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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.
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.
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.
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 (
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.
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 [
The implementation of EHR systems within primary care practices is seen as particularly complex [
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 [
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
In this formula:
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.
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 [
Basic statistics on the size of a primary care clinic in the study.
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Average | SD | Median | Minimum | Maximum | |
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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 | |
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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 |
Given the complexity exhibited by most EHR implementation projects, this study used a mixed-method research approach, particularly a multiphase study design [
In the quantitative study phase, questionnaire modules were designed, based on prior research in the existing literature [
The data collected from 17 sampled primary care clinics were documented and analyzed using statistical analysis and grounded theory [
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.
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
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 |
In addition to clinic net revenue, the sampled clinics showed, on average, positive increases in active patient count, clinician count, clinical support staff count, and clinic revenue in the post-EHR implementation period. These increases are summarized in
Percent increase in clinic net revenue between the pre-EHR and post-EHR periods showed a very strong positive correlation with percent increase in clinic revenue in the same periods (
Average percent changes in active patient count, clinician FTE count, clinical support staff FTE count, clinic revenue, and clinic net revenue between the pre-EHR and post-EHR periods.
Correlations (r-values): clinic net revenue, clinic revenue, active patient count, clinician FTE count, and clinical support staff FTE count.
Interestingly, the percent increases in active patient count, clinician FTE count, and clinical support staff FTE count are not linearly proportional to one another. An average active-patient-count increase of 56% was handled by an average 22% increase for clinician FTEs and an average 39% increase for clinical-support-staff FTEs. This finding suggests change in operational efficiency after EHR implementation, with respect to the active-patients-to-clinician-FTE ratio and the active-patients-to-clinical-support-staff-FTE ratio. The sampled clinics showed an average increase of 27% in the active-patients-to-clinician-FTE ratio and an average increase of 10% in the active-patients-to-clinical-support-staff-FTE ratio, as illustrated in
Percent increase in the number of active patients showed strong positive correlations with percent increases in active-patients-to-clinician-FTE ratio (
These correlations, together with the nonlinear percent changes summarized in
Average percent changes in a clinic’s operational efficiency and financial performance between the pre-EHR and post-EHR periods.
Correlations (r-values): clinic net revenue, clinic revenue, active patient count, clinician FTE count, clinical support staff FTE count, active-patients-to-clinician-FTE ratio, and active-patients-to-clinical-support-staff-FTE ratio.
The percent increase in clinic revenue was also not linearly proportional to the percent increase in the number of active patients—an average increase of 76% versus an average increase of 56%, respectively. In addition to the increase in the number of active patients, there seem to be other factors that contributed to clinic revenue increase in the post-EHR period (detailed analysis on the impact of EHR on the sampled clinics’ billing patterns and revenue management processes, required to identify the contributing elements of the greater than linear increase in clinic revenue over the increase in patient count, is beyond the scope of the study).
The study also finds that percent increase in clinic net revenue was not linearly proportional to percent increase in clinic revenue. The average additional 13% increase in clinic net revenue (89%, which is 13% above the clinic average revenue of 76%) is attributable to the enhanced operational efficiency in the post-EHR period, which suggests the relative cost-savings effect after the EHR implementation.
We further tested the financial and operational impact of EHR in the post-EHR period, in order to assess the degree to which these findings could be extended to the population of clinics implementing EHR. The sign test, as opposed to
Summary of statistical analysis of change in a clinic’s operational efficiency and financial performance between between the pre-EHR and post-EHR periods
Percent changes between the pre-EHR and post-EHR periods | Average | SD | Median | M | Sign test, |
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Percent change in clinic net revenue | 89% | 203% | 23% | 7.5 | <.001 | |||||
Percent change in the number of active patients | 56% | 119% | 10% | 5.0 | .006 | |||||
Percent change in active-patients-to-clinician-FTE ratio | 27% | 53% | 9% | 6.5 | <.001 | |||||
Percent change in the number of clinician FTEs | 22% | 82% | 0% | 0.0 | 1.00 | |||||
Percent change in the number of clinical support staff FTEs | 37% | 75% | 0% | 3.0 | .07 | |||||
Percent change in active-patients-to-clinical-support-staff-FTE ratio | 10% | 29% | 4% | 2.5 | .277 |
The same conclusions can be made for the median percent changes in the active-patients-to-clinician-FTE ratio and in the number of active patients in the same periods (M=6.5 and M=5, respectively). However, for the median percent changes with respect to the number of clinician FTEs, the number of clinical support staff FTEs, and the active-patients-to-clinical-support-staff-FTE ratio, we could not reject with a 95% confidence level the null hypothesis of no change after EHR implementation.
The correlation coefficients shown in
Study participants reported improvements in their ability to manage patient information after the implementation of EHR systems, citing improved ability to manage results such as obtaining test results from laboratories and following the results of an investigation over time (64%, 11/17 clinics). Respondents also reported an improved ability to seek out specific information from patient records (57%, 10/17 clinics), and access complete, up-to-date patient charts and review patient problems (43%, 6/15 clinics). See
Impact of EHR on clinic practices identified by study participants.
Categories | Participant comments |
A. Impact of EHR on a clinic’s ability to manage results | “We receive results electronically and can graph them; graphs help ‘engage’ the patient.” |
“Direct to physician lab results has very positive effect on physician efficiency and patient care.” | |
B. Impact of EHR on a clinic’s ability to seek out specific information from patient records | “Complete chart is always available, anywhere which affects patient safety and means better care.” |
“Integration of information for referral requests is a great benefit.” | |
“Billing codes are up-to-date. (And) billing is automatic by the doctor inside encounter note, which simplifies billing and is easier to manage reconciliation. No missed billing opportunities.” | |
C. Impact of EHR on a clinic’s ability to prepare patient encounter | “Review of patient information prior to encounter is greatly facilitated.” |
“Easier to prepare for encounter; maintenance of problem list /summary is much easier” | |
“Immediate access to patient information—no lost files.” |
Some primary care clinics did better than others in using EHR and achieving faster break-even from EHR investment, which can be observed in
Significant linear regression results of the outcome “break-even point” with explanatory variables (break-even point was log-transformed to approach a Normal distribution).
Explanatory variable | Variable values | Regression coefficient | Standard error of coefficient |
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(a) Age of EHR: Months between Jan 1, 2013, and EHR implementation start date | Number of months | 0.03 | 0.01 | .049 | .64 |
(b) e-Prescriptions complying with national standards | 0 (No) to 1 (Yes) | -1.32 | 0.34 | .006 | .50 |
(c) Extent to which EHR complies to national standards | Continuous (from 0 to 10) | -0.19 | 0.07 | .038 | .54 |
(d) Process change: Use of flow sheets | 0 (No) to 1 (Yes) | -1.29 | 0.46 | .022 | .68 |
Note that in
The first result to note in item (a) of
The second and third results, shown in items (b) and (c) of
Finally, clinics using EHR flow sheets scored consistently better times to break even, shown in item (d) of
Our study finds that despite the limited use of EHR functionalities and limited interoperability, the sampled clinics achieved overall positive operational and financial performance.
Most frequently and routinely used EHR functionalities were related to medication management. Health information exchange and patient engagement portal functionalities saw no significant use (the investigation of why these functionalities were not used is beyond the scope of this study).
Respondents stressed that it typically takes a few months to understand any particular EHR function sufficiently to effectively introduce it in their clinical practices. This finding, coupled with the finding that despite the limited use of EHR functionalities the clinics achieved overall positive improvement in operational and financial performance in the post-EHR period, suggests that a clinic’s ability to embed particular EHR functionalities in their workflow and make use of these functionalities in their day-to-day clinical practices is of more importance in realizing a positive ROI from EHR implementation than implementing an EHR software package with the maximum number of features and functionalities.
EHR functionalities and utilization reported during the study period.
EHR functionalities | % of clinics answering in the affirmative | |
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Alternative presentation formats for clinical information | 100.0 |
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Support for guideline-based data collection and treatment | 94.1 |
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Support for multiple platform access | 88.2 |
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Support for context sensitive alerts, warnings, and guidance | 70.6 |
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Clinical notes capture in narrative form | 23.5 |
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List of all medications taken by an individual patient | 100.0 |
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Provide patients with clinical summaries for each visit | 88.2 |
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List of all laboratory results for an individual patient | 88.2 |
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List of patients by diagnosis (eg, diabetes or cancer) | 82.4 |
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List of patients who are due or overdue for tests or preventive care | 76.5 |
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List of all patients taking a particular medication | 76.5 |
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List of patients by laboratory result (eg, HbA1C>9.0) | 52.3 |
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Clinicians receive a reminder for guideline-based interventions and/or screening tests | 58.8 |
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Clinicians receive an alert or prompt to provide patients with test results | 41.2 |
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Patients are sent reminder notices when it is time for regular preventive or follow-up care | 35.3 |
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All laboratory tests ordered are tracked until results reach clinicians | 29.4 |
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Electronic prescribing of medication | 93.3 |
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Electronic prompts about a potential problem with drug dose or drug interaction | 87.5 |
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Electronic receipt of laboratory results integrated into the EHR system (not scanned) | 62.5 |
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Electronic ordering of laboratory tests | 43.8 |
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Electronic referring to specialists | 37.5 |
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electronic transferring of prescriptions to a pharmacy | 6.7 |
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Electronic exchange outside practice: patient clinical summaries | 25.0 |
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Electronic exchange outside practice: laboratory and diagnostic tests | 18.8 |
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Access alcohol consumption advice online | 11.8 |
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Access advice for informal caregivers online | 11.8 |
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Email about a medical question or concern | 11.8 |
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Access dietary advice online | 11.8 |
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Access advice on physical activity online | 11.8 |
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Access advice on self-management of chronic conditions online | 11.8 |
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Access smoking cessation advice online | 11.8 |
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Request appointments online | 5.9 |
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View a list of medications (current and past) online | 5.9 |
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View other components of their chart (current and past) online | 0.0 |
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View medical imaging results (current and past) online | 0.0 |
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Request refills for prescriptions online | 0.0 |
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View test results (current and past) online | 0.0 |
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Diagnoses are coded using international standards | 94.1 |
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Medications and pharmacological profiles are coded to national standards | 82.4 |
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Patient records are supported by standards-based data migration technology | 50.0 |
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ePrescriptions comply with national standards | 52.9 |
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Patient Identifier is based on national or jurisdictional standard | 58.8 |
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Patient Identifier is supported by aliasing technology to achieve positive ID across systems | 37.5 |
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Findings are coded using international standards | 58.8 |
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Communications with other clinics and institutions use international standards | 31.3 |
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Investigations, referrals, and imaging requests make use of order tracking technology | 35.3 |
This study aimed to complement current insights into the cost recovery concerns related to EHR investments by considering the research question “When implemented in primary care practices, what will be the ROI from EHR?”. The study finds that primary care clinics can realize a positive ROI from the implementation of EHR. Our analysis offers evidence that the increases in net revenue, in the active-patients-to-clinician-FTE ratio, and in the number of active patients are positively associated with the EHR implementation, likely contributing substantially to an average break-even point of 10 months.
In addition, the analysis conducted to understand the variances in financial and operational performance among the sampled clinics provides insights into key differences between those clinics that were highly successful and those less successful in realizing a positive ROI from EHR. Some clinics seem to be more innovative than others in using EHR in their practices to achieve significantly better operational and financial results. The analysis suggests that a clinic’s ability to take advantage of EHR to support process changes has a significant effect on the time required to achieve cost recovery from an investment in EHR. In particular, the clinics that were successful in realizing faster time to break even were better at using EHR in workflow areas involving patient information—such as maintaining patient problem lists, managing test results, and complying with national coding standards, all of which make patient encounter preparation easier and more rapid. We also find that the clinics achieved positive financial performance, even though not all EHR functionalities were used. The alignment of EHR functionalities with clinic workflow plays an important role in achieving positive operational and financial results with EHR. Identified as particularly important EHR-product improvements that would ease adoption of workflow changes are automations that assist clinicians, clinical support staff, and administrative staff both in the overall management of the practice and within the patient encounter, as well as consistent and comprehensive compliance with national standards such as national drug coding standards.
The knowledge gained from this ROI study on EHR is important to practicing primary care physicians who are concerned about how they will fare financially upon investing in EHR, as they face ever increasing pressure to transition from their paper-based records to electronic systems. This study provides evidence to practitioners in primary care that investment in EHR can be a sound decision with a reasonable cost recovery time frame, while providing immediate opportunities for increased operational efficiency and the potential for further improvements in clinic performance and benefits realization from EHR. Practitioners in primary care who are considering the investment in EHR should note the important relationship between EHR functionality, clinic workflow change, and a positive ROI from EHR implementation. Positive ROI does not happen automatically upon implementing an EHR package, and a clinic’s ability to leverage EHR for process changes plays a role in achieving a positive ROI.
This study’s finding on increased active patient count and clinic operational efficiency after the EHR implementation, in particular with respect to improvement in the active-patients-to-clinician-FTE ratio, offers the possibility that EHR can play a role in addressing the shortage in family physicians. As primary care clinics implement EHR systems and discover better ways to take advantage of EHR in their practices, a key question will be how to incorporate such learnings and deliver enhanced EHR products back into the clinics to realize the full potential of EHR. Policies that enable the establishment of a closed-loop feedback mechanism between EHR vendors and health care providers could facilitate targeted enhancements to EHR systems. In addition, policies that provide support to help primary care practices successfully make EHR-enabled changes, such as support of workflow optimization with an EHR system that would ease adoption, could not only facilitate the realization of positive ROI but also help address the shortage in family physicians.
Some of the factors identified in this research as key factors impacting the realization of a positive ROI from EHR implementation, such as improved access to up-to-date patient charts and improved ability to obtain test results from laboratories and follow the results of an investigation over time, have implications to quality of care and patient safety. Thus, future research will be to investigate the relationship between financial ROI and realization of clinical benefits of EHR such as quality, safety, and patient outcomes, as depicted in
Future research: investigate the relationship between return on EHR investment and clinical benefits realization from EHR implementation.
The principal limitation of this study is that the number of primary care clinics examined was limited, due mainly to time constraints of clinics to participate in the study and scarcity of suitably detailed operational and financial data necessary for ROI calculation. For the clinics recruited to the study, the most limiting factor was that of collecting a complete picture of the cost and benefits needed to assess an ROI from EHR implementation. This was due mainly to the absence of standardized financial and business-case approaches to the governance of these independent organizations. The insights gained from the participants in our study, however, provide salient insights into the impact of EHR investment to facilitate the EHR adoption across practicing primary care physicians, with information on time required to achieve cost recovery from an EHR investment and on principal factors impacting cost-recovery performance.
electronic health records
full time equivalent
information technology
return on investment
The authors wish to acknowledge the valuable contributions of Liette Lapointe, PhD, Isabelle Vedele, PhD, John Hughes, MD, Raymond Simkus, MD, and Susan Law, PhD, in all phases of this study.
This study was funded by Canada Health Infoway Inc, an independent, not-for-profit corporation established in Canada to accelerate the adoption of electronic health records and related technologies on a pan-Canadian basis. The opinions, results, and conclusions reported in this manuscript are those of the authors. No endorsement by Canada Health Infoway is intended or should be inferred.
None declared.