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from 10.8 to 13.8 minutes. The mean provider-to-disposition
time increased from 48.0 to 49.8 minutes (P = 0.01). Door-toadmission time was not signiﬁcantly diﬀerent between pre- and
postimplementation (Table).
Even though many of the service intervals were prolonged,
there were improvements after implementation for two separate
timeframes. The ﬁrst of these was for bed-to-provider time. This
service interval was shortened by nearly 1.5 minutes, from 4.2
minutes to 3 minutes (P < 0.01). The second service interval
that improved in average time was the disposition-to-admit,
which improved from 85.8 to 78.6 minutes, a mean diﬀerence
of 7.2 minutes. It should be noted, however, that the P value
of 0.8 implied that this was not a statistically signiﬁcant difference (Table).
The two clinical outcome measures of leaving without being
seen and leaving against medical advice had surprisingly diﬀerent signiﬁcance between the two groups. The number leaving
against medical advice nearly doubled from 6.3 (SE 0.85) patients per month to 11.6 (SE 1.17) per month (P < 0.01), yet
the number leaving without being seen remained similar at 19.5
patients per month before implementation and 15.5 patients
per month after implementation (P = 0.24) (Table).
DISCUSSION
Many facilities struggle to manage the same volume and
acuity of patients in the same timely manner as they had prior
to EHR implementation. This study has added an additional
purview of similar results, with the addition of a longer data collection timeframe. Overall, patient visit metrics appeared to be
mostly negatively impacted during the EHR implementation.
LOS and door-to-door, door-to-bed, and provider-to-disposition times were all found to be longer after implementation,
yet improvements in bed-to-provider and disposition-to-admit
times after EHR implementation were surprising.
The ﬁrst service time noted to have a trend toward improvement was disposition to admit. Soon after EHR implementation, this metric was noted to be a large component of the
overall LOS. The ED had challenges with moving patients who
have a disposition for admission to an inpatient hospital bed
in a timely manner. A departmental goal was implemented in
March 2013 to decrease disposition-to-admit times to a target of
<45 minutes once disposition for admission was determined by
the provider. This eﬀort to improve performance likely resulted
in a shortened disposition-to-admit time. Therefore, it remains
unclear what eﬀect EHR had in improving this metric.
The second improved timeframe was bed to provider, which
decreased by nearly 1.5 minutes. It is likely, however, that this
ﬁnding was a result of a change in procedure. Prior to EHR
implementation, bed-to-provider service times were taken from
providers' documentation of their start time on a paper documentation sheet in the room with the paper medical chart.
However, once EHR was implemented, the bed-to-provider
time was initiated when the provider signed up for the patient
on the computer screen. Additionally, it has been observed that
providers frequently initiate patient encounters prior to their
registration in the computer. This could result in the provider
April 2017

completing a history, physical, and perhaps early electrocardiogram or I-Stat evaluation prior to initiating the mouse click in
the computer, which registers the bed-to-provider time. These
elements are likely the cause for the diﬀerences in service time
and may have resulted in an artiﬁcially lowered time metric.
The inconsistencies in documenting bed-to-provider time portend unreliable data analysis. More research is needed on this
metric to make a more deﬁnitive conclusion regarding EHR's
eﬀect on it.
The negative impact from EHR implementation was seen
in most of the metrics when comparing year-to-year data. With
signiﬁcant and trended increases in LOS, as well as door-todoor, door-to-admission, door-to-bed, and provider-to-disposition times, implementation of EHR had a primarily negative
impact on the ED throughput metrics and service times over
a 12-month period. Similarly to the study of Ward et al (5),
ED physicians described themselves wading through patient
encounters with cumbersome, disjointed movements. Once
user and operations knowledge improved, this began to ease.
Many EHR implementations are all or nothing-i.e., they
are all-encompassing and include medical records/chart reviewing, CPOE, documentation, and disposition paperwork (discharge instructions and prescriptions). At the study institution,
a staged approach was employed. The EHR hospital system went
live on March 1, 2013. The CPOE implementation was delayed
for the ED until May 7, 2013. The hospital-wide CPOE went
live on April 14, 2014. This was also a time when admission
rates increased. Furthermore, the ED's documentation method had little to no change between paper documentation of a
chart to dictation pre- and postimplementation. In many EHR
implementations, a change in the documentation process also
occurs. This may include documentation using point-and-click,
computer dictation, or direct provider entry into the EHR.
Because the department maintained dictation for the entire
study period, the confounders of learning this new system and
comfort with the new system of dictation were absent, also
limiting generalizability.
This study was conducted at a single academic urban ED
with an average ED discharge time well below the national
average (12). Also, this study did not analyze many aspects of
a complex emergency care system such as patient safety, quality,
user satisfaction, patient satisfaction, and diﬀerences in system selection. A baseline period of 12 months and comparison
period of 12 months were selected to attempt to incorporate
the full impact over a 1-year reporting period. Patient volume
dropped 3% over the 12 months prior, admission rates dropped
7%, and transfers dropped 29%. It is uncertain what eﬀect this
change in volume had on the overall ED metrics. Had volume
and acuity level not dropped after EHR implementation, the
increase in time metrics could have been even more signiﬁcant
due to ED crowding.
Other confounders included the disposition-to-admit departmental initiative as well as the method of time documentation pre- and post-EHR implementation. Times prior to EHR
implementation were based on handwritten times on paper
documentation sheets, whereas times in the EHR were obtained

Pre and post hoc analysis of electronic health record implementation on emergency department metrics

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Table of Contents for the Digital Edition of Baylor University Medical Center Proceedings April 2017