This study analyzed healthcare provider attrition rates at a major academic medical center before and after the implementation of a new electronic medical record (EMR) system. The study found 208 provider departures from July 2011 to June 2014, with most (66.4%) occurring before the EMR go-live date in July 2013. The peak in attrition occurred in June 2013, just before implementation. While the cause is unclear, the authors hypothesize EMR implementation may have influenced some providers to retire rather than adapt to the new system.

1. INTRODUCTION
APPROACH
CONCLUSIONS
DISCUSSIONRESULTS
REFERENCES
Figure 2. “Emeritus” attrition type frequencies over study period.
ABSTRACT
CONTACT
Influence of Electronic Medical Record Implementation on Provider Retirement at a Major
Academic Medical Center
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LOGO
OBJECTIVE:
The push for electronic medical record
(EMR) implementation is grounded on
increasing efficiency and cost-savings. With
the increase in dependence on the EMR for
patient care and documentation, we
hypothesized an increase in provider
dissatisfaction. Our objective is to
investigate the effect of EMR
implementation on provider attrition.
APPROACH:
We completed a retrospective study
investigating whether medical provider
attrition, clinical M.D. or equivalent,
coincided with EMR implementation.
Monthly provider attrition rates and mean
age at attrition 24 months preceding the
EMR ‘go-live’ date at our institution and 24
months after were analyzed.
RESULTS:
208 provider departures occurred between
from July 2011 and June 2014. The
attrition categories were classified as
“departure” (n = 137, 65.9%), “emeritus”
(n = 30; 14.4%), “no specified reason” (n =
26; 12.5%), and “not reappointed” (n = 15;
7.2). The most common degree held by
departing providers was “MD” (n = 170;
81.7%). Most departures occurred in June
2013 (n = 24). The mean provider age at
departure was 46.4 years +/- 2.9 years for
June 2012, 48.1 years +/- 2.5 years for June
2013, and 45.0 years +/- 4.1 years for June
2014.
CONCLUSIONS:
EMR implementation may have affect on
provider attrition. Possible reasons include
the steep learning curve for new
technologies as well as the changes to daily
clinical workflow inherent to EMR use.
208 health care provider departures occurred between
July 2011 and June 2014. The ‘go-live’ date for our
institution’s new EMR system was July 2013. The most
common degree held by departing providers was “MD”
(n = 170; 81.7%; Table 1). The attrition categories were
classified as “departure” (n = 137, 65.9%), “emeritus” (n =
30; 14.4%), “no specified reason” (n = 26; 12.5%), and
“not reappointed” (n = 15; 7.2%) (Table 2). Most
departures occurred prior to EMR implementation, and
occurred in June 2013 (n = 24). The mean provider age at
departure was 46.4 years +/- 2.9 years for June 2012,
48.1 years +/- 2.5 years for June 2013, and 45.0 years +/-
4.1 years for June 2014. There was no significant
difference between the mean provider ages when
comparing June 2012, June 2013, and June 2014 monthly
attrition. (p >0.05).
A time series analysis was completed to assess for
temporal trends in or attrition data (Figures 1 & 2). The
trend of the pattern seems to indicate a low level of
monthly attrition. With respect to seasonality, the
pattern demonstrates recurrent peaks of increased
attrition annually every 11 to 12 months (Table 4). This
corresponds with June and July in our time series. There
do not appear to be any overt irregularities or outliers.
Our time series analyses demonstrated a trend for an increase
in number of departing providers on an 11 to 12 month cycle
with the most providers departing in June 2013 – the month
immediately prior to EMR implementation. The cause of the
peak in departures in June 2013 could be attributed to either
a variant of the regular pattern of attrition on an academic
calendar, or associated with the impending EMR
implementation in July 2013. A previously published survey
on health care provider perceptions on EMR have reported
that a providers indicate that an EMR will require a change in
practice style and clinical, and pose a general threat to their
professionalism.[4] Another study suggested that some
providers may retire from an institution rather than
participate in an EMR implementation.[5]
A main barrier to implementation from the perspective of our
providers could be the burden and stress of adopting a new
system. The peak in attrition seen in our attrition data could
be associated with providers’ choosing to retire instead of
completing the implementation process.
Limitations of this study include a short time interval which
limits the amount of attrition data post-implementation. Our
study did not include a qualitative survey of the departing
providers, so we cannot directly attribute the departures to
the EMR.
Attrition data for healthcare providers were obtained from
the Duke University Hospital Department of Human
Resources. We analyzed monthly provider attrition rates and
mean age at attrition 24 months preceding the EMR ‘go-live’
date at our institution and 24 months after.
Statistical analyses were completed using the JMP Pro 11
software suite (Cary, North Carolina, USA). Descriptive
statistics and one-way ANOVA analyses were performed on
all variables. Time series analysis was used to analyze the
attrition frequencies from 24 months prior to, and after EMR
implementation. P-values were reported with statistical
significance fixed at p = 0.05.
To date, no other investigation of the effect of EMR
implementation of provider attrition have been published.
Previous studies have indicated significant barriers exist in the
implementation of a new EMR system.
We demonstrate a significant peak in provider attrition in
the month prior to EMR implementation that may not be
explained by normal attrition patterns with an academic
calendar.
Electronic medical/health record (EMR/EHR) systems have
been developed to serve as an interface between the data
and healthcare providers. The Centers for Medicare and
Medicaid Services (CMS), a branch of the United States
Department of Health and Human Services has recognized
the utility of these EMR and EHR systems.[1]
The push for EMR implementation is grounded on increasing
care quality, efficiency and cost-savings. The enhanced
documentation, decision support capabilities and ‘smart
tools’ inherent to many EMR systems have been reported to
objectively improve quality of care post-implementation in
previous studies using specific quality indicators.[2]
Despite the potential benefits of an EMR, challenges for
implementation are widely reported. Barriers to
implementation have been categorized into four main
domains, namely practice or provider, vendor, attestation
processes, or meaningful use.[3] Perceptions exist that an
EMR will require a change in practice style and clinical
environment, a shift of expertise to younger providers with
more extensive exposure to technology, changes in
interactions with patients, and as a threat to their
professionalism.[4]
Anecdotal reports suggested that implementation of our
EMR prompted an increase in provider attrition secondary to
dissatisfaction. We examined the effect of EMR
implementation on provider attrition. Our hypothesis was
that a significant proportion of providers were influenced
to retire from our institution as a result of the new EMR
system implementation.
1. Centers for M, Medicaid S, Office of the National Coordinator for Health Information Technology
HHS: Medicare and Medicaid programs; modifications to the Medicare and Medicaid Electronic
Health Record (EHR) Incentive Program for 2014 and other changes to EHR Incentive Program;
and health information technology: revision to the certified EHR technology definition and EHR
certification changes related to standards. Final rule. Fed Regist 2014, 79(171):52909-52933.
2. Kern LM, Edwards AM, Pichardo M, Kaushal R: Electronic health records and health care quality
over time in a federally qualified health center. J Am Med Inform Assoc 2015, 22(2):453-458.
3. Heisey-Grove D, Danehy LN, Consolazio M, Lynch K, Mostashari F: A national study of
challenges to electronic health record adoption and meaningful use. Med Care 2014, 52(2):144-148.
4. McAlearney AS, Hefner JL, Sieck C, Rizer M, Huerta TR: Fundamental issues in implementing an
ambulatory care electronic health record. J Am Board Fam Med 2015, 28(1):55-64.
5. McAlearney AS, Hefner JL, Sieck CJ, Huerta TR: The Journey through Grief: Insights from a
Qualitative Study of Electronic Health Record Implementation. Health services research 2015,
50(2):462-488.
Table 4. Time Series diagnostics table with lagged
autocorrelation plot for “Departures” attrition.
Table 1. Attrition frequency by provider credential type.
Matthew G. Crowson, MD
Resident Physician
Duke University Medical Center
Division of Otolaryngology-Head & Neck Surgery
Durham, NC
Tel.: +1 603 306 1182
E-mail address: matthew.crowson@dm.duke.edu
Matthew G. Crowson, MD1; Christopher Vail PA-C MMCi2; Rose J. Eapen, MD1
Duke University Medical Center, 1Division of Otolaryngology-Head & Neck Surgery, 2Department of General Surgery
Provider Credentials n (% of total)
MD 170 (81.7)
MD, PhD 13 (6.3)
MBBCh 4 (1.9)
MBBS 4 (1.9)
MD, MPH 4 (1.9)
DO 3 (1.4)
MD, MS 3 (1.4)
Singletons 7 (3.4)
All 208
Attrition Reason n (% of total)
Departure 137 (65.9)
Emeritus 30 (14.4)
Not Specified 26 (12.5)
Not Reappointed 15 (7.2)
All 208
Attrition Timing n (% of total)
Pre-Implementation 138 (66.4)
Post- Implementation 70 (33.7)
All 208
Table 2. Reason for provider attrition
during study period.
Table 3. Frequency of provider attrition
before and after EMR implementation.
0
5
10
15
20
25
30
1 4 7 10 13 16 19 22 25 28 31
AttritionCounts(n)
Months
0
5
10
15
20
25
30
1 4 7 10 13 16 19 22 25 28 31
AttritionCounts(n)
Months
“Departing” Attrition
“Emeritus” Attrition
Figure 1. “Departing” attrition type frequencies over study period.
EMR Go-Live
EMR Go-Live
Lag Autocorrelation p-value
0 1 .
1 0.0973 0.56
2 -0.0279 0.83
3 -0.2229 0.52
4 -0.1016 0.61
5 -0.0486 0.73
6 -0.0889 0.79
7 -0.1285 0.8
8 -0.2114 0.66
9 -0.0866 0.71
10 0.107 0.74
11 0.54 0.02*
12 0.0232 0.04*