 To identify potentially low-value services for inclusion in value-based insurance design (VBID) programs and to discuss challenges involved in incorporating such information.

 We searched the Tufts Medical Center Cost-Effectiveness Analysis Registry (

) to identify examples of low-value services, defined as interventions that make health worse without saving money or those that cost at least $100,000 per quality-adjusted lifeyear gained. We restricted our attention to papers published since 2000. We supplemented this literature review with a list of services recently rejected by the United Kingdom’s National Institute for Health and Clinical Excellence for coverage by the UK’s National Health Service.

 The list of potentially low-value services includes several drugs to treat cancer, as well as other therapies such as left ventricular assist devices and lung volume reduction surgery. Building negative incentives into VBID programs to discourage use of low-value care will involve a number of challenges, including identification of appropriate candidates; the scope of services to be covered (ie, whether VBID should be expanded beyond drugs to address medical devices, procedures, and diagnostics); and whether VBID programs should target specific subgroups.

 Identifying noncontroversial low-value services and designing VBID programs to discourage their use will not be easy. However, to fulfill their promise of improving value and moderating cost growth, VBID programs should target low-value as well as high-value care.

Articles

The American Journal of Managed Care

Building negative incentives into value-based insurance design programs to discourage use of low-value care will involve a number of challenges.

Low-Value Services in Value-Based Insurance Design

 To examine how cost sharing for prescription drugs affects compliance with antiplatelet therapy and subsequent health outcomes among patients with acute coronary syndrome (ACS).

 Retrospective outcomes study using administrative data from medical and pharmaceutical claims of patients enrolled at health plans offered by 26 large employers drawn from all regions of the country.

 A total of 14,325 patients were diagnosed as having ACS and underwent coronary stent implantation between 2002 and 2005. Each patient was followed up for a maximum of 2 years. Primary outcomes measures were adoption of outpatient antiplatelet therapy, adherence to outpatient therapy, hospital admissions, and healthcare expenditures.

 Patients with ACS who face higher coinsurance are less likely to adopt outpatient antiplatelet therapy within the first month after stent implantation and are more likely to discontinue treatment in the first year after stent implantation (P <.01). Higher coinsurance is also associated with an increased number of ACS rehospitalizations (P <.01). For patients in health plans with higher coinsurance rates, expected costs from ACS hospitalizations are  $2796 (38%) higher in the first year after stent implantation (P <.01).

 Higher copayments for prescription drugs are associated with lower utilization of antiplatelet therapy and with higher likelihood of rehospitalization among patients with ACS. As a consequence, total healthcare spending for patients with ACS increases by approximately $615 in the first year after stent implantation.

This article studies the association of pharmacy cost sharing with antiplatelet therapy utilization and health outcomes for patients with acute coronary syndrome.

Pharmacy Cost Sharing, Antiplatelet Therapy Utilization, and Health Outcomes for Patients With Acute Coronary Syndrome

 To determine whether a behavioral Internet treatment (BIT) program for weight management is a viable, cost-effective option compared with usual care (UC) in a diverse sample of overweight (average body mass index = 29 kg/m2), healthy adults (mean age = 34 years) serving in the US Air Force.

Two-group parallel randomized controlled trial.

Participants were randomly assigned into 2 groups: UC (n = 215) and UC plus BIT (n = 227). Baseline and 6-month assessments were included in the analyses. Primary outcome measures (changes in body weight, percent body fat, and waist circumference) and secondary outcome measures (Weight Efficacy Lifestyle [WEL] questionnaire) were included in an incremental cost-effectiveness analysis (ICEA) model. Costs were computed using the perspective of an agency wanting to replicate the intervention. Sensitivity analyses were performed to measure the robustness of models.

Overall cost for BIT intervention was $11,178.40, or $49.24 per BIT participant. Total staff-time cost was $14.03 per BIT participant. Intervention cost was $25.92 per kilogram of weight loss and $28.96 per centimeter of waist-circumference loss. The cost was $37.88 for each additional point gained on the WEL subscale, where increasing scores indicate increased confidence in managing social pressures to eat.

The BIT program is a cost-effective choice for weight management. It may cost more initially, but it results in long-term cost savings. Such cost-effective, Internet-based behavioral interventions for weight management could provide a valuable tool for preventive care aimed at improving individual and societal health.

	A behavioral Internet treatment (BIT) program for weight management was compared with usual care in a diverse sample of overweight, healthy adults serving in the US Air Force.

		The BIT weight management program was shown to be a cost-effective (only $25.92/kg of weight lost) alternative.

		This program is especially attractive considering its flexibility and improved retention rates and outcomes compared with pharmacotherapy.

From the Department of Pharmacy Practice and Administration, School of Pharmacy (RSR, HMM), School of Nursing (RSR), and Bloch School of Business and Public Administration (RSR, HMM), University of Missouri, Kansas City, MO; the National Institute of Diabetes & Digestive & Kidney Diseases (CMH), National Institutes of Health, Bethesda, MD; the Department of Psychiatry (ALP), University of Texas Health Science Center, San Antonio, TX; the Department of Psychology (ALP), Wilford Hall Medical Center, San Antonio, TX; and the Department of Medicine (JPF), Baylor College of Medicine, Houston, TX.

This study was funded (John P. Foreyt, Principal Investigator) by the US Army Medical Research and Materiel Command’s Peer Review Medical Research Program (DAMD17-02-1-0180). The views expressed in this article are those of the authors and are not the official policy of the Department of Defense or the US Air Force.

The authors (RSR, CMH, ALP, HMM, JPF) 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 (RSR, CMH, ALP); acquisition of data (RSR, CMH, ALP, HMM); analysis and interpretation of data (RSR, CMH, ALP, HMM); drafting of the manuscript (RSR, CMH, ALP, HMM, JPF); critical revision of the manuscript for important intellectual content (RSR, CMH, ALP, JPF); provision of study materials or patients (RSR, ALP); obtaining funding (ALP, JPF); administrative, technical, or logistic support (ALP, JPF); and supervision (RSR).

Rafia S. Rasu, PhD, Department of Pharmacy Practice and Administration, University of Missouri, 2464 Charlotte St, 4247 Health Sciences Bldg, Kansas City, MO 64106. E-mail: 

WEB EXCLUSIVE

An Internet-based weight management program provides a cost-effective alternative for weight management.

Economic Evaluation of an Internet-Based Weight Management Program

Quality care has been defined as providing the right services to the right patient at the right time and place. Health insurance can support the provision of quality care by offering meaningful coverage of the right services for  the right patient—perhaps even at the right time and place. Defining what’s “right,” however, is a critical step in the process.

Health insurance traditionally has offered coverage for services that are generally accepted to be associated with improved outcomes. The advent of managed care brought about a greater scrutiny of the association between medical services and health outcomes, and evidence-based medicine has started to provide a knowledge base from which to examine this association.

 We have made substantial progress in offering coverage for services that are generally associated with improved outcomes.

The value-based insurance design (VBID) movement seeks to push traditional coverage on 2 fronts: (1) beyond yes/no decisions on coverage to gradients based on value and (2) beyond generalities to specific expectations of outcomes for the right patient. If a treatment is impressively effective in improving health  outcomes, but is not provided or adhered to because of health insurance reasons, VBID suggests that enhanced coverage should be provided to encourage patients to do the right thing. Recent evidence shows that this enhanced coverage is possible in specific instances.

 Conversely, if a treatment has little effect on outcomes, limited coverage should be provided to discourage patients from doing the wrong thing. Beyond yes/no decisions (and within the constraints of avoiding unreasonably complex plan designs), coverage should match the value offered by the service to a patient with a particular condition.

A good examination of the available information doesn’t bring us much closer to the goal of providing a complete matrix of coverage for a given treatment based on value and patient conditions.

 Comparative effectiveness research (CER) is much more likely than prior clinical research to include comparisons across treatments that are meaningful to payers. Ensuring that comparisons consider patient conditions and stratify outcomes will require vigilance.

Comparative effectiveness research recognizes the importance of costs, although ensuring that analyses include costs also will require vigilance. The recent Institute of Medicine report on priorities for CER included cost comparisons for 19 of the 100 conditions.

 A further requirement of research for the VBID application of CER will be identification of treatments that are sensitive to patient cost sharing.

At a high level, VBID may be applied to a yes/no coverage decision. Applying gradients of coverage based on value requires that patients respond to cost  sharing at meaningful levels. A clearly high-value or low-value treatment that already has 95% or 5%, respectively, selection and adherence would not benefit  from a VBID approach. Although development of a clinical and economic information base will take time and resources, it is feasible. Tuft’s Center for the Evaluation of Value and Risk in Health is a public service aimed at making such information available (

https://research.tufts-nemc.org/cear/Default.aspx

Successful implementation of VBID requires both information and courage. I suggest that the larger challenge is finding the courage to conduct the research and to implement coverage based on the findings. Among the elephants in the room is the relationship between the profitability of treatments and their outcomes. Administrators would be loath to talk about it publicly, but certain services are more profitable than others. Even before the seminal work of Siu et al,

 we knew that a considerable percentage of hospitalizations are unnecessary. Strict adherence to the evidence-based medicine that is encouraged through VBID may require rethinking our delivery systems. Our delivery systems may not be prepared for VBID.

Nearly a decade ago managed care was making yes/no coverage decisions and moving beyond generalities to assigning expectations of outcomes for specific patients. Granted, the information base for those decisions may not have been ideal and the tools may not have been in the right hands. The backlash against managed care highlighted the limitations of our information. More notably, that backlash highlighted our discomfort with making difficult decisions.

 A new era of VBID may require that medical directors contact patients about coverage, as opposed to speaking only with physicians.

Making coverage decisions on the basis of value for specific patients involves many challenges. Yet we must go down that road. With increased expenses for health insurance, pressures for reforming not only the availability but also the nature of coverage will rise. I hope that we find the courage to provide quality healthcare that is covered in a quality way.

 From the Department of Health Management and Policy, University of Michigan, Ann Arbor, MI.

 Dr Smith reports serving as a consultant to Benefit Based Decisions, LLC.

 Concept and design; drafting of the manuscript; and supervision.

 Dean G. Smith, PhD, Department of Health Management and Policy, University of Michigan, 109 Observatory, Ann Arbor, MI 48109-2029. E-mail: 

 Evidence that value-based insurance can be effective. 

3. Neumann PJ, Auerbach HR, Cohen JT, Greenberg D.

 Low-value services in value-based insurance design. 

 ‘Marginal medicine’: targeting comparative effectiveness research to reduce waste. 

5. McMahon LF Jr, Hayward R, Saint S, Chernew ME, Fendrick AM.

 Univariate solutions in a multivariate world: can we afford to practice as in the “good old days”? 

6. Committee on Comparative Effectiveness Research Prioritization, Institute of Medicine.

 Initial National Priorities for Comparative Effectiveness Research.  Washington, DC: National Academies Press; 2009. 

http://www.iom.edu/Reports/2009/ComparativeEffectivenessResearchPriorities.aspx

7. Siu AL, Leibowitz A, Brook RH, Goldman NS, Lurie N, Newhouse JP.

 Use of the hospital in a randomized trial of prepaid care. 

 The managed care backlash: perceptions and rhetoric in health care policy and the potential for health care reform. 

Provision of quality healthcare covered in a quality way requires both information and courage.

Getting the Right Services Covered by Health Insurance

 To estimate the treatment effect of participation in an asthma intervention that was part of the National  Asthma Control Program.

 Cross-sectional; difference in outcomes between participants and comparable nonparticipants matched by  using propensity scores.

 Data on children who participated in asthma case management (n = 270) and eligible children who did not participate in case management (n = 2742) were extracted from a Medicaid claims database. We constructed measures of healthcare utilization, sociodemographics, and neighborhood characteristics. After creating a comparison group similar to the participants in terms of all characteristics before participation, we estimated the effect of the program on asthma outcomes.

 Participants were more likely to have vaccinations for pulmonary illness (95% confidence interval [CI] = 1.82, 4.81), to fill a prescription for controller medications (95% CI = 1.07, 2.19), and to have a refill for rescue medication (95% CI = 1.07, 2.07) after the program than comparable nonparticipants. There was no statistically significant difference in the number of nebulizer treatments or emergency department visits between the 2 groups.

 The program did increase the use of preventive healthcare by participants. Over the time period we studied, these behaviors did not decrease healthcare utilization for asthma exacerbations. We were unable to discern  whether the lack of effect was because of the nature of the program, heterogeneity of the effects, or barriers outside the program’s control.

CLINICAL

An asthma case management program increased preventive care but did not significantly decrease markers of exacerbations.

Estimated Effect of Asthma Case Management Using Propensity Score Methods

 To investigate the effect of pharmacist management of poorly controlled diabetes mellitus in a community-based primary care group.

 Randomized controlled trial of pharmacist management of diabetes compared with usual medical care.

 Patients 18 years or older with glycosylated hemoglobin (A1C) levels of 9.0% or higher were enrolled. Patients were randomly assigned to an intervention group (n = 52) or a control group (n = 51). Management in the control group included the use of registries and targeted patient outreach. The intervention group participated in the same outreach program plus medication management, patient education, and disease control by a pharmacist.

 Nonparametric data showed median A1C decreases of 1.50% for the intervention group and 

0.40% for the control group (P = .06). Significantly more patients in the intervention group improved their A1C level by at least 1.0% relative to the control group (67.3% vs 41.2%, P = .02). Most of this benefit was seen for patients of nonwhite race/ethnicity compared with control subjects (56.3% vs 22.7%, P = .03). Male patients showed significantly greater benefit as well, with a median A1C decrease of 1.90% vs 0.15% for controls (P = .03).

 Patients with poorly controlled diabetes improved A1C levels significantly when pharmacist management was added to an aggressive organizational diabetes management program. Our results suggest that clinically trained pharmacists can help primary care providers improve diabetes management, especially among male patients and among patients of nonwhite race/ethnicity.

Pharmacist management resulted in improved glycosylated hemoglobin level among patients who had failed systematic outreach and regular use of an all-payer registry.

Improving management of patients with poorly controlled diabetes mellitus using pharmacist intervention is time intensive and resource intensive. 

Our data suggest that male patients and patients of nonwhite race/ethnicity are the greatest responders to pharmacist management. This finding needs to be confirmed by further research.

	Diabetes mellitus affects 23 million people in the United States.

 Adjusting for population age differences, survey data between 2004 and 2006 indicate that 6.6% of non-Hispanic whites, 7.5% of Asian Americans, 10.4% of Hispanics, and 11.8% of non-Hispanic blacks have diabetes mellitus.

 Totaling $174 billion in 2007, diabetes care accounts for 19% of total healthcare costs.1 Among patients with diabetes, 21% to 43% have glycosylated hemoglobin (A1C) levels exceeding 9.0% according to a 2004 study

; evidence suggests that attempting to achieve A1C levels below 7.0% will delay, ameliorate, or prevent the microvascular and neuropathic complications of diabetes.

 Data on trying to lower A1C levels below 7.0% are inconclusive.

 guidelines recommend a goal of less than 7.0% for A1C control.

	The medical literature demonstrates improvement in patient A1C levels with the use of clinical databases and case managers.

 Case managers coordinate all disease-related care and education, as well as support patient self-management. Early investigations showing the value of nurse case managers were conducted in an era of fewer medications and less aggressive management, usually involving patients enrolled in health plans, notably health maintenance organizations.

 Patients in the nurse case management group attained significantly lower A1C levels by 0.6% to 1.0% compared with the control group.

 have used a model whereby pharmacists work in collaboration with primary care physicians. Given the ever-increasing options for treating diabetes, pharmacists should be well positioned to evaluate the merits of each therapeutic option and to provide patient education. Studies showing the benefit of pharmacists serving as case managers have been small and often nonrandomized. The few randomized controlled trials have been performed in large urban academic or diabetes referral centers. As an example, Choe et al10 showed improved A1C levels and process outcome measures in a single-university ambulatory internal medicine clinic for patients with diabetes. We add to the literature a randomized study that describes pharmacist management of patients with diabetes who failed to respond to systematic patient outreach using a regularly updated all-payer registry in the primary care setting.

	This was a 12-month prospective randomized controlled study. The primary outcome measure was the change in A1C level at the end of 1 year. Secondary outcomes were the percentage of patients with a 1.0% decrease in A1C level. This outcome was assessed in the population as a whole and asa function of minority racial/ethnic group status. The research committee and the institutional review board of Saint Mary’s Health Care, Grand Rapids, Michigan, approved this study.

	The study site was the Advantage Health Physician Network (AHPN), which uses an electronic registry that identifies all adults with diabetes mellitus. Thirteen AHPN offices participated, including 3 urban, 9 suburban, and 1 rural site. Clinical practice guidelines, diabetes indicators, and performance thresholds are tracked routinely and are shared regularly with providers and staff. Each office has specific personnel budgeted to support diabetes quality-related initiatives. The latest quality indicators for individual patients are available at each office visit. In addition, there is systematic telephone and mail outreach to patients who are due for diabetes-related care.

	Patients with diabetes 18 years or older having A1C levels of 9.0% or higher or no office visits within 12 months were contacted by a study nurse. The nurse saw the patient at his or her home and determined study eligibility. Patients were excluded from the study if an endocrinologist was managing their diabetes or if they were not expected to live for the duration of the study. Eligible patients were tested using a Bayer DCA 2000 (Bayer Corporation, West Point, CT) point-of-care A1C instrument and were offered enrollment if their A1C level was 9.0% or higher. The study nurse obtained institutional review board—approved informed consent at this point from patients who agreed to participate and to attend all study visits.

	Using sequential envelopes containing computer-generated group assignment, the research nurse randomized patients to the intervention group or to the control group. Envelopes were opened after patients were deemed eligible for the study. Both study groups received the aggressive outreach already described.

	One pharmacist (JPJ) provided all diabetes-related care for the intervention group. The pharmacist is a board-certified pharmacotherapy specialist, who participated in an American Society of Health-System Pharmacists diabetes management traineeship, an American Diabetes Association postgraduate course in diabetes management, and an American Association of Diabetes Educators training program.

	Patients in the intervention group also met with the pharmacist at their respective primary care site for an assessment of adherence, barriers to optimizing blood glucose levels, and current medication regimen.All intervention patients received individualized education regarding diabetes self-management, including diet, exercise, blood glucose level testing, medications, and insulin. The pharmacist followed guidelines of the Management of Hyperglycemia in Type 2 Diabetes.16 This included early switching to insulin therapy after failure of 2 oral medications. The patient’s primary care physician approved any changes in medication or therapy, although the pharmacist was given autonomy to adjust insulin doses as needed. The number of subsequent visits with the pharmacist was based on the need to further educate the patient about diabetes control or to monitor therapeutic changes. Follow-up visits were supplemented with telephone calls as needed for medication management. Patients were followed up for 12 months after study enrollment. We were unable to assess adverse events in the control group. There was 1 severe hypo-glycemic event in the intervention group. 

 were defined as those requiring assistance from another person.

	A power analysis indicated that at least 39 patients were needed in each study group to show a clinically significant decrease in A1C level of at least 1.0% (ß = 0.20 and α = 0.05). 

 Test and Fisher exact test were used where appropriate for nominal data. Mann-Whitney test was used for nonparametric quantitative data and unpaired 

 test for parametric quantitative data. Significance was set at 

 <.05. The statistical software used was NCSS 2004 (Kaysville, UT).

	Recruitment began in May 2006, and final 1-year follow-up for the last patient was in December 2007. Among all AHPN patients with diabetes, 902 had an A1C level of 9.0% or higher or had not been seen by their primary care provider within the previous 12 months. Of these, 491 patients were successfully contacted and were invited to participate in the study. Two hundred thirty-five patients were excluded; 191 (39.0%) declined participation, and 44 (9.0%) were managed by a specialist. An additional 152 (31.0%) had an A1C level of less than 9.0% at the time they saw the study nurse. The remaining 104 patients were randomized, 52 to the control group and 52 to the intervention group.

	One patient in the control group was excluded as an outlier based on A1C level exceeding 3 SDs from the mean A1C change. This patient was hospitalized for 3 weeks because of diabetes complications and subsequently attained an 8.6% decrease in A1C level. Therefore, the final analysis was based on 103 patients, 52 in the intervention group and 51 in the control group. Baseline demographic data are given in 

. The only significant difference between study groups at baseline was lower A1C level in the intervention group.

	The A1C changes were not normally distributed, so median values were used as the measure of central tendency. The overall median A1C reduction in the intervention group was 1.1% greater than that of the control group. This difference approached but did not achieve statistical significance. Post hoc subgroup analysis showed that male patients in the intervention group achieved a statistically significant improvement in their A1C level (median, −1.90%; interquartile range, −0.05% to −2.95%) versus the control group (median, −0.15%; interquartile range, 0.98% to −1.38%]). 

	From the perspective of individual patient improvement, statistically more patients in the intervention group achieved at least a 1.0% improvement in A1C level overall. Twice as many patients of nonwhite race/ethnicity and male patients (post hoc) in the intervention group exceeded the 1.0% improvement mark. No treatment effects using this measure were seen for patients of white race/ethnicity or for female patients (

	This intervention required considerable effort. The pharmacist averaged 6 office visits and 3 telephone calls per patient over the course of a year. Office visits lasted between 30 and 60 minutes. Telephone calls were 10 to 20 minutes in length. The pharmacist changed the drug regimen to basal-bolus insulin in 15 patients (28.8%) and discontinued oral medications completely in 15 patients (28.8%). In the control group, only 1 patient’s regimen was changed to basal-bolus, oral medications were discontinued in 1 patient, and insulin was added for 1 patient. As evidence of aggressive treatment in the control group, there were 6000 patients in the all-payer registry in 2007, and 55.1% had both an A1C level of less than 7.0% and low-density lipoprotein cholesterol (LDL-C) level of less than 100 mg/dL (to convert cholesterol level to millimoles per liter, multiply by 0.0259). This is substantially better than 2007 data in Medicaid beneficiaries, among whom the 90th percentile is 30.2% A1C control and 42.3% LDL-C control.

	This study was undertaken to ascertain the benefit of having a pharmacist work with primary care physicians to reduce A1C levels among patients with poorly controlled diabetes who failed to respond to systematic patient outreach in multiple community-based clinics. Greater health benefits accrue when moving from poor (A1C level >9.0%) to moderate glycemic control compared with moving from moderate to almost normal glycemic control. Vijan et al

 showed that 80% of reductions in blindness and in end-stage renal disease resulted from targeting less than 20% of patients with the worst A1C control. Our overall findings of significantly improved A1C outcomes among individuals in the intervention group are compatible with the findings of 2 recent comprehensive reviews

 of pharmacist management of diabetes. Our study was not originally powered to show subgroup differences. Despite this, statistically more patients of nonwhite race/ethnicity and male patients achieved at least a 1.0% improvement in A1C level. To our knowledge, this subgroup differentiation has not been previously identified and needs to be confirmed by future research. However, our finding of greater improvement with pharmacist intervention among patients of nonwhite race/ethnicity is congruent with results of studies that were limited to specific racial/ethnic groups. Studies

 among subjects of nonwhite race/ethnicity showed much greater improvement over control values than studies

 among subjects of white race/ethnicity. Minority racial/ethnic groups have not only poorer controlled diabetes but also more complications.

 Compared with non-Hispanic whites having diabetes, Mexican Americans and African Americans with diabetes have at least a 4-fold greater incidence of kidney disease, and Mexican Americans with diabetes have a 1.8-fold greater incidence of peripheral vascular disease.

 Given the disproportionate prevalence of diabetes and diabetic complications among patients of minority races/ethnicities, coupled with poorly controlled A1C level, case management with a pharmacist can be of benefit to these populations and is a tool that is likely to have clinically meaningful results.

	The intervention group showed more improvement, despite having lower baseline A1C levels than the control group. This could mean that diabetes in the control group was more difficult to manage. However, previous investigations have found greater improvement with higher baseline A1C levels, suggesting that the intervention group was actually at a disadvantage.10 Among male patients, approximately 2 patients had to be treated by a pharmacy case manager for 1 year to have 1 patient improve his A1C level by at least 1.0%. Among minority races/ethnicities, 3 patients had to be treated by a pharmacy case manager for 1 year to have 1 patient improve his or her A1C level by 1.0%. Among patients in the United Kingdom Prospective Diabetes Study

with type 2 diabetes mellitus, each 1.0% reduction in updated mean A1C level led to a 21% reduction in death, 14% reduction in myocardial infarction, and 37% reduction in microvascular complications. Therefore, male patients and patients of nonwhite race/ethnicity in our intervention group achieved A1C improvements that are likely to be clinically significant. We did not power the study to evaluate blood pressure, cholesterol, retinopathy, or nephropathy screening differences in the study groups. Results of other studies

 suggest that pharmacists can improve these measures as well.

	The fact that basal-bolus insulin was started in 28.8% of patients in the intervention group versus 2.0% in the control group indicates a different approach to diabetes management by the pharmacist. In addition to adding insulin, the pharmacist simplified medication regimens by discontinuing oral medications in 28.8% of patients versus 2.0% in the control group. The average patient was in contact with the pharmacist for more than 300 minutes during the 1-year intervention. This significant time and cost highlight the commitment it takes to help patients overcome the difficulty of intensifying treatment. The time commitment is similar to that seen in the Diabetes Control and Complications Trial.

	This study extends findings of the Diabetes Ten City Challenge

 regarding the benefit of pharmacist intervention. We randomized all patients regardless of insurance status, whereas the Diabetes Ten City Challenge recruited volunteers and excluded one-quarter of them from their final outcomes because of missing economic data. In addition, our patients were selected as the most costly and challenging among patients with diabetes. As expected with higher baseline levels, A1C decrease was greater in our study than in the Diabetes Ten City Challenge. The Asheville Project studied a select insured population and evaluated the economic benefit of using pharmacists, certified diabetes educators, and enhanced benefit plans in a community pharmacy diabetes care program.

	Limitations of our study include the use of a single primary care group, although this is mitigated by the use of diverse sites (ie, urban, suburban, and rural). This study was not designed to prove whether a pharmacist (as opposed to other types of providers) is specifically needed to obtain the benefits of this case management approach. The subsidized nature of the pharmacist time and cost in this study threatens external validity. Finally, the participating pharmacist herein was clinically trained and experienced in patient care activities such as medication management and motivational interviewing. However, community pharmacists without specialized training in diabetes have been shown to improve outcomes in a health maintenance organization population.

 Therefore, the pool of available pharmacists may be greater than expected. Overall, the results of this study should be generalizable to similarly equipped primary care groups employing an experienced clinical pharmacist.

	The findings of this study should stimulate healthcare policymakers to evaluate the cost and benefit of including a pharmacist case manager in primary care networks as part of a systematic approach to diabetes management. Our study demonstrated improvement in disease management by a pharmacist for the most costly segment of the population with diabetes (those with A1C level >9.0%). This poorly controlled group still represents 10% of patients in our baseline population with aggressive interventions already in place (55.1% with A1C level <7.0% and LDL-C level <100 mg/dL). The benefit was seen among insured patients and among uninsured patients. For reasons yet to be explained, male patients and patients of nonwhite race/ethnicity showed disproportionately greater improvement. Our study provides a foundation for future research into pharmacist case management in a community-based primary care practice rather than in a health plan or university setting.

We thank Susan Emelander, RN, for her indispensable role in patient enrollment and follow-up. We thank Andrea Wendling, MD, Sister M. Rosita Schiller, PhD, Alan T. Davis, PhD, and Tracy Frieswyk, BA, for manuscript review and suggestions.

From the Grand Rapids Family Medicine Residency Program (JPJ), Ferris State University, Grand Rapids, MI; Depart-ment of Pharmacy Practice (JPJ), Michigan State University, East Lansing, MI; Saint Mary’s Health Care (JPJ, PJB), Grand Rapids, MI; and Advantage Health Physician Network (PJB), Grand Rapids, MI.

Financial support for this study was provided by Advantage Health Physician Network, Doran Foundation, Michigan Phar-macist Foundation, Priority Health, and Western Michigan Society of Health System Pharmacists.

Dr Jameson reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. Dr Baty is a practicing clinician in the Advantage Health Physician Network, the site where the study was conducted.

Pharmacist management of poorly controlled diabetes mellitus in this randomized trial resulted in more patients decreasing their glycosylated hemoglobin level by at least 1.0%.

Pharmacist Collaborative Management of Poorly Controlled Diabetes Mellitus: A Randomized Controlled Trial

To examine whether physicians' use of information technology (IT) was associated with better knowledge of drug costs.

A 2007 statewide survey of 247 primary care physicians in Hawaii regarding IT use and self-reported knowledge of formularies, copayments, and retail prices.

Approximately 8 in 10 physicians regularly used IT in clinical care: 60% Internet, 54% e-prescribing, 43% electronic health records (EHRs), and 37% personal digital assistants (PDAs). However, fewer than 1 in 5 often knew drug costs when prescribing, and more than 90% said lack of knowledge of formularies and copayments remained a barrier to considering drug costs for patients. In multivariate analyses adjusting for sex, practice size, years in practice, number of formularies, and use of clinical resources (eg, pharmacists), use of the Internet—but not e-prescribing, EHRs, or PDAs&mdash;was associated with physicians reporting slightly better knowledge of copayments (adjusted predicted percentage of 23% vs 11%; P = .04). No type of IT was associated with better knowledge of formularies or retail prices.

 Despite high rates of IT use, there was only a modest association between physicians’ use of IT and better knowledge of drug costs. Future investments in health IT should consider how IT design can be improved to make it easier for physicians to access cost information at the point of care.

In this statewide survey, high rates of health information technology use by physicians were only modestly associated with better knowledge of drug costs.

Approximately 8 in 10 physicians regularly used health information technology in clinical care; however, only 1 in 4 often knew drug costs.

In multivariate analyses, Internet use (but not e-prescribing, electronic health records, or personal digital assistants) was associated with slightly better knowledge of copayments (adjusted predicted percentage of 23% vs 11%; P = .04).

Future investments in health information technology should consider how to improve its design so that physicians can easily access cost information at the point of care.

The United States is preparing to invest billions of dollars in a major reform of its healthcare system.

 A critical step is increasing the use of health information technology (IT).

 Although prior studies have focused on the ability of IT to make clinical information (eg, medical history, labs) available at the point of care,

 IT systems also can be designed to facilitate physicians’ and patients’ access to accurate and timely cost information.

 This access is especially important as policymakers, insurers, physicians, and patients alike grow increasingly concerned with containing healthcare costs.

In this study, we examined the association between physicians’ use of IT and their knowledge of drug costs. Nationally, 26% of patients report nonadherence to medications because of cost.

 Although physicians want to help, they often are hampered by their lack of awareness of drug costs.

 Health IT can improve physicians’ access to cost information. A 2003 survey found that computerized prescription order entry was associated with physicians having greater knowledge of drug costs,

 but since then information regarding formularies, copayments, and retail prices has increasingly become available via other forms of IT such as the Internet and personal digital assistants (PDAs)10 (see www.epocrates.com, www.uptodate.com, www.medicalletter.com, www.medicare.gov, www.drugstore.com, www.costco.com, and www.walmart.com). Understanding whether different types of IT are currently associated with better knowledge of drug costs can highlight if physicians are already successfully obtaining cost information using health IT, or if further improvements in IT design are needed.

The study was a 2007 cross-sectional survey of community physicians in Hawaii who provided adult primary care, regularly saw patients, and were likely to face more than 1 formulary in their clinical practice. We initially identified 819 general internists, family physicians, and general practitioners through the Hawaii Medical Association (which records all licensed Hawaii physicians) and local health plans’ lists of participating physicians. We called physicians’ offices to exclude specialists, Kaiser and military physicians (ie, those employed by staff model institutions with a single formulary), hospitalists, academic teaching faculty, residents in training, retired physicians, and physicians without accurate contact information based on phone directory and Internet searches. A total of 460 physicians remained, similar to the number identified in a separate unpublished study using claims from Hawaii’s largest insurer, Hawaii Medical Services Association, which captures approximately 60% of Hawaii’s residents and contracts with the vast majority of local physicians. (These independent claims analyses identified 415 adult primary care physicians who were the main providers for >20 Hawaii Medical Services Association members with diabetes.) We mailed these physicians a 10-minute, 27-item survey with no financial incentives, followed by 3 mailings to nonrespondents. Of 281 participants (61% response rate), 34 were ineligible (eg, saw <20 patients per week); therefore, final analyses were based on 247 participants. The institutional review board at the University of Hawaii approved this study.

Physicians were asked, “Do you regularly use the following IT in clinical care (yes/no)?” for the Internet, electronic health records (EHRs), e-prescribing, and PDAs.

 This question referred to both software (Internet, EHRs, e-prescribing) and hardware (PDAs) forms of IT often used by physicians in clinical settings. Because potential overlaps can exist between different IT types, we allowed physicians to answer yes to all functionalities that applied. For example, physicians who used EHRs with e-prescribing functionality could respond yes to use of both EHR and e-prescribing. We did not ask physicians about the specific functionalities of the IT types that they reported using.

To assess physicians’ use of clinical resources when prescribing, we asked, “Do you regularly use any of these resources to help you choose a medication for your patient (yes/no): peers/consultants, health plans, pharmaceutical representatives, pharmacists, and medical journals?” 

 With respect to knowledge of drug costs, physicians reported how often they knew copayments, formularies, or retail prices, and how often they checked formularies when unsure of coverage or discussed drug costs with patients (almost always, most of the time, some of the time, rarely/never). Physicians also were asked, “How important is it for you to consider the cost of a medication to your patient?” and “How many of your patients do you think have difficulty paying for their medications?” Lastly, physicians reported demographic (age, sex, specialty) and practice (eg, number of formularies, solo vs group practice) characteristics.

 The survey was tested for ease of understanding and question comprehension in a convenience sample of 5 physicians.

To examine the association between different types of IT use and drug cost knowledge, we used multivariate logistic regression to predict 3 main outcomes: (1) usually or always knew copayments, (2) usually or always knew retail prices, and (3) had no difficulty in determining formulary coverage. We used the 4 separate IT types as our main predictors: Internet, EHR, e-prescribing, and PDA. In sensitivity analyses, we created a composite IT score by assigning 1 point for each type of IT used and summing these points. The composite score (0 to 4) was not statistically significant in predicting any of the 3 outcomes, and detailed results are not presented.

We adjusted our model for sex, solo versus group practice, years in practice, number of formularies, and use of clinical resources. We excluded age and practice setting because they were highly correlated (r >0.40) with years in practice and solo versus group practice. We excluded provider type because it did not approach significance in bivariate analyses. Results are presented as adjusted predicted percentages with 95% confidence intervals for greater ease of understanding.

Most of the 247 respondents were general internists (55%) and family physicians (36%) in solo (43%) or small practices (48%), and 70% faced 6 or more formularies (

). Nearly all reported that their patients had difficulty paying for medications (99%) and that considering drug costs for patients was important (98%). The majority of respondents (65%) said they discussed medication costs with patients most of the time (49%) or always (16%).  The remainder said they discussed these costs sometimes (31%) or rarely (4%).

Approximately 8 in 10 physicians used at least 1 type of IT regularly in clinical care (Table 1): Internet (60%), e-prescribing (54%), EHRs (43%), and PDAs (37%). Nearly two-thirds (62%) regularly used 2 or more IT types. With respect to using clinical resources when choosing medications, physicians reported using information from pharmacists (69%), followed by peers (56%), pharmaceutical representatives (48%), medical journals (46%), and health plans (45%).

Fewer than 1 in 5 physicians often knew copayments or retail prices when prescribing, and 68% reported difficulty knowing formulary coverage (

). Only 1 in 4 physicians regularly checked formularies when unsure whether a drug was covered. Nearly all said that difficulty knowing formulary coverage (94%) and copayments (91%) were significant barriers to considering drug costs for patients.

In analyses adjusting for sex, solo versus group practice, years out from training, number of formularies, and use of clinical resources, physicians who regularly used the Internet in clinical care were slightly more likely to report that they usually knew copayments (adjusted predicted percentage of 23% vs 11%; P = .04) (

). Neither EHR, e-prescribing, nor PDA use was significantly associated with physicians reporting better knowledge of copayments. For formulary coverage and retail prices, none of the 4 types of IT were significantly associated with better knowledge.

Physicians who faced 6 or more formularies in clinical care were less likely to know copayments (15% vs 26%; P = .048) and formulary coverage (26% vs 46%; P = .006). However, physicians who used information from health plans reported better knowledge of copayments (25% vs 13%; P = .01) and formulary coverage (40% vs 26%; P =.03). With respect to retail prices, the use of pharmacists approached but did not reach statistical significance in predicting physicians having better knowledge of prices (24% vs 12%; P = .07).

In this statewide survey of primary care physicians, we were surprised that although 8 in 10 participants said they regularly used at least 1 type of health IT in clinical care, we detected only a modest association between IT use and better knowledge of drug costs. Use of the Internet was associated with slightly better knowledge of copayments (23% vs 11%; P = .04). However, even among those who regularly used the Internet, the majority of physicians still reported poor knowledge of drug costs.

We are aware of only 1 prior study that assessed multiple types of IT use and physicians’ knowledge of drug costs. In this 2003 study, Shrank et al surveyed physicians (49.6% response rate) identified from the California Medical Association, whose members represented one-third of California physicians.

 In contrast with the results from our more recent survey, the authors found that “computer order entry” (not further defined) but not Internet use was associated with greater awareness of drug costs.

 Neither that study nor ours represent national samples, but our findings should alert policymakers, insurers, and IT providers to reevaluate, as additional money is spent on health IT,

 the ability of current and future IT systems to successfully provide cost information to physicians at the point of care.

Our findings should not discount the potential future usefulness of certain types of IT in increasing physicians’ access to drug cost information.

 Although our participants who reported regularly using e-prescribing did not report better knowledge of drug costs, the integration of formulary information support into prescribing software has been shown to decrease both out-of-pocket and total drug costs.

 Fischer et al analyzed claims for 2 Massachusetts insurers 18 months after the implementation of an e-prescribing system with formulary decision support, and found that physicians were more likely to prescribe lower-cost drugs.

 McMullin et al found that use of an e-prescribing system with integrated clinical and cost information decreased the likelihood of physicians prescribing higher-cost drugs from 35.8% (control group) to 17.5% (intervention group).

At the time of our study, an informal review indicated that several health plans in Hawaii made copayment and formulary information available via the Internet, but not necessarily via EHR, e-prescribing, or PDA. A highly promoted type of e-prescribing software in Hawaii contained formulary information only for the single health plan that sponsored its adoption.

 Thus, we speculate that this may have been why use of the Internet, but not other IT types, was associated with slightly better knowledge of copayments.

This study indicates that improving physicians’ knowledge of drug costs will require more than simply increasing physicians’ use of health IT. Given the financial burden of drug costs on patients, there is a critical need to determine if the problem is that drug cost information is not adequately available via various types of IT, physicians are unaware of its availability, or physicians find such cost information too difficult to use. We did not ask physicians who reported using health IT why they still experienced difficulty accessing drug cost information. However, other studies indicate the issue may be the variability among formularies and out-of-pocket costs from patient to patient, as well as the need for further availability of drug cost information via IT.

 Nearly all of our participants recognized the financial burden of drug costs on patients and the importance of considering out-of-pocket drug costs. However, unless health IT is designed to make the costs of drugs (and other medical services) automatically available at the point of care, physicians and patients will likely continue to be hampered in obtaining healthcare that is appropriate from both a cost and a quality perspective.

Among our participants, no single type of IT was used by more than 60% of physicians. National estimates of physicians’ use of different types of IT for clinical care are generally lower than 50%: 41% use the Internet for computerized decision support,

 Thus, cost information will need to be accessible via multiple IT types to achieve a wider impact in improving physicians’ knowledge of drug costs.

There are several important limitations to this study. We sampled physicians from a single state, and the impact of IT use on physicians’ knowledge of drug costs will vary depending on how well cost information is integrated into local IT systems. Our findings, however, show that in at least 1 state, even physicians with very high levels of IT use still experience substantial problems accessing drug costs, and this issue warrants policy intervention. Our results are based on self-report, and physicians could have overreported or unnderreported their actual use of IT. Physicians reported only whether they regularly used IT in general clinical care; we did not ask specifically about their use of IT to retrieve drug cost information. Further studies are needed to determine whether physicians use IT specifically to access drug cost information and if so, how. If they do not use IT to access drug cost information, the reasons why must be investigated. Physicians also self-reported their knowledge of drug costs, and actual knowledge is likely to be even poorer.

 We focused on busy, community-based primary care physicians, and our results cannot be generalized to specialists or physicians who see fewer patients.

Despite high rates of health IT use in clinical care, there was only a modest association between IT use and physicians reporting better knowledge of drug costs. Policymakers and insurers should examine the integration of cost information into future health IT systems and its usability by community physicians at the point of care.

We would like to thank Ms Allison Imamura for her invaluable help with the project.

From the Department of Family Medicine and Community Health (CWT, ALH), University of Hawaii, Honolulu, HI; Pacific Health Research Institute (CWT, RC), Honolulu, HI; RAND Corporation (RHB, EBK, CMM), Santa Monica, CA; Departments of Medicine and Health Services (RHB, CMM), UCLA, Los Angeles, CA; Department of Medicine (GCA), University of Chicago, Chicago, IL; Department of Pharmacy Practice (GCA), University of Illinois at Chicago, Chicago, IL; Department of Family Medicine (EAJ), University of Connecticut, Hartford, CT; and Department of Medicine (RAD), University of California, San Francisco, CA.

Dr Tseng’s work on this study was funded by the Robert Wood Johnson Foundation Generalist Physician Faculty Scholars Program. Dr Dudley’s work was funded by an Investigator Award in Health Policy from the Robert Wood Johnson Foundation. Dr Mangione received support from the Resource Centers for Minority Aging Research/Center for Health Improvement of Minority Elderly (RCMAR/CHIME) funded by the National Institutes of Health/National Institute on Aging (P30 AG021684). The sponsors had no role in study conduct, data analyses, or manuscript preparation.

The authors (CWT, RHB, GCA, ALH, EBK, CMM, RC, EAJ, RAD) 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 (CWT, ALH, RAD); acquisition of data (CWT); analysis and interpretation of data (CWT, RHB, GCA, ALH, EBK, RC, RAD); drafting of the manuscript (CWT, RHB, RC, RAD); critical revision of the manuscript for important intellectual content (CWT, RHB, GCA, ALH, EBK, CMM, RC, EAJ, RAD); statistical analysis (CWT, EBK, RC); obtaining funding (CWT, CMM); administrative, technical, or logistic support (CWT, EAJ); and supervision (RHB, CMM).

High rates of health information technology use by physicians were only modestly associated with better knowledge of drug costs.


Health Information Technology and Physicians' Knowledge of Drug Costs

 To test the hypotheses that older patients with colorectal cancer (CRC) and rural patients are less likely to undergo surgery, radiation, and chemotherapy.

 A total of 6561 patients with CRC between January 1998 and December 2003 were identified by incident International Classification of Diseases for Oncology codes from the Nebraska Cancer Registry. In multivariate logistic regression analyses, we studied the association of age and residence county (rural vs urban and micropolitan) with each of 3 CRC treatments by anatomic site.

 After adjusting for patient demographics, insurance payer, ratio of providers to population, and cancer stage, patients with colon cancer living in micropolitan counties were more likely to receive chemotherapy than those living in rural counties (P <.001). Compared with patients aged 19 to 64 years, patients with colon cancer 85 years and older (P <.001) and patients with rectal cancer 75 years and older (P <.05) were less likely to undergo surgery. Patients with CRC 75 years and older were less likely to receive radiation, and patients with colon cancer 65 years and older and patients with rectal cancer 75 years and older were less likely to receive chemotherapy (P <.001 for both).

 In Nebraska, older patients with CRC were less likely to undergo surgery, radiation, and chemotherapy. Patients with colon cancer in rural counties were less likely to undergo chemotherapy than those in micropolitan counties. Decision makers need to consider issues of age and rural residence in patient access to CRC treatments.

POLICY

Older patients with colorectal cancer were less likely to undergo surgery, radiation, and chemotherapy; rural patients with colon cancer were less likely to undergo chemotherapy.

Age and Rural Residence Effects on Accessing Colorectal Cancer Treatments: A Registry Study

An extensive literature has documented the impact of high copayments on medication use. These studies suggest that for each 10% increase in cost sharing, prescription drug use decreases by 2% to 6%, depending on the class of drug and indication for its use.

 In the case of “essential” medications, these reductions in medication consumption should be associated with adverse clinical consequences.

 After all, if randomized trials and other forms of high-quality evidence have established the relationship between the (greater) use of a drug and improvements in health outcomes, then the opposite must logically be true. Numerous studies demonstrate this apparent association.

 For example, patients who discontinue evidence-based medications after an acute myocardial infraction

The relationship between higher cost sharing and reduced medication use may be confounded by patients' healthy lifestyles or their health choices.

April 2010