To determine if algorithmically generated double-booking recommendations could increase patient volume per clinical session without increasing the burden on physicians.

A randomized controlled trial was conducted with 519 clinical sessions for 13 dermatologists from December 1, 2011, through March 31, 2012.

Sessions were randomly assigned to “Smart-Booking,” an algorithm that generates double-booking recommendations using a missed appointment (no-shows + same-day cancellations) predictive model (c-statistic 0.71), or to a control arm where usual booking rules were applied. The primary outcomes were the average number and variance of arrived patients per session, after controlling by physician. In addition, physicians received a survey after each session to quantify how busy they felt during that session.

257 sessions were randomized to Smart-Booking and 262 sessions were randomized to control booking. Using a generalized multivariate linear model, the average number of arrived patients per session was higher in the Smart-Booking intervention arm than the control (15.7 vs 15.2, difference between groups 4.2; 95% CI, 0.08-0.75; 

 = .014).The variance was also higher in the intervention than control (3.72 vs 3.33, 

 = .38).The survey response rate was 92% and the physicians reported being similarly busy in each study arm.

Algorithmically generated double-booking recommendations of dermatology clinical sessions using individual physician assumptions and predictive modeling can increase the number of arrived patients without overburdening physicians, and is likely scalable to other settings.

Algorithmically generated booking recommendations based on customizable physician assumptions and predictive modeling modestly increased productivity without overburdening physicians in a randomized controlled trial.

		Predictive modeling can identify patients likely to be no-shows.

		Advance scheduling systems can be successfully implemented.

		The role of clinical scheduling decision support should be further studied.

Rising healthcare costs and federal budget deficits continue to put pressure on physicians to more 

efficiently deliver care. Patients who miss outpatient appointments without prior adequate notification, colloquially called “no-shows,” are a frequent source of complaint because they decrease efficiency, have a negative financial impact, and waste appointment slots that could be used by others. One study at a family practice residency clinic concluded that no-shows resulted in a 3% to 14% revenue loss.

 In an era in which access to primary care and several other specialties is constrained, optimal utilization of physician time is paramount.

No-shows have a variety of causes. Logistical issues such as an inability to miss work, find child care, or find transportation are reasons for many patients.

 Simply forgetting is another obvious problem

 and often the most frequent cause of missed appointments (48%9 and 39%8 in 2 studies, for instance). Interestingly, patients’ perceptions or concerns about their visit also affect adherence. A hesitation to hear bad news, endure an uncomfortable procedure, and encounter perceived disrespect by the medical establishment have been reported.

 Self-resolving symptoms have also been also cited as a cause for no-shows.

And not surprisingly, the greater the number of “wait days”—the days between the scheduling of the appointment and the appointment date&mdash; the greater the risk of a no-show.

There are numerous no-show interventions, but most rely on appointment reminders to decrease no-shows or on double booking to compensate for them. Reminder interventions discussed in the literature include staff phone reminders, automated phone reminders, text messages, mailed reminders, other electronic reminders, and financial penalties, all of which have been shown to work to some degree in some settings.

 A systematic review demonstrated a 39% reduction in the non-arrival rate after manual reminders and a 29% reduction for automated reminders; the reminders were shown to be cost-effective.

 Several randomized controlled trials (RCTs) have also concluded that reminders meaningfully reduce no-show rates.

 For example, a 3-arm outpatient RCT resulted in no-show rates of 23.1% with no reminders, 17.3% for automated phone reminders, and 13.6% with staff phone reminders.

 However, in some settings automated reminders have been ineffective.

Overbooking is a common strategy, but most clinics do so with a nonscientific approach, such as, “If we double book early, we will catch up later.” Two groups have published the results of nontraditional scheduling system implementations that used overbooking. A pediatric ophthalmology clinic implemented a system that algorithmically opened appointment slots based on predicted patient demand, physician supply, and scheduling rules; however, their implementation was preliminary at the time of publication.

 In another study, Israeli dermatologists reduced the baseline non attendance rate from 32.9% to 27.9% using managed overbooking and service centralization.

Open access, predictive modeling, and advanced scheduling models have been proposed to improve clinic efficiency. A systematic review of 24 open access studies concluded that no-show rates were lower in practices with a prior baseline >15%; however, other outcome measures were mixed.

 No-show prediction models have typically been designed using association rules,

 Two studies reported C statistics of 0.82 and 0.84; however, neither was externally validated.

 Others have designed and validated advanced scheduling systems that maximize clinic utility through computer simulation and calculation.

Given the relative lack of published outcomes of advanced scheduling system implementations, we developed and validated a model to predict no-shows and same-day cancellations and conducted a randomized controlled trial to determine the following: Can an automated algorithmic approach to double-booking dermatology appointments increase the number of arrived patients per session without overburdening the physicians?

A randomized controlled trial was conducted from December 1, 2011, through March 31, 2012 at the Department of Medical Dermatology at Massachusetts General Hospital (MGH) under the approval of the Partners Institutional Review Board. The department hosts approximately 80 medical dermatology clinical sessions per week, for a total volume of approximately 50,100 patient visits per year. At the time of the study, 8 full-time physicians averaged 6 sessions weekly, and 22 physicians worked 1 to 5 sessions weekly; the practice also employs residents. Clinical sessions run from 8:00 am to 12:00 pm or 1:00 pm to 5:00 pm, and each appointment slot is 15 minutes long except for 30-minute procedures. Physician compensation is based on Relative Value Unit productivity.

Prior to the study, the missed appointment rate was 16.5%, and given this rate, some clinicians routinely double booked their sessions. Appointments were booked using the electronic booking system IDX by 10 schedulers employed by the practice at the front desk or in a small call center adjacent to the practice. Prior to the intervention, schedulers would doublebook by attempting to spread additional patients out evenly across the schedule, and there was an average of 55 days between the scheduling and arrival of each new patient.

Sixteen of 30 physicians were excluded from the study for the following reasons: 7 worked 1 session a week and lacked booking flexibility, 4 were in another scheduling study, 3 worked primarily at other practice sites, 1 was on maternity leave, and 1 new physician’s schedule was not yet standardized. The other 14 physicians were asked to join the study, and all consented to participate. Double booking in designated urgent access, procedural, evening, and weekend clinics was excluded from this pilot.

Develop Missed Appointment Predictive Model 

A missed appointment (no-shows + same-day cancellations) predictive model was developed. Potentially predictive variables were identified through literature review, discussion with the practice leadership, and evaluation of existing administrative data sources. The variables identified were: appointment type, day of week, wait days, language, ethnicity, age, historical diagnoses, insurance, and appointment arrival history. One year of data about approximately 54,000 dermatology appointments were collected to develop and validate the model (see eAppendix A, available at www.ajmc.com for details of the methodology). In addition, missed appointment models were developed using the same methodology for 5 additional departments to quantify the predictive model’s generalizability.

The Smart-Booking approach was developed to algorithmically generate double booking recommendations using individual physician assumptions and missed appointment probabilities. The resulting system was a stochastic algorithm that outputted reports identifying the slots in which appointments should be doublebooked, appointment types to book in open slots, and where open slots should be blocked.

Clinic sessions were randomized 1:1 to the intervention and control, stratified by physician, using a computer-generated list of random numbers. In the control, appointments were booked based on prior methodology; in the intervention, schedulers booked using the Smart-Booking recommendations. Physician scheduling assumptions were collected from all participating physicians and then adjusted based on input from practice leadership.

The Smart-Booking system was used to generate 2 reports. The Follow-up Double-Booking Report (eAppendix B) identified the control and intervention sessions, and in the intervention sessions, time slots were identified to double book follow-up appointments for the next 2 to 60 days. The Next-Day Report (eAppendix C) identified intervention time slots to double book new and follow-up appointments, to convert empty time slots into new or follow-up appointments, and to block empty slots because the schedule was overbooked.

Booking recommendations were electronically delivered at 5:30 am each weekday to the department. Every day the schedulers printed the Follow-up Double-Booking report and booked accordingly. In addition, the Next-Day report was used to modify the next day’s schedule. Schedulers did double-book in the control arm based on each physician’s historical booking maximums. Additionally, patients were double-booked in both arms beyond recommended limits on occasion by physician request or for urgent issues.

Data collected from the IDX scheduling system were used to calculate the number of normalized arrived patients per 4-hour session (referred to as arrived patients) (eAppendix D). The primary outcomes were the average and variance of the arrived patients in each study arm. As a secondary outcome, the average arrived patients were calculated for each individual physician. In addition, the following survey was sent to each physician to quantify how busy they felt after each session.

Based on historical data, the average number of patients arrived per session was 14.0 with a standard deviation of 3.0. This study was powered to detect the difference of 1 additional arrived patient per physician session with an alpha = 0.05 and power = 0.80. This resulted in an initial sample size of 141 sessions per study arm. After accruing 141 sessions in each arm, there were no physician or staff complaints. Therefore, we continued the study for an additional 10 weeks, which resulted in 654 randomized clinical sessions to gain further physician-specific data.

We fit a generalized multivariate linear model to determine the impact of the intervention. Arrived patients was the dependent variable, the arm was the independent variable, and the physician was the random effect. An F-test was used to determine the significance of the difference of the variance in each arm. The physician survey results were plotted for all sessions and summarized descriptively.

One physician was removed from the primary analysis because Smart-Booking was incorrectly calculated and implemented. The threshold for total number of booked patients in the intervention arm was set lower than intended; therefore, when the physician wanted to add extra patients into a schedule, they were systematically added to the control sessions. Sensitivity analyses were performed that included this physician’s data.

Articles

MANAGERIAL

Success of Automated Algorithmic Scheduling in an Outpatient Setting

To examine the impact of Medicaid prior authorization for atypical antipsychotics on the prevalence of schizophrenia among the prison population.

We collected drug-level information on prior authorization restrictions from Medicaid programs in 30 states to determine which states had prior authorization requirements before 2004. We linked the regulatory data to a survey of prison inmates conducted in 2004.

We used a sample of 16,844 inmates from a nationally representative survey and analyzed the data using cross-sectional regression. To capture the impact of prior authorization, we estimated 2 models: the first included an indicator variable for states requiring prior authorization, and a second model used per capita atypical usage.

Evidence indicated that prior authorization restrictions on atypical antipsychotics are associated with an increase in the odds of a schizophrenic resident being imprisoned in a state. State-level prior authorization requirements for atypical antipsychotics are associated with a 2.7% increase in the likelihood that an imprisoned inmate displays psychotic symptoms, and a 1.25 increase in the likelihood that an inmate was previously diagnosed with schizophrenia by a physician. Higher state-level atypical prescriptions per capita are also associated with lower likelihood of psychotic symptoms and of prior schizophrenia diagnosis among prisoners.

Prior authorization requirements for atypical antipsychotics, which are designed to reduce healthcare costs, are associated with greater prevalence of mental illness within the criminal justice system.This association raises important questions about whether increased costs to the criminal justice system might mitigate or offset prescription drug savings created by prior authorization requirements.

We examined the impact of prior authorization formularies on the likelihood that patients with schizophrenia will be arrested and incarcerated.

These findings suggest that prior authorization requirements for atypical antipsychotics, which are designed to reduce healthcare costs, can result in increased expenditures for the criminal justice system. Given the high cost of incarceration, these increased costs to the criminal justice system could easily offset any savings created by prior authorization requirements.

		Prior authorization requirements for atypical antipsychotics are associated with a 22% increase in the likelihood of imprisonment compared with the likelihood in a state without such a requirement.  

		Prior authorization requirements were found to be associated with an increased likelihood that individuals with schizophrenia will be incarcerated for nonviolent drug offenses.

		As the total costs in the United States that are associated with severe psychiatric disorders in jails are very high, new policies on how to treat incarcerated individuals with schizophrenia, particularly nonviolent offenders, are warranted.

Increasingly, public and private healthcare plans are imposing prior authorization requirements to manage drug spending. Prior authorization plans establish a preferred drugs list (PDL) and require authorization before covering drugs not on the PDL. Such programs are designed to reduce costs by steering utilization toward lower-cost medications, but these policies may also lead to poorer drug adherence.

 Poor adherence to or discontinuation of antipsychotic drugs among patients with schizophrenia is likely to cause acute psychotic episodes

 and often results in contact with law enforcement officers due to threatening behaviors brought on by active symptoms, leading to arrest and incarceration.

While schizophrenia affects only about 1% of the US population,

 it is difficult and expensive to treat and can have a devastating impact if not well controlled.

 The effects of losing continuous effective exposure to medication due to nonadherence are immediate and evident. A study on the temporal relationship between medication nonadherence and hospitalization risk for individuals with schizophrenia found that individuals in the first 10 days following a missed prescription refill had a greater than 50% increase in the risk of mental health hospitalization and a 77% increase in the risk of schizophrenia-specific hospitalization.

As a result of their behavior, mentally ill people are more likely to be arrested than other people who are stopped by police, and more likely to be subsequently convicted and incarcerated.

 Enforcement officials may not be sufficiently trained to recognize mentally ill patients, or they may feel obliged to bring the schizophrenic offender to the criminal justice system when the healthcare system is unwilling or unable to accept a violent patient. Prison systems are often poorly equipped to treat the mentally ill, and, when released these prisoners are more likely to recidivate,

which promotes the vicious cycle of mentally ill criminals entering the criminal justice system, suffering further mental health deterioration in prison, and upon release ending up with a significant likelihood of re-arrest due to poorly controlled mental illness.

Evidence also shows that the mentally ill are more costly to incarcerate than those without mental illness and that other types of rehabilitation for the mentally ill may reduce crime at costs lower than those associated with incarceration.

 Over the past several decades, pharmaceutical advancements in the treatment of schizophrenia have been shown to reduce the likelihood of episodes that start the sequence of events leading to incarceration of patients. The second-generation antipsychotics known as atypicals were introduced in the 1990s. These have largely replaced older antipsychotics, because they are associated with lower rates of relapse. About 30% to 40% of patients relapsed with first-generation drugs (relative to 80% without treatment); for second-generation drugs, relapse rates fell to about 25% to 29%.

 Furthermore, many patients respond to only 1 drug, so that increased treatment options increase the likelihood of successful treatment.

 The lower rates of relapse seen with atypicals may lead to fewer incidents with law enforcement and reduced incarceration of schizophrenia patients.

The evidence suggests that atypicals improve outcomes for certain patients with schizophrenia. The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study found that perphenazine (Trilafon), an older typical, worked about as well as several newer atypicals for the average patient in the trial.

 Subsequent research on the CATIE data clarified that a subset of patients in CATIE strictly benefited from atypical usage, and that providing typicals to all patients would have reduced overall health.

 Taken together, this literature suggests that atypicals represent a valuable treatment option for some patients with schizophrenia.

In this study, we examine the association between Medicaid policy and 2 key outcomes-—namely, utilization of antipsychotics and imprisonment of patients with schizophrenia. We compared the prevalence of patients in prison among states that did or did not institute prior authorization programs for atypical antipsychotics. We also compared prevalence across states that have different levels of atypical antipsychotic utilization. Because of the association between prior authorization regulations and increased risk of discontinuing atypical treatment, and because of the potential link between untreated schizophrenia and incarceration, we hypothesized that prior authorization rules will increase the likelihood that a schizophrenia patient is incarcerated.

Ideally, we would like to have measured the percentage of each state’s population with schizophrenia that is incarcerated, and then examined how this percentage is associated with state prior authorization policy. In practice, we were able to obtain only the percentage of the incarcerated population who were diagnosed as having schizophrenia in each state. If it is assumed that Medicaid pharmacy regulations have no impact on the overall prevalence of schizophrenia in a state, and that they have negligible impact on the rate at which nonschizophrenics are imprisoned, then the impact of pharmacy regulations on the prevalence of schizophrenia in prisons will be comparable with the impact of these regulations on the rate of imprisonment of patients with schizophrenia.

A simplified summary of the underlying process illustrates how restrictive pharmacy policies in Medicaid might lead to a higher prevalence of schizophrenia in prison:

Stage 1: A large majority of patients with schizophrenia are covered by Medicaid.

Stage 2: The state’s restrictive prior authorization policy leads to discontinued, reduced, or inappropriate medication.

Stage 3: Suboptimal medication leads to uncontrolled symptoms.

Stage 4: Active symptoms or deteriorated economic conditions that result from uncontrolled symptoms lead to encounters with law enforcement and arrest.

Stage 5: Repeated or serious arrests lead to imprisonment.

Our hypothesis is that Medicaid prior authorization regulations limit access to atypicals, and that this increases the likelihood that a patient will progress down the list from stage 2 to stage 5.

Our analysis made use of the 2004 Survey of Inmates in State Correctional Facilities (SISCF) and the 2004 Survey of Inmates in Federal Correctional Facilities (SIFCF) conducted for the Bureau of Justice Statistics by the Bureau of the Census; these surveys have been used in previous research.

 They provide nationally representative data on inmates held in state and federal prisons obtained through personal interviews of more than 18,000 inmates in about 300 prisons. These data contain individual-level information on inmates’ mental health conditions and various personal characteristics. A benefit of the SISCF data is that they also contain state indicators, which allow us to match the data to our original survey of antipsychotic coverage.

Using a mailed survey that was conducted in 2009, we collected information from 30 state Medicaid programs on their utilization review policies for atypical antipsychotics over the period 1999 to 2008.

 This survey asked whether prior authorization policies applied to a list of drugs identified by US brand name. To supplement the survey information, we examined Medicaid pharmacy program websites for relevant documents and contacted Medicaid program personnel. This allowed us to identify the 4 states (AK, CA, MA, and NY) that had prior authorization for all atypicals before 2003, along with 24 states that we can determine had no prior authorization before the 2004 SISCF survey. For the remaining states, we are unable to determine the prior authorization policy in 2003, because information on the timing of prior authorization implementation is unclear or unavailable from the mailed survey or websites. (See 

State-level utilization data were obtained from the State Drug Utilization Data Files available from CMS. These data, which include the number of prescriptions filled for each drug by each state Medicaid program by calendar quarter, were obtained for 2003 atypical antipsychotics. State-level Medicaid enrollments as of June of each year were obtained from the Kaiser Family Foundation’s State Health Facts database.

We used prevalence of serious psychological distress (SPD) estimated from the 2003-2004 National Surveys on Drug Use and Health, produced by the Substance Abuse and Mental Health Services Administration (SAMHSA), as a proxy for the prevalence of schizophrenia in the general population. Serious psychological distress was measured using the K6 screening instrument for nonspecific psychological distress. In 2003-2004, SPD was noted in about 10% of the population 18 years or older. If we can assume that the percentage of schizophrenia in SPD does not vary by status of prior authorization or level of atypical utilization, then such an approximation does not affect interpretation of our results.

We estimated the probability that an inmate is screened as positive for psychotic symptoms, and whether an inmate reports a prior schizophrenia diagnosis, using a linear probability model. In addition to an indicator variable that equals 1 if the state in which the inmate is incarcerated has a prior authorization rule for atypical antipsychotics, we included the prevalence of schizophrenia in the general population in the state and several individual inmate characteristics found in 

As documented in Table 1, the probability that a prisoner was previously diagnosed with schizophrenia is relatively low, and this suggests the linear probability modeling approach. Logistic regression is known to perform poorly in binary dependent variable models where positive outcomes are statistically rare; in contrast, linear probability model performance is invariant to the mean of the dependent variable.

 In any event, however, the association between prior authorization (or atypical usage) and the prevalence of mental illness within prisons is robust and statistically significant across linear probability, logistic regression, and probit models. Therefore, our results are not primarily dependent on functional form. All our modeling results are presented for the logistic regression model in 

The outcome variable is an indicator of whether an inmate has screened positive for psychotic symptoms. We utilize 2 questions from the survey. In the first, the survey asked inmates whether during the past year they had seen or heard things that other people said were not there, felt that other people were able to read or control their mind, or felt that someone other than the corrections staff had been spying on or plotting against the inmate. Inmates who answered yes to any of these questions were screened as positive for psychotic symptoms in our analysis. This question measures the presence of psychotic symptoms. Note that it is broader than schizophrenia. Our second measure focuses on a much narrower definition, based on a question that asks the inmate if they have ever been diagnosed by a healthcare provider with schizophrenia or a psychotic disorder. This definition will undercount those inmates who are unwilling or unable to identify themselves as schizophrenic but has the advantage of not resulting from self-diagnosis. As shown in Table 1, 2840 inmates screened positive using the more inclusive definition while 775 reported a prior diagnosis. Significantly, all those with a prior diagnosis also tested positive for the presence of psychotic symptoms.

POLICY

This study examined the impact of prior authorization formularies on the likelihood that patients with schizophrenia will be arrested and incarcerated.

Medicaid Prior Authorization Policies and Imprisonment Among Patients With Schizophrenia

Funding for comparative effectiveness research (CER) has focused attention on what treatments work best under what specific clinical circumstances, and for whom. Because not all patients respond in the same way, treatment decisions, clinical guidelines, and coverage policies applied in a “one-size-fits-all” fashion based upon the population “average” response may lead to suboptimal outcomes. Existing frameworks focus on why patients respond differently to treatments. We propose a framework that identifies when these differences are likely to be clinically important. Scenarios are presented in which it may be most critical for clinical decisions and policies to distinguish between the average and the individual patient so that treatment recommendations provide the greatest benefits for the largest number of patients. We provide recommendations for researchers to help identify issues to study, for providers to help assist them in recommending optimal treatment for individual patients, and for payers or public health bodies to help balance societal needs with those of the individual.

Because not all patients respond in the same way, treatment decisions, clinical guidelines, and coverage policies applied in a “one-size-fits-all” fashion based upon the population “average” response may lead to suboptimal outcomes.This article includes:

		A framework to identify when it may be most critical for clinical decisions and policies to distinguish between the “average” and the individual patient so that treatment recommendations provide the greatest benefits for the largest number of patients.

		Recommendations to help researchers identify areas to study, help providers make optimal treatment recommendations for individual patients, and help payers or public health bodies to note when it is necessary to balance societal needs with those of the individual.

The hypothetical Ms Jones, aged 55 years, has successfully managed her recurrent depression with sertraline. She plans to join her new state health exchange. Based upon the Essential Health Benefits Final Rule, the health exchange need only cover as many medications per class as are included in that state’s benchmark plan.

 Her exchange chose to offer fluoxetine rather than sertraline. Some researchers have posited that selective serotonin reuptake inhibitors (SSRIs) do not substantially differ in terms of maintaining remission; however, these studies have generally examined “average” efficacy.

Determining which treatment works best for a population, on average, differs from determining what works best for individual patients.

 Ms Jones asks her provider if she is likely to relapse or see less benefit if she switches to the new preferred drug. To aid in decisions that Ms Jones and other patients have to make, there is increasing interest in and funding of comparative effectiveness research (CER) by the Patient-Centered Outcomes Research Institute (PCORI).

 While many CER studies will compare the benefits and the harms of alternative treatments in large real-world populations, leading to conclusions based on the “average” treatment effect for a certain population, there is increased discussion and funding to attempt to understand which patients respond differently.

Because patients do not all respond in the same way, treatment decisions, clinical guidelines, and coverage policies applied in a “one-size-fits-all” fashion based upon this average response may prove suboptimal. As an example, although percutaneous coronary interventions achieve similar benefits with less morbidity than bypass surgery for many patients, recent studies indicate that bypass surgery leads to better outcomes in patients with complicated diabetes.

Achieving a proper balance between population-based approaches and individualized decision making is critical. Depending upon how it is applied, CER could yield substantial benefit for some, little benefit for many, and even harm to others. Various authors and prior frameworks focused on why treatment responses differ among individuals; they cited such factors as variability in the underlying clinical condition or patient population, differential risk in therapeutic potential, vulnerability to adverse effects, and patient preferences.

 This variability may be attributed to genetic determinants, sociodemographic factors, disease characteristics, or patient comorbidities.

 For further explanation and clinical examples, see the 

In contrast to prior frameworks that address the cause of variability, we focus on the implications of heterogeneity by addressing whether those potential differences are likely to be clinically substantial and should therefore influence the therapeutic choice. Our framework can assist providers as they consider whether to apply a particular CER result to an individual patient, to payers whose policies influence what choices are available, and to policy makers who guide the financing, delivery, access, and quality of healthcare. Both frameworks (why treatment response differs and the implications of treatment response differences for patient care) can be used by the research community to identify clinical areas in need of further investigation.

For Ms Jones’ provider, the ability to predict when treatment response will likely differ for specific patients is important to enable individualized treatment. Ideally, evidence would be available comparing the benefits with the harms of treatment options in patients similar to Ms Jones. However, this level of evidence rarely exists. In an environment of inevitable uncertainty, Ms Jones, her providers, payers, and other policy makers will still have decisions to make. We present a framework that identifies when it may be most critical to distinguish between the average patient and the individual.

Framework to Assess the Impact of Individual Treatment Effects 

When treatment response is unpredictable, how risky is it to apply population average results to individual patients? The answer depends upon: (1) the clinical consequences of delaying optimal treatment; (2) the underlying diversity in patient attributes; (3) the likelihood of response to similar treatments in similar ways (treatment independence); and (4) patient preferences (

). We provide illustrations for each factor below. It should be understood that these factors are not meant to provide a prescriptive or definitive answer, but rather to be considered collectively as a framework for dialogue and presumably improved decision making.

Clinical Consequences of Delaying Optimal Treatment

. For certain diseases (eg, hay fever, fibromyalgia), the patient and provider have time to try several treatments without the threat of irreversible consequences. Patients may have uncomfortable symptoms, but their conditions do not irreversibly deteriorate, and trying additional therapies is not likely to jeopardize long-term symptom control. Ms Jones—whose depression has been successfully controlled on sertraline&mdash;should feel safe to switch her therapy to fluoxetine, under close observation. Should failure occur, she would have the opportunity to begin the next therapeutic option. In other circumstances (ie, a patient with a more acute depressive presentation, or in whose case the switch in therapy results in discontinuation) the risks may well be far greater, and the consequences much less reversible (eg, suicide attempt). Other scenarios include patients who may have only 1 chance for treatment success prior to disease progression (eg, chemotherapy in oncology) or organ damage (eg, sepsis resulting in kidney failure). In circumstances when the consequences of being “wrong” are low&mdash;or when they are high but information exists and is accessible regarding who responds best&mdash;policies that narrow treatment choices may be appropriate. In other circumstances, when there is a high degree of treatment diversity, little evidence to determine which patients are likely to respond well, and the consequences of suboptimal treatment are high, policies should permit greater flexibility and access to treatment choices for the patient and provider.

. Some clinical conditions are well characterized by a set of defining attributes (eg, ST—vs non–ST-elevated myocardial infarction), whereas others are far more heterogeneous (eg, systemic lupus erythematosus presenting with dermatologic, arthritic, or vascular complications).

 Applying population results to individual patients is likely to be more appropriate in the former and more worrisome in the latter. Perhaps Ms Jones’ depressive disorder manifests as anxiety and agitation rather than the classic symptoms of sadness, anorexia, or insomnia. For her, the optimal treatment would address the former symptoms, whereas other patients with the latter manifestations may need alternative treatments.

Population average results might blur these important distinctions and using these blended results could lead to suboptimal patient outcomes. Diversity in clinical attributes, outcomes, and response to treatment may also reflect comorbidities. One patient may have potential adverse consequences due to concomitant medications for a secondary disease while another may have a higher side effect risk due to renal dysfunction. A third patient may not suffer from any of the above. Unless a CER study analysis differentiates those patient subtypes or can predict which outcomes are likely, guidelines based upon the CER study might be appropriate for some patients but clinically inappropriate for others.

In general, the patient’s clinical presentation is known to the provider, who can target the treatment regimen accordingly. In some environments, electronic data systems have the breadth and depth of data and the clinical logic applied to it (eg, prior depressive hospitalization within the last month vs no hospitalization; presence vs absence of comorbidities; or potential drug interactions) so that clinical decision support tools and payer policies that ac- cess that electronic information could similarly tailor appropriate access to needed medications. At other times, the clinical diversity may depend upon patient symptomatic complaints, patient adherence, laboratory test results, or imaging findings—elements not “visible” to most pay- ers or automated systems that influence care. In these circumstances, policies should offer patients and providers more flexibility to select the appropriate treatment. When the condition is well defined, or if the implications of diversity are readily accessible, application of population-level treatment protocols or protocols that can account for this diversity may be appropriate.

Likelihood of not responding to similar treatments in similar ways (treatment independence)

. Based upon the success or failure of prior therapy, variation in response may be predictable. For example, if a patient fails 1 angiotensin-converting-enzyme (ACE) inhibitor for management of hypertension either for reasons of efficacy or safety, she is unlikely to respond well to another ACE inhibitor. Similarly, if amoxicillin is not successful for a urinary tract infection, another penicillin-like agent is less likely to resolve the problem compared with an agent in another therapeutic class (eg, ciprofloxacin, sulfamethoxazole/ trimethoprim). In these scenarios, the responses to available treatments within the same drug class are highly correlated, and treatment options are “dependent” on prior treatment success or failure. In contrast, for patients with depression like Ms Jones who are faced with a change in regimen, success with sertraline may not be matched by similar success with fluoxetine (eg, another SSRI).

 Here, treatment response is more “independent” of prior treatment success or failure.

Independence may vary from illness to illness, across treatment alternatives, or within a class of treatments.

 From a decision-making standpoint, when treatment independence is low (or dependence is high), patients may need to try only 1 therapy in a particular class since related treatments are not likely to be substantially more or less beneficial. In these circumstances, policies that narrow choices within a drug class may be appropriate. However, when treatment independence is high, response to treatment “A” does not eliminate the need for trials of treatments “B” or “C” even when the drugs are within the same therapeutic class. For these patients, access to multiple medications and flexibility to choose among them is needed. For Ms Jones, especially if her disease becomes unstable, the restricted antidepressant formulary of her state exchange that requires her to switch from sertraline to fluoxetine may prove problematic.

. For Ms Jones, tolerability regarding such potential side effects as weight gain or sexual dysfunction may influence her treatment adherence and subsequently her ability to make the optimal therapeutic choice. These types of preferences vary from person to person and reflect attitudes associated with preferred outcomes (eg, increased survival vs greater quality of life); tolerability regardingside effects (eg, willingness to accept being drowsy); differences in socio-cultural preferences (eg, low-sodium diets in the American South); route of administration (eg, oral vs injectable medications), or willingness to make behavioral changes (eg, active vs sedentary lifestyle). Preferences also may change over time as symptoms worsen, conditions progress, or patients adapt to their health status.

There is increasing evidence that patient preferences influence health outcomes and costs.

 In treating hepatitis C, preferences for efficacy and treatment side effects are associated with adherence to antiviral therapy, a key component of achieving sustained viral response.

For several common conditions (eg, cardiac angina, benign uterine fibroids, benign prostatic hyperplasia, hip pain, knee pain, and back pain), overall medical care costs were shown to be reduced when patient preferences were considered in the decision-making process.

For example, when joint replacement candidates were informed of the treatment benefits and risks, fewer patients chose to undergo costly surgeries.

 Unfortunately, population-level comparative studies rarely assess patient preferences and we don’t know whether individual preferences might lead to therapeutic choices which differ from the published "average" results.

For a provider, determining optimal treatment for an individual patient requires factoring in the available population-level evidence on benefits, risks, and costs to inform an individual patient’s preferences. In some conditions (eg, hospital patients with pneumonia or acute myocardial infarction), patient preferences may not greatly differ and a standardized treatment protocol could be more appropriate. However, in patients with preference-sensitive conditions or conditions in which the potential risks and benefits of treatments differ greatly (eg, prostate cancer, bleeding due to uterine fibroids), it would be important to elicit those preferences and enable patients to choose among the alternative--approaches: to not, in other words, strictly apply population-level findings. In scenarios where patient preference plays a critical role in determining the optimal choice among alternatives with different costs (eg, when adherence or patient expectations are important), a tension will exist between efficient resource allocation and patient choice. In some cases, ï¿¼ï¿¼ï¿¼ï¿¼ï¿¼Higher risk and clinical impact of heterogeneity ï¿¼ï¿¼there may be limited evidence to justify a costly treatment for a terminal condition. In other cases, patient preference may come at a cost to other health plan members. In these cases, health plans and policy makers will need to consider the plan’s purpose and deliberate on how benefit designs or policy statements could account for these tradeoffs to minimize costs and optimize health outcomes.

Although genomic information will continue to offer the potential to predict individual response, it will not resolve all sources of variability. Research directly comparing treatment alternatives in the same patient is needed. However, direct assessment of treatment alternatives in the form of crossover studies or n-of-1 designs infrequently occurs. It is beyond the scope of this paper to address, but others researchers have discussed the benefits and limitations of using alternative research methods and analyses (eg, risk prediction models, latent growth mixture models, classification and regression analyses, and non-parametric models) to elucidate who will likely respond or will not to particular treatment approaches.

For experimental and non-experimental studies, pre-specification of subgroups and collection of multiple baseline patient characteristics (eg, clinical, demographic, patient preferences) can help identify which patients differ in their treatment response and point to potential causal effects. Methods research on how to best elicit patient preferences in clinical practice, and how to utilize information generated by patient-powered information sources and networks, could improve our ability to identify patient preferences; demonstration projects could incorporate these approaches into shared decision making. For payers and policy makers, studies on the clinical and economic impact of preferences on patients’ ability to manage their health are needed.

 The information on the clincial and economic impact of patient preferences when treating depression would likely assist Ms Jones’ healthcare provider in answering her question about the advisability of switching to the health exchange’s preferred medication, and it would enable her payer to support rational decision making and balance the tension between costs and preferences.

PCORI’s focus on patient variation and patient sub- groups will improve our knowledge of heterogeneity.

 However, simply funding research on the topic will not be sufficient. Among top-tier journals, only 1 in 3 trials reported heterogeneity in treatment response.

 To expand the availability of this evidence, journal editors could adopt usage of a CONSORT-like statement, in which authors would indicate the likelihood of heterogeneity (eg, high, medium, or low); report whether measures of heterogeneity were assessed; and list the factors that explain any heterogeneity.

 Transparent pre-specification of subgroups (eg, via time-stamped analysis plans on clinicaltrials.gov) and standardized subgroup reporting (eg, via online appendices, in databases managed by clinical condition consortia, or as specified by data standards groups) would enable meta-analysts to use this information and better explore patient differences. In turn, these meta-analyses would be able to identify areas in which individual treatment response varies or to suggest future research priorities.

As detailed CER results become available, providers will need to focus beyond the study-level conclusions and understand the patient associated with treatment response characteristics. With the large number of CER studies anticipated,

 it is likely that providers will increasingly depend upon professional societies and clinical practice guidelines to assist them in sorting through this voluminous and oftenconflicting information. Providers would be greatly assisted if guideline developers would synthesize the information on treatment variability, treatment independence, and patient preferences, and then formulate practice recommendations with this level of detail. These same concepts should be considered by those responsible for developing quality metrics, so that their clinical and policy decisions will not be affectedinappropriate conclusions based on misleading population-level data.

In the future as mobile health (mHealth) communications between patients and providers become more widely adopted, providers will be able to quickly view a patient’s symptoms, preferences, and clinical markers and determine if subsequent trials of alternative treatments are needed. As bedside information technology becomes more prevalent and sophisticated, providers will gain greater ability to offer evidence-based and individualized treatment recommendations. By mining large electronic databases, Ms Jones’ provider could identify patients “similar” to her and determine how they responded to a switch from sertraline to fluoxetine.

Evidence regarding treatment independence could also guide providers on the likely “success rate” with second-line treatment. When treatment independence is high, rather than immediately switching to a medication in a different class, it may be appropriate to switch to other agents in the same class. In cases where treatment dependence is high (eg, treatment response is correlated with prior treatment success or failure due to similar pharmacologic or pharmacodynamic characteristics), altering the treatment strategy by using a drug in a different therapeutic class may be the preferred course. Finally, tools and training are needed to facilitate the provider-patient discussion of treatment options that incorporate individual preferences for possible risks and benefits.

Payers typically make population-based rather than highly individualized coverage and reimbursement decisions. This choice reflects limitations in the available information as well as the need to avoid creating an overly complex benefit design. When treatment independence is low, there is either minimal variation in treatment response or more variability that is predictable based on information accessible to the payer. In such scenarios, there is little risk of clinical deterioration; therefore, insurance designs that direct therapy toward preferred options through utilization management (eg, narrow or closed formularies, therapeutic substitution, step-therapy, or prior authorization) or by instituting financial incentives (eg, lower copayments for preferred tiers) may be acceptable.

Conversely, logistical burdens and financial incentives to access what the payer has deemed optimal treatment may be less acceptable when 1 or more of the following is present: 1) marked variation in treatment effects; 2) minimal ability to predict individual response by the payer or clinical system; 3) treatment response with 1 treatment is not correlated to response with alternative treatments; 4) the consequences of suboptimal initial therapy are high. In these cases, more flexible utilization management designs or provider-directed decision making (within a range of evidence-based therapies) may be preferable.

Treatment heterogeneity also raises ethical dilemmas in reimbursement. Patients with a given biologic predisposition may find that treatments that are optimal for them are not the preferred agents based upon population “average” results. Currently, these patients would be required to pay higher amounts to access the non-preferred medications. To account for ethical standards of fairness,

patient co-payments for these patients should be similar to those of the preferred treatment options for the population “average.” With the increased sophistication of benefit design and electronic records, it may be feasible to account for biology or genetics and tailor co-payments accordingly.

CER will increasingly provide evidence about what treatments work best. For CER to achieve the greatest benefit, we need that evidence for groups of patients as well as for those individuals who may respond differently. We also need to understand the implications of applying population results to particular patients. In some circumstances, population results can and should be applied broadly. In other circumstances, caution is warranted. It is our hope that the framework discussed here begins to address this choice. We hope to encourage discussion about the importance of recognizing when additional research is needed, and about why providers, payers, and policy makers should acknowledge the implications of variation in treatment effect. Doing so should ultimately lead to improvements in health not only for the population as a whole, but also for individual patients like Ms Jones.

 National Pharmaceutical Council, Washington, DC (JSG, RWD); Cognigen Corporation, Buffalo, NY (TG); Center for Health and the Social Sciences, University of Chicago, Chicago, IL (DOM).

 Drs Graff and DuBois are employed by the Na- tional Pharmaceutical Council, a policy research organization supported by the nation’s major research-based pharmaceutical companies. Dr Grasela is an employee of Cognigen, which provides contract research services in the area of pharmacokinetic and pharmacodynamic modeling. Cognigen also receives consulting fees and fees for public speaking on behalf of client products and projects. Dr Meltzer is a consultant for Patient-Centered Outcomes Research Institute (PCORI) as a member of the methodology committee, and has received research grants for other projects from the National Pharmaceutical Council.

 Concept and design (JSG, RWD, DOM, TG); acquisition of data (JSG, RWD, TG); analysis and interpretation of data (JSG, RWD); drafting of the manuscript (JSG, RWD, DOM, TG); criti- cal revision of the manuscript for important intellectual content (RWD, DOM, TG); obtaining funding (RWD); administrative, technical, or logis- tic support (JSG); supervision (RWD).

 Jennifer S. Graff, PharmD, National Pharmaceutical Council, 1717 Pennsylvania Ave, Ste 800, Washington, DC 20006. E-mail: jgraff@npcnow.org.1. Patient Protection and Affordable Care Act; standards related to essential health benefits, actuarial value, and accreditation: a rule by the Health and Human Services Department on 02/25/13. 78 Fed Reg 156.122:12833-12872. https://www.federalregister.gov/articles/2013/ 02/25/2013-04084/patient-protection-and-affordable-care-act-standards-related-to-essential-health-benefits-actuarial. Accessed September 27,2013.

2. Gartlehner G, Hansen RA, Morgan LC, et al. Second-generation antidepressants in the pharmacologic treatment of adult depression: an update of the 2007 comparative effectiveness review. (Prepared by the RTI International—University of North Carolina Evidence-based Practice Center, Contract No. 290-2007-10056-I.) AHRQ Publication No. 12-EHC012-EF. Rockville, MD: Agency for Healthcare Research and Quality. www.effectivehealthcare.ahrq.gov/reports/final.cfm. Published December 2011. Accessed November 13, 2012.

3. Kent D, Hayward R. When averages hide individual differences in clinical trials. Am Sci. 2007;95(1):60-68.

4. US Government Accountability Office. Public Law 111-148—March 23, 2010: Subtitle D&mdash;Patient-Centered Outcomes Research. http:// www.gao.gov/about/hcac/pcor_sec_6301.pdf. Accessed September 13, 2013.

5. Patient-Centered Outcomes Research Institute. Funding announcement: improving methods for conducting patient-centered outcomes research. http://pcori.org/assets/PFA-Improving-Methods.pdf. Published November 16, 2012. Updated January 28, 2013. Accessed September 13, 2013.

6. Selby J; Patient-Centered Outcomes Research Institute. Putting the individual patient at the center in clinical research. http://www. npcnow.org/system/files/conferences/download/2012myth_selby.pdf. Published November 30, 2012.

7. Patient-Centered Outcomes Research Institute. PCORI funding awards: cycle III and inaugural improving methods for conducting PCOR. http://pcori.org/assets/2013/09/PCORI-Funding-Awards-Cycle- III-and-Inaugural-Improving-Methods-for-Conducting-PCOR.pdf?2. Accessed September 13, 2013.

8. Epstein AJ, Polsky D,Yang F,Yang L, Groeneveld PW. Coronary revascularization trends in the United States: 2001-2008. JAMA. 2011; 305(17):1769-1776.

9. Kravitz RL, Duan N, Braslow J. Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q. 2004;82(4):661-687.

COMMENTARY

A framework for researchers, providers, payers, or public health bodies identifies when it is most critical to distinguish between "average" population and individual patient response.

Individual Treatment Effects: Implications for Research, Clinical Practice, and Policy

To evaluate associations between patients’ preferences for attributes of different colorectal (CRC) screening modalities, physician CRC screening recommendations during periodic health exams, and subsequent utilization of screening 12 months later in a large health maintenance organization (HMO).

Multi-method study including baseline surveys from average-risk HMO members joined with audio recordings of 415 periodic health exams (PHEs) and electronic medical record (EMR) data.

Patient ratings of test attributes were used to create an algorithm reflecting type and strength of CRC screening modality preference at baseline. Physician recommendations were obtained from audio recordings. Attribute-based test preferences and physician recommendations were compared with CRC test use using chisquare tests. Associations between attribute-based preferences and physician recommendations were assessed using logistic regression.

Based on attribute rankings, most participants had a weak preference for colonoscopy (COL) (41%), an unclear preference (22.4%), or a weak preference for fecal occult blood testing (FOBT) (18.6%). About half (56%) of patients were screened at 12 months and there was no statistical association between attribute preferences and type of test received. Patients were significantly more likely to receive a recommendation including a test other than COL when they had an attribute-based test preference for FOBT (odds ratio [OR]: 2.17; 95% CI, 1.26-3.71; 

CRC screening test use in this large HMO was generally low. It was not associated with patients’ preferences for different attributes of CRC screening tests but was associated with physician recommendations. Physicians may have better success in getting patients to screen if they consider preferences for test attributes.

		Colorectal cancer (CRC) screening test use in a large health maintenance organization (HMO) was relatively low (56%).

		Patients had CRC screening preferences that could be linked to attributes of different tests.

		HMO members more often received the screening test that was recommended by their physician, usually colonoscopy, than the test that was consistent with their preferences.

		Physicians appeared willing to consider preferences for tests other than colonoscopy, so adherence with CRC may be increased if patients' preferences can be incorporated into recommendations.

Studies have found variation in the proportion of adults who are adherent with colorectal cancer (CRC) screening, with reports ranging from 45% to 70%,

 yet there remains consensus that there is room for improvement, particularly in managed care settings where there is generally coverage for all types of CRC screening. There are several options for CRC screening,

 but increasingly, only colonoscopy (COL) and fecal occult blood testing (FOBT) are used in clinical practice.

 Low rates of CRC screening uptake, combined with the existence of more than 1 appropriate test, have led some to suggest that offering patients the test they prefer may be an effective method for increasing CRC screening adherence.

This argument is supported by several studies that have documented variation in preferences for CRC screening tests across populations.

 Overall, these studies have shown that patient preferences can be linked to specific attributes of the screening tests, such as the preparation involved.

 Yet only a few studies have evaluated associations among patient preferences for key attributes of CRC screening tests, test ordering by physician, and test utilization.

Missing from these studies is an examination of whether attribute-based test preferences are incorporated into physician CRC screening recommendations. The importance of having a physician recommendation in screening uptake has been well documented.

 Yet increasingly, physicians are recommending COL and often do not offer patients a choice of screening options.

 The recent National Institutes of Health State-of-the-Science Conference Statement on CRC screening has called for a continued need to understand the role of patient preferences in CRC screening adherence.

We used data from an observational study conducted in a managed care setting to better understand associations among the preferences of average-risk adults for key attributes of CRC screening tests, physician test recommendations, and subsequent use of CRC screening. We had 3 objectives: 1) to describe the distribution of preferences for key attributes of CRC screening tests and to link these attributes to existing CRC screening modalities; 2) to evaluate the association between physician recommendation for CRC screening modalities and attribute-based CRC screening test preferences; and 3) to compare CRC screening utilization 12 months post visit with attribute-based CRC screening test preferences and physician recommendation.

The data used in this analysis came from a study of patient-provider discussions about CRC screening conducted in a large health maintenance organization (HMO) located in southeast Michigan (R01CA112379). Additional details about the study are provided elsewhere.

Participant Eligibility Criteria and Recruitment 

Participating physicians (N = 64) were salaried family and general internal medicine physicians affiliated with a managed care group in southeast Michigan. Participating physicians agreed to allow scheduled periodic health exam (PHE) visits of their eligible patients to be audiorecorded, with patient consent.

Patients (N = 500) were insured, aged 50 to 80 years, and due for CRC screening (eg, never screened with any of the recommended tests or overdue for screening based on US Preventive Services Task Force [USPSTF] guidelines) at the time of a PHE with a study-participating physician from February 2007 to June 2009. All patient participants in the study were able to receive either FOBT or COL for screening purposes without a co-payment as part of their managed care coverage. Patients identi- fied via electronic medical record (EMR) and appointment scheduling data were sent an introduction letter and called approximately 2 weeks prior to their PHE to confirm eligibility, and to complete a pre-visit telephone survey. Participants were asked to arrive at their scheduled PHE early to enable completion of informed consent prior to audio-recording of the PHE. Participants received a $25 gift card. All aspects of the study were approved by the Institutional Review Boards of the Henry Ford Medical Group, Case Western Reserve University, and Virginia Commonwealth University.

There were 3 primary outcomes for this analysis: 1) attribute-based CRC screening test preferences prior to the PHE, generated from patient survey responses to a list of key CRC screening attributes; 2) primary care physician recommendation for CRC screening test(s) that occurred during the PHE obtained via office visit audio recording; and 3) CRC test utilization 12 months following the PHE obtained via EMR data. Each is described below.

Attribute-based CRC screening test preferences

: In the pre-visit telephone survey, patients ranked 7 key attributes of screening tests. These attributes were obtained from prior literature that had assessed CRC screening preferences via a list of attributes.

Based on this literature, the attributes selected for the survey were test accuracy, preparation required, complications/side effects, need for sedation, frequency of the test, degree of pain/discomfort, and whether a stool sample was necessary. Participants ranked each attribute first, second, and third most important when deciding which screening test to use. We developed an algorithm (

) from patient rankings to categorize patients into attribute-based CRC test preference groups. Our algorithm was based on other attribute-based preference elicitation methods, including conjoint analysis (CA) and the analytic hierarchy process (AHP), which require respondents to engage with complex scenarios or systematic deconstruction of decisions into subsets of attributes. Both CA and AHP have been used to assess preferences for CRC screening, and research has found that certain key attributes can be linked to preferences for specific CRC tests.

 Because these methods are conceptually difficult and typically cannot be done via a telephone survey, we developed a telephone-based approach to assessing them in our study and used our algorithm to link them to preferences for different CRC tests.

Using participant rankings, the following CRC screening test preference structures were generated: 1) strong COL preference (accuracy first, preparation and complications not ranked); 2) weak COL preference (accuracy first,preparation or complications second or third); 3) strong FOBT preference (preparation or complications first, accuracy not ranked); and 4) weak FOBT preference (preparation or complications second, accuracy second or third). Those whose attribute rankings did not fall into one of these 4 groups were categorized into a fifth group labeled “unclear preference.” An unclear preference included individuals who ranked any of the other attributes in their top 3, but where it was not possible to link these preferences to a test. Because of the small number of patients with strong preferences for either FOBT or COL, for analyses we consolidated the 2 FOBT preference groups and the 2 COL preference groups to create a final preference measure with 3 groups: FOBT, COL, or unclear.

Physician CRC screening recommendations: 

Physician CRC modality recommendation during the PHE was obtained from audio recordings. Details regarding the coding of the content of the PHE related to CRC screening discussions are provided elsewhere.

 Because of the high rate of recommendation for COL, we categorized recommendations into 2 groups: COL-only versus COL-plus another test, which was almost universally FOBT (eg, “COL-plus”). All visits included in this analysis were PHEs of patients who were eligible or overdue for CRC screening. Visits by patients who had received a prior screening test in the recommended time frame were not eligible for this study, and visits in which COL was recommended as an appropriate follow-up to a positive FOBT were not included in this study.

CRC test use in the 12 months post PHE was collected from the EMR. Because of the small number of patients receiving barium enema or sigmoidoscopy (N = 5), we excluded those patients and created a variable measuring CRC screening receipt at 12 months with the following categorization: 1) COL only; 2) FOBT only; 3) COL and FOBT; 4) no screening.

The primary covariates used in this analysis were patient demographics that were assessed via patient self- report from the pre-visit survey. These characteristics included: age, race (white vs African American/other), gender, education (high school or less vs some college or more) and income (<$40,000 vs $40,000 to <$80,000 vs >$80,000 or more).

The analyses followed the research objectives and were conducted accordingly in 3 phases.

Attribute-Based CRC Screening Test Preferences

First, we generated a description of the sample according to the patients’ demographic factors. To assess the importance of each attribute, we calculated the proportion of patients who ranked each attribute 1st, 2nd, and 3rd. We then applied our algorithm to categorize baseline CRC test preferences into the 5 categories as described above. Next, we evaluated associations between baseline preferences and patient demographic factors using 

CRC screening rates in a large managed care organization were low. Among those screened, use was associated more with physicians' recommendations than with patient preferences.

July 2014

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