication.  Medical documentation
errors can lead to some causes in injury and or even patient deaths, and is
also important for the care correlation between providers.  Studies show that about 5 million errors per
year are tied to wrong documentation involving drugs that look and sound alike
such as; Altenol vs. Alendol or Lyrica vs. Lamictal.  These spelling errors can happen due to
non-word errors such as; Humulog for Humalog and are commonly due to free-text
entries (typed notes) or real-word errors (words spelled correctly but are
contextually wrong, such as; (there for or their) Speech Recognition (SR)
generated text.  Studies used sources
including a couple of standard medical terminology such as; UMLS, SNOMED, CT,
RxNorm and Cetera. The error correction was based on Shannon’s noisy channel
model; specifically using the Daneray-Levenshtein edit distance between
misspellings and the suggestions, both in terms of their orthography and
phonetics.  To evaluate and compare their
systems performance, they used ASpell Default setting as their baseline. It is
an open, free software spell checker, which helped to show a significant
improvement in the spell check regarding precision, and accuracy.  Although, speech recognition technology has
been widely used in medical practices, the quality and accuracy of clinical
documents hasn’t been thoroughly studied or reported.  The limited scope and sample size, for the
study used a discharge summary and a progress note, and another involved two
physicians dictating 47 emergency department