Journal:Return on investment in electronic health records in primary care practices: A mixed-methods study

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Full article title Return on investment in electronic health records in primary care practices: A mixed-methods study
Journal JMIR Medical Informatics
Author(s) Jang, Yeona; Lortie, Michel A.; Sanche, Steven
Author affiliation(s) McGill University, Desautels Faculty of Management; St Mary's Research Centre, Montreal
Primary contact Email: yeona.jang@mcgill.ca; Phone: 1.514.398.8489
Year published 2014
Volume and issue 2 (2)
Page(s) e25
DOI 10.2196/medinform.3631
ISSN 2291-9694
Distribution license Creative Commons Attribution 2.0
Website http://medinform.jmir.org/2014/2/e25/
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Abstract

Background: The use of electronic health records (EHR) in clinical settings is considered pivotal to a patient-centered health care delivery system. However, uncertainty in cost recovery from EHR investments remains a significant concern in primary care practices.

Objective: Guided by the question of “When implemented in primary care practices, what will be the return on investment (ROI) from an EHR implementation?”, the objectives of this study are two-fold: (1) to assess ROI from EHR in primary care practices and (2) to identify principal factors affecting the realization of positive ROI from EHR. We used a break-even point, that is, the time required to achieve cost recovery from an EHR investment, as an ROI indicator of an EHR investment.

Methods: Given the complexity exhibited by most EHR implementation projects, this study adopted a retrospective mixed-method research approach, particularly a multiphase study design approach. For this study, data were collected from community-based primary care clinics using EHR systems.

Results: We collected data from 17 primary care clinics using EHR systems. Our data show that the sampled primary care clinics recovered their EHR investments within an average period of 10 months (95% CI 6.2-17.4 months), seeing more patients with an average increase of 27% in the active-patients-to-clinician-FTE (full time equivalent) ratio and an average increase of 10% in the active-patients-to-clinical-support-staff-FTE ratio after an EHR implementation. Our analysis suggests, with a 95% confidence level, that the increase in the number of active patients (P=.006), the increase in the active-patients-to-clinician-FTE ratio (P<.001), and the increase in the clinic net revenue (P<.001) are positively associated with the EHR implementation, likely contributing substantially to an average break-even point of 10 months.

Conclusions: We found that primary care clinics can realize a positive ROI with EHR. Our analysis of the variances in the time required to achieve cost recovery from EHR investments suggests that a positive ROI does not appear automatically upon implementing an EHR and that a clinic’s ability to leverage EHR for process changes seems to play a role. Policies that provide support to help primary care practices successfully make EHR-enabled changes, such as support of clinic workflow optimization with an EHR system, could facilitate the realization of positive ROI from EHR in primary care practices.

Keywords: return on investment in electronic health records; cost recovery from EHR implementation; ROI indicator; physician satisfaction with EHR; primary care practices

Introduction

Context

The use of electronic health records (EHR) in clinical settings is widely recommended as an innovation enabler with potential benefits of reducing health care costs, while improving quality and safety, and is considered central to achieving patient-centered health care.[1][2][3][4] As a wide array of EHR projects have been implemented within various health care settings, the health care field is rich with volumes of work examining the benefits of EHR. However, the existing literature reports mixed results in benefits realized from EHR implementation.[5][6] Such mixed results suggest that the implementation of EHR systems does not automatically guarantee the conversion of potential benefits into realized benefits.

The implementation of EHR systems within primary care practices is seen as particularly complex[7][8][9][10], with physicians and other staff in primary care practices citing obstacles such as difficulty in adapting to the significant changes in workflow and the time commitment required to learn to use the new software while prioritizing patient care.[11][12][13][14] While there is a growing body of evidence that EHR can be a valuable tool for improving quality of care and patient safety with relatively positive perceptions about EHR benefits[15][16][17], uncertainty about cost recovery of an EHR investment remains a significant concern in primary care practices.[7][8][18][19] Various studies on EHR impact and adoption also raise the need for cost-benefit analysis of EHR investments.[5][20] Thus, this study seeks to assess the return on investment (ROI) from an EHR implementation in primary care settings, aiming to complement the current insights on cost recovery concerns in existing literature.

Measurement

Return on investment is a common approach to measuring rates of return on money invested, in terms of increased profit attributable to the investment. A standard ROI is defined as follows:

ROI = (Gain from investment - Cost of investment)/Cost of investment

Results reported by various studies regarding ROI from EHR systems in primary care settings are mixed.[21][22][23][24][25] Most of the existing literature used a bottom-up approach identifying specific cost-saving areas and collecting the data on financial savings made in these areas attributable to EHR systems. However, EHR is a process-enabling information technology (IT) that offers the opportunity to streamline information-intensive workflow, remove manual hand-off of data and information, and facilitate coordination — thus facilitating the execution of entire business processes rather than individual tasks. Due mainly to the context-sensitive nature of benefits realization from a process-enabling IT such as EHR and the scarcity of detailed financial data relating to gains and/or savings directly attributable to an EHR system in primary care clinics, this study used break-even-point analysis as an indicator of ROI, instead of standard ROI analysis.

The break-even point of an EHR investment is defined as the number of months it takes a clinic to recover the cost of the EHR system and other associated implementation costs, with increased revenues and/or decreased expenses. Increases in revenues and/or decreases in expenses are assessed by considering net revenues during three distinct periods of time: pre-EHR, peri-EHR, and post-EHR. The pre-EHR period is defined as the full fiscal year before the implementation of an EHR system started. The peri-EHR period is defined as the fiscal year(s) containing the EHR implementation period (ie, during EHR implementation). If the peri-EHR period covers more than one fiscal year, the net revenue is averaged over these fiscal years. The post-EHR period is defined as the full fiscal year following the end of the peri-EHR period.

To calculate the break-even point of implementing an EHR system in a clinic, the cost of EHR implementation is set equal to the difference in the clinic’s net revenue between the pre-EHR and peri-EHR periods, plus the difference in the clinic’s net revenue between the pre-EHR and post-EHR periods, as summarized in the following formula:

C EHR= [(NR peri−NR pre)/12]*M imp + [(NR post−NR pre)/12] * (M break-even−M imp)

In this formula: CEHR=cost of EHR implementation, NRperi=annual clinic net revenue in the peri-EHR period, NRpre=annual clinic net revenue in the pre-EHR period, NRpost=annual clinic net revenue in the post-EHR period, Mimp=the number of months taken to complete the EHR implementation in the clinic, and Mbreak-even=months to break even. The net revenue of a clinic is defined as the sum of the physicians’ billings for work done in the clinic minus any expenses that the clinic pays to maintain its ongoing practice. In the case where the months to break even were less than the months of EHR implementation, in other words, the net revenue difference between pre-EHR and peri-EHR periods is large enough to recover the cost of EHR implementation, the formula was adjusted by setting the cost of EHR implementation equal to the difference in a clinic’s net revenue between the pre-EHR and peri-EHR periods, or:

CEHR = [(NRperi – NRpre)/12]* Mbreak-even

Objectives

Guided by the research question “When implemented in primary care practices, what will be the ROI from an EHR implementation?”, the objectives of this research are twofold: (1) to assess the ROI from an EHR implementation in primary care practices by measuring the time required to recover the cost of converting a clinic from a paper-based environment to an EHR-enabled environment and (2) to identify principal factors affecting the realization of a positive ROI from an EHR implementation in primary care practices. Such ROI information related to cost recovery of an EHR investment would be helpful to both clinics considering implementing EHR systems and to policy makers designing EHR-adoption funding programs and policies.

Methods

Sample

Community-based, primary care clinics meeting the following four eligibility criteria were recruited for this study on ROI from EHR in primary-care settings. First, this study focused on community-based, primary care clinics. Thus, specialty care clinics and walk-in clinics were excluded. Second, clinics were required to have implemented EHR systems. Third, clinics were required to have been paper-based in the past, in order to ensure that the comparison between pre-EHR and post-EHR implementation performance was possible for the ROI calculation. Fourth, clinics were required to provide operational and financial data necessary to calculate ROI, as well as the information on challenges and opportunities that they had experienced both during and after the EHR implementation.

The research team contacted 132 randomly selected community-based, primary care clinics in Canada that met the first two eligibility criteria for recruitment to the study. Of the 132 clinics, 62 clinics declined to participate, mostly citing time constraints. Of the 70 clinics remaining, 34 clinics were not eligible, mainly because they were unable to provide the operational and financial data necessary to calculate ROI. Of the 36 eligible clinics, 19 clinics later declined to participate, due mainly to time constraints. Thus, data were collected from a total of the 17 eligible clinics, resulting in the study participation rate of 13%, which is relatively consistent with typical participation rates of family physicians reported in other studies involving interviews and observations.[26] No statistically significant differences were observed between participating and non-participating primary care clinics in terms of geographic location (P=.315), the number of physicians or other clinicians (P<.001), or the number of patients per physician (P=.192). Table 1 summarizes the basic statistics on the size of the sampled clinics. We used Full Time Equivalent (FTE) in comparing the size of primary care clinics.

Table 1. Basic statistics on the size of a primary care clinic in the study
Average SD Median Minimum Maximum
Clinic size: clinician FTEs
Pre-EHR period 3.4 2.6 3.0 1.0 8.5
Post-EHR period 3.6 2.4 3.0 0.8 8.0
Clinic size: clinical support staff FTEs
Pre-EHR period 3.4 2.9 2.8 1.0 12.0
Post-EHR period 4.2 3.1 3.0 0.9 12.0

Methodology

Given the complexity exhibited by most EHR implementation projects, this study used a mixed-method research approach, particularly a multiphase study design.[27] By combining quantitative and qualitative data, mixed-method research can provide a fuller understanding of the complex and multidimensional world of primary care clinics than would otherwise be achieved using either approach alone.

In the quantitative study phase, questionnaire modules were designed, based on prior research in the existing literature[28][29][30][31][32][33], to collect data on EHR implementation costs, EHR functionalities in use, physician satisfaction with EHR, and physicians’ perceptions about the impact of EHR on operational efficiency and on quality of care. Each clinic respondent was required to complete the study instruments using the online questionnaires or researcher-assisted telephone questionnaires. The minimum financial data required for the study include clinic revenue and clinic net revenue as well as EHR implementation cost that consisted of EHR software costs, hardware costs, support costs, and labor costs associated with EHR system implementation and training, in the three different periods—before EHR implementation, during EHR implementation, and after EHR implementation. The minimum operational data required for this ROI study include the number of active patients, clinician FTEs, and clinical support staff FTEs, in the same three periods. The lead researcher served as dedicated support liaison for clinics, in order to ensure that the costs of the EHR implementation, as well as other financial and operational data before and after EHR implementation, were abstracted from clinic records in a consistent fashion. In the subsequent qualitative study, semistructured interviews and observations were conducted with clinic staff and physicians identified as responsible for such functions as patient appointment management, patient record management, test results management, patient encounters, and billing, to assess factors affecting the realization of a positive ROI from an EHR implementation in primary care practices.

The data collected from 17 sampled primary care clinics were documented and analyzed using statistical analysis and grounded theory.[34] As break-even points were analyzed, we compared those clinics that were successful in realizing a positive ROI from EHR implementation to those that were less successful, in an attempt to identify principal factors impacting the realization of positive ROI from EHR. In particular, we used linear regression analysis to estimate the relationships of the outcome variable “break-even point” with the explanatory variables that include the codes identified from the qualitative data through the coding process.

Results

Analysis of break-even point as indicator of return on investment

Overview

Our analysis suggests that the sampled primary care clinics typically recovered their investment in EHR within an average of 10 months (95% confidence interval: 6.2 months, 17.4 months), seeing more patients with improved active-patients-to-clinician-FTE and active-patients-to-clinical-support-staff-FTE ratios in the post-EHR implementation period.

Change in clinic net revenue after implementation of electronic health records

Once an EHR system is implemented, a key factor that impacts the time required to achieve cost recovery from the EHR investments is clinic net revenue. With respect to how clinics fared financially upon adopting EHR systems, all but one of the primary care clinics in our study achieved an increase in clinic net revenue in the post-EHR period, as shown in Table 2.

Table 2. Percent changes in clinic revenues, net revenues, and clinician FTEs between the pre-EHR and post-EHR periods
Clinic # Percent change between the pre-EHR and post-EHR periods (in ascending order by percent change in number of clinician FTEs), %
In number of clinician FTEs In clinic revenue In clinic’s net revenue
Clinic 1 -29 23 23
Clinic 2 -20 -28 22
Clinic 3 -14 27 4
Clinic 4 -2 29 26
Clinic 7 0 55 9
Clinic 5 0 50 63
Clinic 9 0 33 8
Clinic 10 0 31 28
Clinic 8 0 23 28
Clinic 11 0 19 16
Clinic 6 0 3 15
Clinic 12 0 -10 20
Clinic 13 0 -15 -30
Clinic 14 10 120 116
Clinic 15 47 223 227
Clinic 16 53 103 98
Clinic 17 329 603 845
Average 22 76 89

References

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Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In several cases the PubMed ID was missing and was added to make the reference more useful.

Per the distribution agreement, the following copyright information is also being added:

©Yeona Jang, Michel A Lortie, Steven Sanche. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 29.09.2014.

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