In the context of clinical research, electronic health records (EHR) can help physicians to quickly locate patients that meet the inclusion/exclusion criteria for participation in trials and thereby make it easier for them to generate referrals and enrollments. For example, one EHR-based approach that has been utilized is a clinical trial alert (CTA) system, which is designed to notify physicians of ongoing trials and their patients’ potential eligibility (if patients’ EHRs indicate that they meet selected trial criteria). One study found that the CTA intervention at The Cleveland Clinic in Cleveland, Ohio, contributed to a 10-fold increase in physicians’ referral rate and a doubling of their enrollment rate (Embi, et al., 2005).
To translate these recruitment benefits to impacts on parameters in our trial cost estimation model, we consulted a 2009 report produced by Deloitte on secondary uses of EHR data in life sciences, which includes an illustrative example of the potential benefits of integrating EHR with drug development. According to the report, use of EHR data and patient alerts reduces the attrition rate by 50 percent (Deloitte, 2009), which would reduce the number of patients that must be initially recruited. In the example, 2,000 patients are enrolled in anticipation of a 25 percent attrition rate. The target number of patients is therefore 1,500. If the attrition rate is reduced by 50 percent (to 12.5 percent), sponsors only need to enroll 1,714 patients to end up with the same number of patients (1,500) for the trial. This amounts to a 14.3 percent reduction in the number of patients that must be enrolled (relative to 2,000).
Additionally, the 2009 Deloitte report cited previous studies indicating that EHR can drive a 28 percent increase in eligible patient identification and a doubling of monthly patient enrollment rates. We translated these figures to a reduction in patient recruitment costs of roughly 30 to 50 percent and settled on a midpoint of 40 percent. While it is also possible that EHR could impact patient retention and associated per-patient costs, it was not clear from the literature how one might adjust those costs (aside from reducing the number of patients by which they were multiplied). Depending on how EHR is used, it may also contribute to lower data collection costs, but these effects are also yet unquantifiable.
As EHR is already used to some degree in clinical research, it is necessary to adjust our estimated impacts by the appropriate rate of adoption. In other words, the cost data we have from Medidata already reflect the fact that some use of EHR is already taking place; therefore, the average percentage reduction in costs resulting from wider use of EHR will not be as high as it would be if it were not yet being used at all. According to a figure reported by HHS in a news release, the EHR adoption rate was 35 percent in 2011, up from 16 percent in 2009 (hospital settings) (U.S. Department of Health & Human Services, 2012). As our data from Medidata spans the period between 2004 and 2012, we chose to use the 16 percent adoption rate from 2009 to adjust our estimates because it is closer to the midpoint of the time period covered by our data and therefore more likely to approximate the average adoption rate across all trials observed for our cost model. Having made this adjustment, we arrive at a 12.3 percent reduction in the number of patients that must be enrolled and a 35.9 percent reduction in patient recruitment costs (per patient).
Table 4 below provides estimates of expected reductions in per-study costs by phase and overall due to EHR adoption in clinical research across the different therapeutic areas.
Table 4: Projected Impacts of EHR Use on Clinical Trial Costs (in $ Millions and in Percentages), by Therapeutic Area and Phase [a]
|Therapeutic Area||Phase 1||Phase 2||Phase 3||Phase 4|
|Central Nervous System||-$0.2657||-6.78%||-$1.1428||-8.24%||-$1.6784||-8.72%||-$0.7480||-5.29%|
|Pain and Anesthesia||-$0.0565||-3.97%||-$0.9166||-5.40%||-$2.5282||-4.78%||-$1.5528||-4.83%|
[a] The numbers in bold represent the highest savings in dollars and in percentages within that phase. Note that sometimes the highest dollar reduction does not necessarily correspond to the highest reduction in percentage terms.
In Phase 1 studies, cost savings due to EHR adoption are highest for ophthalmology ($0.5 million, representing 8.6 percent of study cost). Cost savings range from $0.4 million (cardiovascular and dermatology) to as high as $1.1 million (central nervous system and immunomodulation) in Phase 2 studies. According to our model, the largest savings in costs from EHR adoption are achievable in Phase 3 studies with ranges from $0.5 million (hematology) to $2.5 million (pain and anesthesia). Similarly, Phase 4 study savings could be as high as $4.7 million (respiratory system) due to EHR implementation.