Michael A. Fischer, MD, MS

Given rising pharmaceutical expenditures and the widespread use of reference pricing as a costcontainment instrument abroad, we systematically reviewed the evidence evaluating reference pricing policies. We performed a structured electronic search of peer-reviewed journals for studies published before that reported on the effects of reference pricing policies on medication use, payer and patient spending, and resource consumption. Our search yielded 16 studies describing 9 reference-pricing policies from 6 countries. Reference-pricing policies led to decreases in drug prices and increases in utilization of targeted medications, while also reducing payer and patient expenditures. In addition, these policies did not lead to increased use of medical services, such as physician office visits and hospitalization. These results suggest that reference pricing may be an attractive policy strategy for the US healthcare system.

		Reference pricing has been widely used in other countries as a cost-containment instrument.

		Reference pricing policies lead to decreases in drug prices, as well as payer and patient expenditures.

		Reference pricing policies do not lead to increased use of medical services.

		Reference pricing may be an attractive policy strategy for the US healthcare system.

Rising expenditures for prescription drugs are a major problem for public and private payers in many countries. In the United States, prescription sales reached $300.3 billion in 2009, a 5.3% increase from the previous year.

 Pharmaceutical cost-control measures frequently involve increasing patient cost-sharing, for example through tiered formularies.

While these approaches effectively reduce drug spending for payers, they can also cause patients to reduce their use of essential medications; this may have adverse clinical consequences which can increase rather than decrease total healthcare expenditure.

 In other countries, “reference pricing” has been used as a cost-containment instrument that appears not to have the undesirable effects of other pharmacy benefit designs.

 This policy strategy sets a standard price or reimbursement level for a group of therapeutically interchangeable drugs, often based on the price of the lowest cost member in the class. Manufacturers of other products may price their products above or below this level and patients are responsible for any costs above the reference price. In the American context, the policy is sometimes described as a “maximum allowable cost (MAC) program.”

Reference drug pricing has been advocated as a policy solution in the United States.

 For example, Pearson and Bach have proposed that reference pricing be used by Medicare, with coverage and reimbursement decisions informed by comparative effectiveness research.

 Even though domestic experience with reference pricing has been extremely limited, its widespread international adoption, beginning in Germany in 1989, followed by the Netherlands in 1991 and New Zealand in 1993 and more than half a dozen countries since, provides an evidence base on which to estimate its potential role in the United States.

Accordingly, we systematically reviewed the scientific literature to understand the effects of reference pricing on medication use, payer and patient spending, and resource consumption.

We performed a structured electronic search of peer-reviewed journals using PubMed, EconLit, Embase, Business Source Complete, and the National Bureau of Economic Research for studies published before May 2012 that reported on the effects of reference drug pricing policies on medication use, payer and patient spending, and resource consumption.

Our electronic search strategy included medical subject headings and keywords related to pharmaceuticals (eg, “economics, pharmaceutical,” “drug utilization,” or “fees, pharmaceutical”), healthcare policy (eg “health government policy regulation,” “health policies”), and policy analysis (eg “health economics,” “cost and cost analysis”), in addition to those specifically related to reference pricing by name (“reference pricing,” “reference price,” “reference drug pricing”). Search terms were adjusted for each database while maintaining a common overall architecture.

Using predefined inclusion and exclusion criteria, abstracts were evaluated to identify potentially relevant articles. We retrieved the published version of all candidate articles and reviewed their reference lists to identify additional relevant studies. The review was limited to papers that evaluated the introduction of reference price policies on either specific or all drug classes within a health system, leaving out studies that evaluated incremental policy changes on reference pricing. In addition, we excluded studies that (1) did not evaluate the effects of a reference pricing policy on drugs, (2) did not present original data, or (3) did not assess our outcomes of interest.

Data on study populations and characteristics, results, and study quality were extracted from each article using a standardized protocol and reporting form. Specific information collected included study and analysis design (ie, cohort, cross-sectional, randomized control trial), policy design (ie, targeted medications, how the price is set), patient sample (ie national, provincial, private), drug classes, implementation date, and outcomes. Outcomes and study results were categorized into 3 groups: (1) drug prices, (2) utilization, switching, and adherence, and (3) expenditures and resource consumption. When not explicitly presented, we calculated percent change in expenditure and/or per capita savings based on the published findings.

Study quality was assessed with the Agency for Healthcare Research and Quality (AHRQ)

 tool for rating observational cohort studies. A study quality score from each study was calculated as a proportion of total points that each paper received. The studies were scored with a maximum of 9 points, 1 for each study domain evaluated, and judged on the presence of key study elements such as study question, comparability of subjects, and outcome measurement.

Our search yielded 16 studies describing 9 reference-pricing policies from 6 countries (

 All the studies were published in the last decade. Of the policies examined, 1 was applied regionally in British Columbia, Canada, 2 were applied in private, employer-sponsored health plans (1 in Canada and 1 in the United States), and the remaining policies were implemented on a national level in Germany, Norway, and Spain (

). The 9 policies encompassed 2 types of reference pricing, “generic reference pricing” and “therapeutic reference pricing.” Four of the 9 policies (evaluated by 5 out of 16 studies) pertained to generic reference pricing and the other 5 (11 out of 16 studies) pertained to therapeutic reference pricing. Generic reference pricing (or “maximum allowable cost”) involves only off-patent drugs within a certain therapeutic class while therapeutic reference pricing (or “therapeutic maximum allowable cost”) pertains to all eligible products, on- and off-patent, within a therapeutic class.

In Germany, a committee of healthcare providers and sickness funds, with input from manufacturers, decides how drugs are to be grouped and sets the reference prices. The reference prices were always below the price of the highest cost product in the group and above the lowest third of the market price.

 Norway and Spain follow similar practices and manufacturers may adjust their product prices in response. Norway’s national referencing pricing policies have since been repealed because savings in expenditures were not as great as expected.

 Regulators in British Columbia, Canada, select specific products to be reference drugs rather than set a reference price. An independent advisory committee decides which therapeutic group of products will be subject to the program as well as the products that will not face any cost sharing under reference pricing.

 In Canada, the reference price program for nonsteroidal anti-inflammatory drugs (NSAIDs) was introduced 18 months after a generic substitution policy was put in place for the same class.

 In Spain, several other cost-containment policies were implemented around the time of the introduction of the reference pricing system in 2000, including markup adjustments of the margins obtained by manufacturers, wholesalers, and pharmacies in 1999 and 2000 and the compulsory reduction of ex-factory prices in 1999 and 2000.

 No other policies had these concurrent changes.

The studies were generally of good methodological quality, with scores ranging from 56% to 100% on the AHRQ scale (mean of 82%). While the majority of studies focused on 1 or 2 classes of drugs, others focused on a wider range of unrelated classes. Most of the studies on the British Columbia policy, for example, focused on individual classes of cardiovascular medication (eg, angiotensin-converting enzyme [ACE] inhibitors, nitrates, calcium channel blockers) while the study by Brekke et al on Norway’s policy targeted antidepressants, antiulcerants, antihistamines, ACE inhibitors, and statins.

Target classes were not specified by Augurzky et al, while Puig-Junoy examined the effects of reference pricing on 4 top-selling representative products rather than specific drug classes.

Four of the 9 reference price policies were associated with significant reductions in the price of the targeted drug classes, with a mean reduction of 11.5% (range 7%-24%) (

). In Spain, prices fell for the highest priced products of 3 out of the 4 targeted classes, but there was no change in the price of omeprazole.

 In British Columbia, Canada, the price of calcium channel blockers fell by $0.80 per median monthly dose (standard deviation: $0.60), but this change was not statistically significant, likely because drug prices are set at a national level in Canada and thus manufacturers cannot easily adjust the price of products in response to the introduction of a regionally applied reference pricing policy.

The reference pricing policies had varying effects on utilization of the targeted drug classes (

). Grootendorst found a 101% increase in the use of unrestricted (referent) NSAIDs associated with the reference pricing policy introduced in British Columbia in 1997.

 Pavnick also observed an increase in the use of anti-ulcerants after Germany began its policy in 1989, but the 6.4% (standard error 16.2%) change was not statistically significant.

In several evaluations, results were presented separately for those agents for which prices fell in response to reference pricing. Mabasa and Ma observed a 12% decrease in the use of proton pump inhibitors as a class; the use of the referent drug, rabeprazole, increased by 21%.

Likewise, Schneeweiss et al observed a 60% increase in the use of calcium channel blockers whose prices were affected (

 <.0001), but saw no changes in utilization of other antihypertensive medications.

 Puig-Junoy found a decrease of 1% to 12% in the use of drugs whose prices fell after the change to reference pricing, perhaps because the Spanish reference pricing system evaluated was introduced in conjunction with a number of other policies aimed at decreasing overall drug utilization.

The policies led to an increase in switching from more expensive drugs to those that fell in price because of reference pricing and a decrease in switching away from referent drugs to more expensive drugs. For example, Schneeweiss et al observed that 9% of patients switched from a cost-sharing calcium channel blocker to one with a price that fell below the reference price.

 Stargardt et al observed that 49% of patients on atorvastatin, which was above the reference price, switched to another statin following policy implementation, while only 4% of patients on statins at or below the price switched to another statin. The policies were also associated with significant improvements in medication adherence.

 Stargardt reported nonadherence to be significantly lower for patients treated with statins at the reference price compared with atorvastatin, which was above the reference price (31.1 vs 39.0%, 

Reference price policies significantly decreased both patient and payer expenditures (

). Three studies that evaluated changes in patient expenditures found out-of-pocket savings ranging from 12% to 18% per month. The 4 studies that reported the impact of reference pricing on payer expenditures found reductions of 14% to 52% on targeted drug classes. These correspond to per capita savings of $81 to $650.

Although the policies reduced payer spending, the 3 studies that evaluated the effects of reference pricing on hospitalizations and physician visits found no significant changes in these outcomes. While Schneeweiss et al found a temporary 11% (95% confidence interval [CI] 1.07-1.15) increase in physician visits shortly after British Columbia’s ACE inhibitor policy went into effect, perhaps as a result of patients visiting their physicians to switch to reference products, 3 to 10 months after the policy, there were no significant changes in physician visits compared with baseline, —3% (95% CI 0.86-0.91).

The analysis by Schneeweiss et al of a reference pricing program for calcium channel blockers also revealed non-significant changes in physician visits and hospitalizations shortly after the implementation of the policy (95% CI 1.00-1.03 and 0.89-1.06), followed by significant decreases (4% and 15% respectively, 95% CI 0.95-0.98 and 0.79-0.93) at 3 to 10 months after implementation. The evaluation by Hazlet and Blough of British Columbia’s H2 antagonist policy found very similar results.

Only 1 study directly evaluated the impact of reference pricing on clinical end points, and found non-significant differences in cardiovascular death rates between users exposed to reference pricing for ACE inhibitors, calcium channel blockers, and nitrates and those who were not (

Our meta-analysis of 16 studies evaluating the impact of 9 reference pricing policies suggests that this strategy reduced drug prices, increased utilization of and adherence to targeted drugs, and promoted switching behavior from expensive products to alternatives at or below the reference price. These outcomes were associated with significant reductions in both patient out-of-pocket and total payer expenditures. Although the rate of physician visits increased for a short period after policy implementation, reductions in visits and hospitalizations over a longer time period were not consistently observed. Thus, the policies appeared to achieve cost savings without negative effects on resource consumption. Furthermore, Moreno-Torres’s study on the incremental effects of reference pricing in Spain suggests that while generic reference pricing reduced payer costs, by including brand name products of the same active ingredient in reference pricing policies, per capita pharmaceutical costs also decreased, benefiting insurers and consumers (4.06€ per capita savings, a 1.54% saving).

Reference pricing appears to have effects different from many other strategies to contain prescription drug spending. While prescription cap limits protect payers from excessive cost, the limits cannot distinguish between medically necessary and unnecessary drug use and may prevent patients from purchasing drugs that they need. Among frail, low-income, elderly patients, these caps lead to increased risk of institutionalization. 

Therapeutic interchange is a widely used procedure that allows pharmacists to substitute a brand name product for a generic product from the same therapeutic class of drugs. Savings in payer expenditures have been observed in settings in which therapeutic interchange has been implemented, especially within the Veterans Affairs population.

 However, the success of therapeutic interchange depends on the active cooperation of physicians, consumers, and pharmacies. As such, some US plans have found that therapeutic interchange has not been as effective as hoped, especially in influencing physicians.

 Virtually all publicly and privately insured individuals in the United States receive coverage through plans that include tiered formularies,

 which create incentives for patients to use generic medications or lower-cost brand-name drugs for which discounts have been negotiated.

 Their introduction has been shown to result in lower spending on drugs by payers but to increase spending for patients and to lead to gaps in use in some cases.

Implementation of reference pricing in the United States could potentially occur in several different ways. For example, in line with current interest in comparative effectiveness, Pearson and Bach call for Medicare to use reference pricing to set coverage and reimbursement standards.

 Under this paradigm, a reference price would be set for new products of comparable clinical effectiveness. Although the Affordable Care Act legislation prohibits the use of comparative effectiveness in coverage decisions, the law does appear to allow for research findings to be used for setting reimbursement rates as part of a larger, “iterative and transparent process which includes public comment and considers the effect on subpopulations.”

 As the German and Spanish experiences suggest, this could be done through an incremental approach, first implementing reference pricing for only a few classes of drugs, such as statins and proton pump inhibitors, before scaling up to include more classes.

Alternatively, reference pricing could be embedded in tiered formularies. For example, in contrast to placing all generic products in the lowest tier of incentive formularies as is currently practiced, 1 generic product in each therapeutic class could be set as the referent drug and the prices of other generics set according to this. For brand name products, reference pricing could be used to set the same price for multiple products in the same therapeutic class. Currently, preferred prices are set through negotiations between pharmacy benefit management and manufacturers and only 1 brand name product in each class has preferred status. By setting a fixed level of reimbursement, more than 1 product may effectively obtain preferred status, making the prices more competitive and increasing the transparency of the price negotiation and setting process. On the other hand, this process may eliminate incentives for manufacturers to provide pharmacy benefit managers with discounts in exchange for preferred formulary status, and thus the application of reference pricing to brand-name products may have unclear implications for pricing.

Of course, these mechanisms of introducing reference pricing in the US healthcare system add complexity to patient benefit plans, which may also undermine the benefits that the policy strategy offers. It is reassuring that the introduction of reference drug pricing in other settings did not adversely affect patient health and utilization results.

Articles

Reference pricing is an effective cost-containment tool widely used in other countries; it may be an attractive policy strategy for the US healthcare system.

A Systematic Review of Reference Pricing: Implications for US Prescription Drug Spending

To determine the efficacy of healthcare information technology (HIT) interventions in improving adherence.

 Systematic search of randomized controlled trials of HIT interventions to improve medication adherence in cardiovascular disease or diabetes.

 Interventions were classified as 1-way patient reminder systems, 2-way interactive systems, and systems to enhance patient—provider interaction. Studies were subclassified into those with and without real-time provider feedback. Cohen’s d effect sizes were calculated to assess each intervention’s magnitude of effectiveness.

 We identified 7190 articles, only 13 of which met inclusion criteria. The majority of included studies (54%, 7 studies) showed a very small ES. The effect size was small in 15%, large in 8%, and was not amenable to calculation in the remainder. Reminder systems were consistently effective, showing the largest effect sizes in this review. Education/counseling HIT systems were less successful, as was the addition of realtime adherence feedback to healthcare providers. Interactive systems were rudimentary and not integrated into electronic health records; they exhibited very small effect sizes. Studies aiming to improve patient—provider communication also had very small effect sizes.

 There is a paucity of data about HIT’s efficacy in improving adherence to medications for cardiovascular disease and diabetes, although simple patient reminder systems appear effective. Future studies should focus on more sophisticated interactive interventions that expand the functionality and capabilities of HIT and better engage patients in care.

(Am J Manag Care. 2010;16(12 Spec No.):SP82-SP92)

We performed a systematic review to assess the efficacy of healthcare information technology (HIT) interventions for improving patients' medication adherence.

There is a striking paucity of clinical data despite increasing availability of HIT. 

Existing HIT adherence interventions are promising, with simple patient reminder systems providing the best evidence for use. 

The tested interactive systems (eg, education and counseling via interactive computer interface) were rudimentary and showed limited benefit.

Nonadherence to essential medications is an important public health problem.

 Patients commonly do not adhere to chronic medication therapy, leading to substantial morbidity, mortality, and excess healthcare costs.

 Despite the significant impact of nonadherence on health, solutions are elusive. Meta-analyses evaluating interventions to improve adherence have consistently supported the use of resource-intensive, multifactorial approaches with multiple elements delivered over time, such as self-management plans, reinforcement, or rewards.

In an effort to identify more cost-effective solutions, researchers and clinicians have begun exploring the role of healthcare information technology (HIT) in medication adherence interventions.

 There are numerous potential applications for HIT in a medication adherence intervention. Electronic pharmacy data may allow for identification of nonadherence and facilitate data delivery to prescribers and pharmacists.

 Electronic systems might inexpensively remind patients and providers about refills. Interactive electronic systems may be used to educate  patients about appropriate medication use, and widespread online connectivity should accommodate more sophisticated monitoring, interaction, and communication.

 Although rigorous evaluation of the effectiveness of HIT adherence interventions is essential, little systematic assessment has been done to date. We conducted a systematic review of HIT interventions designed to improve medication adherence in cardiovascular disease and diabetes. Our goal was to assess the state of evidence in this field, identify interventions that were successful, and ascertain specific features of interventions that seem to be most associated with success.

 We performed a systematic search of peer-reviewed journals between 1966 and 2010 using MEDLINE and EMBASE. We limited our search to randomized controlled trials.

 Our search terms related to the type of study (ie, randomized controlled trial); adherence (ie, adherence OR compliance OR medication adherence OR treatment adherence); prescription drugs (ie, drug OR medication OR antihypertensive OR antihyperlipidemic OR hypoglycemic); and cardiovascular disease and diabetes (ie, myocardial infarction, coronary heart disease, heart failure, hypertension, hyperlipidemia, OR diabetes). Articles with at least 1 search term in 3 of the main categories (study type AND adherence AND either drug OR disease) met criteria for review.

 Search terms and parameters were adjusted for both databases (MEDLINE and EMBASE) while maintaining a common overall architecture. Search results were then screened for duplicate entries, which were removed.

 Studies were included if they reported results of randomized controlled trials studying interventions to improve adherence to medications used for prevention or treatment of diabetes or cardiovascular disease, the greatest source of mortality in the United States.

 We included only randomized trials in order to promote interventions based on the  highest quality of evidence. Studies were limited to adult subjects (aged ≥18 years). Of these interventions, we included only interventions with any electronic component. Examples included the identification of patients with electronic tracking of adherence, electronic reminders to take medication, or electronically enhanced communication with patients or providers. Non-English studies were excluded.

) were classified into 2 groups. The first group described the type of interaction with the patient: 1-way patient reminder systems, 2-way interactive systems, or systems to enhance patient—provider interaction. We selected these categories to assess how to best deliver interventions to patients, whether by simple patient reminders, engagement of patients with an electronic system, or enhancement of communication with the provider. Studies of reminder systems featured interventions providing audio and/ or visual reminders of medication dosing. Interactive systems included computer-based tools aimed at patient education, counseling, and/or promoting favorable patient behaviors. Systems interacted with patients either immediately or via delayed feedback (eg, customized reading material).

 The second group described the type of physician engagement. These types of interventions included those in which no real-time adherence information was passed on to providers and those that incorporated real-time feedback to providers. We included this categorization in an attempt to assess the incremental value of delivering additional feedback to the provider. In a third type of intervention, providers (or in 1 case, research assistants) directly interacted with patients as part of the intervention.

 Data were extracted by 3 investigators (ASM, SLC, WHS), with disagreements resolved by consensus. We assessed a number of variables related to the organization and outcome of studies including study design, setting, characteristics of population studied, number of participants, mean age (or age range) of participants, characteristics of the intervention, methods used to measure medication adherence, and medication adherence outcomes. Confidence intervals (CIs) were reported where they were available and P values where no CIs were available.

 We identified those randomized controlled trials where means and standard deviations for medication adherence outcomes were presented. For these studies, we computed Cohen’s d effect size (ES) statistics, which can be calculated for outcomes that are either binary (eg, survey responses or predefined adherence cutoffs) or continuous (eg, proportion of days covered).

 The ESs compare the difference in effect between the study groups divided by the standard deviation of this difference. When standard deviations  were not reported, we derived them from the P value or t test statistic.

 Using standard methods, we considered an ES of less than 0.2 to be very small, an ES of 0.2 to less than 0.5 to be small, an ES of 0.5 to less than 0.8 to be medium, and an ES of 0.8 or greater to be large. We assumed that the estimated Cohen’s d statistics were independent of scale, sample size, and the standard deviation of the outcome studied.

 Thirteen studies met criteria for our literature review (

). Five studies used 1-way patient reminder systems, of which only 1 incorporated provider feedback. Six studies examined 2-way interactive systems, of which 3 included provider feedback. The remaining 2 studies were designed to test systems to enhance patient—provider interaction. Five studies used patient’s self-reported adherence as an end point, whereas 8 studies used pill count, pill cap monitoring, or some other quantitative measure to determine adherence. A total of 11 studies included patients with cardiovascular disease, and 2 studies were conducted with diabetic patients. Seven studies had a very small ES, 2 studies had a small ES, no studies had a medium ES, and 1 study had a large ES. There were 3 studies where the ES could not be determined from the published data.

 One-Way Patient Reminder Systems Without Real-Time Provider Feedback

 Four studies featuring reminder systems without provider feedback were identifi ed in our search. Studies varied in length from 12 weeks to 1 year. Only 1 study used a self-reported adherence measure. The ES could be calculated for 2 of the 4 studies, resulting in a very small effect and a large effect.

 Christensen et al studied the impact of an electronic reminder device on adherence in hypertensive patients taking telmisartan.

 Patients with “untreated or ineffectively treated hypertension” were recruited from private practice or hospital ambulatory centers in Poland; 1577 patients were given the monitoring device, and 784 patients began using it. A total of 135 patients did not return the device, and 251 patients were excluded from analysis because they did not answer survey questions appropriately or investigators had “doubts about the authenticity of electronic monitoring data.” The intervention was an electronic blister card device, which recorded dispensing and provided audiovisual medication  reminders. The study was a crossover design with 6 months per arm. At 6 months, before crossover, patients’ self-reported compliance was 90.6% in the intervention group and 85.1% in the control group (P = .072). The ES for this intervention was very small (ES = 0.06; 95% CI, 0.01-0.12).

 McKenney et al followed 70 adult patients on antihypertensive medication in an ambulatory patient population from a retirement community or a primary care center.

 The intervention was a pill cap displaying the last time and day when the container was opened, as well as an audiovisual alarm reminder. The alarm sounded when a dose was due, and the digital face fl ashed if the alarm was ignored. The study was conducted in two 12-week phases. In each phase, the electronic cap was compared with standard vials. After  phase 1, the intervention arm had adherence of 95.1% versus 78% in the control arm (P = .0002). After phase 2, the electronic cap arm had adherence of 94.6% versus 79% in the control arm (P = .003). The ES for the cap intervention was large (ES = 0.85; 95% CI, 0.14-1.56).

 Mengden et al studied 44 patients with uncontrolled hypertension from a hospital outpatient department in Germany.

 After a 4-week run-in period to identify patients with uncontrolled hypertension, patients had 1 antihypertensive substituted by candesartan/hydrochlorothiazide (16/12.5 mg). Adherence was tracked using a Medication Event Monitoring System (MEMS) cap, which was placed on each participant’s medication bottles. Patients were randomized to receive hypertension teaching and an interactive MEMS with visual reminders or usual care with noninteractive MEMS for monitoring. Adherence was calculated from MEMS data as the percentage of prescribed doses taken. Although adherence in patients with uncontrolled hypertension dropped signifi cantly during the run-in (P <.001), it rebounded to excellent levels after drug substitution. At 12 weeks, patient adherence was remarkably high in all study arms: the intervention arm had adherence of 99.7% compared with 97.7% in the control arm and 97.8% in patients whose hypertension was considered to be controlled after the run-in period (no statistically significant differences). The ES could not be calculated. However, the high levels of adherence in control patients in this larger study are not representative of most patients.

 Santschi et al compared 2 different electronic reminder systems in 25 patients with hypertension taking irbesartan being followed at 1 of 2 outpatient clinics in Switzerland.

 This crossover study compared the MEMS system with the Intelligent Drug Administration System, a blister pack—based medication tracking and reminder system. Each crossover period lasted 2 months. Adherence was calculated as the percentage of doses taken. At the end of the study, median adherence was 100% for each device, with no statistically significant difference. The ES could not be calculated.

 One-Way Patient Reminder Systems With Real-Time Provider Feedback

 One study fit the criteria for a reminder system with provider feedback. Rosen et al used interactive MEMS as part of a cue dose training regimen.17 A total of 33 type 2 diabetes patients receiving metformin were recruited from a primary care clinic at the Veterans Administration Connecticut Healthcare System. The intervention included interactive MEMS with audiovisual reminders to take medication. Data from the interactive MEMS were collected every 4 weeks and shared with healthcare providers. Study coordinators contacted providers and urged them to discuss the MEMS findings with their patients. Control patients were monitored with noninteractive MEMS. The intervention lasted 16 weeks with a 12-week follow-up period. Adherence was defined as a percentage of prescribed doses taken. At baseline, adherence was approximately 60% in both groups. At 16 weeks, adherence in the intervention arm was 80% versus 60% in the control (P = .017). The ES was small (ES = 0.43; 95% CI, -0.27-1.14). Adherence was noted to fall after the intervention period concluded. Investigators also surveyed patients and doctors about the experience of discussing MEMS data. Both parties generally found the discussions to be moderately helpful and comfortable.

 Two-Way Interactive Systems Without Real-Time Provider Feedback

 We found 3 articles describing interactive systems without provider feedback. All 3 systems sought to educate/counsel patients and influence adherence behavior either via realtime interaction or customized feedback reports. Of the 3 studies, 2 used self-reported adherence measures. The ESs for all 3 studies were very small.

 Stacy et al recruited 497 of 1219 commercially insured subjects from a large health plan who had been newly prescribed a statin.

 Case and control patients received at least 1 interactive voice recognition (IVR) call providing behavioral advice. Patients randomized to the intervention received tailored behavioral support and print material and 2 additional tailored IVR calls. Tailored print material provided personalized advice based on patients’ responses during the IVR call. More than 25% of patients participated in all 3 calls; 42% of patients participated in 2 of 3 calls. Patients in the control arm received 1 nontailored IVR call and generic print materials. The adherence end point was calculated as percentage of pills taken by using insurance claims data, with an 80% adherence threshold. At 6 months, 47.0% of the intervention and 38.9% of the control patients, respectively, were adherent to at least 80% of their medications. The ES was very small (ES = 0.08; 95% CI, 0.01-0.17).

 Johnson et al studied the impact of an individualized transtheoretical model expert system intervention on 404 adults with hyperlipidemia prescribed a cholesterol-lowering medication.

 The transtheoretical model explains and predicts how and when individuals change behaviors with stages classified as precontemplation, contemplation,  preparation, action, and maintenance. The expert system was built on normative databases to mimic reasoning and problem solving of human experts, and generated individualized reports for patients relevant to their current stage of change. Adherence in both the intervention and control arms was assessed by the system at 6-month intervals over 18 months on a 5-point Likert scale. The odds ratios of improved adherence in the intervention group compared with the control group were 2.03 (P >.05) at 6 months, 3.67 (P <.05) at 12 months, and 2.86 (P <. 05) at 18 months. The ES at the conclusion of the study was very small (ES = 0.18; 95% CI, -0.08-0.45).

Despite increasing availability of healthcare information technology, a literature review showed few clinical data on medication adherence interventions using this technology.

Michael A. Fischer, MD, MS

Articles

A Systematic Review of Reference Pricing: Implications for US Prescription Drug Spending

Healthcare Information Technology Interventions to Improve Cardiovascular and Diabetes Medication Adherence