The objective of this study is to describe the growth in provider (physician, nurse practitioner, and physician assistant) adoption of e-prescribing and the growth in pharmacies actively accepting e-prescriptions using nationally representative data from December 2008 to December 2012. Additionally, this study explored e-prescribing adoption variation by urban and rural  counties.

 Descriptive analysis of nationally representative, transactional e-prescribing data.

 Data for this analysis were from Surescripts. Surescripts is a leading e-prescription network utilized by a majority of all chain, franchise, or independently owned pharmacies in the United States routing prescriptions for more than 240 million patients through their network.

The total number of prescribers, including physicians, nurse practitioners, and physician assistants e-prescribing via an electronic health record (EHR) on the Surescripts network has increased from 7% to 54%. Additionally, the number of pharmacies actively accepting e-prescriptions is 94%. These increases in pharmacies actively accepting e-prescriptions and the provider’s eprescribing mirror the increase in the volume of e-prescriptions sent on the Surescripts network.

This analysis shows that the vast majority of pharmacies in the United States are able to accept e-prescriptions and over half of providers are e-prescribing via an EHR.

Electronic prescribing among pharmacies and providers (including nurse practitioners, physician assistants, and physicians) has increased.

		Providers e-prescribing via an electronic health record has increased from 7% in December 2008 to 54% in December 2012.

		Pharmacies actively e-prescribing has increased from 70% in December 2008 to 94% in December 2012.

		No significant differences currently exist among provider or pharmacy adoption in rural and urban areas.

Electronic prescribing (e-prescribing) is the electronic transmittal of a prescription to a pharmacy from the provider and is a tool used to send accurate, error-free, and legible prescriptions to pharmacies.

 Providers can e-prescribe via electronic health records (EHRs) or standalone e-prescribing systems. EHRs have advantages such as clinical notes, laboratory results and  orders, and a broad range of clinical decision support that standalone systems do not offer.

 E-prescribing through EHRs improves the availability of pharmacy benefits information and patient medication histories, making potentially life-saving information available immediately.

 Evidence of the benefits of e-prescribing is mounting. E-prescribing has been found to significantly reduce prescription errors in communitybased ambulatory practices and eliminate prescription errors due to   illegibility.

 In 2000, the Institute of Medicine (IOM) detailed the rate of preventable medication errors associated with paper prescribing practices.

 In addition, the IOM called for the transformation of healthcare through the use of health information technology (Health IT) such as e-prescribing in “Crossing the Quality Chasm.”

E-prescribing has been encouraged by the Federal government for nearly 10 years. The first time was when the Medicare Modernization Act (MMA) was passed in 2003.

Federal regulations passed in 2006 and all states enacted laws to allow the electronic exchange of most types of prescriptions,

 thereby eliminating legal barriers to the adoption of e-prescribing. The Medicare Improvements for Patients and Providers Act of 2008 (MIPPA) authorized e-prescribing incentive payments for Medicare providers, starting in 2009.

Most recently, The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 was passed.

The goal of the HITECH act was to help meet the objectives of the “triple aim”: to improve care, improve population health  overall, and reduce the costs of healthcare.

 To help spur health information technology (IT) adoption, “meaningful use” incentive payments were designed to help with the initial costs of EHRs for eligible providers. These payments are designed to encourage and facilitate the adoption of health IT including e-prescribing. Additionally, the State Health Information Exchange Cooperative Agreement Program (State HIE Program) in the Office of the National Coordinator for Health Information Technology (ONC) awarded grants to 56 states and eligible territories.

 This program specifically focuses on pharmacy adoption of e-prescribing, and encourages grantees to employ various strategies to advance pharmacy eprescribing. ONC has also funded 62 regional extension centers (RECs) to help more than 100,000 primary care providers from individual and small practice settings adopt and use EHRs.

 Despite the potential benefits of implementing e-prescribing, due to the technical, cost, and/or regulatory barriers, studies have indicated that the adoption of e-prescribing has been slow.

 Additionally, studies have held that technical challenges such as availability of reliable high-speed network connections to operate e-prescribing systems especially in the rural areas may affect preparedness of the pharmacies to accept e-prescribing.

 Therefore, federal, state, and local governments have devoted significant efforts to the adoption of e-prescribing.

The objective of this study is to describe the growth in provider (physician, nurse practitioner, and physician assistant) adoption of e-prescribing and the growth in pharmacies actively accepting e-prescriptions using nationally representative data.

Surescripts is a leading e-prescription network utilized by a majority of all chain, franchise, or independently owned pharmacies in the United States routing prescriptions for more than 240 million patients through their network, excluding closed systems such as Kaiser Permanente.

 For national results, data from all 50 states and the District of Columbia were included in the analysis. The data represent transactions from December 2008 to December 2012. The area resource file was used to determine county level urban and rural characteristics.

Surescripts pharmacy data include all pharmacies registered with the National Council for Prescription Drug Programs (NCPDP). The NCPDP files include indicators of whether each pharmacy is connected to the Surescripts network and whether each pharmacy processed a prescription on the Surescripts network in the given month. In this analysis, an active pharmacy is a pharmacy that has processed at least 1 electronic prescription in the given month. To support a realistic denominator of pharmacies that have the ability to e-prescribe on the Surescripts network, this analysis included chain, franchise, and independent pharmacies. Medical device manufacturers, nuclear, intravenous infusion, and government/military pharmacies were excluded.

For providers, including physicians, nurse practitioners, and physician assistants, Surescripts data provide an e-prescribing method. For penetration rates of providers eprescribing via an EHR, a method of identifying provider denominators was developed with SK&A, a proprietary data set using a combination of the title and specialty variables.

 The database is designed to comprise a census of ambulatory healthcare sites with at least 1 provider with prescribing authority. All sites are contacted twice a year and asked to confirm information on practice location, the providers who work at the site, and other site characteristics. The counts were de-duplicated to correct for individual providers who are observed at multiple sites. Data for annual percentages of new and renewal prescriptions routed through the Surescripts network data exclude controlled substances.

The total number of prescribers, including physicians, nurse practitioners, and physician assistants e-prescribing via EHR on the Surescripts network has increased, as displayed in 

. In December 2008 the total number of prescribers using an EHR on the Surescripts network was approximately 47,000, representing 7% of the provider population in the United States. As of December 2012, the total number increased to 398,000, representing 54% of providers in the United States. Among current prescribers on the Surescripts network, 86% use an EHR while 14% use standalone e-prescribing systems (data not shown).

In order for providers to successfully use their e-prescribing systems, they must have pharmacies with the ability to accept these e-prescriptions. The growth in pharmacies actively e-prescribing in the United States during this study period is displayed in 

. The percent of retail pharmacies actively e-prescribing on the Surescripts network increased from 43,000 pharmacies, representing 70% of all chain, franchise, and independent pharmacies in December 2008 to over 59,000 pharmacies, representing 94%, in December 2012, therefore showing a 24% increase in the past 4 years.

These increases in pharmacies actively accepting e-prescriptions and in providers’ e-prescribing mirror the increase in the volume of e-prescriptions sent on the Surescripts network. In 2008, 4% of all new and renewal prescriptions were sent electronically in the United States. It is forecasted that 45% of new and renewal prescriptions will be sent electronically in 2012. In December 2008, 61% of pharmacies in rural counties were actively accepting e-prescriptions, compared with 75% of urban pharmacies (

 <.001). This 14% gap has closed during the study period. In December 2012, 94% of urban pharmacies and 93% of rural pharmacies were actively accepting e-prescriptions. For providers, adoption has remained consistent between urban and rural providers (data shown in 

). Additional results regarding new and renewal prescriptions and variations in e-prescribing among pharmacies and providers in rural and urban counties are also given in the eAppendix.

The majority of pharmacies in the United States have been able to accept e-prescriptions since 2008. This suggests that e-prescribing among physicians was not hindered by the lack of pharmacies able to receive e-prescriptions. In order to implement health information technologies, providers need technologies in the marketplace, be able to implement the technology in practice, and perceive that the technologies are worthwhile.18 The HITECH act and resulting programs such as the State HIE and REC have assisted with the first 3 points. However, provider perceptions are less influenced by governmental policies and programs than by their practice experience. The large increase in e-prescribers (7%-54%) suggests accumulating positive perceptions as experience grows. Over half of providers have implemented EHRs and e-prescribe via those systems. This is consistent with current literature.

Cost and work flow concerns are most commonly cited as the primary challenges to health IT adoption.

Studies have noted that e-prescribing has doubled prescribing time for office-based physicians. This time is partially offset by reducing other record-keeping tasks.

 The IOM reported that although ubiquitous in other major industries, the diffusion of relevant technologies in healthcare is still in its very early stages. The report states that EHRs have the potential to improve patient outcomes and also improve adherence to medications.

Federal incentives, meaningful use requirements, and the federal programs detailed above may have helped lead toward increased positive experience, and thus driven this remarkable increase in adoption of e-prescribing.

E-prescribing is an essential component of meaningful use, an important milestone for health information exchange (HIE), and is important to meeting the triple aim.

 Tracking the distribution of new prescriptions and renewal requests on the Surescripts network is a useful way to assess the robustness to be able to afford the technologies, have access to those of HIE, as renewal requests require bidirectional exchange between pharmacist and prescriber. Additionally, patient adherence to medications is better with e-prescribing.

A recent review article found that 92% of health IT studies during that time showed either positive or mixed positive outcomes. 

 Complete EHR use may add additional quality and outcome benefits. Payers and prescribers are   enabled to communicate information that will lead to improved quality care for patients. Formulary benefit alerts, safety alerts, adherence reminders, and gaps in care alerts are some of the initial innovations that HIE via EHRs makes possible.

In addition to the challenges faced by urban providers, rural providers and pharmacies face unique issues with access, resources, and connection. With this in mind, the RECs have worked with over 50% of eligible providers in rural areas to provide assistance regarding e-prescribing and other requirements of meaningful use. Additionally, the State HIE Program has reached out to rural pharmacies to help facilitate e-prescribing. Our study found no current major differences in provider adoption by rurality. This is in agreement with 2 early studies of physician offices which showed that Health IT adoption and use in rural offices was not lower than in urban offices.

Despite the progress made in the use of e-prescribing, there is also a concern of lack of ability to receive and process e-prescriptions by independent or rural pharmacies for reasons such as availability of broadband Internet and concern over transaction fees. Studies have suggested that incentive programs or grants to help with infrastructure would help to overcome that issue.

 It is important to note that our study reports no difference in ability of pharmacies in rural and urban counties which are actively e-prescribing. This indicates the potential success of incentives, grants, and technical assistance provided to such pharmacies.

With the increase of health IT use among providers and pharmacies, concerns have been raised regarding patient privacy and increased third-party access to health information. It is of note that in the period of this study, those issues do not appear to impede rapid growth in utilization and adoption. However, questions regarding the cost and health benefits of health information technologies such as e-prescribing and EHR use continue to be voiced. This analysis suggests that increasing experience with various HIE technologies such as e-prescribing results in their rapid growth. This is a trend encouraging continued exploration of the clinical and economic benefits of HIE.

Data used for this analysis were generated from prescribers and pharmacies connected to the Surescripts Network and e-prescribing transactions that flow through the network. Due to the strength of market share, Surescripts can serve as a proxy for national trends analysis. While Surescripts captures the vast majority of outpatient transactions, it may not include transactions from a number of sources such as inpatient e-prescribing where 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.

E-prescribing is proving its potential to create a gateway to the improved patient care that health IT promises. The majority of pharmacies in the United States are able to accept e-prescriptions and nearly half of providers are e-prescribing via an EHR. These percentages have increased significantly as pharmacy and prescribing practitioner experience have grown. This study shows positive emerging trends in electronic prescribing by demonstrating accelerated growth in adoption of electronic prescribing at both provider and pharmacy level. Continuous efforts and focused investments can be expected to diminish most of the barriers to implementation in the future.

From Office of the National Coordinator for Health IT (MHG, MFF), Washington, DC; College of Pharmacy (VV), University of Toledo, Toledo, OH.

 This study was funded by the Office of the National Coordinator for Health Information Technology. The data used for this analysis were procured by ONC/HHS. MFF and MHG are federal government employees at ONC/HHS.

The authors (MHG, MFF, VV) report no relationship or financial interest with any entity that would pose a  conflict of interest with the subject matter of this article.

Concept and design (MHG, MFF); acquisition of data (MHG); analysis and interpretation of data (MHG, MFF, VV); drafting of the manuscript (MHG, MFF, VV); critical revision of the manuscript for important intellectual content (MHG, MFF, VV); statistical analysis (MHG, VV); obtaining funding (MHG, MFF); administrative, technical, or logistic support (MHG); and supervision (MFF).

Meghan H. Gabriel, PhD, Office of the National Coordinator for Health Information Technology, US  Department of Health and Human Services, 200 Independence Ave, S.W. Washington, DC 20201. E-mail: Meghan.Gabriel@hhs.gov.1. Centers for Medicare & Medicaid Services. E-Prescribing overview. https://www.cms.gov/eprescribing. Revised April 2, 2013. Accessed January 2013.

2. Ryan J. Nuts and bolts of e-prescribing. American Academy of Orthopadic Surgeons. http://www.aaos.org/news/aaosnow/oct08/managing6.asp. Published October 2008. Accessed Accessed January 2013.

3. Institute of Medicine. To Err is Human: Building a Safer Health System. National Academies Press; November 1999.

4. Kaushal R, Kern LM, Barron Y, Quaresimo J, Abramson EL. Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med. 2010;25(6):530-536.

5. Abramson EL, Barron Y, Quaresimo J, Kaushal R. Electronic prescribing within an electronic health record reduces ambulatory prescribing errors. Jt Comm J Qual Patient Saf. 2011;37(10):470-478.

6. Leavitt M. Medscape’s response to the Institute of Medicine Report: crossing the quality chasm: a new health system for the 21st century. MedGenMed. 2001;3(2):2.

7. The Center for Improving Medication Management. A Clinician’s Guide to E-Prescribing. http://www.ama-assn.org/resources/doc/hit/clinicians-guide-erx.pdf. Published 2011. Accessed January 2013.

8. Centers for Medicare & Medicaid Services. Medicare E-Prescribing Incentive Program. http://www.cms.gov/Medicare/E-Health/Eprescribing/Medicare-Incentives.html. Published 2013. Accessed January 2013.

9. Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363(6):501-504.

10. Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health Aff (Millwood). 2008;27(3):759-769.

11. Williams C, Mostashari F, Mertz K, Hogin E, Atwal P. From the office of the national coordinator: the strategy for advancing the exchange of health information. Health Aff (Millwood). 2012;31(3):527-536.

Articles

POLICY

Using nationally representative transactional e-prescribing data, this study describes the trends of e-prescribing adoption by  providers and pharmacies.

Emerging and Encouraging Trends in E-Prescribing Adoption Among Providers and Pharmacies

 To describe changes in rosiglitazone prescribing following the US Food and Drug Administration (FDA) warning of  potentially increased risk of myocardial infarction and removal from the Department of Veterans Affairs National Formulary (VANF), assess patient-level factors associated with rosiglitazone discontinuation, and evaluate changes in glucose control.

 Veterans with an active outpatient prescription for rosiglitazone on April 1, 2007, were followed until June 30, 2008. Incidence rate ratios (IRRs) of rosiglitazone discontinuation were compared over time using Poisson methods. We identified patient-level factors associated with stopping rosiglitazone using multivariable Poisson regression and compared glycated hemoglobin (A1C) values across time among patients who discontinued/continued rosiglitazone using linear mixed models.

Of 95,539 veterans with an active outpatient rosiglitazone prescription, 86.7% discontinued rosiglitazone. Discontinuation rates increased significantly after the FDA warning, with IRRs from 1.6 to 1.8. After removal from the VANF, rosiglitazone discontinuation rates again increased significantly. Discontinuing rosiglitazone was associated with the FDA warning, removal from the VANF, female sex, black race, Hispanic ethnicity, comorbidity, A1C greater than 9%, and use of rosiglitazone as first- or second-line therapy. Among patients who did and did not receive a replacement medication, the mean  changes in A1C from baseline were 0.12% and 0.46%, respectively. For those who continued rosiglitazone, the mean change in A1C was —0.02%.

The rosiglitazone discontinuation rate increased following the FDA warning and increased further following removal of rosiglitazone from the VANF. Glucose control may have declined among those who discontinued rosiglitazone.

After the US Food and Drug Administration (FDA) issued a safety alert in May 2007 regarding potentially increased risk of myocardial infarction in patients receiving rosiglitazone, the Department of Veterans Affairs (VA) removed it from its national formulary in October 2007.

		The rosiglitazone discontinuation rate increased following the FDA warning and increased further after its removal from the VA formulary.

		Patients receiving insulin concurrently tended to have the highest discontinuation rates, and patients receiving rosiglitazone as third-line agent had the lowest discontinuation rates.

		Among patients who discontinued rosiglitazone and did not receive replacement medication, the mean change from baseline in glycated hemoglobin was 0.46%.

Before October 4, 2007, rosiglitazone was available on the Department of Veterans Affairs National Formulary (VANF) for treatment of type 2 diabetes mellitus (T2DM); pioglitazone could be obtained via the nonformulary request process. However, rosiglitazone use likely changed following the US Food and Drug Administration (FDA) safety alert issued May 21, 2007, regarding the potential increased risk of myocardial infarction (MI) and cardiovascular death in patients receiving rosiglitazone and the subsequent removal of rosiglitazone from the VANF on October 4, 2007.

 The FDA alert, which was sent to all Department of Veterans Affairs (VA) providers via established electronic distribution lists, was largely prompted by the online publication of a meta-analysis in which rosiglitazone was associated with increased MI risk (odds ratio = 1.43; 95% confidence interval [CI],1.03-1.98) compared with other diabetes medications or placebo.2 A subsequent meta-analysis of trials with 12 or more months of follow-up (vs 24 weeks in the previous meta-analysis) confirmed the increased MI risk (relative risk = 1.42; 95% CI, 1.06-1.91).

The only other thiazolidinedione (TZD) available for T2DM treatment during this time was pioglitazone. A meta-analysis of pioglitazone versus non-TZDs showed a lower risk of the composite end point of death, MI, or stroke with pioglitazone (hazard ratio = 0.82; 95% CI, 0.72-0.94).

 In addition, a nested case-control study of older patients with T2DM showed that rosiglitazone monotherapy, but not pioglitazone, was associated with increased MI risk.

Given these results, as well as similar findings in a retrospective cohort study conducted by the VA,

regional pharmacy leaders and the VA Medical Advisory Panel removed rosiglitazone from the VANF at their October 4, 2007, meeting. Using the electronic distribution lists, VA prescribers were sent guidance recommending a discussion of the risks and benefits of continued rosiglitazone use with their patients. If patients and providers made an informed choice to continue TZD therapy, then patients could remain on rosiglitazone or change to pioglitazone. Pioglitazone was the only TZD available for new starts, but required completion of a nonformulary request.

Other studies examined the impact of safety warnings on rosiglitazone  use, but none described changes in use based on its place in therapy for the treatment of T2DM.

 This is important because providers may be more likely to discontinue rosiglitazone if other options remain available for controlling blood glucose. In addition, our study describes the effect on glucose control (ie, glycated hemoglobin [A1C]) of removing rosiglitazone from the VANF. Formulary changes are frequently made within healthcare systems to promote the use of specific medications, but the clinical consequences of these decisions rarely are examined and reported.

The 4 primary objectives of this study were to: (1) describe changes in rosiglitazone prescribing following the FDA warning of a potentially increased risk of MI and removal of rosiglitazone from the VANF; (2) describe these changes in the context of diabetes medications used concomitantly with rosiglitazone; (3) examine the medications replacing rosiglitazone; and (4) assess patient-level factors associated with rosiglitazone discontinuation. The secondary objective was to evaluate changes in  A1C levels over time among patients who did and did not discontinue rosiglitazone.

The cohort included veterans with an active outpatient prescription for rosiglitazone in the VA on April 1, 2007, who were chronic users of the medication. Chronic use was defined as having another prescription for rosiglitazone within the 180 days preceding the release date of the prescription that was active on April 1. The release date is the date the prescription is mailed to the patient or picked up from the VA pharmacy. For each patient, the study period was the time between the baseline date of April 1, 2007, and June 30, 2008.

The study cohort was identified retrospectively using the Pharmacy Benefits Management Services’ database (version 3.0) for rosiglitazone prescriptions. The Pharmacy Benefits Management database also was used to obtain information on all antidiabetic medications prescribed in the VA during the study period. VA Medical SAS Datasets (Austin Information Technology Center, Austin, Texas) provided patient demographics and comorbidities. A1C values were obtained from the Decision Support Services database. All-cause mortality was identified from the VA Beneficiary Identification and Record Locator System files.

Discontinuation of the baseline rosiglitazone prescription during the study period was defined as a subsequent prescription for pioglitazone or no additional prescriptions for rosiglitazone during the 60 days following the end of the days of supply for the rosiglitazone prescription (ie, release  date of rosiglitazone prescription plus days of supply plus 60 days).

The place of rosiglitazone in therapy was defined in terms of concurrent prescriptions for antidiabetic medications that were active within 1 week of the baseline date. The place of rosiglitazone in therapy was categorized as; (1) first line if the patient had no concurrent medications at baseline (ie, monotherapy); (2) second line if the patient had concurrent metformin or sulfonylurea, but not both (with or without additional medications); (3) third line if the patient had concurrent metformin and sulfonylurea (with or without additional medications); (4) with insulin if the patient had concurrent insulin (with or without additional medications); or (5) other if the patient had concurrent medications other than those listed previously (eg, acarbose). For those patients discontinuing rosiglitazone, we assessed the medication(s) that replaced rosiglitazone (ie, the medications released within the time period of the days of supply of rosiglitazone plus 60 days, but not dispensed during the 180 days before the rosiglitazone release date). The secondary outcome was change in A1C. The baseline A1C value was the most recent result within 3 months of the baseline date. The follow-up A1C level was the most recent result within 3 to 9 months of stopping rosiglitazone (for those who discontinued rosiglitazone) or the most recent result within 3 to 9 months of June 30, 2008 (for those who continued rosiglitazone).

Outcomes were assessed in the context of 2 key events: the May 21, 2007, FDA safety warning and the removal of rosiglitazone from the VANF on October 4, 2007. The study period was split into 3 phases accordingly: pre-FDA warning (April 1, 2007, to May 20, 2007), post FDA warning (May 21,  2007, to October 4, 2007), and after removal from the VANF (October 5, 2007, to June 30, 2008).

Patient baseline characteristics including demographics, comorbidities as defined in the adaptation by Deyo and colleagues of the Charlson Comorbidity Index (a weighted index in which a higher score is associated with an increased risk of health outcomes such as mortality and resource use based on comorbidities coded within the year prior to baseline),

 A1C, and place of rosiglitazone in therapy were described by rosiglitazone discontinuation status (yes or no). We compared baseline characteristics with 

We determined the monthly rosiglitazone discontinuation rates. Using Poisson methods, we compared monthly incidence rate ratios (IRRs) in the month prior to the FDA warning (May 2007) with the IRRs in the month prior to removing rosiglitazone from the VANF (September 2007) to assess the impact of the 2 events separately. We also examined the rosiglitazone discontinuation rates across time and described the proportions of patients receiving medications to replace rosiglitazone by the place of rosiglitazone in therapy at the index date. We estimated the intervention effects (IRRs for post FDA warning vs pre-FDA warning, after removal from the VANF vs pre-FDA warning, and after removal from the VANF vs post FDA warning) and identified patient-level factors associated with stopping rosiglitazone by using multivariable Poisson regression with a robust variance estimator and fixed effects for VA medical centers. Patients who died before rosiglitazone was discontinued were coded as continuing on rosiglitazone.

To evaluate the effect on glucose control of removing rosiglitazone from the VANF, we compared A1C levels across time among patients who discontinued and continued rosiglitazone. We used a linear mixed model to account for withinpatient correlation and conducted subgroup analyses of A1C levels according to insulin use at baseline. All P values were 2-sided, and 

 values less than .05 were considered statistically significant. Because most tests were statistically significant due to the large sample size, we focused our interpretation on differences of a clinically significant magnitude. We used Stata release 11 (StataCorp LP, College Station, Texas) to fit the Poisson models; the remaining analyses were conducted using SAS version 9.2 (SAS Institute Inc, Cary, North Carolina).

Baseline Characteristics by Rosiglitazone Discontinuation Status

On April 1, 2007, 95,539 veterans had an active outpatient prescription for rosiglitazone and were chronic users of the medication. The average age of the cohort was 67 years; 97.8% were male; 60% were white; 9.3% were black; and 30% were other/unknown race (Table 1). At baseline, the mean A1C was 7.4%, and 82.1% of patients received other antidiabetic medications concurrently with rosiglitazone. Rosiglitazone was first-line therapy in 17.9% of patients, second-line in 35.6%, third-line in 26.6%, used with insulin in 19.6%, and rarely prescribed with other medications not included in the second-line or third-line definitions (0.3%). By the end of the study period, 82,797 (86.7%) patients discontinued rosiglitazone. The baseline  characteristics of these patients were essentially the same as those of the total cohort. Patients who continued rosiglitazone were clinically similar to the patients who discontinued the medication, except that they were less likely to be black, have a baseline A1C value higher than 9%, or receive insulin concurrently at baseline, and relatively more likely to receive rosiglitazone as thirdline therapy.

The rosiglitazone discontinuation rate over the entire study period was 4.0 per 1000 person-days, which means that during 1000 days of observation (eg, 10 patients on rosiglitazone for100 days), 4 would have discontinued the medication. The period-specific rosiglitazone discontinuation rates were about 2.3 per 1000 person-days of rosiglitazone use before the FDA warning, 3.9 per 1000 person-days after the FDA warning, and 5.3 per 1000 person-days after removal from the VANF (

). Discontinuation rates increased significantly during the 4 months following the May 21, 2007, FDA warning, with the IRRs ranging from 1.6 to 1.8 (Table 2, 

). After removal from the VANF on October 4, 2007, discontinuation rates increased significantly through May 2008; compared with September 2007, the monthly IRRs ranged from 1.2 to 1.6. Discontinuation rates returned to the September 2007 level in June 2008. Rosiglitazone discontinuation rates varied with the place of rosiglitazone in therapy at the baseline date (

). Patients receiving insulin concurrently tended to have the highest discontinuation rates,  and those on rosiglitazone as a third-line agent had the lowest discontinuation rates.

When rosiglitazone was discontinued, the proportions of patients who received another medication in its place ranged from 17.1% pre-FDA warning to 22.5% post FDA warning and 37.8% after the VANF removal. An abrupt increase in the proportion of patients starting pioglitazone in place of rosiglitazone, from 1% or fewer pre-FDA warning to 22% after VANF removal of rosiglitazone, accounts for the large increase in the proportion of patients receiving prescriptions for replacement medications (

). In addition, patients receiving rosiglitazone as first-line therapy were most likely to receive a replacement  medication, whereas those receiving insulin concurrently at baseline were least likely (

Factors Associated With Discontinuation of Rosiglitazone

Compared with the pre-FDA warning period, patients were more likely (IRR = 1.81; 95% CI, 1.77-1.85) to have rosiglitazone discontinued after the FDA warning (

). During the post VANF removal period, patients were more than twice as likely to discontinue rosiglitazone relative to baseline (IRR = 2.73; 95% CI, 2.67-2.79). The discontinuation rate was significantly higher after rosiglitazone was removed  from the formulary than it was immediately after the FDA warning (IRR = 1.51; 95% CI, 1.49-1.53).

In the multivariable model (Table 3), rosiglitazone was more likely to be discontinued for patients who were female (IRR = 1.10; 95% CI, 1.05-1.15) or black (IRR = 1.05; 95% CI, 1.03-1.08). A 1-point increase in the Charlson Comorbidity Index score was associated with a 3% increase in the risk of discontinuing rosiglitazone. Compared with patients whose baseline A1C was within the range of 7% to 9%, patients whose A1C was less than 7% were less likely (IRR = 0.91, 95% CI, 0.89-0.92), and patients whose A1C was greater than 9% were more likely (IRR = 1.24; 95% CI, 1.2-1.27), to have rosiglitazone discontinued. Patients who were not receiving rosiglitazone as third-line therapy at the index date were 11% to 24% more likely to have it discontinued.

Among patients who had rosiglitazone discontinued and received replacement medication(s), mean A1C values at baseline and after discontinuation were 7.4% and 7.5%, respectively (

). For those patients who had rosiglitazone discontinued and received no new medication, mean A1C values were 7.4% at baseline and 7.9% after discontinuation. Among patients who remained on rosiglitazone, mean A1C values were the same at baseline and the end of the study (ie, 7.3% vs 7.3%). In the subset of patients who did not receive insulin concurrently  at baseline, those who had rosiglitazone discontinued and received a replacement medication had mean A1C values of 7.3% at baseline and 7.5% after discontinuation. Those who did not receive a replacement medication had mean A1C values at baseline and after discontinuation of 7.2% and 7.7%, respectively. Patients who received insulin concurrently with rosiglitazone had higher A1C values on average. Among those taking insulin who received other medication(s) to replace rosiglitazone, the mean A1C values were comparable at baseline and after discontinuation of rosiglitazone (7.9% vs 8.0%). For patients receiving insulin at baseline who had rosiglitazone discontinued and received no new medication, mean A1C values were 7.9% at baseline and 8.3% after discontinuation.

 we found an increase in the rosiglitazone discontinuation rate following the May 21, 2007, FDA warning.

The rate increased still more following the removal of the medication from the VANF; 86.7% of the VA cohort had rosiglitazone discontinued, which is higher than the 62% reported in a similar study by Orrico and colleagues.

 However, our overall follow-up period was longer (13 vs 5 months), and we further assessed rosiglitazone use following its change in formulary status. We conducted a similar study of an intervention to decrease glyburide use in elderly veterans with renal insufficiency; in that study, the baseline glyburide discontinuation rate was approximately 3 per 1000 person-days in the preintervention period and 6.1, 4.8, and 4.2 per 1000 person-days at 1, 2, and 3 months, respectively, after the intervention.

Although the antidiabetic  medications and “interventions” were different, the discontinuation rates in the glyburide and rosiglitazone studies (ie, 2.3 per 1000 person-days at baseline and 4.6, 5.4, and 6.0 per 1000 person-days at 1, 2, and 3 months, respectively, after removal from the VANF) are fairly similar.

We also found important differences in the discontinuation rates according to rosiglitazone’s place in therapy, which was not evaluated in prior studies. The discontinuation rate was lowest when rosiglitazone was used as third-line therapy and generally highest when it was used in combination with insulin. Patients receiving rosiglitazone in combination with metformin and a sulfonylurea (ie, third-line therapy) likely had long-standing and more difficult to control diabetes and may have preferred to avoid insulin, so providers may have been reluctant to stop rosiglitazone. Among patients receiving concomitant insulin, providers could increase the dose of insulin as necessary when rosiglitazone was discontinued. Also, the FDA warned of an increased MI risk in patients on both insulin and rosiglitazone on November 19, 2007; this warning did not apply to pioglitazone.

For patients who had rosiglitazone discontinued, approximately 29% received another medication in its place during the time periods before and after the FDA warning. The FDA warning increased the rosiglitazone discontinuation rate, but did not markedly influence the rate of replacement of rosiglitazone with other medications. However, this replacement rate increased sharply to 37.8% when rosiglitazone was removed from the VANF because providers could switch patients to pioglitazone without completing a nonformulary request. This made prescribing pioglitazone easier and accounted for the observed increase during the period after the VANF removal as the proportions of patients prescribed metformin, a sulfonylurea, or insulin in place of rosiglitazone decreased slightly. It is somewhat surprising that the proportion of patients who received pioglitazone  was not higher; it is possible that some medical centers did not follow VA guidelines and limited the availability of pioglitazone.

MANAGERIAL

After the FDA warning and removal of rosiglitazone from the VA National Formulary, glucose control may have declined among those discontinuing rosiglitazone without receiving replacement medication.

FDA Warning and Removal of Rosiglitazone From VA National Formulary

 To determine the relationship between benefit design, out-of-pocket costs, and prescription reversals, and the impact of reversals on rehospitalizations and total healthcare costs among Medicare members prescribed oral linezolid.

Medicare members from a national health plan prescribed oral linezolid posthospitalization for skin and soft tissue infection (SSTI) or pneumonia were followed retrospectively.

 Members were identified by an oral linezolid prescription between June 1, 2007, and April 30, 2011, where the index event was a prescription fill or reversal less than 2 days before or 10 days after discharge. Associations between out-of-pocket costs and reversal, and between reversal and rehospitalization 30 days postindex, were compared for prescription fills versus reversals. A generalized linear model calculated adjusted total healthcare costs per member controlling for age, sex, geographic region, and clinical characteristics.

 Reversal rates rose progressively from 2% for members with out-of-pocket costs of $0 to 27% for members with out-of-pocket costs higher than $100 (

 <.0001). Infection-related rehospitalizations were 23% versus 9% for members with a  prescription reversal versus a fill (

 <.0001). While postdischarge prescription drug costs were $1228.78 lower (

 <.0001), adjusted mean medical costs were $2061.69 higher (

 = .0033) and total healthcare costs were $1280.93 higher (

 = .0349) for reversal versus fill members.

 Higher out-of-pocket costs were associated with higher rates of reversal, and reversals were associated with higher rates of rehospitalization and adjusted total healthcare costs among Medicare members prescribed oral linezolid posthospitalization for SSTI or pneumonia.

High out-of-pocket costs (coinsurance rather than copay) were associated with prescription reversals among Medicare members prescribed oral linezolid after hospitalization for an uncomplicated or complicated skin infection or pneumonia. Also, members with prescription reversals experienced higher rates of rehospitalization and higher total healthcare costs than members with prescription fills.

		Reversal rates rose from 2% for out-of-pocket costs of $0 to 27% for out-of-pocket costs of more than $100.

		Infection-related rehospitalizations were 23% for reversal members versus 9% for fill members.

		Adjusted mean postdischarge healthcare costs were $1280.93 higher for members with reversals than for fill members.

Linezolid is indicated for the treatment of vancomycin-resistant Enterococcus faecium infections, complicated and uncomplicated skin and soft tissue infections (SSTIs), and nosocomial and community-acquired pneumonia (

 Pathogens for such infections include Staphylococcus aureus (methicillin-susceptible and resistant strains), Streptococcus pneumoniae (including multidrug-resistant strains), Streptococcus pyogenes, and Streptococcus agalactiae. Among these, methicillin-resistant Staphylococcus aureus (MRSA) infections have received particular  attention in the 2000s commensurate with evidence indicating a dramatic rise in the rate of MRSA-related hospital admissions.

Whereas more recent studies suggest community-onset and hospital-onset MRSA infections may have tapered and declined somewhat since 2005,

 the burden of SSTIs overall remains substantial, and if these infections are not appropriately treated they can result in serious life-threatening consequences.

Among antibiotics used to treat SSTIs and pneumonia, linezolid is available in both intravenous and oral forms. Oral  bioavailability is approximately 100%,

allowing for sequential intravenous to oral administration without changing the drug or dosing regimen. The availability of intravenous and oral forms may allow for a shortened length of hospital stay if treatment is continued orally after discharge, resulting in lower total costs of treating the infection.

 However, due to oral linezolid’s high out-of-pocket costs, patients have been known to decline purchase of (reversals) their prescriptions and receive treatment with an alternative antibiotic or forgo treatment altogether.

 based on Humana Inc health plan members examined whether rehospitalization rates and total healthcare costs increased for members with a prescription reversal versus a fill of oral linezolid. Although members with a claim reversal were rehospitalized at higher rates and had higher medical costs compared with those who had a fill, they did not have higher total healthcare costs than members with a prescription fill for oral linezolid. This implied that oral linezolid and  alternative antibiotics or the absence of treatment were similarly cost-effective. However, one limitation of the study by Ball and colleagues was that diagnosis for the initial hospitalization was not confirmed for an infection indicated for linezolid treatment. A second limitation of the study by Ball and colleagues was that members were followed for as long as 60 days postdischarge, which potentially led to inclusion of healthcare utilization and costs unrelated to the initial infection and its consequences.

This research sought to refine the study by Ball and colleagues by restricting the identification of members to those whose initial hospitalization was for complicated or uncomplicated SSTI or pneumonia diagnoses (eAppendix), as well as shortening the postdischarge time period to 30 days to examine acute consequences more likely related to the initial hospitalization. Members from both commercial and Medicare lines of business were initially identified for the study, but all analyses were conducted separately by line of business because of significant differences in plan reimbursement. This study focuses on the Medicare line of business, which comprised a majority (71%) of the total membership.

This study utilized data from Humana’s SAS database which contains enrollment, medical, and pharmacy claims data for Humana’s Medicare membership. All data sources were merged using de-identified member data. The finalized protocol was approved by a central independent institutional review board.

We performed a retrospective cohort analysis of all Medicare members identified as having a prescription claim for oral linezolid after discharge from an inpatient stay for complicated  or uncomplicated SSTI or pneumonia (eAppendix).Members were identified during the time period of June 1, 2007, to April 30, 2011. The service date of the first linezolid prescription claim was  used to identify the date of the index event (fill or reversal). Members who were identified with a reversal in the pharmacy claims  but who filled the prescription for oral linezolid within 3 days after the reversal were recategorized as fill members. Members with a claim reversal were compared with those without a reversal in terms of plan design (whether members paid a copay or had coinsurance), member out-of-pocket costs, infection-related (complicated or uncomplicated SSTI or pneumonia) and all-cause rehospitalization (inpatient admission with overnight stay), and healthcare costs (prescription, medical, and total) in  the 30 days post discharge.

Study subjects were fully insured Medicare Advantage members aged 18 to 89 years who were continuously enrolled for 120 days before and 30 days after the index event (linezolid  fill or reversal) and whose index linezolid claim was processed no more than 2 days before or 10 days after hospital discharge. A diagnosis of complicated or uncomplicated SSTI or pneumonia in any position on the inpatient claim was required for the initial hospitalization.

Members were excluded from analysis if they were enrolled as administrative services—only populations (members of  employer-funded plans excluded by contract from research), had a claim for pregnancy (

International Classification of  Diseases, Ninth Revision

 codes 630.xx-679.xx, V22.xx, and V23.xx), or were in a skilled nursing facility/nursing home any time during the study period.

Claims data for Medicare members prescribed oral linezolid and discharged from an inpatient stay for complicated or uncomplicated SSTI or pneumonia were examined for benefit design, out-of-pocket costs, reversals, rehospitalizations, and healthcare costs. Visual inspection was used to distinguish between copay and coinsurance because the medical claims did not contain an explicit indicator for copay or coinsurance. All integer values (ie, $6, $10, $25, $30, $100, $150) and known noninteger values of Medicare Part D copays (ie, $5.60, $6.30) were classified as copay, while all remaining noninteger values (ie, $57.43, $238.59) were classified as coinsurance. For all descriptive analyses, means were compared using 2-sample t tests and count variables were compared using 

 tests. Demographic and clinical characteristics included age, sex, geographic region, low-income status and dual eligibility (Medicare and Medicaid eligibility), characteristics of the initial broader window typically necessary for medical claims—based comorbidity scores such as the Charlson Comorbidity Index score.

The impact of the reversal on postindex healthcare costs (plan payment plus member medical cost) was modeled using a generalized linear model (GLM) with a gamma distribution as its probability distribution and log link as its link function. Generalized linear models are commonly utilized to account for distributions of highly skewed data, characteristic of medical expenditures.

The log link function for the GLM is specified below, where μ refers to postindex healthcare costs:

Covariates included in the model were age, sex, geographic region, low-income subsidy/dual eligibility, baseline RxRisk-V score, a surgical procedure or an intensive care unithospitalization such as surgery or intensive care unit stay, and the RxRisk-V comorbidity score, a surgical procedure or an intensive care unit stay during the initial hospitalization, out-of-pocket costs, and preindex healthcare costs (per $1000). In order to include data on members with no costs during the follow-up period, $1 was added to all member costs. The models provided adjusted mean costs and 95% confidence intervals for the prescription fill and reversal groups.

A total 1062 Medicare members were available for analysis. Among the total sample, 16.5% of the members reversed their prescription for oral linezolid. Demographic and clinical characteristics by fill versus reversal groups indicated there were no statistical differences in age, sex, or geographic region (

). However, a higher percentage of the members filling their linezolid prescription had low-income subsidy/ dual-eligibility status compared with members reversing their linezolid prescription (

 <.0001). A majority of the characteristics of the initial hospitalization were similar, with the exception that a statistically higher percentage of reversal members were hospitalized for complicated or uncomplicated SSTI (

 = .0148). That corresponded to a higher percentage of fill members hospitalized for pneumonia (

 = .0106). The RxRisk-V comorbidity scores and preindex total healthcare costs were not statistically different between the 2 groups.

 shows that as out-of-pocket costs increased the percentage of members reversing their prescriptions also increased, with out-of-pocket costs above $100 resulting in a reversal rate as high as 27% (

 <.0001). It is assumed that patients with out-of-pocket costs of more than $100 were subject to a coinsurance benefit versus a copay for most patients with out-of-pocket costs of less than $100. Mean (standard deviation) out-of-pocket costs for members with a copay were $7.05 ($14.89); for members with coinsurance the mean out-of-pocket cost was $466.52 ($574.67).

Among members who reversed their prescriptions for oral linezolid, 73% filled a prescription for an alternative antibiotic during the 30 days after discharge, while 27% received no antibiotic. Among patients with prescriptions for alternative antibiotics, a small percentage (9%) were treated with oral or parenteral vancomycin, and 0.6% were treated with parenteral tigecycline. Other, more common antibiotics with prescriptions filled during the 30 days after discharge were amoxicillin (10.5%), ciprofloxacin (15.7%), clindamycin (11.0%), doxycycline (10.5%), and levofloxacin (7.0%) (complete list provided upon request).

, infection-related (complicated or uncomplicated SSTI or pneumonia) rehospitalizations were 14 percentage points higher (

 <.0001) and all-cause rehospitalizations were 10 percentage points higher (

= .0027) in the reversal group versus the fill group. For each type of infection (complicated or uncomplicated SSTI, pneumonia, or both SSTI  and pneumonia), no significant difference was detected between the fill and reversal groups. When grouping prescription fill versus reversal groups by the percentage readmitted for the same infection as the one diagnosed at initial hospitalization, a higher percentage of the reversal group was hospitalized for the same infection: 20% reversal group versus 9% fill group (

<.0001) for complicated or uncomplicated SSTI and 3% reversal group versus 1% fill group (

Unadjusted postindex prescription drug, medical, and total healthcare costs are reported in 

. Whereas postindex prescription drug costs were significantly lower for members with a reversal (

 <.0001), postindex medical costs were  significantly higher for these members (

 = .0013) compared with members with a fill. The combined total unadjusted healthcare costs were not statistically different between the 2 groups (

Adjusted prescription drug, medical, and total healthcare costs are reported in 

. After adjusting for demographic and clinical characteristics, differences in the prescription drug and medical costs remained statistically significant between the fill and reversal groups. Notably, with adjustment, the difference in total healthcare costs between the fill and reversal groups became statistically significant (

 = .0349),  with the mean healthcare cost for the reversal group being $1280.93 higher than that for the fill group.

Parameter estimates (including exponentiated estimates for ease of interpretation) from the GLM for adjusted costs are reported in

. The parameter estimate for the reversalvariable was statistically significant, indicating that adjusted costs for members with a reversal were 19.4% higher than those for members with a fill (

= .0349). Male sex was associated with a 20.1% increase in adjusted costs (

 = .0027), and an incremental point increase in the RxRisk-V score was associated with a 4.8% increase in adjusted costs (

 <.0001). The parameter estimate on preindex healthcare costs was statistically significant, but indicated minimal magnitude. Theparameter estimate for members whose out-of-pocket costs were more than $100 was associated with lower adjusted total healthcare costs (

 = .0080). Parameter estimates for the remaining variables examined were not statistically significant.

Several sensitivity analyses further explored the GLM results by varying cutoffs for the out-of-pocket cost categorical variable (above and below $60, above and below $125), as well as including an interaction term between the reversal term and out-of-pocket-cost term. Results (provided upon request) were generally similar to those specified in Table 5.

The current study found that Medicare members with an oral linezolid fill had fewer infection-related and 30-day all-cause hospital readmissions than members who reversed their prescriptions and either did not receive any antibiotic or received a different antibiotic following their reversal. A higher readmission rate, combined with all other types of medical encounters,  resulted in higher medical costs during the 30 days after discharge from the initial hospitalization for SSTI or pneumonia. Whereas treatment with oral linezolid was associated with higher postindex prescription drug costs, higher prescription drug costs were offset by lower medical costs for the fill group, resulting in total healthcare costs that were $1280.93 lower for the fill group versus the reversal group. This clearly highlights the need to examine prescription drug costs and benefit design in the context of total healthcare costs.

The fill and reversal groups were similar with respect to the vast majority of their demographic and clinical characteristics, suggesting that an economic perspective might have factored in the decision to fill or reverse the linezolid prescription. Consistent with this interpretation is the significantly higher distribution of low-income subsidy/dual-eligibility status among members with a fill versus members with a reversal (42% vs 10%, respectively). Low-income subsidy/dual-eligible members are more likely to fill the prescription for oral linezolid as it is probable they will have low or no out-of-pocket costs. If economic factors did indeed influence the decision to fill or reverse the linezolid prescription, then strategies to reduce member out-of-pocket costs (eg, benefit design) for all health plan members could enable better member access, and in turn, reduce total healthcare costs.

Among members who reversed their prescriptions for oral linezolid, 27% went untreated and 73% filled prescriptions for alternative antibiotics in the 30 days after discharge. As to whether these other antibiotics could be considered alternative therapies to linezolid from a clinical point of view, the specific infection diagnosis and pathogen for each member, along with the specific antibiotic prescription filled postdischarge, would need to be taken into consideration on a case-by-case basis.

An examination of the literature to date regarding the cost-effectiveness of oral linezolid treatment versus comparators indicates that several formal cost-effectiveness analyses have been conducted (primarily vs vancomycin), with results suggesting linezolid was cost-effective in each of the analyses, particularly in the cases of shorter hospital length of stays.

 As pathogens, disease states, additional indications for existing comparators, and emerging new comparators change the clinical and economic landscape of the treatments for SSTIs and pneumonia over time, additional research including cost-effectiveness analyses will need to be conducted.

One limitation of this study was its focus on members with an inpatient stay, which may not be generalizable to those  prescribed oral linezolid in an ambulatory setting. In addition, the length of treatment for oral linezolid or other antibiotic therapies was not evaluated in this study and may have had an impact on postdischarge outcomes. Furthermore, the distinction between copay and coinsurance was made via visual inspection due to the fact that the medical claims did not contain an indicator for copay or coinsurance. Future work will need to more accurately reflect the distinction between copay and  coinsurance.

Additionally, limitations common to studies using administrative claims data apply. These include lack of certain information in the database (eg, lab results, weight, health behavior information) and errors in claims coding. No causal inference can be  ascertained from this study, as it was an observational study using retrospective claims data. Although multivariate regression modeling was used to reduce selection bias and strengthen the causal inference, this approach can only reduce bias caused by measured covariates. Finally, because this study used data from Humana members only, the results may not be generalized to the general population. However, Humana is a large national health plan with members residing in a broad array of US regions.

Associations between out-of-pocket costs and prescription reversals, as well as impact of reversals on rehospitalizations and healthcare costs, were examined among patients prescribed oral linezolid.

Out-of-Pocket Costs and Prescription Reversals With Oral Linezolid

Care coordination is a key component of the patient-centered medical home. However, the mechanism for identifying primary care patients who may benefit the most from this model of care is unclear.

To evaluate the performance of several risk-adjustment/stratification instruments in predicting healthcare utilization.

 All adults empaneled in 2009 and 2010 (n = 83,187) in a primary care practice were studied. We evaluated 6 models: Adjusted Clinical Groups (ACGs), Hierarchical Condition Categories (HCCs), Elder Risk Assessment, Chronic Comorbidity Count, Charlson Comorbidity Index, and Minnesota Health Care Home Tiering. A seventh model combining Minnesota Tiering with ERA score was also assessed. Logistic regression models using demographic characteristics and diagnoses from 2009 were used to predict healthcare utilization and costs for 2010 with binary outcomes (emergency department [ED] visits, hospitalizations, 30-day readmissions, and highcost users in the top 10%), using the C statistic and goodness of fit among the top decile.

The ACG model outperformed the others in predicting hospitalizations with a C statistic range of 0.67 (CMS-HCC) to 0.73. In predicting ED visits, the C statistic ranged from 0.58 (CMSHCC) to 0.67 (ACG). When predicting the top 10% highest cost users, the performance of the ACG model was good (area under the curve = 0.76) and superior to the others.

 Although ACG models generally performed better in predicting utilization, use of any of these models will help practices implement care coordination more efficiently.

For care coordination to be used effectively, it is important to identify which patients are likely to benefit from it. Little evidence exists as to which risk-stratification method performs better in predicting hospitalizations, emergency department visits, 30-day readmissions, and high-cost users.

		There appeared to be good concordance among 6 different risk screening instruments in predicting hospitalization.

		Adjusted Clinical Group classification was generally better than other models in predicting healthcare utilization.n

		Focusing care coordination efforts within the medical home on patients likely to benefit most requires appropriate identification of the highest risk, highest utilizing patients.

With rising prevalence of chronic disease, patient-centered medical homes (PCMHs) were created to deliver higherquality, more cost-effective primary care in the United States.

Identifying which patients are likely to benefit from care coordination is important. We evaluated the performance of 6 risk-screening instruments in predicting healthcare utilization.

September 2013

Emerging and Encouraging Trends in E-Prescribing Adoption Among Providers and Pharmacies

FDA Warning and Removal of Rosiglitazone From VA National Formulary

Out-of-Pocket Costs and Prescription Reversals With Oral Linezolid

Risk-Stratification Methods for Identifying Patients for Care Coordination

Adherence, Persistence, and Switching Patterns of Dabigatran Etexilate

Referring Patients for Telephone Counseling to Promote Colorectal Cancer Screening

Improving BP Control Through Electronic Communications: An Economic Evaluation

Outpatient Parenteral Antimicrobial Therapy at Large Veterans Administration Medical Center

Testimonials Do Not Convert Patients From Brand to Generic Medication

Improving Pneumococcal and Herpes Zoster Vaccination Uptake: Expanding Pharmacist Privileges