: Little is known about variation in rate of uptake or potentially inappropriate prescribing of non-warfarin direct-acting oral anticoagulants (DOACs). Our objective was to examine variations in overall use, and potentially inappropriate use, of DOACs across sites within the Veterans Health Administration (VA).

: We examined 32,719 unique patients who received a DOAC from 1 of the 130 VA sites of care between October 1, 2010, and September 30, 2014 (fiscal year [FY] 2011-FY2014). We examined the growth of DOAC use over time, and how it varied by site. We examined the proportion of patients receiving DOACs who were potentially inappropriate recipients, because of the presence of a prosthetic heart valve or insufficient renal function.

: Use of DOACs within VA increased, with more initial prescriptions in FY2014 (18,369 prescriptions) than all previous years combined (14,350). This use was uneven by site; the top 10 sites collectively accounted for 26% of DOAC prescriptions in FY2014, while the bottom 10 sites accounted for 0.6%. Overall, 4.58% of DOAC prescriptions were potentially inappropriate: 1.59% because of a prosthetic heart valve, and 3.04% because of insufficient renal function. The rate of inappropriate prescribing varied widely among sites, from a low of 0% (3 sites) to a high of 12.6%.

: Uptake of DOACs within VA is accelerating but is uneven across sites. While potentially inappropriate prescribing of DOACs is relatively uncommon, it varies among sites from completely absent to as high as 12.6% of prescriptions. Understanding this variation will have implications for pharmacy budgets and for patient safety.

The introduction of non-warfarin direct-acting oral anticoagulants (DOACs) has generated considerable enthusiasm. Clinicians and patients, alike, have been eager to adopt effective long-term oral anticoagulants that do not require the close monitoring or frequent dose titrations that are needed to effectively use warfarin. However, DOACs are not appropriate for all patients and in all situations.

Specifically, their use is proscribed when the patient has insufficient renal function, and their use is not recommended for patients with prosthetic heart valves or who have other, less-common indications for anticoagulation.

 Relatively few studies have been published about how these new agents are being adopted into clinical practice, although one such study suggested that they are frequently used for off-label indications, such as hypertensive heart disease and coronary artery disease.

In this study, we examined the increasing use of DOACs within the Veterans Health Administration (VA). We also examined the rates of potentially inappropriate use of DOACs within VA, due to inappropriate indications or insufficient renal function. Finally, we examined differences among VA sites, in terms of overall utilizations of these agents, as well as potentially inappropriate utilization.

The increasing uptake of DOACs by health systems, over time, will have potentially important implications for resource utilization, while ensuring that appropriate prescribing of these agents will have important implications for patient safety. The role of the present study is to provide a barometer of how these trends are progressing in the nation’s largest integrated healthcare system, the VA.

We identified all patients who received any of the DOACs from a VA pharmacy from October 1, 2010, through September 30, 2014, which corresponds to VA fiscal years (FYs) 2011-2014. DOACs were first included in the VA formulary during FY2011. During our study period, there were 3 DOACs in use: dabigatran, rivaroxaban, and apixaban. The study was approved by the Institutional Review Board of the Bedford [Massachusetts] VA Medical Center.

Part of our analysis was to examine variation in rates of use of DOACs, and of potentially inappropriate prescribing, by VA site. We included 130 VA sites of care, each of which includes a hospital, an outpatient care center, and several outlying community-based clinics. If a patient visited more than 1 site (2.8% of patients), we assigned the site at which the medication was initiated.

Inappropriate Use of DOACs: Prosthetic Heart Valves

One major way that a prescription for a DOAC might be inappropriate would be to use it in a patient who has a prosthetic heart valve. For the present analysis, short-term primary prophylaxis of venous thromboembolism (VTE) was not considered; we focused on longer-term indications for anticoagulation therapy. All 3 DOACs eventually received FDA indications for 2 such indications: stroke prevention in non-valvular atrial fibrillation and treatment of VTE (including prevention of recurrent VTE).

In contrast, the use of DOACs for some indications not only lacks an FDA indication, but also is contraindicated or discouraged. A prominent example would be using a DOAC in a patient with a prosthetic heart valve. The RE-ALIGN study found that dabigatran is inferior to warfarin in patients with a mechanical prosthetic heart valve, resulting in, among other problems,  higher rates of both bleeding and thromboembolism.

Other DOACs have not been studied with prosthetic heart valves, but in the absence of direct evidence we should also assume that warfarin would be preferred over them as well. Therefore, the package insert for dabigatran states that its use is contraindicated in patients with mechanical heart valves and is “not recommended” for those with bioprosthetic valves

; the inserts for the other DOACs state that use with any prosthetic heart valve is “not recommended.”

 VA guidance documents reiterate these recommendations.

International Classification of Diseases, Ninth Revision, Clinical Modification

) codes to identify indications for therapy, then established a hierarchy of indications.

 Prosthetic valve was considered the most important indication when present, followed by VTE (any further treatment was assumed to be for VTE treatment and prevention of recurrence), followed by atrial fibrillation/atrial flutter.

Other, less-common indications for anticoagulation included systemic embolus, mural thrombus, and others. A list of 

 codes used to define indications for therapy can be found in the eAppendix (available at 

 codes do not reliably differentiate between a mechanical and a bioprosthetic heart valve, although neither is considered appropriate for DOAC use in VA.

Inappropriate Use of DOACs: Kidney Function

VA guidelines do not recommend the use of dabigatran or rivaroxaban if the patient has an estimated glomerular filtration rate (eGFR) below 30 mL/min/1.73m

, or of apixaban if the eGFR is below 25 mL/min/1.73m

 FDA-mandated package inserts for all 3 DOACs do allow for administration of a reduced dose in severe renal failure, despite an absence of direct evidence for such use.

The package insert for dabigatran allows a reduced dose of 75 mg twice daily for eGFR 15-30 mL/min/1.73m

 despite the fact that no clinical trial studied this dose or this level of renal function.

 The package inserts for rivaroxaban and apixaban similarly allow reduced doses in severe renal failure,

 despite the absence of direct trial data.

 VA guidance documents conservatively recommend that only doses which have directly been studied should be given, and only at levels of renal function which were studied.

Therefore, within VA, it is recommended that dabigatran and rivaroxaban should not be used below an eGFR of 30 mL/min/1.73m

, or apixaban below an eGFR of 25 mL/min/1.73m

 Anytime a patient whose kidney function fell below these cutoffs received a DOAC, we considered it to be potentially inappropriate. If kidney function improved upon re-measurement, receipt of a DOAC was considered appropriate thereafter. Of note, reduced doses of DOACs (such as dabigatran 75 mg) are not approved for use within VA, and are therefore not an option, as stated in VA Criteria for Use guidance.

We recalculated the eGFR for a patient whenever a new creatinine value was recorded, using the 4-variable version of the Modification of Diet in Renal Disease (MDRD) equation.

 Several methods can be used to calculate kidney function based on creatinine values, including the Chronic Kidney Disease Epidemiology Collaboration equation, the Cockcroft-Gault equation, and MDRD.

We chose MDRD for this study because it was the most appropriate match for the creatinine assay used by VA during our study period,

 and because it does not require the patient’s weight, which can be missing. In addition, the major difference among the prevalent methods lies in their ability to detect Stage II chronic kidney disease—that is, slight decrements in eGFR which have prognostic significance, but no immediate clinical significance.

 Because we only considered an eGFR below 30 mL/min/1.73m

) to be potentially problematic in combination with DOAC use, this distinction is not relevant to our study.

We examined the characteristics of patients in our source population as of the date of each patient’s first prescription for a DOAC. We examined the rates of use for each DOAC and for warfarin by year: overall, by indication, and by medical center. We then examined the rates of potentially inappropriate use for all DOACs and for each DOAC individually: due to inappropriate indications, due to insufficient renal function, and overall.

For the cross-section of patients who used DOACs in FY2014, whether as new or continuing users, we examined the variation in potentially inappropriate use by site. We examined the relationship between site volume of DOAC prescriptions and the rate of potentially inappropriate prescribing. Finally, we examined changes in the rate of potentially inappropriate prescribing over time. A 

 value of <.05 was considered statistically significant. All analyses were conducted using SAS, version 9.4 (SAS Corporation, Cary, NC).

We studied 32,719 unique patients who received DOACs from VA from October 1, 2010, through September 30, 2014. Patient characteristics as of each patient’s first DOAC “fill” from a VA pharmacy are described in Table. Most patients receiving DOACs (75%) were aged 65 years or older. Reflecting the VA population, 97% were male and 77% were white, non-Hispanic.

Dabigatran accounted for the majority of DOAC prescriptions (56%), with rivaroxaban accounting for much of the remainder (38%) and relatively few patients receiving apixaban (5%). A considerable proportion of DOAC recipients (14.8% in total) either had an unusual indication (5.1%) for receiving a DOAC (see eAppendix for a list of such indications) or no recorded indication at all (9.7%). Atrial fibrillation was by far the most common reason to receive DOAC therapy (66% of all prescriptions).

Patients receiving DOAC therapy for atrial fibrillation were somewhat older than other patients, with 82% of them being aged 65 years or older. In keeping with the timing of FDA indications present during most of the study period, dabigatran was the predominant DOAC for atrial fibrillation (74%), while rivaroxaban was the predominant DOAC for venous thromboembolism (75%).

As would be expected, the use of DOACs increased during the study period, as clinicians gained familiarity with these agents and as new indications received FDA approval (Figure 1). There was evidence of acceleration in this trend, with more new starts for DOACs in FY2014 (18,369) than all previous years combined (14,350).

Use varied widely by site. For example, during FY2014, out of 130 VA sites of care, the 10 highest-utilizing sites collectively accounted for 26% of DOAC prescriptions, while the 10 lowest-utilizing sites collectively accounted for 0.6%. At 5 of the 10 highest-utilizing sites, there were more new starts for a DOAC in FY2014 than for warfarin (data not shown). For example, at the highest-utilizing site, there were 709 DOAC starts in FY2014 and 420 warfarin starts. We also characterized the duration for all 225,238 DOAC prescriptions written during the study period. Most of them were for 30 days’ worth of medication (84%), followed by 90 days (10%), 60 days (2%), and other durations (4%).

A small proportion of DOAC prescribing over the entire study period was for patients who had prosthetic heart valves (519 patients; 1.59% of all DOAC patients). Of those patients, 470 (1.44%) were inappropriate at the start of therapy, and 49 (0.15%) later had such a code, yet continued DOAC therapy thereafter. Similarly, a small number of patients received DOACs despite having eGFR below 30 mL/min/1.73m

 for dabigatran or rivaroxaban, or 25 mL/min/1.73m

 for apixaban (995 patients; 3.04% of all DOAC patients).

Of those patients, 371 (1.13%) had an inappropriately low eGFR at the time of their first prescription, and 624 (1.91%) developed inappropriately low eGFR at a later date, but received at least 1 additional fill for a DOAC after that date. Of the patients who developed inappropriately low eGFR after their first fill and remained on DOAC, 269 (43%) received only 1 additional fill, while 355 (57%) received at least 2.

Overall, 4.58% of patients received a potentially inappropriate DOAC fill; 2.54% were not appropriate candidates at the time of inception, while 2.04% initially appropriate candidates became inappropriate but continued to receive DOAC therapy.

The phenomenon of potentially inappropriate prescribing was also highly variable among sites (Figure 2). Of 130 VA sites, we focused on 115 sites with at least 50 patients with DOAC fills, to avoid problems with small cell sizes and unstable estimates. At 3 of these 115 sites, none of the DOAC fills was potentially inappropriate. However, 7 sites had 9% or more of patients with a DOAC fill flagged as potentially inappropriate. At 1 site, 12.5% of 104 DOAC patients had a potentially inappropriate fill.

We examined whether sites with a higher overall volume of DOAC prescriptions would have a higher (or lower) rate of potentially inappropriate prescribing, again limiting our analysis to sites with at least 50 total patients receiving a DOAC. There was no correlation between volume of prescriptions and the rate of potentially inappropriate prescriptions (r  = 0.06, 

 = .56). We did find that the rate of potentially inappropriate prescribing of DOACs decreased over time across VA: 7.9% in FY2011, 6.6% in FY2012, 5.2% in FY2013, and 3.5% in FY2014.

In this study, we examined the growing use of DOACs over time in VA, the largest integrated healthcare system in the United States. We found that DOAC use is growing at an increasing rate, with more new starts in FY2014 than all previous years combined. We also found that the expanding use of DOACs is highly variable across sites, with some sites continuing to use almost no DOACs, while other sites adopting them at very high rates.

At a few sites, more patients began DOACs in FY2014 than warfarin. We also found that a small but appreciable proportion of DOAC use in VA appeared to be potentially inappropriate, either because of insufficient renal function or the presence of a prosthetic heart valve. Our best estimate of the overall rate of potentially inappropriate prescribing was 4.58%; however, this rate was also highly variable by site.

Of sites with at least 50 new starts for DOACs in FY2014, 3 sites had no potentially inappropriate prescriptions, while 5 sites had a rate of potentially inappropriate prescribing of more than 9%. It is encouraging that we found that the rate of potentially inappropriate prescribing has been decreasing over time in VA.

Understanding patterns of prescribing of DOACs has implications in terms of cost and patient safety. DOACs have important implications for pharmacy budgets, for 4 main reasons: (a) their use is growing, as we have shown; (b) their cost is much greater than that of warfarin, even considering the labor of managing warfarin

; (c) anticoagulants are indicated for a large and ever-growing number of patients

; and (d) patients often continue to use anticoagulants for many years. In a forthcoming study, we will examine the reasons why some VA sites have embraced DOACs much more avidly than others, using qualitative methods to examine key informant interviews.

We do know that VA guidance changed early in FY2014 from an implicit “fail first” policy,

 where patients were expected to try warfarin first, to a policy that explicitly allowed using DOACs without a trial of warfarin first.

 We will be asking about how that change in policy impacted local pharmacy policy. We also know that while VA Pharmacy Benefits Management provides uniform guidance in the form of Criteria for Use documents, each site still may differ somewhat in how they choose to implement those recommendations.

Qualitative methods will be ideal to examine these site-level differences in the implementation of nominally uniform guidelines. Our findings will have implications not only for DOACs, but for guiding a value-based approach to evaluating many new agents that are developed, almost all of which are much more expensive than previous medications.

In addition, our findings also have implications for patient safety. Overall, we found a relatively low rate of potentially inappropriate prescriptions for DOACs across VA, which is, in part, a testament to vigorous oversight by VA pharmacists, who are in turn supported by resources from the national VA pharmacy service, including data dashboards, medication use evaluations, and internally produced criteria-for-use documents.

Together with other strengths of the VA system (highly functional electronic medical record [EMR] system, well-educated clinicians, integrated healthcare system, etc), this may explain why we saw a relatively low overall rate of potentially inappropriate prescribing of DOACs. However, we did find opportunities for improvement, including an appreciable number of patients who received DOACs despite the presence of a prosthetic heart valve or insufficient renal function.

 In addition, we saw that the rate of potentially inappropriate prescribing at some sites was twice or even 3 times the VA average. Over the coming months and years, VA should work to ensure that potentially inappropriate prescribing of DOACs, which runs contrary to official VA guidelines

 should occur at a rate approximating zero, as is already the case at a few VA sites.

There have been few previous studies about potentially inappropriate prescribing of DOACs, possibly because few databases contain detailed enough information to explore the issue. One early report examined the uptake of dabigatran in 2011, when other DOACs were not yet in use.

 Like our study, that study suggested that uptake of dabigatran was increasing at an accelerating rate.

The previous study also found that off-label use of dabigatran was considerable, although much of the off-label use in that study was for uncommon indications, and relatively little was for patients with prosthetic heart valves.

 In addition, that study did not examine renal function as a contraindication to DOAC use.

Another recent study examined potentially inappropriate prescribing practices with dabigatran, but that paper focused on co-administration with interacting medications (such as nonsteroidal anti-inflammatory medications).

 Thus, to our knowledge, our report is largely new information, and points to an important quality issue that needs to be addressed.

Articles

Original Research

This study examines variations in overall use, and potentially inappropriate use, of non-warfarin direct-acting oral anticoagulants across sites within the Veterans Health Administration.

Potentially Inappropriate Prescribing of Direct-acting Oral Anticoagulants in the Veterans Health Administration

: To evaluate whether provider adoption and use of the formulary decision support element of e-prescribing were directly associated with significant prescriber and patient behavior change.

: Retrospective, observational prepost study comparing a treatment group with a control group.

: Pharmacy fill information and provider enrollment records from January 2009 to December 2011 were used to compare fill volumes—primary fill and subsequent refill volume by retail and mail order fulfillment channels&mdash;of prescribers who had adopted e-prescribing (treatment group) with those who had not (control group). Propensity scores were used to match treatment and control groups by baseline prescribing patterns. Primary outcomes of interest were prepost differences for 90-day retail fills, initial fills (primary adherence), and subsequent refills.

: E-prescribers demonstrated significantly higher average increases in 90-day retail fills (+2.8 fills per provider, 

 <.001), primary adherence (+12.0 fills per provider, 

 <.001), and subsequent adherence (second fill: +4.5 fills per provider, 

 <.001; and third fill: +2.4 fills per provider, 

 <.001) compared with non—e-prescribers.

: Provider adoption and use of e-prescribing applications with formulary decision support were associated with significant prescriber and patient behavior change in a large, nationally representative population. E-prescribing adoption was positively associated with prescribers’ selection of longer supply prescriptions and improvements in adherence. E-prescribing infrastructure may allow for additional provider messaging opportunities designed to improve quality of care and patient outcomes.

Pharmacy benefit managers (PBMs) are critical stakeholders in the healthcare industry, responsible for administering prescription benefit programs for the vast majority of insured Americans. Prescription drug spending represents 10% of total healthcare expenditures, or roughly 1.8% of US gross domestic product.

Importantly, prescription drugs are first- or second-line treatments for many chronic diseases associated with an outsized portion of healthcare spend.

 Appropriate and persistent use of prescription drugs to treat chronic diseases has also been demonstrated to reduce downstream medical expenditures,

In addition, pharmacies are the most frequently utilized healthcare service, with the average patient filling nearly 4 times as many prescriptions as the number of their visits to office-based physicians.

 Given these facts and additional demographic context—an aging population with increasing prescription drug use on a per capita basis

—it becomes clear that PBMs can play a significant role in the current healthcare reform landscape.

PBMs are adopting benefit design strategies that reflect the broader healthcare trend of aligning payment to value and outcomes. A common theme among the evolving strategies is an effort to address and improve patient adherence to medication. Due to their position of processing prescription claims, PBMs are in a unique position to have the most comprehensive view of a patient’s prescription treatment regimen.

PBMs can leverage claims, eligibility, pharmacy data, and provider data to understand prescribing patterns and patient behavior by identifying gaps in care and suboptimal adherence, and potentially to effect positive behavior change relating to medication adherence through the timely delivery of information to providers, patients, and pharmacies.

Surescripts is a health information network that connects PBMs, pharmacies, and providers using electronic health records (EHRs) for the purpose of enabling real-time electronic prescribing. Through Surescripts, PBMs can electronically deliver prescription formulary, eligibility, and co-pay information to providers using a certified EHR. MedImpact Healthcare Systems, Inc, is one such PBM, managing pharmacy benefits for more than 47 million individuals nationwide, connected to the Surescripts network. Provider use of the benefit information through Surescripts is associated with increased levels of preferred brand and generic prescribing, resulting in lower co-pays that are in turn linked to improved adherence.

Surescripts also provides PBM-supplied decision support information about availability of retail 90-day supply and mail-order benefits, both of which have been linked to improved adherence.

 The ability of providers to route prescriptions electronically provides an auditable trail between what the provider prescribed and whether the patient filled the prescription at the pharmacy, addressing a current challenge with quality measures relating to primary adherence.

Additionally, provider use of electronic prescribing (e-prescribing) is a core measure in the EHR Incentive Program, a federal incentive program to promote provider adoption and meaningful use of EHRs.

 The e-prescribing core measure requirement is currently limited to the routing component of e-prescribing; however, e-prescribing infrastructure allows numerous opportunities for formulary decision support and clinical messaging.

Current widely used methods of measuring medication adherence (such as proportion of days covered or medication possession ratio) rely on the presence of a claim for the first prescription fill, which is available only if the patient successfully picked up the initial prescription from the pharmacy. A challenge with these methods is that as many as 28% of first-fill prescriptions are abandoned before they reach the pharmacy, and therefore, such measures may be overstating true levels of medication adherence.

Our study is novel in that we sought to understand whether provider adoption and use of the formulary decision support element of e-prescribing was directly associated with improved medication adherence as indicated by increases in the number of claims for 90 days’ supply, first fill, and subsequent refills. We did this by pairing data from Surescripts’ provider network and MedImpact’s pharmacy information.

We investigated 3 core questions: first, we assessed whether e-prescribing resulted in prescribing behavior change, as indicated by selection of 90-day prescriptions; second, whether e-prescribing is associated with an increase in primary adherence, as indicated by the number of claims for first-fill prescriptions; and third, whether there was a discernible subsequent impact on second and third medication fills.

A prepost, retrospective analysis was performed by linking summary prescriber-level fill information from MedImpact and prescriber enrollment data from Surescripts. MedImpact’s pharmacy fill information from January 2009 to December 2011 was summarized to monthly prescriber-level data.

More than 75 variables were aggregated by prescriber, using National Provider Identifier (NPI) codes, to the following levels: a) fulfillment channel and days’ supply amount: retail 30-day supply, retail 90-day supply, and mail 90-day supply; and b) adherence defined as the number of prescriptions filled for members’ first, second, and third fills. We measured the raw volume of these claims associated with each prescriber (treatment and control) for 1-year pre- and post e-prescribing adoption.

Total prescription claims was defined as the sum of retail 30-day supply, retail 90-day supply, and mail pharmacy claims for primary and subsequent fills. For the purpose of this analysis, comparisons of prescribing behavior were conducted based on a treatment versus control group research design. Prescribers adopting e-prescribing were included in the treatment group.

Prescribers not using e-prescribing technology were included in the control group. The pre-period was defined as the 12 months prior to a provider’s first use of the e-prescribing formulary decision support element, and the subsequent 12 months was defined as the post period. Differences among provider-level variables over the pre- and post periods were assessed to measure the impact of electronic prescribing on the study population. The primary outcomes of interest were prepost differences in claims for 90-day retail fills, initial fill (primary adherence), and subsequent refills.

A 2-stage matching algorithm was employed to enhance statistical rigor and to include adjustments for baseline differences in prescribing behavior between treatment and control groups. The goal of the 2-stage matching approach was to improve the comparability of the treatment and control groups while limiting potential confounding.

Robust matching procedures combined simulation-based adjustments (1-stage matched methods), and propensity score matching (2-stage matched methods) was used to minimize the effect of bias that may have occurred in the absence of such advanced statistical techniques. Both 1- and 2-stage matched results are presented to describe the impact of advanced matching methods between e-prescribers and non—e-prescribers, and to validate findings of 1-stage matching methods.

First, an adoption month and year were randomly assigned to each control group prescriber to simulate the introduction of e-prescribing into the population and to control for rapid changes in adoption of e-prescribing during the study period. This approach was used to avoid measuring pre- and post prescribing rates for a control prescriber in January 2010 to a treatment prescriber in December 2010.

Random assignment of proxy exposure dates to unexposed populations is a research design used to identify the onset of risk for exposure to the unexposed control group.

 Frequency distributions were calculated based on eligibility transaction dates from providers in the treatment group. Each control group prescriber was assigned a proxy eligibility date to replicate the observed distribution of the treatment group. Establishing this proxy or simulated eligibility transaction date in the control group created a basis for comparison over the study period between the treatment and control groups.

Second, propensity score methods were used to balance treatment and control groups by baseline prescribing patterns.

 Propensity scores were based on estimated probabilities from a multivariable logistic regression model used to predict the probability of prescribers’ membership in the treatment group. Covariates placed in the generalized linear model with logit link function included baseline prescribing patterns for retail 90-day, retail 30-day, mail 90-day, first fill, second fill, and third fill.

A 1-to-1 matching algorithm was performed using the nearest available neighbor without replacement method to match control group prescribers to prescribers in the treatment group based on the propensity scores of the treatment group.

Outcomes were calculated for both 1-stage and 2-stage matching scenarios. The treatment group was compared with 1- and 2-stage matched control groups. Changes for each outcome, day supply, and adherence were calculated to determine the relative percent difference in claim volume for each group, and the absolute difference of percent differences between treatment and control groups. Relative percent difference for each group was determined as %▲ = (Post —Pre)/Pre. For example, the treatment group was measured as follows: %▲

Absolute percent differences between treatment and control groups were defined as e-prescribing value

, where n = 1 for 1-stage matching and n = 2 for 2-stage matching. E-prescribing value serves as a proxy to measure the impact of e-prescribing on each measure, as the differences among the treatment and control groups were estimated to be attributed to e-prescribing. Postperiod data were compared with baseline results by group using parametric 

tests. Statistical significance was determined at 

<.05 level. All analyses were conducted with SAS version 9.4 (SAS Institute, Inc, Cary, North Carolina).

Selection of the treatment (e-prescribers) and control (non—e-prescribers) groups included a multistep exclusion process (

). Approximately 20,372 prescribers who had adopted e-prescribing during the 2010 calendar year were identified using the Surescripts provider network. E-prescribing adoption was evidenced by a prescriber’s registration, activation, and first e-prescribing transaction. To be selected, prescribers must have completed at least 1 eligibility transaction with MedImpact through the Surescripts network.

An eligibility transaction is a request for drug coverage and formulary information generated at the point of care and routed electronically to the corresponding PBM through the Surescripts network. The eligible provider must have been successfully matched in the Surescripts directory and must have had at least 25 claims, decreasing the sample by 3404 providers.

Eligibility transactions dates were divided into 2 periods: the 12 months prior to the first eligibility transaction date (pre-period), and the 12 months following the first eligibility transaction date (post period). Prescribers with claim totals of 0 in either the pre- or post periods (426) were excluded.

As both treatment and control groups were expected to have no e-prescription claims in the pre-period, such providers were removed from the population. Omitting e-prescription claims derived from integrated healthcare delivery systems led to an additional reduction in prescriber count by 10,247 providers. The final treatment group consisted of 6295 providers.

The control group was selected from providers who had not adopted e-prescribing prior to and during the study period by using the nationwide Surescripts provider directory. 63,305 prescribers were removed in the exclusion process using a combination of the provider’s Drug Enforcement Administration number, NPI code, and last name to remove all prescribers registered on the Surescripts network, in addition to removing prescribers with fewer than 25 claims. 

This resulted in retaining only the providers who had not adopted e-prescribing during the study period, with more than 25 claims. The final control group consisted of 18,934 providers after removing 810 providers due to 0 total claims in the pre- or post period, and 16,591 providers due to affiliations with integrated healthcare systems.

Average prepost volume of retail 90-day fills increased by 3.3 fills per provider (R

/provider) in the treatment group versus 0.5 R

/provider in the 1-stage matched control group, an absolute difference of +2.8 R

<.001) (Table). The absolute difference between treatment and 2-stage matched control group was +3.2 R

<.001). Increases in retail 30-day fills were greater in the treatment group. The overall absolute difference in retail 30-day fills indicated greater increases among e-prescribers, 16.4 R

<.001) for 1-stage matched comparisons and a 21.2 R

<.001) change for 2-stage matched comparisons.

, estimated e-prescribing value, as measured by differences in retail 90-day fills, ranged from 17.2% to 29.9%, indicating that prepost differences were substantially higher for the treatment group compared with both control groups. This trend was consistent for both mail 90-day and retail 30-day measures as well.

Although the absolute difference from pre- to post periods was not statistically significant and indicated a decrease in mail 90-day fills of 0.1 R

 <.846) for the treatment group, significant differences using 2-stage matched methods showed an overall absolute increase for the treatment group in mail 90-day fills of +1.8 R

 <.001). Calculations of percent difference for mail 90-day indicated a decrease of 10.0% for the treatment group, a decrease of 23.4% for the control group 1-stage, and a decrease of 29.1% for control group 2-stage.

Decreases in mail fills for the treatment group were significantly less than decreases for the control groups (1- and 2-stage), yielding estimated e-prescribing values ranging from 13.4% to 19.1% for 1- and 2-stage matched results, respectively. E-prescribing value for retail 30-day ranged from 6.2% to 17.4%, indicating greater increases in retail for the treatment group compared with both control groups (1- and 2-stage).

E-prescribing providers experienced significantly higher increases in both primary and subsequent fills compared with the 1-stage and 2-stage control groups (Table). For primary adherence, prepost differences between treatment and control groups were 12.0 R

 <.001), respectively, for 1-stage and 2-stage matched groups. This was observed for subsequent (second and third) fills as well. The treatment group exceeded the 1-stage control group in second fills by 4.5 R

For third fills, increases in the treatment group were significantly higher than in the control groups. Differences between treatment group and 1-stage matched groups were 2.4 R

 <.001) and greater differences occurred for 2-stage matched groups (3.0 R

 <.001). Overall, the estimated value of e-prescribing on primary adherence ranged from 14.3% to 20.5%, indicating greater increases in primary fills for e-prescribers (Figure 2). E-prescribing value on subsequent fills ranged from 10.9% to 28.3% for second fill and 5.9% to 23.8% for third fills, indicating significantly greater results for the treatment group when compared with the control groups.

Nonadherence to medications results in substantial downstream medical costs, and therefore warrants the significant attention and accompanying investments that payers and care providers have created with programs and resources to improve patient adherence. Our findings suggest that provider adoption and use of e-prescribing applications with formulary decision support were associated with improvements in medication adherence in a nationally representative population.

Specifically, we found that e-prescribing adoption was positively associated with providers’ selection of longer-supply prescriptions, which in turn has been linked to increased medication adherence, and improvements in adherence to first fill, as well as subsequent fills. Considering the growth in e-prescribing adoption in 2013, with close to 640,000 prescribers routing roughly half of all prescriptions electronically, the potential improvements in medication adherence and health outcomes are significant.

We observed a marked increase among e-prescribers in the selection of 90-day supply prescriptions when compared with the control group. Importantly, the increase in 90-day supply prescriptions—both to mail (+13.4% to +19.1%) and retail pharmacies (+17.2% to +29.9%)&mdash; among e-prescribers was comparatively greater than that of 30-day supply prescriptions. When providers adopt EHRs and are made aware of 90-day channel (mail and at retail) benefit information conveyed through e-prescribing, they may be more likely to: a) pay attention to this information when presented, and b) shift their overall prescribing habits and choose 90-day rather than 30-day prescriptions for maintenance medications.

Our findings show that providers who adopted e-prescribing during the study period saw a substantial increase in primary medication adherence (+14.3% to +20.5% in first fills), suggesting that electronically routed prescriptions increased the likelihood of patients starting therapy. This finding is consistent with other studies observing that between 22% and 31% of paper prescriptions (ie, those printed out and handed to patients) do not make it to the pharmacy.

E-prescription routing addresses this issue of “leakage,” which is a contributing factor to the increase in primary adherence. Many pharmacies have developed outreach programs to remind patients that their prescriptions are ready, programs that are likely benefited by the increase in prescriptions submitted electronically. We suspect that prescriber use of the benefit information from e-prescribing, and patient knowledge that the prescription is likely to have lower co-pays (due to increased use of generic prescriptions and 90-day supply prescriptions), were also factors in the improved primary adherence numbers.

This study evaluates whether provider adoption and use of formulary decision support for e-prescribing are directly associated with significant prescriber and patient behavior change.

E-Prescribing With Decision Support Is Associated With Improvements in Medication Adherence

There are more than 7000 known rare diseases that affect a worldwide patient population that, if aggregated together, would be the third most populous country in the world. However, timely and accurate diagnosis, the use of effective treatment and maintenance therapies, and known prognoses for these patient populations are often challenging due to a number of factors.

These challenges include access to, and validation of, clinical knowledge for a timely and effective diagnosis, treatment and maintenance therapies, and long-term prognosis due to very limited patient populations and individual provider expertise within each disease state.

Since the passage of the Orphan Drug Act in 1983, there has been significant acceleration in the number of pharmaceutical compounds identified, developed, and approved for the treatment of rare diseases, particularly within the past decade. The development and implementation of a rare disease strategy and management program by healthcare provider organizations and payers could greatly assist in improving the clinical, psychosocial, and economic impacts for this patient population.

Today’s pharmaceutical market is increasingly driven, both from a marketing and a financial perspective, by drug products with narrow therapeutic indications and/or patient populations. This is in direct contrast to the last several decades in which the development of medications for acute or chronic disease states, affecting millions of patients, was the primary target for the pharmaceutical industry.

Also contributing to this market shift are the development of “lifestyle” drugs and other medications for newly discovered and sometimes vaguely defined disorders or disease syndromes that, heretofore, may have been traditionally considered part of the human living experience and aging process. But, within the past decade, the accelerated application and approval of drugs designated for orphan status to treat rare diseases in very limited numbers of patient have become increasing concerns for all stakeholders in the healthcare marketplace.

These disease states and drugs often have significant health, psychosocial, and economic impacts. The purpose of this paper is to provide an overview of this information as a baseline for understanding this rapidly growing field within drug development and utilization.

As there is no agreed-upon, worldwide threshold prevalence or incidence for what constitutes a rare disease, the definition often varies by country. In the United States, if the total number of individuals affected by a disease or disorder is less than 200,000—which equates to a prevalence rate of approximately 6.4 cases per 10,000 citizens—then it is considered a rare disease.

 By contrast, a disorder or disease is considered rare in the United Kingdom if it affects less than 50,000 individuals.

In the United States, it is estimated that 25 to 30 million people are afflicted with a rare disease, or approximately 8% to 10% of the population. Worldwide, it is estimated that 350 million people suffer from a rare disease. If all of these affected individuals were from a single country, it would be the third most populous country in the world. Rare diseases also account for 35% of all deaths during the first year of life, with children representing approximately 50% of patients with a rare disease. The devastating fact for this patient population is that 30% will not live to see their 5th birthday.

 There are now more than 7000 rare diseases recognized by the various organizations and governmental agencies that track, provide support, and coordinate research for the diagnosis and treatment of these disorders and diseases. However, a small number of these rare diseases (approximately 350) account for approximately 80% of the total number of patients affected.

Although rare diseases vary widely in their clinical symptoms and disease progression, most have a strong genetic basis in common. Approximately 80% of rare diseases are caused by a genetic defect, and these diseases are often categorized according to their clinical system impact.

 Gastrointestinal disorders account for 33%; oncologic, 17%; hematologic, 17%; neurologic, 8%; and rheumatologic, 8% of rare diseases. Other clinical system impacts account for the remaining 17%, and include cardiovascular, endocrine, infectious, lysosomal, musculoskeletal, renal, and respiratory disorders.

Although it is useful to discuss rare diseases in aggregated terms, the reality is that, like common adult and pediatric acute and chronic diseases, prevalence rates vary widely among rare diseases. For example, a relatively well-known rare disease, cystic fibrosis, affects approximately 30,000 individuals in the United States, well below the 200,000 threshold limit, but still representing a substantial number of patients.

At the other end of the spectrum are rare diseases, such as paroxysmal nocturnal hemoglobinuria, in which red blood cells breakdown prematurely: less than 500 patients in the United States are estimated to suffer from this disorder. Thus, the key for physicians and other caregivers is to recognize that while aggregate statistics are helpful in understanding the overall problem, they do little to assist with the diagnosis, management, and treatment of the specific rare disease, let alone the psychosocial and economic impacts.

The treatment of rare diseases, like common diseases, generally falls into 1 of 3 strategies. For congenital anomalies and other similar physical organ or structure defects, surgery may be an option. Diet and lifestyle changes may also be used to mitigate symptoms or disease impact or progression in a limited number of rare diseases.

Overwhelmingly, however, the discovery, development, and use of pharmaceutical agents to provide curative, disease-modifying, and/or symptom and function-modifying relief for patients with rare diseases continues to be a significant area of focus.

Prior to the 1980s, pharmaceutical manufacturers focused their attention on acute and chronic disease states that affected millions of patients. For example, it is currently estimated that in the United States, kidney disease has one of the smaller patient populations, at approximately 4.5 million.

At the other end of the spectrum, cancer, cardiovascular disease, and diabetes consist of approximately 20, 28, and 29 million US patients, respectively. Hypertension tops the list at 75 million Americans. One can easily see the market potential and financial incentive alignment for drug manufacturers during these decades.

The passage of the Orphan Drug Act in 1983, which provided significant financial incentives consisting of market exclusivity, tax incentives for certain R&D costs, user fee waivers, and “fast track” review by the FDA, began to change the landscape considerably for pharmaceutical manufacturers.

 Prior to the act, only 38 drugs (now known as orphan drugs) for rare diseases received FDA approval.

By contrast, in 2014 and 2015, 47 and 41 new drug entities, respectively, were approved for rare diseases.

 Today, a total of 552 orphan drug products are FDA-approved, with 233 approved in the last decade alone.

 Further, since 1983, the FDA has designated more than 3600 molecular entities as qualifying for orphan drug status, with a record 422 applications received in 2015.

As of 2014, there were more than 450 compounds being actively studied for further development.

 But even with all of this activity, most rare diseases currently do not have therapeutic drugs that are commercially available for treatment.

The Orphan Drug Act did not modify the historical FDA requirements for drug review and approvals, even given the fast track approval process.

 Dating back to the 1962 Food, Drug, and Cosmetic Act, the FDA must ensure that there is substantial evidence of clinical efficacy demonstrated through “adequate and well-controlled” clinical trials, in addition to assuring the overall safety of drugs from a risk-and-benefit-analysis perspective.

However, 2 key events in the late 1980s and late 1990s clarified the FDA’s potential flexibility in reviewing all drugs, especially those for rare diseases. In 1988, the HIV/AIDS crisis lead to the development of IND Subpart E of the Food and Drug Chapter in Title 21, which focused on expediting the development, evaluation, and marketing of “Drugs Intended to Treat Life Threatening Illnesses and Severely Debilitating Illnesses.”

 In addition, in 1998, the FDA issued seminal guidance on “Providing Clinical Evidence of Effectiveness,” which delineated 9 different methods in which a single clinical trial could be used to file for drug approval versus the historical requirement of 2 clinical trials. A recent review of the FDA’s use of this flexibility in reviewing orphan drugs for approval noted a favorable response in terms of the unique limitations often found in rare disease clinical trials (eg, limited numbers of patients, longitudinal safety monitoring, etc) compared with “common” disease state drug trials.

It is clear that the development of orphan drugs for rare disease states is regarded by several observers of the pharmaceutical industry as the next major market opportunity for drug manufacturers and shareholders, as the markets for common disease drugs plateau.

 Unlike so-called lifestyle and other similar drugs, well-defined and agreed-upon clinical disorders and diseases exist for rare diseases and do not require convincing providers and payers that there is a need to treat a newly discovered, and often vaguely described, syndrome.

There is a readily identifiable target patient population and provider base, which, although dispersed, does not require the marketing investment of historical drug launches. Combined with the now all too common pricing of at least $100,000 in annual drug therapy costs, or multiples thereof, paid predominantly by commercial insurers and government programs, the business potential for rare disease drugs is extremely high.

The impact of a rare disease on a patient and their family and friends can be devastating from a health, psychosocial, and economic perspective. The path to an accurate diagnosis is often very lengthy: an average of 7.6 years in the United States.

 Depending on the specific rare disease and its clinical manifestations, this can range from just a few months to several decades.

Along the way, patients experience an average of 2 to 3 misdiagnoses from an average of 4 primary care physicians and 4 specialists, usually with conflicting treatment advice and disease state information. Patients often have difficulty finding a doctor who is knowledgeable about their specific rare disease and typically wind up educating their provider about their disease history, progress, maintenance, and treatment.

 Patients may need to travel significant distances in order to be seen by a physician with the specific rare disease state knowledge and expertise they need.

The vast majority of patients with a rare disease often experience significant depression (75%), anxiety, and stress (86%); reduced interactions or isolation from family and friends (70% and 65%, respectively), and worry based on lack of information about their disease (90%) and future prognosis (83%).

 Family and friends serving as caregivers report very similar symptoms and percentages as the patients.

The ability to connect with other patients and support groups for a specific rare disease, which is so essential in other common disease states to manage psychosocial concerns, is often limited or very difficult due to the limited numbers of patients within a geographic area or in aggregate. Likewise, the ability to access trustworthy information regarding their disease state online, or elsewhere, is often a challenge. Finally, quality-of-life scores for patients with rare diseases are typically 10% to 20% lower than for common disease state patients and are often reported at less than 50%.

The economic consequences for patients with rare diseases can also be considerable. The ability of the patient and the caregiver to continue working in their chosen field, or elsewhere, may be significantly compromised, if not eliminated, in order to manage the rare disease impact on the patient’s and family’s lives.

Lack of insurance coverage and uncertainty regarding covered therapies and procedures can lead to additional significant financial burdens on the patient and their family. As noted previously, rare disease drug therapies are expensive, easily running into 6 figures on an annual basis.

Fortunately, for these patients in particular, the Affordable Care Act eliminated upper coverage limits and pre-existing disease clauses and forced the coverage of routine costs associated with clinical trials. Still, 55% of these patients have incurred direct medical expenses, and one-third of these patients have borrowed money from family members and friends to pay for their rare disease treatments and maintenance therapies.

Physicians are confronted with a variety of challenges in treating patients with rare diseases. As mentioned previously, the pathway to an accurate diagnosis is often lengthy and filled with conflicting information and misdiagnosis. The root cause of this is the small number of patients a physician may see with a specific rare disease in order to build his or her personal knowledge and experience base in treating these individuals.

Combined with the usual education, training processes, and practice experience of physicians of routinely seeing patients with common diseases, the potential for initial and subsequent misdiagnoses is understandable. This phenomenon is frequently described as “the horse and zebra problem.”

 If a rancher hears hoof beats, they assume it is a horse not a zebra, and they are correct more than 99.9% of the time. Fortunately, for ranchers, a quick visual check can immediately remedy their conclusion about what they are dealing with. With physicians and patients with rare disease, the clinical symptoms presented may not be so unique and discerning.

Physicians may also be frustrated about the lack of readily available information, diagnosis, and treatment guidelines in order to enable them to effectively manage patients with rare diseases. Identifying and connecting with other physicians knowledgeable in the specific disease area may be as difficult for providers as it can be for patients. In fact, more than 50% of all rare diseases have no disease-specific foundation or organization that can provide research updates, treatment guidelines, or medical education for providers.

The uncertainty regarding disease progression, short- and long-term prognoses, and the unknown impact of long-term drug therapy treatment outcomes and side effects can also be major concerns for physicians and patients. Finally, the psychosocial impact and economic concerns and stresses of the patient, family members, and caregivers can be challenging for physicians and other healthcare providers to manage.

The development and implementation of a rare disease management program by healthcare providers and payors could greatly assist in improving the clinical, psycho-social and economic impacts for this patient population.   

July/August 2016

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E-Prescribing With Decision Support Is Associated With Improvements in Medication Adherence

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