 To collect normative data, assess differences between demographic groups, and indirectly compare US and Canadian medical systems relative to patient expectations of involvement in cancer treatment decision making.

 Individual patient data were compiled across 6 clinical studies among 3491 patients with cancer who completed the 2-item Control Preferences Scale indicating the roles they preferred versus actually experienced in treatment decision making.

The roles in treatment decision making that patients preferred were 26% active, 49% collaborative, and 25% passive. The roles that patients reported actually experiencing were 30% active, 34% collaborative, and 36% passive. Roughly 61% of patients reported having their preferred role; only 6% experienced extreme discordance between their preferred versus actual roles. More men than women (66% vs 60%, 

 = .001) and more US patients than Canadian patients (84% vs 54%, 

 <.001) reported concordance between their preferred versus actual roles. More Canadian patients than US patients preferred and actually experienced (42% vs 18%, 

 <.001) passive roles. More women than men reported taking a passive role (40% vs 24%,

 <.001). Older patients preferred and were more likely than younger patients to assume a passive role.

 Roughly half of the studied patients with cancer indicated that they preferred to have a collaborative relationship with physicians. Although most patients had the decision-making role they preferred, about 40% experienced discordance. This highlights the need for incorporation of individualized patient communication styles into treatment plans.

Roughly 50% of patients with cancer preferred to have a collaborative relationship with physicians in treatment decision making, and the remaining were split equally between preferring active and passive roles.

Most patients had the decision-making role they preferred, but about 40% of patients experienced discordance between their preferred and actual roles. n Being American and younger was associated with patient preference for a more active role.

Being Canadian was associated with greater discordance between preferred and actual roles experienced by the patient.

This study highlighted that individualized patient communication styles should be incorporated into treatment plans.

Control preferences regarding decision making among patients with cancer have been associated with patient outcomes.

 Several studies have demonstrated that patients who are involved in making medical decisions fare better

 than patients who do not. Based on the unfolding theory by Coombs,

 developed a reliable and easy-to-administer instrument to measure the degree of control a patient desires over his or her medical decisions wherein patients were asked to rank 5 different roles in order of preference. The measure was used in multiple nursing investigations. Hack et al found that there was potential for discordance among patients with cancer between the decision-making roles patients preferred and the roles they experienced (or were allowed to have) in the active or collaborative experience. The Control Preferences Scale (CPS) (

) asks patients 2 questions about how much decision-making control they would like to have and how much decision-making control patients perceived that they actually experienced. There are 5 response categories with associated stick figure drawings for each of the following 5 control options: active, active shared, collaborative, passive shared, and passive. The responses can be further collapsed into the following 3 categories of role responses: active, collaborative, and  passive.

There has been renewed interest in examining patient preferences regarding decision making because of the focus on patient-centered care,

 and the CPS is a brief and valid measure of this construct.

The CPS has been applied in studies across the world, including Sweden,

The CPS has been shown to have good psychometric properties.

 However, to date there has not been a compilation effort to provide collective normative data for others to reference. This article aims to fill that void.

have attempted to determine what factors are associated with desired levels of control preference in treatment decision making. Many investigations have shown that higher education level is associated with preference for a more active role. In a sample of 1012 Canadian women with breast cancer, Degner et al

 found that patients who were more educated and younger desired a more active role. Education level and length of follow-up were associated with choice of an active or collaborative role among 140 Italian patients with multiple sclerosis.

 In a national survey of 1000 Norwegian women older than 18 years, characteristics associated with more active role preferences were age 45 to 59 years (relative to younger and older age groups), higher education level, better general health status, and a negative attitude toward therapy.

of 5199 adults from Wisconsin, female sex, higher education level, better self-rated health, and fewer prescription medications predicted a significantly higher probability of the most active involvement in decision making. Role preference showed little variability over time. Hence, role preference may be more of an intrinsic personality trait that is consistent over time and situations.

Several other factors are also known to influence role preference. In a community survey of 514 men aged 50 to 70 years in Sydney, Australia, occupational skill level predicted men’s preferences for a passive role.

Patients with occupational skill levels 3 through 5 were significantly more likely to delegate decision making to physicians than were  managers and professionals. Shields and colleagues

 found that patients having prostate or breast cancer, less fatalistic orientation, and professional or managerial occupations preferred a shared or active role compared with patients having other cancers, more fatalistic orientation, and other occupations. In a population- based survey of 2765 adults, age younger than 45 years, excellent self-rated general health, female sex, and white (vs Hispanic) race/ethnicity were associated with preferences being more patient oriented (ie, more active role).

In a survey of Norwegian women with urinary incontinence, older age and being married were associated with lower likelihood of preferring an active role.

The differences between US and Canadian healthcare systems have been well documented.

 Despite the growing body of evidence for incorporating patient control preferences in medical care, it is unknown if preferences vary by country. This study pooled individual patient data from 6 studies conducted in the United States or Canada and included 3491 patients. The meta-analysis sought to determine (1) estimates of the overall distribution for preferences among patients with cancer in terms of role expectation and experience in treatment decision making, (2) the effect of selected demographic and clinical variables on patient preference and experience in treatment decision making, and (3) differences in control preferences between US and Canadian patients.

Data were compiled from 6 studies that administered the CPS to patient populations. The Study of Cancer Survivors I by the American Cancer Society

was intended to be a national (US) prospective longitudinal study among patients surviving 1 of 10 common cancers, including prostate, female breast, lung, colorectal, bladder, uterine, kidney, ovarian, skin melanoma, and non-Hodgkin’s lymphoma. We included patients from the state of Minnesota for the pooled analysis.

The Mayo Clinic Multi-Disciplinary Quality of Life Study

 investigated interventions to maintain overall quality of life in 103 patients undergoing radiation therapy for advanced-stage cancer. The patients were randomized to standard care or to a structured multidisciplinary intervention that included 8 sessions addressing cognitive, physical, emotional, spiritual, and social functioning domains relative to quality of life.

 studied a consecutive sample of 1012 women with a confirmed diagnosis of breast cancer at 2 tertiary oncology referral clinics and 2 community hospital oncology clinics. They performed a cross-sectional survey querying about patient preferences regarding participation in decision making for their care.

 investigated patient preferences in decision making among 434 consecutive adult patients with a new cancer diagnosis at 2 tertiary care referral clinics in Winnipeg, Manitoba, Canada. All patients were queried within 6 months of an initial diagnosis of cancer.

Hack and colleagues35 studied the efficacy of audiotaping primary adjuvant therapy consultations on information communication, satisfaction, and quality of life among 628 women with newly diagnosed breast cancer from 6 cancer centers in Canada. The patients were block randomized to 1 of the following 4 consultation groups: (1) standard care control and not audiotaped, (2) audiotaped but no audiotape given, (3) audiotaped and patient given audiotape, or (4) audiotaped and patient offered choice of receiving audiotape or not.

 studied the efficacy of providing an audiotape of primary treatment consultations to 472 men with newly diagnosed prostate cancer undergoing radiation therapy. Patients were recruited from 4 cancer centers in Canada and were block randomized to 1 of 4 consultation groups as described for the breast cancer study.

Data evaluated in this pooled analysis were CPS findings and patient demographics, including country, sex, and age. Among all studies, patients were categorized as having corcordance or discordance between the role in treatment decision making that they preferred and the role that they experienced. For each CPS question, the distribution of control (role preference and role experience) was summarized by simple proportions and by graphical representations of bar charts. Cross-tabulation of control preferences with demographic variables followed. The Fisher exact test was used to compare role distribution concordance and to measure association with clinical and demographic variables. For ease of interpretation, CPS preferences were categorized as active, collaborative, or passive as in previous studies.

 Multivariate logistic regression models were used to assess the effect of the univariately significant variables (age, sex, country, and tumor stage) in combination with each other to predict role preference and concordance between role preference and experience.

As suggested by a reviewer, we performed additional sensitivity analyses adjusting the aforedescribed multivariate-adjusted models for education level (grade school, high school,

more than high school, or not reported) to assess if the country-related and other associations with role preference and concordance were confounded by patient education. In addition, we performed these multivariate analyses considering all 5 CPS categories, instead of the 3 collapsed levels, per the reviewer’s suggestion.

The study cohort consisted of 3491 patients enrolled in 6 different cancer treatment trials (

). There were 2591 patients (74% of the total sample) from Canadian clinical trials and 900 patients (26% of the total sample) from US studies. Overall, the study included 68% female patients, 61% aged 65 years or older, and 29% with tumor stages 0 to 2 (

). Comparisons between the US and Canadian patient cohorts indicate more women, more patients 50 years or older, and more patients with early-stage disease among US patients. There is a considerable amount of missing data on tumor stage, so the latter finding needs to be interpreted with caution. All studies involved CPS assessment at the time of diagnosis or before treatment initiation except for the American Cancer Society study,

which was a cross-sectional study of survivors.

Overall Preferred and Actual Roles in Treatment Decision Making

Four percent of patients preferred an active role in treatment decision making, 22% an active shared role, 49% a collaborative role, 15% a passive shared role, and 9% a passive role (

). Collapsing these into 3 categories indicates that 26% of 3276 patients preferred an active role in treatment decision making, 49% a collaborative role, and 25% a passive role. The percentage of patients preferring an active role ranged from 12% in the multitumor study by Degner and Sloan

 to 36% in the American Cancer Society study.

The decision making that patients reported actually experiencing were 4% active, 26% active shared, 34% collaborative, 14% passive shared, and 22% passive (Table 3). Collapsing these into 3 categories indicates that 30% of patients reported that they experienced an active role, 34% a collaborative role, and 36% a passive role. The percentage of patients experiencing an active role ranged from 18% in the Mayo Clinic Multi- Disciplinary Quality of Life Study33 to 40% in the prostate cancer study by Hack et al.

Role preferences were compared to determine whether preferred roles were in concordance with actual roles (

). Overall, there was roughly 61% concordance (ie, patients’ preferred and actual roles were the same). Only 6% of patients experienced extreme discordance between their preferred and actual roles (ie, wanting an active role and experiencing a passive role, or vice versa). Twenty-six percent of patients preferred an active role, and 30% experienced an active role. Conversely, 36% of patients preferred a passive role, and 25% experienced a passive role. Among 1058 of 2742 patients (39%) reporting discordance between their preferred and actual roles, 453 (43%) had a more active role than they preferred, whereas 605 (57%) had a more passive role than they preferred.

Role Distributions by Sex, Country, Age, Tumor Stage, and Education Level

The distribution of patients by the roles they preferred versus actually experienced is broken down by sex in 

. There was little difference in the role distributions preferred by men versus women (Figure 1A). However, there was a significant difference in the distribution of the actual roles patients experienced, with more women than men reporting taking a passive role (40% vs 24%, 

More US patients than Canadian patients preferred active roles (32% vs 22%, 

<.001), whereas more Canadian patients than US patients preferred (33% vs 14%, 

<.001) and actually experienced (42% vs 18%, 

). American men preferred and actually experienced more active and collaborative roles than Canadian men (

). Canadian men actually experienced a more active role versus what they preferred (40% versus 24%, 

= .004). American women preferred a less passive role than Canadian women (10% vs 33%, 

<.001) and actually experienced a less passive role than Canadian women (17% vs 45%, 

The preferred and experienced role distributions varied across age groups. More older patients (>50 years) than younger patients preferred a passive role in treatment decision making (32% vs 21%, 

<.001). More younger patients (<50 years) than older patients preferred an active role (31% vs 22%, 

<.001). More older patients than younger patients actually experienced a passive role (38% vs 30%, 

<.001). More younger patients than older patients actually experienced an active role (34% vs 29%, 

In the subset of patients for whom tumor grade was available, there was no significant difference in the preferred role distribution between patients having tumor stages 0 to 2 (24% active and 33% passive) versus patients having tumor stages 3 to 5 (21% active and 36% passive) (

= .50). However, there was a difference in the actual role distribution, with more patients having tumor stages 0 to 2 than those having tumor stages 3 to 5 actually experiencing an active role (30% vs 22%, 

The preferred role differed by education level. Grade school, high school, and more than high school education levels were associated with preference for a passive role in 49%, 32%, and 22% of patients, respectively (

<.001). A passive actual role was experienced by 64%, 35%, and 23% of patients with grade school, high school, and more than high school education levels, respectively (

Concordance Between Preferred and Actual Roles by Sex, Country, Age, and Tumor Stage

= .001) reported concordance between their preferred and actual roles. More US patients than Canadian patients (84% vs 54%, 

<.001) reported concordance between their preferred and actual roles (

). Examination within sex and country of residence shows that US men had greater role concordance than Canadian men (85% vs 52%, 

<.001). This was also true for US women compared with Canadian women (85% vs 54%, 

 <.001). Among patients 65 years or older, 59% reported role concordance compared with 63% among patients aged 50 to 64 years and 62% among patients younger than 50 years (

= .18). Tumor stage did not affect concordance between preferred and actual roles (62% concordance for patients with tumor stages 0 to 2 vs 56% concordance for patients with tumor stages 3 to 5,

= .10). Patients with grade school education had 59% concordance between preferred and actual roles, those with high school education had 59% concordance, and those with more than high school education had 64% concordance (

Multivariate Modeling of Role Preference and Role Concordance

A polytomous logistic regression model was constructed using the preferred trichotomous role categorization (active, collaborative, and passive) to assess which demographic and clinical variables (age, sex, country, and tumor stage) were related to patients’ role preferences. Patients tended to prefer a more active role if they were American (odds ratio [OR],

Articles

ONCOLOGY FOCUS

This pooled analysis assesses preferred roles in treatment decision making, actual roles, and preferred versus actual discordance among 6 studies of patients with cancer.


Preferred Roles in Treatment Decision Making Among Patients With Cancer: A Pooled Analysis of Studies Using the Control Preferences Scale

 To evaluate the impact of utilization management methods on use of smoking cessation medication.

Retrospective cohort analysis of pharmacy claims data.

 The study population included patients at least 18 years of age, naive to varenicline, and with a rejected varenicline claim between January 2007 and April 2008 because of 1 of 4 utilization management restrictions: drug not covered, prior authorization, step therapy, or quantity limits. The outcome variable was whether patients used any smoking cessation medication including varenicline, bupropion, and nicotine replacement therapy (prescribed and over-the-counter) within 6 months of the rejected varenicline claim. Multivariable logistic regression was conducted to evaluate the probability of filling a prescription for any smoking cessation medication.

 A total of 15,597 patients were identified. Within 6 months after the rejected claims, 8393 (53.8%) patients filled at least 1 smoking cessation medication, 7864 (93.7%) of whom filled varenicline. Compared with quantity limits, the odds ratios for filling any smoking cessation medication after the rejected varenicline claim were 0.01 (95% confidence interval [CI], 0.01-0.02) for varenicline not covered, 0.07 (95% CI, 0.06-0.09) for step therapy, and 0.18 (95% CI, 0.16-0.20) for prior authorization. Higher out-of-pocket expense was associated with a lower probability of filling any smoking cessation medication.

 About half of the patients in this study did not fill any smoking cessation medication following a rejected varenicline claim. It is important to address this treatment gap in support of patients seeking smoking cessation therapy.

This research examines the use of smoking cessation medication among patients whose varenicline claims were rejected because of 1 of 4 utilization management restrictions (drug not covered, prior authorization, step therapy, or quantity limits). Six months after the rejected varenicline claim:

Only 53.8% of patients filled a prescription for any smoking cessation medication. 

Of patients who did fill a prescription, 93.7% received varenicline and 6.3% received other smoking cessation medication without getting varenicline. 

Higher out-of-pocket expense was associated with a lower filling rate for any smoking cessation medication.

Smoking is a serious health problem associated with significant mortality, morbidity, and costs. It is known to be a cause of many deadly diseases such as cancer, stroke, heart diseases, and lung diseases.

The Centers for Disease Control and Prevention estimated that smoking or exposure to smoking led to 443,000 premature deaths annually from 2000 to 2004 in the United States. Three leading causes of smoking-related death were lung cancer, ischemic heart disease, and chronic obstructive pulmonary disease. The total  annual economic burden of smoking was $193 billion per year in the United States during 2001 to 2004, with $96 billion for direct healthcare costs and $97 billion for productivity losses.

It was estimated that if all smokers covered by Medicaid stopped smoking, Medicaid could save $9.7 billion every year after 5 years.

Further, at a global level, the World Bank has estimated that smoking accounts for about 6% to 15% of total health expenditures in developed countries.

 Although smoking cessation (SC) is extremely beneficial, it is known to be difficult. Among all of the untreated smokers who try to quit smoking, only 4% to 7% are likely to be successful.

Current clinical guidelines call for aggressive treatment of tobacco dependence through a combination of clinical counseling and medication interventions to improve the success rates.

Seven medications have been identified as first-line treatment for smoking, including bupropion SR, nicotine gum, nicotine inhaler, nicotine lozenge, nicotine nasal spray, nicotine patch, and varenicline.

 Unfortunately, SC treatments have been severely underused. The National Health Interview Survey shows that of smokers attempting to quit smoking in 2000, only 21.7% used any pharmacotherapy and only 1.3% used behavioral counseling.6 Clinical guidelines call for coverage of tobacco dependence treatments to improve the use of SC treatments and cessation rates.

 Research on the impact of benefit design on SC medication is limited. Existing studies generally focused on the effects of copayments on SC medication use.

 The impact of utilization management methods such as prior authorization, step therapy, restrictive formulary, and quantity limits is not well understood. Previous research on rejected claims in other therapeutic areas showed that some patients did not fill prescriptions after their claims were rejected. For example, research on step-therapy restrictions on angiotensin-receptor blockers showed that 12 months after a claim for an angiotensinreceptor blocker was rejected because of step therapy, 7% of patients did not have any antihypertensive medication claims.

Another study showed that up to 70% of patients did not try to overcome a prior-authorization restriction after having a rejected claim for a cyclooxygenase-2  inhibitor. 

If a large percentage of patients did not fill any SC medication after being denied an SC medication claim, such an observation should raise concerns for health plans and policy makers. In the long run, decreased use of SC medication could lead to worse health outcomes and higher healthcare costs.

 We investigated the relationship between utilization management for varenicline and the initiation of therapy with SC medication. Specifically, this study reports the proportion of patients who actually used SC medication 6 months after a varenicline claim rejection and examines factors that influenced patients’ decision to use SC medication. We focusedon varenicline because utilization management of SC medication generally focuses on this drug, as it is the only newly approved SC medication in over a decade. In this study, 94.3% of the patients whose claims for SC medication were rejected because of utilization management had prescriptions for varenicline. Sensitivity analysis was performed to include patients whose claims for other prescribed SC medication were rejected.

 A retrospective cohort data analysis was conducted based on MedImpact’s claims database. MedImpact is a large private national pharmacy benefits management company. The claims database contains detailed information of the adjudication process, including rejected, reversed, and approved claims. If a patient decides to pay cash to purchase a medication after a claim is rejected because of utilization management, he or she receives a network discount, typically around 10% to 15%, and the claim is recorded in the data set.

 The target study population included patients who attempted to fill prescriptions for varenicline that were rejected because of utilization management. The study population met the following inclusion criteria:

 • At least 1 rejected varenicline claim from January 2007 to April 2008 because of 1 of 4 utilization management restrictions: drug not covered, prior authorization, step therapy, or quantity limits.

 • No approved or rejected varenicline claims before the identification period.

 • Continuously enrolled in the same health plan 1 year before and at least 183 days after the initial rejected varenicline claim.

 • Had coverage for over-the-counter (OTC) SC medication.

 • Age 18 years or older at the time of attempting to fill a varenicline claim.

 This research focused on patients whose varenicline claims were rejected because of 1 of 4 utilization management restrictions: drug not covered, prior authorization, step therapy, or quantity limits. These 4 restrictions accounted for approximately 80.9% (140,228 of 173,314) of the total number of rejected varenicline claims (

). The remaining varenicline claims were turned down for reasons unrelated to benefit design such as member benefit expired, incorrect spelling, not a network pharmacy, etc.

 Patients whose claims for varenicline were rejected because the drug was not covered had a closed formulary in which medications not listed in the formulary were excluded from coverage. Closed formularies are commonly used by health plans to control costs.

Patients who had an open formulary, in which medications not in the preferred formulary were covered with a higher copayment, were not included in this analysis because their varenicline claims were not rejected.

 Patients whose claims were rejected because of prior authorization usually were required to enroll in an approved counseling and behavior-modification program. Both clinical guidelines and the prescribing information for varenicline recommend the use of clinical counseling along with varenicline to improve cessation rates.

 For this reason, many health plans require patients to enroll in a clinical counseling program before varenicline can be dispensed. Upon enrollment in such a program, patients receive a certificate of enrollment that they then can fax to health plans to meet the prior-authorization requirement and obtain varenicline. Prescribing physicians also can certify this enrollment on behalf of patients to help them obtain the drug.

 The step-therapy restriction typically requires patients to try bupropion or nicotine replacement therapy before trying varenicline. Health plans usually impose the steptherapy requirement to encourage patients to use a low-cost alternative. If a significant portion of patients can be diverted to low-cost alternatives, health plans can save on medication costs.

 Finally, the quantity-limit restriction usually restricts days of supply of the initial varenicline claim to fewer than 21 to 34 days, depending on the health plan. Health plans usually put a quantity limit on SC medication to avoid waste. For example, some patients may stop using varenicline, like other SC medication, after several days. In that case, the rest of the medication could be wasted if a large quantity of varenicline were dispensed.

 The outcome variable was whether patients used any SC medication within 6 months of the rejected varenicline claim. In general, the intent of utilization management is to encourage patient compliance with clinical guidelines or to channel patients to use cheaper alternatives, rather than to deter patients from initiating SC pharmacotherapy treatment altogether. For this reason, the percentage of patients using any SC medication was used as a measure of success for these tools. The SC medication considered in this research included any form of bupropion, prescribed and OTC nicotine replacement therapy, and varenicline.

 Price Index for Smoking Cessation Medication

 When a patient’s claim for an SC medication is rejected, he or she may consider alternative medications. The health plan’s generosity in covering SC medication may be an important factor in the patient’s decision. We constructed a price index to quantify health plan’s coverage of SC medication.

 A price index is the average out-of-pocket (OOP) cost for SC medication weighted by the number of claims. It can be interpreted as the OOP cost for a market basket of SC medication. The 

) provides the technical details about the price index.

 Multivariable logistic regression modeling was done to examine factors that could influence patient response to a rejected varenicline claim. The covariates included age, sex, type of insurance, geographic locations, comorbidities, type of benefit restrictions, price index, and history of use of bupropion or nicotine replacement therapy before the rejected varenicline claim. Comorbidities were measured by RxRisk, a method to determine a patient’s risk profile based on pharmacy data only.

Only comorbidities that influenced more than 5% of the population were included in the final regression analysis.

MANAGERIAL

About half of the patients in this study did not fill any smoking cessation medication following a rejected claim for varenicline.

Utilization Management for Smoking Cessation Pharmacotherapy: Varenicline Rejected Claims Analysis

About 32 million Americans have uncontrolled hypertension, and the impending shortage of primary care physicians could result in worsening blood pressure control. The "Treat to Target" approach to hypertension in primary care has the potential to increase hypertension control while off-loading a portion of hypertension management from physicians to other team members. "Treat to Target" involves 3 components: home self-monitoring of blood pressure, regular health coaching for patients with elevated blood pressure, and home titration of blood pressure medications based on standing orders from the patient's physician. To implement "Treat to Target" in primary care, public and private health insurance plans and primary care practices should receive payment for health coaching sessions that train patients to take their blood pressure, teach patients about hypertension and its medications, and assist patients by telephone in home titration. Reimbursing primary care practices that implement effective blood pressure programs and save physician time is a logical step to reducing long-term costs from stroke, heart attack, and kidney failure.

Primary care practices can improve the management of hypertension, the single most effective clinical service for reducing mortality in the United States.

Health plans can help by paying for patients' home blood pressure monitors and by paying primary care practices to provide health coaching. 

Blood pressure medications can be titrated at home based on standing orders from the patient's physician. 

The advantage for health plans is a reduction in the serious and costly complications of uncontrolled hypertension.

Hypertension affects 65 million people in the United States. Of this group, 50% have achieved blood pressure control below 140/90 mm Hg, leaving more than 32 million Americans at risk for the serious consequences of uncontrolled hypertension.

 Because controlling hypertension could avoid an estimated 46,000 deaths per year among persons younger than age 80 years, it is the single most effective clinical service for reducing mortality.

 With such high incidence of poor blood pressure control, the need to transform hypertension management is pressing.

 Because 79% of physician visits for hypertension take place in primary care, the failure of blood pressure control is a failure of primary care. Given the growing shortage of primary care physicians, one might expect blood pressure control to worsen in the near future. Primary care needs a new approach to hypertension management that asks less of overburdened physicians while improving outcomes. We describe a  strategy that may accomplish both goals.

 Hypertensive patients can achieve normal blood pressure through an approach called “Treat to Target,” which melds 3 complementary components. First, patients with high blood pressure are given a home blood pressure monitor and encouraged to enter their home recordings into a log book. Second, patients receive regular phone calls from a nonphysician

 team member who reinforces blood pressure goals and provides coaching on diet, exercise, and medication adherence. These team members—also referred to as health coaches&mdash;may be registered nurses, pharmacists, medical assistants, or other nonclinicians trained in behavior- change counseling. Third, using physician-approved treatment protocols or standing orders, patients and health coaches can jointly decide by telephone to intensify antihypertensive medications. The process of coach-assisted home titration should reduce the need for physician visits. If medication doses are changed, the health coach notifies the physician.

 Examples of successful blood pressure treatment strategies using the “Treat to Target” model have been described in the peer-reviewed literature. In a small study, 31 patients were randomly assigned to a patient- directed management group or usual care. In the patient-directed group, participants were trained in how to use a home blood pressure cuff. If home blood pressure readings were consistently elevated, enrollees could augment antihypertensive medications according to an algorithm developed for each patient. After 8 weeks, patients in the intervention group had lower mean arterial blood pressures than the control group.

 In a second study without a control group, 111 patients were trained to take blood pressure measurements at home and to use a simple protocol for selftitration. The study found improved blood pressure control, no significant safety issues, and a high level of patient and physician satisfaction.

 Another study randomly assigned patients to usual care, home blood pressure monitoring, or home blood pressure monitoring plus home titration managed by pharmacists. In the home-titration arm, patients regularly sent blood pressure values to a  pharmacist via the Internet; the pharmacist responded with specific recommendations including medication changes. After a 12-month intervention, 56% of patients interacting with pharmacists had blood pressure under 140/90 mm Hg compared with 36% of patients using home blood pressure cuffs and 31% of patients receiving usual care.

 In a larger trial, 527 patients with elevated blood pressure were randomly assigned to a self-monitoring and self-titration group or a control group. After 12 months, blood pressure in the self-titration group was lower than that in the control group; the difference was statistically significant. The patients in the intervention group and their family physicians agreed on the medication titration changes, with patients increasing their medications if their home blood pressure measurements remained high.

 An ongoing study involves health coaches without professional training assisting with hypertension management in primary care. Low-income and mostly minority patients with hypertension were randomly selected to receive home blood pressure monitoring, regular health coach phone calls, and home titration of antihypertensive medications or home monitoring and coaching without home titration.

 Early results show that of 179 patients with elevated blood pressure, 109 (61%) had a follow-up blood pressure below 140/90 mm Hg.

 The 3 interrelated components of the “Treat to Target” model—home monitoring, coaching, and home titration&mdash;can revolutionize blood pressure treatment in primary care while off-loading tasks from an overworked, shrinking adult primary care physician workforce. This approach works because it addresses 4 factors that contribute to poor blood pressure control: poor understanding of physician instructions, lack of patient participation in decision making, low adherence, and “clinical inertia.”

 It’s not surprising that we’re failing in our efforts to help patients achieve blood pressure control when we consider our “success rates” in these 4 areas. First, 50% of patients leave the physician visit not understanding the physician’s instructions. Second, although patients have better outcomes when they are actively engaged in their care, patients actively participate in decisions in only 9% of visits.

Third, only one-third  of people with chronic conditions have excellent medication adherence.

Adherence rates among patients with hypertension vary depending on the measurement method used and are significantly lower for minorities; a clinic caring for vulnerable populations found only 36% good adherence among patients with hypertension.

Lower adherence is associated with poor blood pressure control, disease progression, disability, and death.

 Fourth, clinicians often fail to intensify medications during visits when blood pressure recordings are high. This phenomenon, called clinical inertia, often is related to competing priorities—visits in which too many agenda items must be addressed in the time available.

 In one study, 83% of ambulatory visits in which hypertension patients had elevated blood pressure readings were associated with either poor adherence or the failure of clinicians to appropriately intensify medications.

 “Treat to Target” addresses all 4 of these factors. Patients take their blood pressure at home and are actively engaged in their own care. Health coaches (registered nurses, pharmacists, or nonprofessional team members) spend time with patients—mainly over the phone&mdash;explaining hypertension and the medications used to treat it, and helping patients to problem-solve the numerous reasons for poor medication adherence. Home titration of medications, using physician-created algorithms and conducted by the patients themselves with the help of health coaching, can overcome clinical inertia and speed up the intensification of medications without placing added stress on primary care physicians.

 To implement “Treat to Target” in primary care, several preconditions need to be met. Health insurance plans, including Medicare and Medicaid, should pay for home blood pressure monitors so that lower-income people are able to obtain them. Primary care practices should receive payment for health coaching sessions that train patients to take their blood pressure, teach patients about hypertension and the medications used to treat it, and assist patients by telephone in home titration. Without these changes, practices are unlikely to adopt this innovation. Reimbursing primary care practices that implement effective blood pressure programs and save physician time is a logical step to reducing long-term costs from stroke, heart attack, and kidney failure.

 From the Warren Alpert Medical School (DM), Brown University, Providence, RI; and the Department of Family and Community Medicine (TB), University of California at San Francisco, San Francisco, CA. Mr Margolius is a 4th year medical student at Brown University.

 The authors report no external funding for this study.  Author Disclosures: The authors (DM, TB) 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 (DM, TB); acquisition of data (DM); analysis and interpretation of data (TB); drafting of the manuscript (DM); critical revision of the manuscript for important intellectual content (DM, TB); and administrative, technical, or logistic support (TB).

 Thomas Bodenheimer, MD, Department of Family and Community Medicine, University of California at San Francisco, 995 Potrero Ave, Bldg 80-83, San Francisco, CA 94110. E-mail: tbodenheimer@fcm.ucsf.edu.

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COMMENTARY

Blood pressure control can be improved through a "Treat to Target" approach including home blood pressure monitoring, health coaching, and home titration of antihypertensive medications.

Controlling Hypertension Requires a New Primary Care Model

 To compare outcome measures of nurse-directed diabetes management for 9 to 12 months between a nonintegrated model (patients removed from the primary care clinic and followed up in a separate diabetes clinic with supervision by an endocrinologist) and an integrated model (nurse placed in the primary care clinic with supervision by primary care physicians).

A nurse trained to follow approved detailed treatment algorithms (glycemia and dyslipidemia algorithms for both models plus a hypertension algorithm for the integrated model) was given prescription authority.

 A total of 367 patients were randomly selected from a primary care clinic for the nonintegrated model, and 178 patients were referred to the nurse by primary care physicians for the subsequent integrated model. Ultimately, one quarter of patients in the nonintegrated model were using insulin (mostly bedtime insulin only), while three quarters of patients in the integrated model were using insulin (mostly intensified regimens). The initial mean (SD) glycosylated hemoglobin (A1C) levels fell from 8.9% (2.6%) to 7.0% (1.2%) of total hemoglobin in the nonintegrated model and from 11.1% (2.3%) to 7.2% (0.9%) of total hemoglobin in the integrated model (to convert A1C level to proportion of total hemoglobin, multiply by 0.01). Taking initial values into account, the final A1C levels were not statistically different (P = .61). In the nonintegrated and integrated models, respectively, 60% and 49% met the American Diabetes Association (ADA) A1C goal, and 82% and 96% met the low-density lipoprotein cholesterol (LDLC) goal. In the integrated model, 90% met the blood pressure (BP) goal, and 47% met all 3 goals (ADA A1C, LDL- C, and BP).

 An integrated model of diabetes care is generalizable and should be considered by policy makers to improve diabetes outcomes, especially among underserved minority populations.

Incorporation of an integrated model of diabetes care (nurse placed in the primary care clinic with supervision by primary care physicians) will markedly improve diabetes surrogate outcome measures of glycemia, lipids, and blood pressure (BP) in the short term and will decrease clinical events and save money in the long term.

In the nonintegrated model (patients removed from the primary care clinic and followed up in a separate diabetes clinic with supervision by an endocrinologist) and integrated model, respectively, 60% and 49% met the American Diabetes Association (ADA) glycosylated hemoglobin (A1C) goal, and 82% and 96% met the low-density lipoprotein cholesterol (LDL-C) goal. 

In the integrated model, 90% met the BP goal, and 47% met all 3 goals (ADA A1C, LDL-C, and BP.

Diabetic complications can be devastating.

 Microvascular complications of diabetes are the leading causes of blindness (in adults aged 20-74 years), end-stage renal disease, and lower extremity nontraumatic amputations. Macrovascular complications (coronary artery disease, strokes, and peripheral arterial disease) are increased 2-fold to 4-fold in patients with diabetes mellitus versus age-matched control subjects without diabetes. Meeting the goals of the American Diabetes Association (ADA) for outcome measures (glycosylated hemoglobin [A1C] level <7.0% of total hemoglobin, low-density lipoprotein cholesterol [LDL-C] concentration <100 mg/ dL, and blood pressure [BP] <130/80 mm Hg)

 (to convert A1C level to proportion of total hemoglobin, multiply by 0.01; to convert LDL-C concentration to millimoles per liter, multiply by 0.0259). These goals are not being met by most patients with diabetes.

 Efforts to improve outcome measures of diabetes care have generally been ineffective.

 A major reason is  that these efforts do not usually lead to timely and appropriate treatment decisions.

 A notable exception is case managers who could adjust medications without waiting for physician approval.

 Observational investigations have demonstrated the effectiveness of an integrative model (ie, patients were cared for in their usual primary care settings), with reduced 1-year utilization and costs and improved process measures (A1C level, LDL-C concentration, and eye and kidney screenings), as well as a decrease in the percentage of the population with A1C levels exceeding 9.5% of total hemoglobin.

 The investigators have taken the next step by evaluating outcome measures in randomized clinical trials and have shown that nurses and pharmacists (under the supervision of physicians) following approved treatment algorithms and given prescription authority (ie, they can prescribe medications independently according to the algorithms) can lower A1C levels among patients with diabetes 3-fold more than is typical with patients in usual-care control groups.

However, in these approaches, patients were followed up in a nonintegrated model in which they were removed from their primary medical home to receive diabetes care, and the nurses and pharmacists were almost always supervised by endocrinologists. This nonintegrated model of diabetes care is not generalizable because (1) there are far too few endocrinologists to provide adequate supervision, and (2) most important, patients are removed from their medical home so that care is fragmented (ie, important aspects of healthcare are being performed at separate sites, with duplication of care, suggestions of conflicting treatments that result in patient confusion, potential communication problems between the patient and more than 1 provider, etc).

 Our group published results of a nonintegrated model of nurse-directed diabetes management in this journal several years ago.

 Herein, we evaluate an integrated model of diabetes management based on the nonintegrated model in which the same registered nurse (not a nurse practitioner), MB-C, still with prescription authority and following treatment algorithms, was placed in the primary care site and was supervised by primary care physicians. Outcome measures of the nonintegrated and integrated models of diabetes management are compared. If the results are similar, the more generalizable integrated model of care has the potential to markedly reduce morbidity and mortality associated with diabetic complications, especially among an underserved minority population such as that studied herein.

 The site for these models of diabetes care was a county health center serving a challenging, mostly uninsured minority population. The glycemia-, dyslipidemia-, and hypertension-detailed treatment algorithms followed by the nurse have recently been published.

The hypertension algorithm was unavailable in the nonintegrated model. The nurse was able to follow approximately 175 patients at any one time in both models. (Among a more middle-class population, a nurse following these algorithms can manage up to 250 patients at a time because many of the interactions can be performed by telephone, fax, or e-mail, which is often not possible among the minority population studied herein.) A special computer program (NuMedics, Inc, Tigard, Oregon) was used to provide weekly reports of patients who missed appointments or laboratory tests so that appropriate follow-up could be performed by a medical assistant, who also entered data into the computer program.

 For the nonintegrated model of diabetes management, patients were randomly selected from an internal medicine clinic. The first person with diabetes from morning or afternoon clinic sessions who agreed to participate was placed under the care of a Spanish-speaking nurse trained to follow the glycemia and dyslipidemia treatment algorithms at a separate clinic in the health center. The nurse was supervised by an endocrinologist (MBD), who met with her weekly and was available by telephone at all other times. All other patient care unrelated to diabetes was performed by a primary care provider in the internal medicine clinic. For the integrated model of diabetes management, this same nurse was placed in the family medicine clinic and was supervised by the primary care physicians there, who referred their patients to her. The nurse cared for these patients in the family medicine clinic. (Both the internal medicine and family medicine clinics in this county health center were primary care clinics but were staffed by physicians with different training.) In addition to the glycemia and dyslipidemia treatment algorithms, a hypertension algorithm was used in the integrated model of care. The endocrinologist was not involved in the care of these patients. It is important to point out that results are compared between randomly selected patients in the nonintegrated model and referred patients in the integrated model.

 Patients were followed by the nurse for 9 to 12 months before being discharged back to usual care in both models. Patient education occurred at individual visits with the nurse and could be focused on specific problems encountered by the patient. For patients taking pills, the first goal of the glycemia treatment algorithm is a fasting plasma glucose (FPG) level of less than 130 mg/dL, which necessitated measurement every 2 to 3 weeks until that goal was achieved (to convert glucose level to millimoles per liter, multiply by 0.0555). At that point, patients were seen every 3 months to determine if the A1C goal of less than 7.0% of total hemoglobin was achieved. If the FPG level remained at least 180 mg/dL at 3 weeks after maximal (tolerated) doses of metformin plus a sulfonylurea agent were reached—or if the FPG level was less than 180 mg/dL but the A1C level was 7.0% or higher of total hemoglobin 3 months later&mdash;a maximal dose of pioglitazonehydrochloride (45 mg) was added, and the A1C level was measured 4 months later. If that value was 7.5% or higher, bedtime insulin was added, and pioglitazone was discontinued. Patients starting insulin regimens were seen every week or every 2 weeks initially until FPG levels measured at home were less than 200 mg/dL, every 3 weeks until they were less than 150 mg/dL, and then every 6 weeks. Three months after more than 50% of these values had dropped to less than 130 mg/dL, the A1C level was measured. If it was 7.5% or higher, insulin therapy was intensified by switching the patient to a split-mixed regimen (usually selected because it required only 2 insulin injections per day) or to a basal-bolus regimen with 4 injections per day for those with irregular eating patterns. The sulfonylurea agent and pioglitazone were discontinued, but metformin was continued in overweight and obese patients to mitigate weight gain.

 In the dyslipidemia treatment algorithm, statins were adjusted monthly until the LDL-C target was reached (<100 mg/dL [<70 mg/dL for patients with clinical cardiovascular disease]), at which time lipids were measured every 4 to 6 months. In the hypertension treatment algorithm, BP medications were adjusted monthly until the goal of less than 130/80 mm Hg was achieved. All patients were seen at least every 3 months.

 The final A1C levels were compared between the nonintegrated and integrated models of diabetes management by 1-way analysis of covariance, controlling for baseline values. The percentages of patients achieving the ADA A1C and LDL-C goals were analyzed by χ2 test for homogeneity. Significance was set at the 5% level (2-tailed).

A nurse following treatment alogrithms with prescription authority in a primary care setting markedly improved diabetes outcomes; 47% met ADA A1C, LDL-C, and BP goals.

September 2010

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