Jaime Y. Smith, MAE

: The objective of this study is to present the first information available regarding enablement and use by state and area characteristics of electronic prescriptions of controlled substances (EPCS) by prescribers and pharmacy systems in the United States using 15 months of transactional data, from January 2014 to March 2015, from an e-prescribing network.

: The data used in this study represent electronic transactions of controlled substances from all states and the District of Columbia, from Surescripts. Data in this analysis include prescriber enablement for EPCS, pharmacies with certified and audit-approved software enabled to receive EPCS transactions from prescribers, and number of EPCS transactions. In addition, area characteristics were determined using the Area Health Resource File to define metropolitan and non-metropolitan areas.

: During the study period from January 2014 to March 2015, pharmacies enabled for EPCS grew minimally, from 78% to 79% nationally. National EPCS enablement among prescribers increased 3.7%, from 0.6% to 4.3%. Differences were observed between metropolitan and rural counties. The percent of transactions nationally grew from 0.16% to 1.8% during the study period, more than a 10-fold increase in transactions.

: There has been progress in the adoption and use of EPCS. However, there are marked differences at the state level and between urban and rural areas. EPCS is an important tool to help prescribers provide better care for their patients. Additionally, EPCS provides a complete electronic record of controlled substance prescriptions, critical for public health and law enforcement initiatives that address the misuse and diversion of opioid medications.

The growth of the prescribing of controlled substances over the past decade has paralleled the growth of prescription drug addictions and overdoses in the United States.

There is an increasing association of controlled substances, especially opioid analgesics, with drug overdose deaths, misuse, and drug diversion. The highest rates of death, opioid sales, and nonmedical use are clustered in the Southeast, especially the Appalachian region, and in the Northwest.

 Overdose rates also vary by urban-rural county type, with more rural areas having higher overdose death rates for prescription opioids compared with urban areas.

Until recently, controlled substances were not permitted to be e-prescribed.

 Currently, a prescriber is able to issue electronic prescriptions for controlled substances (EPCS) only when the electronic prescription or electronic health record (EHR) application the practitioner is using complies with the requirements set forth in The Drug Enforcement Administration Interim Final Rule (IFR) passed in 2010.

The rule requires prescribers to apply to certain federally approved credential service prescribers or certification authorities to obtain their 2-factor authentication credential or digital certificate. Additionally, the IFR set out security requirements for providers and pharmacies to lawfully send and receive EPCS.

These security requirements include: (a) an in-person or remote knowledge-based identity proofing process for providers, (b) use of a 2-factor authentication procedure by providers when creating and transmitting an EPCS, (c) use of a digital signature associated with each EPCS transaction, (d) pharmacy capability to digitally sign or receive digitally signed EPCS and store records of such transactions, and (e) the required completion of a third-party audit by both e-prescribing vendors and pharmacy system vendors. If these requirements are met, prescribers are now legally able to electronically send controlled substance prescriptions to pharmacies for dispensing.

Currently, all 50 states and the District of Columbia allow EPCS. Missouri was the most recent state to allow EPCS, with updated controlled substance rules and regulations enacted in July 2015.

 In states where EPCS is legal, all states allow the electronic prescribing of Schedules II-V. New York is the only state that has required mandatory EPCS.

The New York legislature required the date of implementation of this public health law begin March 2016. This will require practitioners to become compliant with the requirements, including the registration of their software applications with the Bureau of Narcotic Enforcement.

 This mandate applies to all New York pharmacies, including rural areas.

Health information technology (health IT), and specifically EPCS, offers the potential to help enhance prescription monitoring and support patient care. EPCS can play an important role in delivery of care by eliminating paper-based fraud and improving work flow to reduce error.

The ability to electronically prescribe controlled substances provides prescribers and their patients the same benefits made possible by e-prescribing, most importantly access to enhanced safety features and clinical decision support. From a public health perspective, the additional security requirements necessary for EPCS are a crucial step towards curbing fraud and diversion that contribute to prescription drug abuse.

 This is because EPCS can support data sharing by prescribers to facilitate appropriate prescribing and improve clinical decision making.

Due to the federal and state-level policy initiatives, it is important to explore the national and state level trends of EPCS. The objective of this study is to present the first information available regarding enablement and use by state and area characteristics of EPCS by prescribers and pharmacy systems in the United States, using 15 months of transactional data from January 2014 to March 2015 from an e-prescribing network.

The data used in this study represent electronic transactions of controlled substances from all states and the District of Columbia from Surescripts, an e-prescription network utilized by a majority of chain, franchise, and independently owned retail pharmacies in the United States. This analysis describes prescriber enablement for EPCS, pharmacies with certified and audit-approved software enabled to receive EPCS transactions from prescribers, and number of EPCS transaction trends from January 2014 to March 2015. We also describe EPCS trends in urban and rural areas.

To determine prescriber enablement for EPCS, we included physicians, nurse practitioners, and physician assistants as prescribers. Using transactional e-prescribing network data as the numerator, we examined trends in the number of pharmacies enabled to accept EPCS, the percent of prescribers enabled to electronically prescribe controlled substances, and the percent of EPCS transactions as measured by dividing the number of total EPCS by the denominator of total controlled substances prescribed.

Counts of total controlled substance prescriptions (electronic and non-electronic) were obtained from the National Association of Chain Drug Stores (NACDS) and used as the denominator to calculate percent EPCS transactions.

 NACDS provides total counts of all electronic and non-electronic controlled substance prescriptions routed through all community pharmacies in the United States.

Area characteristics were determined using the Area Health Resource File to define metropolitan and non-metropolitan areas within each state.

 Metropolitan counties are defined as those that contain a core urban area with a population of at least 50,000, while non-metropolitan counties are defined as those that have a core urban area of less than 50,000, including those counties that are in non—core-based statistical areas.

During the study period from January 2014 to March 2015, pharmacies enabled for EPCS grew minimally, from 78% (46,711 pharmacies) to 79% (47,971 pharmacies) nationally. State enablement varied greatly. For example, pharmacy enablement by state ranged from 30% to 92% in March 2015. Rhode Island had the highest rate of pharmacy enablement for EPCS (96%) and North Dakota had the lowest (30%) (data not shown).

National EPCS enablement among prescribers increased 3.7%, from 0.6% to 4.3% between January 2014 and March 2015. Differences were observed between metropolitan and rural counties. For metropolitan areas, the rise in EPCS enablement mirrored the national trend; however, by the end of the study period, EPCS enablement was slightly higher in metropolitan counties (4.5%) than the national enablement rate (4.3%).

Although the rise in prescriber enablement in rural counties lagged behind national and metropolitan trends, enablement in these counties was 2.6% in March 2015, up from 0.4% in January 2014. Of note, compared with growth observed in the first 3 quarters of 2014 (0.8%), an increase in prescriber enablement was observed in Q4 2014 and Q1 2015 (2.5%) (Figure 1).

As of March 2015, 7 states had an enablement rate above the national average (4.3%), with New York having the highest rate (23.2%) followed by Nebraska (14.3%), Oregon (12.5%), Michigan (11.7%), California (8.0%), Massachusetts (4.8%), and Iowa (4.4%). North Dakota (0.2%), South Carolina (0.3%), Minnesota (0.3%), and Missouri (0.4%) had the lowest enablement rates (Figures 2 and 3).

The percent of transactions nationally grew from 0.16% to 1.8% during the study period, more than a 10-fold increase in transactions. States varied significantly in their use of EPCS. The states with the highest percentages were New York (7.6%), Nebraska (7.5%), California (5.5%), and Michigan (4.7%). The most significant growth occurred in the first quarter of 2015. In January 2014, 16 states had no EPCS transactions. As of March 2015, only 1 state, Missouri, had none (see eAppendix Table, available at www.ajpb.com, for more detailed information).

While the majority (79%) of retail pharmacies and a growing number of prescribers across the United States are enabled for EPCS, challenges remain to the widespread use of EPCS. Until recently, the majority of EHR vendors did not support the security requirements for prescribers to send EPCS; now, nearly 100 vendors do.

 This analysis showed differences in enablement between prescribers in urban and rural counties. Although EPCS was legal in all but 1 US state at the time of this study,

Of the 7 states that have enablement rates above the national average, New York and Nebraska have the highest uptake rates. New York issued a mandate requiring the electronic prescription of all medications as of March 2016.

 This mandate has likely driven the EPCS growth in New York. However, government initiatives are not the only method that have spearheaded the growth of EPCS.

Health systems such as Nebraska Methodist have been at the forefront of EPCS use for the state, thus leading the way for EPCS adoption in Nebraska. This was facilitated, in part, by the Nebraska Information Technology Commission and the fact that the State of Nebraska passed a law allowing for EPCS in 2013, earlier than many states.

An important reason for the promotion of EPCS is its ability to provide key medication information to physicians who may otherwise have little knowledge about a patient’s history with controlled substances. EPCS enables physicians to track and audit controlled-medication usage by their patients.

EHR applications for EPCS make this streamlined and incorporated into the physician workflow. In addition, EPCS serves as an important complement to state-run electronic databases that track the prescribing and dispensing of controlled prescription drugs to patients. These Prescription Drug Monitoring Programs (PDMPs), developed as tools to help curtail the abuse and diversion of prescription drugs, currently 37 states have operational programs and 11 states and territories are developing programs.

 PDMP databases can be accessed via an e-prescribing application, or an EHR.

Advances in information systems and health IT have made it possible for states to collect historical data on controlled substances dispensed by pharmacies by establishing PDMPs. This application of technology has shown success in addressing public health concerns of prescription-opioid misuse and abuse.

The US Department of Health and Human Services has made the epidemic of prescription drug abuse, misuse, and diversion a high priority, putting forth an initiative to decrease opioid overdoses, overdose mortality, and the prevalence of opioid use disorder.

 State-led PDMPs are a tool to help curb opioid abuse. PDMPs are often used to help behavioral health prescribers ensure the best care for their patients. In addition, prescribers can use PDMPs to detect patterns of controlled-substance prescribing to support clinical decision making.

For example, 1 year after requiring that prescribers access their state PDMP before prescribing, New York and Tennessee saw decreases in “doctor-shopping” by 75% and 36%, respectively.

 In addition, after implementation of a PDMP program in Florida, oxycodone-caused deaths decreased by 25%.

The ability to maintain an entirely electronic record of controlled substances from EPCS through monitoring will further boost public health efforts to curb misuse and abuse. This is because PDMPs—when universal, real-time, actively managed, and populated by EPCS&mdash;are an effective tool in mitigating this crisis.

In fact, New York State has enacted the Internet System for Tracking Over-Prescribing (I-STOP) through the state’s PDMP to help curb New York’s opioid abuse and diversion. The I-STOP legislation requires prescribers to check the state PDMP prior to prescribing a controlled substance.

This study showed that a majority of pharmacies are enabled for EPCS. However, provider enablement is still low. Educational resources and technical assistance for prescribers may help them adopt EPCS.18 Technical assistance may be especially helpful for prescribers serving rural communities, who face additional difficulties in the task of balancing benefits and risks of controlled substances.

As the adoption and use of EPCS grows, future studies should explore EPCS prescribing patterns by provider type and specialty. In addition, it will be important to explore clinical outcomes associated with increased use of EPCS, specifically opioid-related overdoses and deaths.

The current study has several limitations. First, data used for this analysis were composed of prescribers and pharmacies connected to the Surescripts Network and e-prescribing transactions for controlled substances that flow through the network. Due to the strength of market share, Surescripts can serve as a proxy for national trends analysis.

However, while Surescripts captures the vast majority of outpatient transactions, it does not include transactions from a number of sources, including inpatient e-prescribing in which the prescription goes directly to the hospital pharmacy, e-prescribing that occurs within a closed integrated delivery network (eg, Kaiser Permanente), and transactions that occur solely on competing networks.

From January 2014 to March 2015, there was progress in the adoption and use of EPCS. However, there are marked differences at the state level and between urban and rural areas. State-level regulatory barriers and initiatives have impacted EPCS. EPCS may serve as an important tool to help prescribers provide better care for their patients.

Additionally, EPCS provides a complete electronic record of controlled substances, critical for public health and law enforcement initiatives addressing the misuse and diversion of opioid medications. A multi-stakeholder approach is needed to further the adoption and use of EPCS technology. Policy makers, public health entities, the health IT industry, and providers each have an important role to play.

: Department of Health Management and Informatics, College of Health & Public Affairs, University of Central Florida (MHG), Orlando; Surescripts (JYS, MS, SJ), Arlington, VA; Office of the National Coordinator for Health IT (TLW), Washington, DC.

: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. MHG is an employee of the University of Central Florida. JYS, MS, and SJ are all employees of Surescripts. TLW is an employee of the US Department of Health and Human Services. The views expressed in this publication are solely the opinions of the authors and do not necessarily reflect the official policies of the US Department of Health and Human Services, the University of Central Florida, or Surescripts.

: Concept and design (MHG, JYS, MS, SJ, TLW); acquisition of data (JYS, MS, SJ); analysis and interpretation of data (MHG, JYS, MS, SJ, TLW); drafting of the manuscript (MHG, JYS, MS, SJ, TLW); critical revision of the manuscript for important intellectual content (MHG, JYS, MS, SJ, TLW); statistical analysis (JYS); administrative, technical, or logistic support (MHG, JYS); and supervision (MHG, JYS).

: Meghan Hufstader Gabriel, PhD, Assistant Professor, Department of Health Management and Informatics, College of Health & Public Affairs, University of Central Florida, 4364 Scorpius St, HPAII - Rm 213, Orlando, FL 32816.

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https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/HAC-Reduction-Program.html. Published February 4

2. Hospital-specific reports: Hospital-Acquired Condition (HAC) Reduction Program. QualityNet website. 

https://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier3&cid=1228774298662

. Published September 1, 2016. Accessed September 6, 2016.

3. PSI 90 fact sheet. Agency for Healthcare Research and Quality website. 

http://www.qualityindicators.ahrq.gov/News/PSI90_Factsheet_FAQ.pdf

. Published 2016. Accessed September 6, 2016.

4. FY 2017 HAC Reduction Program HSR User Guide 

5. FY2015 IPPS final rule home page. CMS website. 

https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/FY2015-IPPS-Final-Rule-Home-Page.html

. Published August 6, 2014. Accessed September 6, 2016.

https://www.medicare.gov/hospitalcompare/search.html

. Scheduled 12/2016. Accessed 09/06/2016.

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http://www.cdc.gov/hai/surveillance/progress-report/index.html

. Published March 3, 2016. Accessed September 6, 2016.

8. Kelly W, Trowbridge J. Managing antibiotic resistance—an imperative for future medical care. Am J Pharm Benefits. 2013;5(3): 97-101.

Articles

This study presents the first information available regarding enablement and use by state and area characteristics of electronic prescribing of controlled substances by prescribers and pharmacy systems in from an e-prescribing network.

Electronic Prescribing of Controlled Substances: A Tool to Help Promote Better Patient Care

: 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

Providers with electronic prescribing (e-prescribing) systems—whether as stand-alone applications or as part of an electronic health record (EHR)&mdash;have the ability to electronically request and receive information about a patient’s medication history and plan eligibility, and to submit new prescriptions electronically to the patient’s preferred pharmacy.

 Provider use of e-prescribing has been demonstrated to improve use of on-formulary and generic prescribing, to decrease adverse drug events, and to be associated with increases in first-fill medication adherence.

Growth in provider adoption of e-prescribing and EHR systems has risen significantly over the past several years, in large part because of federal incentive programs such as the Medicare Improvement for Patients and Providers Act and the Health Information Technology for Economic and Clinical Health Act (HITECH).

 Today, almost three-fourths of office-based providers and 95% of community pharmacies now actively use e-prescribing systems.

 Furthermore, and contrary to reported disparities in adoption of EHRs among urban and rural hospitals,

 the adoption of e-prescribing has been uniform across rural and urban care settings and practice sizes.

There is mounting interest in and research around provider adoption of EHRs. However, substantially fewer data are publicly available, and fewer studies have been published regarding provider utilization of EHR functions once they have been adopted. Previous survey- and interview-based studies have shown that despite the benefits, prescribers continue to experience barriers that can inhibit use, including among others: 1) managing separate work flows for e-prescribing for controlled substances, 2) inconsistent pharmacy use of e-renewals, 3) variability between e-prescribing systems, and 4) application usability issues.

The purpose of this study is to understand provider utilization of the e-prescribing functionality within an EHR through analysis of empirical transaction data provided by Surescripts, a national health information network. In particular, we sought to understand whether providers utilized or discontinued use of the e-prescribing functionality after the point of adoption, whether there were any trends or consistent patterns over time, whether providers using different e-prescribing and EHR systems had different patterns of use, and whether there were disparities or trends across specialties and geographic regions.

At a time when the majority of providers have adopted an EHR but are not yet using the majority of available functions, we believe the study of available utilization data can be instructive for EHR software vendors, clinic information technology leaders, consultants, and policy makers.

A longitudinal study was conducted using transactional data from the Surescripts network. E-prescriptions (eRxs) were evaluated for a 6-year period from January 2007 to December 2012. Data from all 50 states and the District of Columbia were included for this study. More than 450,000 office-based providers—including physicians, nurse practitioners, physician assistants, and other medical professionals with prescriptive authority&mdash;were evaluated. Surescripts estimates that this number represents roughly two-thirds of all office-based physicians and providers. The prescriber population varies over time. Providers’ length of time using e-prescribing systems on the Surescripts network were categorized into 4 subgroups based on length of time on the Surescripts network. Intervals of time were as follows: less than 1 year, 1 to 2 years, 2 to 3 years, and more than 3 years.

Prescription transactions included new prescription and renewal authorization responses sent electronically by providers as the key metric used to calculate provider utilization levels. Utilization was observed following the providers’ point of adoption and defined by the count of prescription messages sent electronically over the Surescripts network for the 6-year period. Providers who sent at least 1 eRx message across the Surescripts network were considered active prescribers and were included in the study. Monthly prescriptions were gathered for each provider on the Surescripts network who sent at least 1 eRx message during a specific month and were aggregated by time on the network. Data anomalies, such as prescribers with no associated specialty group, region, or EHR system, were removed from the analysis. A total of 24 providers exhibited extreme prescribing behavior, with more than 25 million total prescriptions over the study period, and were subsequently removed from the population. An outlier analysis found that these 24 prescribers accounted for more than 1% of the total number of prescriptions in the study. Although the prescriptions are authentic, the number of prescribers associated with these transactions is drastically underrepresented.  

This analysis assessed 3 provider characteristics to describe utilization trends over time: e-prescribing system used when sending eRxs, geographic location of prescribers, and prescriber specialty. (System names were removed for the purpose of this analysis to preserve confidentiality.) Data were summarized and aggregated by month and year, over each time period. The outcome measure, provider utilization rate, was defined as:

[(sum (new prescriptions) + sum (refill responses)) / number of prescribers]                                                         (1)

in order to calculate the number of prescriptions sent electronically per provider. The primary explanatory variable of interest was time on the network, followed by the interaction of time and geographic region, provider specialty, and prescriber technology.

Data were stratified by provider specialty and ranked by overall percentage of active prescribers. The top 10 specialty groups were identified in the ranking step and analyzed by time on the Surescripts network; these 10 specialty groups were developed using 9 specialties and aggregating all other specialty groups into “other specialties.” A similar method was applied to evaluate utilization over time by e-prescribing technology, to include both EHR and stand-alone applications. Prescriber technologies were masked for confidentiality and presented here as System 1-System 10. The final measure assessed was provider geographic region. Utilization patterns were tracked over the study period and compared using the categories: west, northeast, south, and midwest.

Descriptive statistics were generated for each variable of interest. Univariate analysis indicated non-normal distributions of data. Kruskal-Wallis nonparametric analysis of variance (ANOVA) was performed to determine whether there were statistical differences in prescriber utilization over time by region, specialty, and EHR system technology. Post hoc multiple comparison methods were generated by applying Wilcoxon tests to pairwise data for all statistically significant results. (Pairwise comparisons were conducted using the Dwass-Steel-Critchlow-Fligner method [Hollander and Wolfe (1999)] or Bonferroni/Dunn’s correction.) Statistical significance was accepted at 

 <.05. Data analysis was performed using SAS version 9.3 (SAS Institute Inc, Cary, North Carolina).

Overall utilization increased as prescribers’ use of e-prescribing on the Surescripts network grew over time. Specifically, provider utilization increased (over the time of the study) from an overall monthly utilization rate of 93 eRxs per provider in December 2007 to 156 eRxs per provider by the end of the study in December 2012. 

 displays median utilization rates per provider over the 4 time intervals. Active providers on the network for less than 1 year exhibited median utilization rates of 72 eRxs per provider. Rates increased to 112 eRxs per provider for the 1- to 2-year time period, 137 eRxs per provider for 2 to 3 years, and 178 eRxs per provider for more than 3 years. Differences between utilization rates across each time interval were statistically significant (

Provider utilization patterns were further illustrated by analyzing associations between median eRx-utilization rates over time by prescriber geographic location, provider specialty groups, and e-prescribing technology. All results indicated overall significance between utilization rates over time and geographic region, provider specialty, and technology. Each application displayed a similar pattern of increased utilization over time. Among the top 10 systems, system 1 and system 2 both experienced the greatest increase for more than 3 years. 

 shows median utilization rates per provider by prescriber technology over time. Increases in utilization rates over time and across systems were statistically significant (

displays provider utilization rate by 10 specialty groups over time on the network. Patterns of increased rates persisted over time across provider specialty. Results were statistically significant (

 <.0001). Family practitioners and internists accounted for nearly 35% of all specialists in the data. Increases in utilization rates over the 4 time periods for family practitioners and internists were greater than differences between all other indicated groups.

Provider geographic location was the final measure of analysis. Provider locations were divided into 4 regions: midwest, northeast, south, and west, as shown in 

. Each region displayed consistent increases over the 4 time periods (

 <.0001). The south had the largest number of active prescribers on the Surescripts network with the highest volume of prescriptions, followed by the midwest, northeast, and west. (See the 

Our results suggest that provider utilization of e-prescribing increases following the point of adoption and that utilization continues to increase over time. We observed this behavior consistently across choice of EHR software vendor, across practice settings and provider specialty, and across geographic regions. The results strongly suggest that, once adopted, the use of EHR systems to e-prescribe is “sticky”—meaning that once prescribers enact the requisite change management process to adjust their clinical work flow, they are likely to continue to use and increase their use of e-prescribing. This is true despite the fact that, for the majority of the study period, e-prescribing for controlled substances was not legal; therefore, separate prescribing work flows were required for a portion of prescriptions.

Surescripts’ National Progress Report suggests that in 2013 a total of 1.04 billion prescriptions were routed electronically; based on available data from IMS Health regarding total prescriptions written, this suggests that more than 50% of eligible prescriptions written in 2013 were routed electronically.

 (Surescripts’ analysis of IMS Health data assumes that 11% of all dispensed prescriptions are for controlled substances and therefore were not legal in many states at the time of the study.) Combined with information about current levels of adoption, this information does provide a guideline as to how long we should expect average utilization to continue to increase.

Our findings have relevance to policy makers and the administrators of the HITECH program. The Office of the National Coordinator for Health Information Technology is responsible for setting thresholds for core measures of Meaningful Use. Historically, it has relied on input from provider organizations, hospital organizations, the vendor community, and others regarding how to set those measures, including the thresholds for e-prescribing. Our findings have several implications: 1) overall utilization of e-prescribing is quite high, with a simple calculation suggesting that the average e-prescriber routes roughly two-thirds of eligible prescriptions electronically today; 2) e-prescribing utilization behavior is dynamic and increases over time, suggesting that average utilization at any one time is not an appropriate reference point for setting Meaningful Use thresholds; and 3) utilization is likely to continue beyond HITECH and the Meaningful Use program given the high overall levels of utilization and the trend of increased use over time.

There are several limitations to this study. First, Surescripts does not have visibility to all eRxs. For example, eRxs routed between prescribers and pharmacies within a fully integrated delivery network will not show up on the Surescripts network. Despite this, we believe that Surescripts’ connectivity to more than 600 e-prescribing applications and 62,000 community and mail pharmacies allows it to serve as a proxy for utilization analysis at the national level. Second, utilization data were aggregated by year and month, provider specialty, region, and technology, therefore not allowing for analyses of individual prescribers and potential variability. Third, to evaluate utilization for active prescribers, only those providers who had activated e-prescribing on the Surescripts network and who had remained active over the period of the study were included. Future research on prescribers who activate but never use e-prescribing, or those who discontinue use, may be warranted. Fourth, controlled substances—estimated to account for 11% of all prescriptions&mdash;are significantly understated in this analysis because barriers to adoption and use of e-prescribing of controlled substances have minimized overall e-prescribing utilization rates. As regulatory and technological constraints to e-prescribing of controlled substances diminish over time, e-prescribing adoption and utilization trends are expected to rise.

We believe that e-prescribing can be viewed as a gateway technology that may lead to scaled interoperability. Specifically, e-prescribing is the first form of health information being exchanged ubiquitously, across geographic regions, practice settings, and prescriber specialties. We believe this is because the e-prescribing ecosystem is mature, with: 1) clear transaction standards, 2) an established governance forum for development of those standards in the form of the National Council for Prescription Drug Plans, and 3) early adoption among pharmacies, which allowed for positive network effects as prescribers adopted.

Our findings are promising and suggest that providers are making fundamental changes in the way they practice medicine and that those changes are sticking. Furthermore, the consistency of our findings across EHR systems, geographic regions, and practice settings suggests that providers are finding value in e-prescribing, irrespective of participation in or support from Regional Extension Centers or other programs. The findings are an encouraging sign of provider willingness to use EHRs to send and receive electronic clinical messages. Questions remain as to whether these findings can be extrapolated to suggest how other EHR functionality will be used over time, and if so, to what extent. We believe additional research is warranted to explore these questions and suggest that observation of transactional data from a health information exchange, rather than traditional survey-based methods, can be helpful in addressing these questions.

The authors would like to acknowledge the support provided by Jill M. Mulligan and the executive sponsorship provided by Mary Ann Chaffee.

E-prescribing adoption has increased dramatically over the past decade. This study provides empirical evidence that utilization among adopters continues to increase, suggesting application â€œstickiness.â€