The concept of learning healthcare system (Perlin 2014) based on electronic health records (EHR) has faced stiff resistance in the healthcare management community. Fear of failed implementations, reluctance to change well-established practices and the lack of a standard costing model has held back change initiatives. Return on investment (ROI) for EHR implementation is quantifiable, but a consensus approach to the process has yet to emerge.
Perceptions Holding Back EHR Development
One roadblock to the implementation of EHR systems has been the belief held by physicians that it would not bring real improvements in healthcare delivery. This reluctance was the finding in a study by Deloitte Healthcare that also discovered that doctors believed the disruption of switching would be a step backward, leading to reduced productivity.
The indications are that primary healthcare providers can gain a positive ROI by deploying an EHR system. However, such improvements must be measurable and clearly discernable from data sources. However, there is as yet no standard model to compare the results of implementing ERH systems consistently. Most of the cases under study have shown no clear pattern of results because there has been no convention for analysis methods.
A Model Based On Reaching Break-Even
The simplest method to obtain ROI is to divide the difference between the gain and the cost of investment by the cost of the investment. Authors in the JMIR Medical Informatics (Yang, Lortie and Sanche 2015) propose a methodology based on costs to break-even. They then demonstrated that a more appropriate measure, which could become an industry standard, might be a break-even point analysis. This approach calculates the difference between the pre-EHR and post-EHR periods to give the minimum revenue at which an EHR could become profitable.
Yang et al. define the formula as:
CEHR = [(NR peri – NR pre)/12]*M imp + [(NR post – NR pre)/12] * (M break-even – M imp)
- C EHR – EHR implementation cost
- NR peri – Annual net revenue for the fiscal year that includes the implementation
- NR pre – Net revenue for the fiscal year before the implementation
- NR post – Net revenue for the fiscal year after the implementation
- M imp – Months to complete the implementation
- M break-even – Months to break even
In the case where the time to break even is less than the implementation period, the formula becomes the difference between pre and peri incomes as follows:
CEHR = [(NR peri – NR pre)/12]* M break-even
The results published by Yang et al. were based on the study of seventeen clinics that measured results of implementing an EHR system and showed a significant increase in the average net revenue post implementation; this is one study that perhaps shows a methodology that could become a standard measure of success.
Toward A Standard EHR Model
However, there may be more factors to include gaining a balanced assessment of EHR implementation success or failure; these will only appear as healthcare managers test models and measure them against real world outcomes. Perlin calls for a set of standards that are analogous to the GAAP standards of accountancy practice. Considering the potential impact on healthcare and quality of life for patients such global standards could be a reasonable aim.
Determining the value and long-term viability of EHR will help to convince skeptical organizations and influencers of its value. A standard model, such as that suggested by Yang et al., or something similar, will assist in reaching that objective. Such a model will also determine the direction that developments in healthcare must progress to achieve the goal of a system that measures healthcare achievement and payment in terms of outcomes.
Cho, Jonathan. Finding ROI through EHR optimization. October 7, 2015. https://managedhealthcareexecutive.modernmedicine.com/managed-healthcare-executive/news/finding-roi-through-ehr-optimization (accessed September 28, 2016).
Perlin, Jonathan. A Standard Model For Evaluating Return On Investment From Electronic Health Record Implementation. january 6, 2014. https://healthaffairs.org/blog/2014/01/06/a-standard-model-for-evaluating-return-on-investment-from-electronic-health-record-implementation/ (accessed September 28, 2016).
Yang, Yeona, Michael Lortie, and Steven Sanche. Journal:Return on investment in electronic health records in primary care practices: A mixed-methods study. December 28, 2015. https://www.limswiki.org/index.php/Journal:Return_on_investment_in_electronic_health_records_in_primary_care_practices:_A_mixed-methods_study (accessed September 26, 2016).