David H. Smith, RPh, PhD

 Examine association of comprehensiveness of colorectal cancer (CRC) screening discussion by primary care physicians (PCPs) with completion of CRC screening.

Observational study in Kaiser Permanente Northwest, a group-model health maintenance organization.

 A total of 883 participants overdue for CRC screening received an automated telephone call (ATC) between April and June 2009 encouraging CRC screening. Between January and March 2010, participants completed a survey on PCPs’ discussion of CRC screening and patient beliefs regarding screening. Primary outcome measure: receipt of CRC screening (assessed by electronic medical record [EMR], 9 months after ATC). Primary independent variable: comprehensiveness of CRC screening discussion by PCPs (7-item scale). Secondary independent variables: perceived benefits of screening (4-item scale assessing respondents’ agreement with benefits of timely screening) and primary care utilization (EMR; 9 months after ATC). The independent association of variables with CRC screening was assessed with logistic regression.

 Average scores for comprehensiveness of CRC discussion and perceived benefits were 0.4 (range 0-1) and 4.0 (range 1-5), respectively. A total of 28.2% (n = 249) completed screening, 84% of whom had survey assessments after their screening date. Of screeners, 95.2% completed the fecal immunochemical test. More comprehensive discussion of CRC screening was associated with increased screening (odds ratio [OR] = 1.51, 95% confidence interval [CI] = 1.03-2.21). Higher perceived benefits (OR = 1.46, 95% CI = 1.13-1.90) and 1 or more PCP visits (OR = 5.82, 95% CI = 3.87-8.74) were also associated with increased screening.

 More comprehensive discussion of CRC screening was independently associated with increased CRC screening. Primary care utilization was even more strongly associated with CRC screening, irrespective of discussion of CRC screening.

 More comprehensive discussion of CRC screening by PCPs was associated with higher screening rates among adults aged 51 to 80 years who were overdue for screening.

 Higher perceived benefits of CRC screening by patients was also associated with increased CRC screening.

 1 or more PCP visits was strongly associated with increased CRC screening, irrespective of discussion of CRC screening.

Colorectal cancer (CRC) is the third-most common cancer and the second-leading cause of cancer-related death in the United States.

 Recent estimates suggest that CRC will be newly diagnosed in about 112,340 people annually, resulting in over 52,180 deaths per year.

Total direct costs associated with treating CRC are $17 billion annually, with 9.8 million work days lost annually to hospitalization for colon cancer.

The early detection of high-risk lesions, or CRC itself, through appropriate screening is associated with decreased incidence of and mortality from CRC.

 The US Preventive Services Task Force recommends that men and women of average risk begin screening for CRC starting at age 50 years,

 and specialty care guidelines provide recommendations for higher-risk groups.

 Currently, recommended CRC screening modalities for average-risk patients include annual fecal occult blood testing (FOBT), colonoscopy every 10 years, or flexible sigmoidoscopy every 5 years, with or without interval FOBT.

 In addition, as a result of improved test performance and usability, in 2008 multiple professional societies endorsed the use of 4 types of fecal immunochemical tests (FITs) for CRC screening to replace FOBT.

Despite the proven benefits of CRC screening, screening rates remain suboptimal. Although more than half of adults 50 years and older in the United States have ever received a CRC screening test, less than half receive screening tests at recommended intervals.

and specific health-system characteristics are associated with receipt of CRC screening (having usual sources of care,

 and assistance with scheduling of colonoscopy),

 less is known about how providerpatient interactions and comprehensiveness of CRC discussion by primary care physicians (PCPs) influence CRC screening. Previous studies indicate that physician recommendation for screening

 may increase CRC screening. However, less is known about how the comprehensiveness of discussion by PCPs about testing options, screening intervals, and potential test complications may influence actual CRC screening. Study of the association of the comprehensiveness of CRC discussion by PCPs with receipt of CRC screening can inform the development of quality-improvement interventions.

The primary objective of this study was to determine the independent association of comprehensiveness of CRC screening discussion by PCPs, as reported by patients, with receipt of CRC screening.

The protocol for this study was approved by the institutional review board within the study health maintenance organization (HMO).

Study Site, Survey, and Electronic Data Sources

The study was conducted at Kaiser Permanente Northwest (KPNW), a not-for-profit HMO with about 485,000 members in southwest Washington and the Portland, Oregon, metro area. The members’ demographic characteristics (age, gender, race/ethnicity) are similar to those of the area population.

KPNW regional electronic databases provided data on patient membership, demographics, primary care assignment, clinical data (including weight and height, laboratory results, and other healthcare utilization, including CRC screening), and clinician data. These data capture over 95% of all medical care and pharmacy services members receive, and data are linked through each member’s health record number.

We included HMO members aged 50 to 80 years who were at average risk for developing CRC, were overdue for CRC screening, and received an automated telephone call as part of a CRC screening-outreach program between March and June 2009.

 The specifics of the automated call program, including the definition of “average risk,” have been described previously.

 Essentially, the average-risk population includes those who are overdue for CRC screening and do not meet any of the following

criteria: (1) active colon cancer/risk factors that would indicate need for non-routine screening, (2) ever diagnosed with adenomatous polyps or human immunodeficiency virus/acquired immunodeficiency syndrome, (3) referred for colonoscopy or sigmoidoscopy in previous 3 months, or (4) receipt of plavix or warfarin medications in previous 4 months that may increase risk of false-positive FIT. The brief automated telephone calls (ATCs) included information about the benefits of CRC screening, encouraged FOBT as a relatively simple and lowrisk method of screening, and allowed patients to request an FOBT by pushing a number on their telephone. The date of the telephone call was defined as the intervention index date.

Of the population receiving automated telephone calls, a random sample of 2000 adults were mailed a survey in January 2010. We chose to begin survey outreach efforts in January 2010 in order to have the most time for those efforts, while still ensuring that sufficient time elapsed (6-9 months) for patients who received ATCs between March and June 2009 to complete screening.

The process flow of the survey administration process is described in the 

 We sent initial surveys to study respondents on January 15, 2010. Next, we sent a reminder postcard to non-responders 2 weeks after the initial mailing. Finally, we mailed the survey a second time to non-responders about 3 weeks after the reminder postcard. Survey enrollment was completed by March 31, 2010. Of the whole population, 1816 (90.8%) were contacted. A total of 184 participants were not contacted due to incorrect addresses. Of those contacted, 883 completed the survey, for a response rate of 48.6% (883/1816). Compared with non-respondents, respondents were more likely to be white (respondents = 95.3% vs non-respondents = 87.5%, 

<.0001), female (respondents = 58.3% vs non-respondents = 48.4%, 

<.0001), and taking more medications (respondents [mean ± standard deviation (SD)] = [3.1 ± 3.8]). Responders and non-responders did not differ in age or length of health plan membership.

This survey was designed to better understand the barriers and facilitators that patients encountered in their efforts to complete CRC screening. We focused on 2 patient-reported domains: (1) patients’ perceptions of clinician-patient counseling, such as comprehensiveness of CRC screening discussion by the PCP, and (2) patient beliefs; namely, perceived benefits of CRC screening. Comprehensiveness of CRC discussion was the primary independent variable, while primary care utilization and perceived benefits of CRC screening were the secondary independent variables.

Comprehensiveness of CRC Screening Discussion, Based on Patient Self-Report

Comprehensiveness of CRC screening discussion.

 These items were based on previous work from Braddock and colleagues

 that assessed levels of informed decision making occurring during patient-provider communications regarding CRC screening. Informed decision making, a means for facilitating patient participation in decision making, includes (1) providing relevant information about the clinical situation, alternatives, and risks and benefits; (2) assessing the patient’s understanding; and (3) giving the patient a clear opportunity to voice a preference.

 Seven yes/no items measured the content of patients’ discussions with their PCPs. We asked patients whether their PCPs did each of the following at any time in the 2 years prior to survey: (1) explained benefits of CRC screening, (2) discussed how often each screening test should be done, (3) provided information on how to do various tests, (4) explained the accuracy of each screening test, (5) explained potential test complications from screening tests, (6) asked if the patient understood or had any questions about CRC screening, and (7) asked if the patient had the information he or she wanted about CRC screening. These 7 items had high internal consistency (Cronbach’s alpha = 0.92). To calculate an average score, each of the 7 items was summed together (0.0 = no, 1.0 = yes) and divided by 7. A score of 1 indicates that patients answered “yes” to all 7 questions, while a score of 0 indicates a “no” answer to all 7 questions. Only the responses of patients who answered all 7 questions were included in the variable describing this scale.

Perceived Benefits of CRC Screening, Based on Patient Self-Report

Four items measured perceived benefits of CRC screening, scored on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Similar to the scale for comprehensiveness of CRC discussion, the perceived-benefits scale also had good internal consistency (Cronbach’s alpha = 0.80). To calculate an average score, each of the 4 items was summed together (1 = strongly disagree, 5 = strongly agree) and divided by 4. A score of 5 indicates that the respondent answered “strongly agree” to all 4 questions, and a score of 1 indicates an answer of “strongly disagree” to all 4 questions. Only the responses of patients who answered all 4 questions were included in the variable describing this scale. These questions were adapted from Myers and colleagues28-30 and measured general agreement with the following statements: (1) CRC screening is necessary even if no family history exists, (2) getting screening for CRC can protect health, (3) when colon polyps are found and removed, CRC can be prevented, and (4) when CRC is found early, it can be cured. The rationale for selecting perceived benefits is that beliefs about colon cancer have been identified in the literature as an important characteristic associated with CRC screening.

All patients were due for screening at the time of the ATC; the primary outcome measure was whether the patient completed CRC screening in the 9 months following the ATC. CRC screening included any of the following: 1 sample FIT, receipt of colonoscopy, and receipt of flexible sigmoidoscopy. The distribution of first completed CRC screening test for this study population was FIT (95.2%), colonoscopy (2.8%), and sigmoidoscopy (2.0%).

We identified patient age in relation to the intervention index date, and we identified gender from member eligibility files. We also determined length of KPNW membership. Individual racial categories were obtained from electronic databases for 267 (30.4%) participants, and missing data were imputed using the census tract block corresponding to each subject’s mailing address. Patient race was categorized as white or non-white (African American, Pacific Islander, Asian, and Native American). In addition, as a measure intended to reflect disease burden,

we determined the mean number (± SD) of unique generic drugs dispensed within the 6 months prior to index date for each participant. We also determined whether survey respondents had any primary care visits (internal medicine, family practice; >1 visits vs none) in the 9 months after the intervention index date.

We used logistic regression to model CRC screening during the 9 months following index date (receipt of ATC). Final variables were entered simultaneously and included age (continuous, by 10 years), gender (female vs male), number of medications (continuous), comprehensiveness of CRC discussion scale (continuous), perceived benefits scale (continuous), and having any primary care visit during the follow-up period. With the exception of age and gender, all variables with significant bivariate associations with CRC screening completion were included in the final multivariable model. Although not significant in bivariate analysis, age and gender were retained in final logistic models to adjust for demographics, given that age and gender have been associated with CRC screening in previous research. We lacked complete data on some of the central variables (ie, PCP discussion scale, perceived benefits scale), and we imputed missing data by multiple imputation (MI). Multiple imputation is considered a scientifically rigorous approach to handle missing data.

The MI and MIANALYZE procedures in SAS used 5 replications. All analyses were conducted using SAS version 9.2 (SAS Institute, Cary, North Carolina).

 presents the population characteristics of patients who completed the patient survey. The mean age was 59 years, nearly 60% were female, 95% were white, and the average number of years with KPNW was 14. The average number of medications taken at baseline was 3.1. The average score on the comprehensiveness of CRC discussion scale was less than 0.5 (mean = 0.4, SD = 0.4, possible range 0 to 1), indicating that patients, on average, answered “yes” to fewer than 4 of the 7 screening discussion questions. In addition, the average score on the perceived benefits scale was high (mean = 4.0, SD = 0.7; possible range 1 to 5), suggesting that most believed in the benefits of CRC screening. Most (60%) reported 1 or more primary care visits in the 9 months after the intervention index date.

presents unadjusted and adjusted logistic regression results. Patients’ perceptions of the comprehensiveness of CRC screening discussions and perceived benefits had independent significant (

 <.05) associations with completion of CRC screening, after simultaneous adjustment. A higher score on the comprehensiveness of CRC screening discussion scale was associated with greater odds of completing CRC screening in both unadjusted (OR, 1.84; 95% CI, 1.20-2.81, 

 = .005) and adjusted (OR, 1.51; 95% CI, 1.03-2.21, 

 = .035) analyses. A higher score on the perceived benefits scale was associated with greater odds of completing CRC screening in both unadjusted (OR, 1.70; 95% CI, 1.32-2.18, 

<.0001) and adjusted (OR, 1.46; 95% CI, 1.13-1.90,

Of patient-demographic and clinical-characteristic covariates, 1 or more primary care visits in the follow-up period (9 months after ATC) was associated with greater odds of completing CRC screening in both unadjusted (OR, 6.16; 95% CI, 4.16-9.14, 

 <.0001) and adjusted (OR, 5.82; 95% CI, 3.87- 8.74, 

 <.0001) analyses. Having more medications (OR, 1.05; 95% CI, 1.01-1.09, 

= .012) was associated with greater odds of completing CRC screening in the unadjusted analysis; however, this finding did not remain significant in multivariable analysis.

Articles

More comprehensive discussion of colorectal screening by primary care physicians was associated with higher screening rates among adults aged 51 to 80 years who were overdue for screening.

More Comprehensive Discussion of CRC Screening Associated With Higher Screening

To estimate the cost-effectiveness of an automated telephone intervention for colorectal cancer screening from a managed care perspective, using data from a pragmatic randomized controlled trial.

Intervention patients received calls for fecal occult blood testing (FOBT) screening. We searched patients’ electronic medical records for any screening (defined as FOBT, flexible sigmoidoscopy, double-contrast barium enema, or colonoscopy) during follow-up. Intervention costs included project implementation and management, telephone calls, patient identification, and tracking. Screening costs included FOBT (kits, mailing, and processing) and any completed screening tests during follow-up. We estimated the incremental cost-effectiveness ratio (ICER) of the cost per additional screen.

 At 6 months, average costs for intervention and control patients were $37 (25% screened) and $34 (19% screened), respectively. The ICER at 6 months was $42 per additional screen, less than half what other studies have reported. Cost-effectiveness probability was 0.49, 0.84, and 0.99 for willingness-to-pay thresholds of $40, $100, and $200, respectively. Similar results were seen at 9 months. A greater increase in FOBT testing was seen for patients aged 

70 years (45/100 intervention, 33/100 control) compared with younger patients (25/100 intervention, 21/100 control). The intervention was dominant for patients aged 

70 years and was $73 per additional screen for younger patients. It increased screening rates by about 6% and costs by $3 per patient.

At willingness to pay of $100 or more per additional screening test, an automated telephone reminder intervention can be an optimal use of resources.

Colorectal cancer screening has been shown to be a cost-effective intervention, but uncertainty remains over the most efficient methods for increasing screening rates.

Automated phone calling increased screening rates by about 6% and costs by $3 per patient.

The incremental cost-effectiveness ratio per additional screen of $40 is less than half what other studies have reported.

At a willingness to pay of $100 or higher per additional screening test, our study suggests an automated telephone reminder intervention can be an optimal use of resources.

Colorectal cancer (CRC) is a common and often lethal disease with a lifetime risk of about 1 in 20; it causes more than 51,000 deaths annually in the United States. When diagnosed at late stages the 5-year survival rate is less than 12%, but patients with localized tumors have a 5-year survival rate greater than 90%.

 Early detection is clearly a key to improving survival, and screening has been shown to be cost-effective (eg, at a commonly used threshold of $50,000 per year of life gained), with some analyses even suggesting that screening is cost-saving.

 While CRC screening rates have increased in recent years, they are still below 60% for people aged 

Because the favorable cost-effectiveness of CRC screening is now established, attention has shifted to optimizing methods for increasing adherence with CRC screening. The cost-effectiveness of several intervention types has been reported, including mailing stool cards with and without targeted calls, where the cost per additional patient screened has ranged from about $100 to more than $6000 depending on the intensity of the intervention

 where the cost per additional patient screened has ranged from about $100 to $1000.

Automated telephone calls have been used to remind and inform patients about a wide range of preventive and therapeutic healthcare interventions.

 A recently reported pragmatic trial by our group showed that automated telephone reminders significantly improved CRC screening rates.

 Using data from that randomized control trial, this report focuses on the cost-effectiveness of telephone reminders compared with usual care per additional patient screened.

We used findings from a practical randomized control trial that enrolled patients due for CRC screening (as defined by the National Council for Quality Assurance) to estimate the cost-effectiveness of an automated telephone intervention for CRC screening relative to usual care. The cost analysis focused on replication costs

 from the perspective of a health maintenance organization (HMO) and included any method of screening rather than just fecal occult blood testing (FOBT). Our follow-up was less than 1 year, so costs and effects were not discounted; costs were inflated to reflect 2008 dollars.

A complete description of the trial has been reported elsewhere.

 The trial was conducted at Kaiser Permanente Northwest, a not-for-profit, group-model HMO, and was approved by its institutional review board. 

 details how patients were selected in the randomized control trial; 6000 patients (out of 32,320) were randomly allocated to automated telephone contact or usual care. Consistent with US Preventive Services Task Force CRC screening guidelines in place at the time of trial initiation,

we selected average-risk patients aged 51 to 80 years who were eligible for screening (eg, had no evidence of relevant screening in the HMO’s records and did not have risk factors indicating need for disease monitoring).

Intervention group patients were exposed to calls prompting FOBT screening (call type 1) and FOBT return reminder calls (call type 2); while these calls were not personalized, they were directed toward specific individuals. 

details the timing of these calls. Call type 1 was brief (1 minute), provided information about the benefits of CRC screening, and encouraged FOBT as a relatively simple and low-risk method of cancer screening. Recipients who answered could immediately request a mailed FOBT kit by touch-tone. A detailed message that included a telephone number to call for FOBT kit requests was left via voice mail for unanswered calls. Patients who did not get CRC screening received up to 2 “general” automated reminder calls, 6 weeks apart. One additional reminder call (call type 2) was made to patients who had requested an FOBT kit but did not return it within 5 weeks from the date of request; those patients were given the opportunity to request an additional FOBT kit. All FOBT kits used the guaiac method (Hemoccult II; Beckman Coulter, Inc, Brea, California), which requires samples from 3 consecutive stools, and were delivered via standard mail.

Patients randomized to usual care did not receive the telephone contact intervention. Their CRC screening, if any, occurred via normal care processes. At the time of the study, clinicians had access to national guidelines, Intranetbased screening guidelines, and a “panel support tool” (an electronic prompt system embedded in the electronic medical record that informed clinicians which patients were due for preventive services, including CRC screening). For usual care patients, FOBT kits were obtained after a clinician order by picking the kit up either from the laboratory at the clinic where they received their care or from the HMO’s influenza immunization service, located in primary care clinics.

We used the HMO’s electronic data systems to identify patient outcomes. We specified 2 outcomes for the analysis: the cost per any completed screen and the cost per positive FOBT screen. Even though the intervention promoted FOBT, we defined a completed screen as a record of any US Preventive Services Task Force—recognized screening test (FOBT, flexible sigmoidoscopy, double-contrast barium enema [DCBE], or colonoscopy) during follow-up. For both outcomes, a patient was counted as screened only once, even if he or she had multiple tests; but as detailed below, costs for all screening tests during follow-up were included. We used the term “nonfecal tests” to collectively refer to those more invasive testing methods including flexible sigmoidoscopy, DCBE, and colonoscopy. We examined the effect of the intervention on both FOBT and nonfecal tests separately.

We estimated staff costs associated with the intervention from clinical trial records and study staff time estimates. These costs included project management time to oversee the entire process and computer programming efforts for patient identification, electronic file preparation for the automated telephone call vendor, and patient tracking (eg, for repeat calls to nonresponders). To improve generalizability to other systems, salary costs were taken from the US Bureau of Labor and Statistics.

 To fully allocate the costs we added a fringe benefit rate of 30% and overhead rate of 20%. The cost of the calls and their content was based on vendor charges. The cost of developing and maintaining the automated telephone system was embedded in the vendor charge to the HMO. FOBT kit mailing costs were estimated based on bulk mailing rates.

For patients in both groups, the costs associated with the FOBT testing kit, processing, and reporting was estimated from the HMO’s laboratory charges to nonmembers. Clinician time and cost associated with FOBT laboratory result review and follow-up with patients was estimated from a previous economic evaluation in laboratory medicine

 and from interviews with those doing the work. Costs for other types of testing, including DCBE, flexible sigmoidoscopy, and endoscopy, came from the HMO’s accounting database. While a patient was counted as screened only 1 time, all CRC testing-related costs during the follow-up were enumerated and assigned to that patient. We did this because the intervention may have had knock-on effects for other types of CRC testing, including both diagnostic (eg, following a positive FOBT) and screening (eg, encouraging a patient to request colonoscopy screening), and we wanted to more fully account for the economic consequences of the intervention. For example, if a patient was screened with FOBT at month 1 and then subsequently had a colonoscopy during month 8, the cost of both tests was counted in the 9-month analysis. We made no attempt to discern the clinical rationale for a given screening test (eg, a screening vs a diagnostic colonoscopy); rather, we conservatively included all tests for both outcomes and costs. Usual care patients may have gotten their screening advice at an office visit, but we assumed CRC screening advice was unlikely to be the chief reason for the visit, so we did not attempt to include those costs. Any resulting bias from that decision would make usual care appear less costly.

In the clinical trial, the mean age in both groups was about 60 years, with an even female/male split. There were 2943 patients in the intervention group, and 2962 patients in the usual care group. In the intervention group, 22.5% completed FOBT within 6 months, compared with 16.0% in the usual care group. Screening by any method was completed for 23.9% in the intervention versus 17.6% in the usual care group. In the intervention group, 2943 (100%) received the initial call type 1 (general reminder), 2406 (81.8%) received a second type 1 call, and 1632 (55.5%) received a total of 3 calls of type 1. Call type 2 (FOBT return reminder) was made to 18.3% of the intervention group.

We calculated the incremental cost-effectiveness ratio (ICER) as (cost

 = control, and effect = number of patients with a screening test. We used net benefit regression methods

 to estimate the intervention’s probability of being cost-effective. We report the probability of the intervention being cost-effective at several relevant thresholds of willingness to pay for an additional outcome (eg, additional screen) to aid decision makers’ translation of the results to their own setting and specific considerations. We also computed bootstrapped 95% confidence intervals on total costs using the percentile method.

The parent clinical trial revealed a significant intervention effect that depended on patient age, so we undertook a similar subgroup analysis. As was done in the clinical trial, we followed patients for 6 months in our primary analysis. Since fecal screening is recommended annually, the health plan encourages screening on that basis; therefore, we also wanted to examine a time period more compatible with that interval. To accomplish this goal and to avoid the potential overlapping effects from the next annual screening efforts, we undertook a secondary analysis and followed patients for completed tests up to 9 months after their index date. SAS version 9.1 (SAS Institute, Cary, North Carolina) and STATA versions 10 and 11 (STATA Corporation, College Station, Texas) were used for all analyses.

enumerates the resources necessary to implement and sustain the automated calling program in the intervention arm and to carry out all types of CRC screening tests in both arms. 

 shows the resource use and average cost per person in both groups at 6 and 9 months and by age. Accounting for the staff time associated with project management, computer programming, patient tracking and reporting, and the calls themselves, the intervention cost was about $4.20 per person.

For patients of all ages, the average cost per patient for FOBT testing (kits and their processing) was slightly higher in the intervention group, while the per patient cost associated with nonfecal tests (endoscopy, DCBE, and flexible sigmoidoscopy) was slightly higher in the usual care group. We found that the number of FOBT tests completed at 9 months was markedly higher in the intervention group (ie, 812 vs 669). On the other hand, we observed similar overall proportions of patients with nonfecal tests (3.2% intervention and 3.0% usual care, data not shown). But there were differences by age; intervention patients aged 70 years or older had 5.6 nonfecal tests per 100 persons compared with 7.8 per 100 persons in usual care. However, for patients younger than 70 years, nonfecal test use was similar in both groups (7.9 and 8.0 nonfecal tests per 100 persons in the intervention and usual care groups, respectively). FOBT testing increased in both age groups, but the FOBT increase was greater in the older age group (45/100 intervention patients and 33/100 control patients) than in the younger age group (25/100 intervention patients and 21/100 control patients). As shown in Table 2, these observed differences by age in the use of FOBT and nonfecal tests translated into notable differences in costs at both 6 and 9 months. For example, at 9 months the average costs of FOBT and nonfecal tests were similar for patients younger than 70 years in the intervention and usual care groups. However, at 9 months, for patients 70 years or older, the costs of FOBT were 25% higher in the intervention group ($1.50 vs $1.20), while the costs for nonfecal tests were more than 40%

 in the intervention group ($34.60 vs $48.70). While these component costs showed some variation, total costs were similar in both groups, as evidenced by the overlapping confidence limits.

 shows the cost-effectiveness at 6 and 9 months by age using each of the 2 measures of effectiveness: any completed screen and the positive FOBT screen. Overall, we found that the cost for an additional screened patient was $42 at 6 months and $40 at 9 months. At 6 months, the chance of the intervention being cost-effective was about 50% at a willingness to pay of $40 for an additional patient screened. It increased to 84% for a willingness to pay of $100 and 99% for a willingness to pay of $200.

The intervention was dominant (ie, lower costs with a greater proportion of patients screened) at 6 months for patients 70 years or older, but it had an ICER of $73 for younger patients. In both age groups the intervention had a high probability (

95%) of being cost-effective at a willingness to pay of $200 for an additional screened patient at 6 months, but cost-effectiveness was markedly different at willingness-to-pay thresholds of $100 (0.97 and 0.64 for older and younger patients, respectively) and $40 (0.75 and 0.35 for older and younger patients, respectively). We found a similar pattern of results at 9 months.

The cost per additional positive FOBT screen was $1156 at 9 months. The probability of cost-effectiveness was 0.47 at a willingness to pay of $1000 and increased to 0.70 at a willingness to pay of $7000.

Depending on one’s willingness to pay for an additional screen, we found that automated telephone calls can be costeffective. For example, at a willingness to pay of $100 per additional screen, the probability of cost-effectiveness is 0.84 and increases to near-certainty (0.99) at a willingness to pay of $200 per additional screen. But at lower willingness-to-pay levels (ie, $40 per additional screen), there is considerable uncertainty about the cost-effectiveness (probability of being cost-effective was 0.49).

A recent systematic review noted that, compared with no screening, all types of CRC screening are cost-effective at common thresholds (eg, $50,000 per year of life gained) and some are even estimated to be cost-saving.

 Our study focused on the cost and cost-effectiveness of using automated telephone calling to promote FOBT screening relative to usual care. We found that automated calls were low cost and compared favorably with other reported CRC screening programs at about $40 per additional patient screened with any test. For example, a recent report reviewed several studies that analyzed the cost-effectiveness of comparable interventions to increase CRC screening.

 Those authors noted that the cost per additional patient screened ranged from more than $6000 to $94 for a range of interventions including physician communication training and patient education, physician feedback, and patient mailings.

Our intervention differed from previous interventions in several important ways that may have had a direct effect on the differential program costs. First, we did not send stool cards unless the patient requested the kit. By requiring a patient request before mailing a kit (and thus before incurring associated costs), a relatively costly component of the intervention was avoided by presumably targeting those patients most likely to complete testing. Additionally, by requiring a patient commitment (ie, a positive response to the call), the intervention potentially further enhanced patient follow-through. Our results suggest that future reviews on this topic might usefully compare the costs and cost-effectiveness of patient-directed versus provider-directed interventions and consider whether automated systems are more cost-effective, perhaps in a stepped approach.

Automated telephone calls can increase colorectal cancer screening rates at a cost of about $40 per additional screen.

Automated Telephone Calls to Enhance Colorectal Cancer Screening: Economic Analysis

 To test the efficiency and cost-effectiveness of interventions aimed at enhancing laboratory monitoring of medication.

 Patients of a not-for-profit, group-model HMO were randomized to 1 of 4 interventions: an electronic medical record reminder to the clinician, an automated voice message to patients, pharmacy-led outreach, or usual care. Patients were followed for 25 days to determine completion of all recommended baseline laboratory monitoring tests. We measured the rate of laboratory test completion and the cost-effectiveness of each intervention. Direct medical care costs to the HMO (repeated testing, extra visits, and intervention costs) were determined using trial data and a mix of other data sources.

The average cost of patient contact was $5.45 in the pharmacy-led intervention, $7.00 in the electronic reminder intervention, and $4.64 in the automated voice message reminder intervention. The electronic medical record intervention was more costly and less effective than other methods. The automated voice message intervention had an incremental cost-effectiveness ratio (ICER) of $47 per additional completed case, and the pharmacy intervention had an ICER of $64 per additional completed case.

 Using the data available to compare strategies to enhance baseline monitoring, direct clinician messaging was not an efficient use of resources. Depending on a decision maker’s willingness to pay, automated voice messaging and pharmacy-led efforts can be efficient choices to prompt therapeutic baseline monitoring, but direct clinician messaging is probably a less efficient use of resources.

Patients of a not-for-profit, group-model HMO were randomized to 1 of 4 interventions: an electronic medical record reminder to the clinician, an automated voice message to patients, pharmacy-led outreach, or usual care.

The electronic medical record intervention was more costly and less effective than other

Depending on a decision maker’s willingness to pay, pharmacy-led outreach or automated

	voice messages to patients can be cost-effective methods to enhance laboratory monitoring of

	medications at initiation of therapy compared with usual care.

Recent studies indicate that laboratory monitoring of medications at initiation of therapy is below the level recommended by guidelines, with as many as 39% of patients not receiving recommended testing.

 Lack of monitoring is a concern because of potential adverse events (eg, hyperkalemia associated with inhibitors of angiotensin) and because of failure to achieve therapeutic benefit due to inadequate blood levels of medication. Additionally, failure to establish baseline levels makes it difficult to determine a patient’s trends in laboratory values.

These concerns regarding patient safety and clinical effectiveness have led researchers to test methods to enhance laboratory-based medication monitoring. Several types of interventions (including pharmacyled efforts, electronic reminders to clinicians, and automated telephone call reminders to patients) to improve laboratory monitoring of medications at therapy initiation have been effective in randomized trials.

 Although all these interventions improve monitoring, the most efficient intervention methods are not clear, and no economic analyses have been done to inform policy makers in this area. Some efforts may be particularly resource intensive, but could be worth the added expenditure when the potential adverse outcome is severe. Without careful analysis of the balance between costs and benefits, one cannot determine which (if any) interventions ought to be funded by healthcare payers. The efficiency of alternative approaches to therapeutic monitoring is of growing importance to healthcare providers as this monitoring is now a focus of quality measurement.

 To help with decision making regarding laboratory-monitoring interventions, we undertook a preplanned costeffectiveness analysis of a randomized trial that tested several interventions aimed at enhancing laboratory monitoring of medication.

Complete details of the trial design are available elsewhere.

 The study was conducted at a not-for-profit, group-model HMO and was approved by its institutional review board. All HMO patients who had not received baseline laboratory tests (defined as within 6 months before or 5 days after a newly dispensed study medication) were randomized to 1 of 4 conditions: an electronic medical record reminder to the patient’s primary care provider (EMR arm), an automated voice message to patients (AVM), pharmacy team outreach (Pharmacy), or usual care (UC) (

). In the EMR intervention, a patient-specific electronic message was sent to the primary care clinician from the chair of the HMO’s patient safety committee stating that computer records indicated the patient had received a new medication, that laboratory monitoring was recommended, and the patient had not received the test(s) between 6 months before and 5 days after the dispensing. The message referenced internal and external guideline resources, recommended specific tests, and provided a sample letter the clinician could send to the patient. The AVM intervention included telephone messages advising the patient that laboratory tests were required for a medication the patient had received; the patient was advised that the testing had been ordered and could be completed at any HMO laboratory. The Pharmacy intervention began with a telephone call from a nurse in the pharmacy department to the patient to encourage laboratory testing. If the nurse successfully contacted the patient, a follow-up letter reminded the patient to obtain the laboratory test(s). If telephone contact was not successful, the nurse sent a letter suggesting that the patient go in for testing. If patients had questions or concerns about their medication during the contacts, a pharmacist was available for consultation.

Study medications (and lab tests required) were angiotensinconverting enzyme inhibitors or angiotensin receptor blockers (serum creatinine, serum potassium), allopurinol (serum creatinine), carbamazepine (aspartate aminotransferase [AST] or alanine aminotransferase [ALT], complete blood count, serum sodium), diuretics (serum creatinine, serum potassium), metformin (serum creatinine), phenytoin (AST/ALT, complete blood count), pioglitazone (AST/ALT), potassium supplements (serum potassium, serum creatinine), statins (AST/ALT), and terbinafine (AST/ALT, serum creatinine). The primary outcome was laboratory test completion, defined as the proportion of patients with all recommended baseline laboratory monitoring tests completed at 25 days after the intervention date. In the year before randomization, the laboratory-monitoring rates at the initiation of therapy (those who had initiated a study medication and had completed all recommended baseline laboratory testing) were similar in the study groups (about 60%). Other characteristics of the study groups also were similar, but the AVM group had a smaller proportion of female primary care physicians (24% vs ~40%). A total of 961 patients were included in the clinical trial. By day 25 after the intervention, 22.4% (53 of 237 patients) in the UC arm, 48.5% (95 of 196 patients) in the EMR arm, 66.3% (177 of 267 patients) in the AVM arm, and 82.0% (214 of 261 patients) in the Pharmacy arm all had completed recommended monitoring (P <.001). A total of 72 abnormal test results were found among the 961 patients (7.5%).

We followed best practice in economic evaluation as outlined by the US Public Health Service.

 Our economic analysis examined the incremental cost per additional case completed (defined as enrollees that had all guideline-specified laboratory tests completed) and the incremental cost per abnormal case detected. We calculated the incremental cost-effectiveness ratio (ICER) by dividing the difference in cost by the difference in cases completed (or abnormal cases detected); interventions with lower ICERs are a better value for the money. Interventions with a higher ICER also may be cost-effective, depending on a decision maker’s willingness to pay for each additional unit of effect. Interventions were ranked on cost, and dominated options (ie, more costly but less effective) were identified. To account for the uncertainty due to sampling variation in cost-effectiveness analysis, we plotted cost-effectiveness acceptability curves5; these curves show the probability of each intervention being cost-effective at a given willingness to pay for an additional completed case (or abnormal case detected). All analyses were conducted using STATA release 9.0 (StataCorp, College Station, TX) and Microsoft Excel 2003 (Microsoft, Redmond, WA).

Most of the cost data were collected directly from the trial, with some expert opinion based on formal data-gathering techniques as described below. The perspective of the analysis was the HMO. Thus, we included only direct medical care costs incurred by the HMO. The scope of the analysis included the costs within the 25 days after the intervention of (1) all recommended laboratory tests (including repeated testing), (2) extra visits associated with abnormal tests (validated from pharmacist chart review), and (3) performing the intervention. The analysis does not include potential offsets of poor outcomes averted (eg, lactic acidosis, liver toxicity) because those data were too sparse to answer those questions effectively. Also excluded from the analysis were development costs and patient costs, like travel time and copayments. To maintain consistency with the efficacy analysis, the primary outcome was the cost per completed case within 25 days of dispensing, with a secondary analysis of the cost per addition al enrollee with 1 or more abnormal laboratory tests within 25 days of dispensing.

 details the unit costs (ie, prices), activities, data sources, and resource assumptions used in the analysis. To improve generalizability to other systems, salary costs were taken from sources reflecting the prevailing wage rate in Portland, Oregon, with a fringe benefit rate of 30% and overhead rate of 20% added to fully allocate the costs. Laboratory testing costs come from the HMO’s laboratory accounting system and include patient intake, phlebotomy, testing, and reporting. Mailing costs were applied based on estimates for bulk mailing, and costs for clinic visits came from the HMO’s cost structure.

Resources used in the performance of all the interventions included chart review to ensure patient eligibility, tracking systems for patient follow-up, and noting the intervention delivery in the patient’s medical record. Additional tasks were study arm specific. In the Pharmacy arm they included time for mailings and outreach phone calls and their documentation. The AVM intervention required time to upload files. The cost of maintaining the automated telephone system was embedded in the vendor charge to the HMO. The EMR intervention costs included nurse time to send the message and clinician followup activities. Because existing EMR functionality was used to provide the EMR messages, no incremental programming resources were necessary to provide the intervention. The time (in minutes) taken to complete these tasks was recorded for a sample of patients in the Pharmacy, AVM, and EMR arms. To establish patient-level resource use, each patient in the appropriate arm was assigned an imputed value randomly from the sample, preserving the sample’s underlying (observed) distribution. Analyst time to create and maintain the patient lists from automated data were taken from the trial.

In the base-case analysis, formal interviews were undertaken with 13 HMO clinicians to estimate time (in minutes) for all aspects of responding to the EMR message: reading the message, ordering tests, and review and follow-up (for normal and abnormal results separately) on laboratory results. We then imputed patient-level resource use for these items by randomly selecting from a triangular distribution, using the expert’s estimates of the mode and range. Because they came from expert opinion, we undertook sensitivity analyses by assigning the low estimates and, separately, the high estimates of time required for ordering, review, and follow-up on normal and abnormal test results. Additionally, because of uncertainty regarding the frequency and type of contact for patients in the EMR arm (eg, telephone, letter, during visits), we undertook a sensitivity analysis that reduced the cost of contact in that arm.

presents the resources used per patient by arm (ie, unit costs from Table 1 multiplied by actual units of use). The increase in laboratory testing associated with each of the interventions is evident from changes in the average cost of testing ($18.65 for UC, vs $26.69, $32.44, and $40.93 for EMR, AVM, and Pharmacy, respectively); the cost for review and follow-up of both normal and abnormal test results followed a similar pattern. The average cost of patient contact (including chart review, notification tracking, and charting) was $5.45 in the Pharmacy arm. The EMR arm used about $7 ($3.18 + $3.77) for patient contact, while the AVM arm cost for patient contact came to $4.64 ($3.55 + $1.09).

 shows that enhanced effectiveness comes with increases in total cost. Pharmacy, the intervention with the greatest proportion of completed cases at 82 per 100 patients, is also the most expensive at $5160 per 100 patients, whereas the AVM arm yielded 66 completed cases per 100 patients at a cost of $4159. The EMR arm was “dominated,” because using a mix of UC (eg, for 40% of patients) and AVM (eg, for 60% of patients) would be both less expensive and more effective; the EMR strategy is therefore a suboptimal choice. The AVM intervention had an ICER of $47 per additional completed case; the Pharmacy arm had an ICER of $64 per additional completed case. At a lower level of willingness to pay for an additional completed case (eg, $40), UC was the intervention with the highest probability of being cost-effective; as willingness to pay increased, however, other interventions became more likely to be cost-effective.

 shows an examination of the cost-effectiveness of finding abnormal cases. There was a similar pattern of increasing effectiveness at higher cost, and the EMR intervention method was again dominated. As with completed cases, at a lower level of willingness to pay for finding an abnormal case (eg, $400), UC had the highest probability of being cost-effective, but above $600 AVM and Pharmacy became more likely to be cost-effective. The sensitivity analysis based on low and high estimates of time used in ordering, reviewing, and follow-up of normal and abnormal tests  showed no difference in ICER ranking and nearly identical results overall (eg, ICERs of $44 and $50 for the AVM arm vs $47 for the base-case analysis). The sensitivity analysis on the cost of contact for patients in the EMR arm revealed that, even if the cost of patient contact was reduced to zero (eg, the marginal cost might be close to zero if all contact took place during visits), the EMR arm would never be the optimal strategy.

 shows the cost-effectiveness acceptability curves. For example, at a willingness-to-pay level for a completed case between $47 and $64, AVM had the highest probability of being cost-effective, but the Pharmacy intervention had the highest probability of being cost-effective at greater levels. There is considerable uncertainty in the estimates of cost-effectiveness, particularly for abnormal case detection. For example, at levels of $800 and higher AVM had a probability of being cost-effective of about 0.32, while Pharmacy was cost-effective with a probability of about 0.50.

We found that direct-to-clinician messaging (EMR) to prompt therapeutic baseline monitoring is probably not an efficient use of resources. But our analysis indicates that depending on a decision maker’s willingness to pay for a completed case, both AVM and Pharmacy can be efficient choices. Deciding which choice is optimal requires additional information about the harms prevented from completing baseline monitoring; our analysis did not include these costs because of limitations in study budget and design. However, it is worth noting that compared with the ICER for the AVM arm, the ICER for the Pharmacy arm was almost 40% greater for the “cases completed” analysis, but was less than 10% greater for the “abnormal cases detected” analysis, suggesting that the relative efficiency of the Pharmacy intervention may increase when considering harms.

As noted, our findings are most useful for an efficiency comparison between the alternative methods in the analysis. The findings give less guidance to help with the decision about what (if any) program should be adopted; a cost-benefit or cost-utility analysis would be needed to answer that question. But an informal analysis can be made using the data in Table 3 and making assumptions about the cost and probability of events that might be avoided by a monitoring program. For example, if one assumes that the cost of an event avoided would be $10,000 (ie, short inpatient stay) and the probability of the event for those with an abnormal test is 5%, the willingness-to-pay amount to avoid the event would be at least $500 (ie, $10,000 × 5%); this example does not incorporate potential losses in health-related quality of life. Empirical work on the probability of abnormal tests leading to adverse events, the cost of those events, and the health benefits gained is necessary to further inform decision makers’ choices in this area. Reports of simulation modeling on the economics of preventing medication errors using information technology and pharmacy personnel indicate that health-related quality-of-life benefits (from avoiding medican tion errors) are a critical parameter; their inclusion reversed the estimates from negative to positive net benefits.6

Our intervention did not differentiate patients with regard to potential for poor outcomes; rather it was applied to all patients with a missing laboratory test at baseline. Another approach would be to administer the intervention to patients who are predicted to be at particularly high risk of poor outcomes. By targeting high-risk patients, the proportion of patients with a greater likelihood of adverse events (given an abnormal test) would increase. Such an approach might increase the efficiency of the strategies but would require identification of those patients, perhaps by using a risk score approach.

The clinical trial on which we based our economic analysis showed that each of the 3 interventions was superior to UC in improving monitoring at 25 days; patients in the EMR arm were 2.5 times more likely to have recommended monitoring.

 But when we included costs (ie, increase in clinician time associated with contacting the patient and follow-up), the EMR arm was shown to be relatively inefficient. Off-loading these tasks to others enhances the overall effects and lowers costs. We included the effort required by clinicians to review laboratory test results in our cost estimates. Another approach would have been to assume that clinicians can absorb the cost of reviewing the extra tests into their slack time. But as suggested by best practice guidelines in economic evaluation,

 we estimated the “opportunity cost” of providing the interventions. The concept of opportunity cost requires measurement of all resources necessary to produce an intervention, even if those resources may be viewed as free (eg, the effort required to review laboratory tests); this concept recognizes that doing one thing means that another thing cannot be done (because that opportunity is lost).

We found relatively high levels of certainty associated with willingness to pay for an additional completed case. At a willingness-to-pay level of less than $40 the probability of UC being the most cost-effective was about 0.9, and at a willingness-to-pay level greater than about $85, the Pharmacy intervention was the most cost-effective with a probability of 0.9. With intermediate levels of willingness to pay for a completed case (ie, $50-$65) AVM was the most cost-effective, but with less certainty (probability about 0.6). On the other hand, we found considerable uncertainty with the abnormal case detection analysis. No single intervention showed costeffectiveness with probability greater than 0.5 at a willingness to pay between $450 and $850. The uncertainty estimates are loosely analogous to a wide confidence interval (see van Hout et al

 for further details) and point out the lack of precision in our estimates. More precise estimates require further data collection.

Our results were not sensitive to the estimates of time required to order, review, and follow-up on tests, suggesting that additional data collection should focus on other areas like estimating the potential cost-offsets from avoiding poor outcomes.

Some items we included in the performance of these interventions, for example, the individual chart review of each patient prior to intervention to ensure eligibility (ie, we did not rely entirely on electronic data), may not have been strictly necessary. Excluding these potentially protocol-driven costs may change the relative cost-effectiveness of the interventions. We felt that this was an important feature to include, however, because the effectiveness of the intervention was based on that level of rigor, and because the HMO would likely not be willing to implement any of the programs without that double-check. Additionally, we included patient contact costs in the EMR arm for all patients, but this may have overcounted costs in that arm to the extent that the rate of contact was lower. But our sensitivity analysis on that variable indicated that even if the contact costs were reduced to zero the EMR arm would never have been the optimal strategy.

The clinical trial allowed us to evaluate strategies that would off-load clinicians’ work (ie, AVM and Pharmacy arms) as compared with more intrusive strategies (the EMR arm). Because we did not set out to balance resources available in the intervention arms, we cannot determine whether the effect and cost difference we observed was solely based on the intervention delivery mechanism, the intensity of the intervention, or other factors. Also, although a laboratorymonitoring protocol was available and communicated to all clinical staff, clinicians in the EMR arm may have been more likely to exercise clinical judgment on a case-by-case basis and thus not intervene for every patient. Our analysis assumes that all patients who received interventions were at the same risk of poor outcomes. Because we did not measure outcomes in our analysis, to the extent that the clinical judgment exercised by EMR clinicians improved the likelihood of interventions for high-risk patients (thus improving their group’s potential to benefit), our analysis underestimated the true effect in the EMR arm.

Our study has limitations. The clinical trial on which it was based was undertaken in a single HMO, so the effect of the interventions (especially EMR) may not be completely generalizable to other practice settings. Additionally, the parent study was not designed to determine whether the interventions led to improved detection of abnormal baseline laboratory test results or significant changes in patient care and outcomes. Our findings in this cost-effectiveness analysis help to put the clinical trial results in a decision-making context. We think a hybrid approach to the intervention may be a particularly useful approach. For example, one that includes AVM followed by Pharmacy outreach for nonresponders may prove to be efficient.

The efficient use of health information technology is of great interest to clinicians and healthcare payers, and our study is one of the first to estimate the relative cost-effectiveness of these technologies. Using automated telephone technology has been shown to have promise in many clinical areas, for example post-inpatient discharge,

 enhancing immunization rates in children,

 but the cost-effectiveness of these systems is less clear. The use of automated medical records has been the subject of several systematic reviews.

 Like automated telephone technology, the EMR systems have promise, but the economic benefits are less certain. Our study shows that providing clinical decision support directly to clinicians using EMR technology is more costly and less effective than automated telephone calls for increasing laboratory monitoring. This finding is particularly of interest given (1) that we did not include costs for programming of EMR messages because existing EMR functionality was used and (2) the “sunk cost” of the EMR was not included in our analysis because from the HMO’s perspective the EMR technology was already owned. But it should be noted that the costs of chart review and tracking activities would likely have been substantially higher in a health system that lacked an EMR. Our study may serve as a cautionary example of when EMR-based intervention methods are not the most efficient choice.

Depending on one’s willingness to pay, both AVM and a pharmacy-led intervention may be efficient methods to enhance the level of laboratory monitoring of medications at initiation. Our analysis gives guidance, but further empirical work to estimate the cost and probability of adverse events associated with abnormal laboratory tests is important to further inform funding decisions.

: From the Center for Health Research (DHS, ACF, NAP, XY, MMR), Kaiser Permanente and Northwest Permanente (ACF), Portland, OR; the Institute for Health Research (MAR, DJM), Kaiser Permanente Colorado, Denver; Harvard Medical School (SRS, SBS) and Harvard Pilgrim Health Care (SRS, SBS), Boston, MA. Funding Source: The study was funded by Kaiser Permanente’s Garfield Memorial Fund.

: The authors (DHS, ACF, NAP, XY, MMR, MAR, DJM, SRS, SBS) 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 (DHS, ACF, NAP, MMR, MAR, DJM, SRS, SBS); acquisition of data (DHS, ACF, XY, MMR); analysis and interpretation of data (DHS, ACF, NAP, XY, MMR, MAR, DJM, SRS, SBS); drafting of the manuscript (DHS, MMR); critical revision of the manuscript for important intellectual content (DHS, ACF, NAP, MAR, DJM, SRS, SBS); statistical analysis (DHS, NAP, XY, SBS); provision of study materials or patients (DHS); obtaining funding (DHS, ACF); administrative, technical, or logistic support (DHS, ACF, MMR); and supervision (DHS, ACF, SBS).

: David H. Smith, RPh, PhD, Center for Health Research, Kaiser Permanente, 3800 N Interstate Ave, Portland, OR 97227. E-mail: 

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Although some interventions may enhance medication safety, an electronic medical record reminder to providers may not be an efficient use of resources.


David H. Smith, RPh, PhD

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