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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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, MDResident PhysicianDuke University Medical CenterDivision of Otolaryngology-Head & Neck SurgeryDurham, NCTel.: +1 603 306 1182E-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.

1 4 7 10 13 16 19 22 25 28 310

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Figure 1. “Departing” attrition type frequencies over study period.

EMR Go-Live

EMR Go-Live

Lag Autocorrelation p-value 0 1 .1 0.0973 0.562 -0.0279 0.833 -0.2229 0.524 -0.1016 0.615 -0.0486 0.736 -0.0889 0.797 -0.1285 0.88 -0.2114 0.669 -0.0866 0.71

10 0.107 0.7411 0.54 0.02*12 0.0232 0.04*