Soeren Mattke, MD, DSc

 The growing burden of chronic disease necessitates innovative approaches to help patients and to ensure the sustainability of our healthcare system. Health plans have introduced chronic care management models, but systematic data on the type and prevalence of different approaches are lacking. Our goal was to conduct a systematic examination of chronic care management programs offered by health plans in the commercial market (ie, in products sold to employers and individuals).

 We undertook a national survey of a representative sample of health plans (70 plans, 36% response rate) and 6 case studies on health plans’ programs to improve chronic care in the commercial market. The data underwent descriptive and bivariate analyses.

All plans, regardless of size, location, and ownership, offer chronic care management programs, which identify eligible members from claims data and match them to interventions based on overall risk and specific care gaps. Plans then report information on care gaps to providers and offer self-management support to their members. While internal evaluations suggest that the interventions improve care and reduce cost, plans report difficulties in engaging members and providers. To overcome those obstacles, plans are integrating their programs into provider work flow, collaborating with providers on care redesign and leveraging patient support technologies.

 Our study shows that chronic care management programs have become a standard component of the overall approach used by health plans to manage the health of their members.

We conducted a national survey of health plans and case studies to document current approaches to chronic care management in the commercially enrolled market segment. We found that all plans, regardless of size, location, or ownership, offer programs for members with chronic conditions. While internal evaluations suggest that these programs improve care and reduce cost, plans report difficulties in engaging members and providers. To overcome those obstacles, plans are integrating their programs into provider work flow, collaborating with providers on care redesign and leveraging patient support technologies.

Health plans offer a broad range of services to support chronically ill members, but those resources remain underutilized.

Improved coordination of these services with providers can improve utilization and impact.

Better matching of interventions to member needs and preferences can increase engagement.

The number of people in the United States with 1 or more chronic conditions is expected to grow from 141 million in 2010 to 171 million by 2030, which means that almost 1 in 2 Americans will suffer from a chronic disease in the not-so-distant future.

 In addition to affecting an individual’s quality of life, the costs associated with treating chronic conditions increase as individuals’ number of conditions increase,

 and additionally, productivity losses due to chronic diseases are projected to triple to $3.4 trillion from the current $1.1 trillion.

 Therefore, improved management of chronic conditions is urgently needed.

	In the past several years, health plans have introduced efforts to achieve this goal that include health coaching for members with unhealthy lifestyles, predictive modeling to identify and support members at risk for disease exacerbation, and remote monitoring of members in the critical transition phase after hospital discharge.

 While examples of health plan programs can be found in the gray (and sometimes the peer-reviewed) literature, we are unaware of any systematic studies that have examined the prevalence and scope of chronic care management programs offered by health plans.

	Our study goal was to conduct a systematic examination of chronic care management programs offered by health plans in the commercial market (ie, in products sold to employers and individuals). Specifically, we sought to understand the current range of chronic care management services, to document how programs engage with patients and providers, and to elicit emerging industry trends. In addition, we explored factors that can impede a health plan’s ability to design and implement effective chronic care programs.

	Our study will be of interest to policy makers, providers, health plans, and other stakeholders. As a result of the Affordable Care Act, many individuals who have newly gained access to health insurance are likely to be diagnosed with chronic conditions and subsequently will require effective management. CMS is seeking to promote better chronic care management in the Medicare fee-for-service population through separate payments for such services under the current fee schedule. Finally, emerging payment and care delivery models such as accountable care organizations and patient-centered medical homes will increasingly require providers to focus on chronic care management for their patients. A better understanding of chronic care management approaches based on health plan experience in the commercial population can help to inform policy makers and providers alike.

We used a mixed methods approach that combined a telephonic survey and in-depth case studies. The study was approved by RAND’s Institutional Review Board. For the telephonic survey, a random sample (stratified by commercial enrollment) of 70 health plans in the United States was drawn from a sampling frame consisting of plans with commercial enrollment of 50,000 or more that were listed in the 2011 Atlantic Information Services Directory of Health Plans. Of note, this directory lists health plans and includes both the parent companies and their subsidiaries. We chose the parent companies as a sampling frame because, for reasons of economies of scale, chronic care management programs are typically developed and operated at that level. The sample included for-profit and nonprofit plans, as well as integrated delivery systems that combine health insurance and care provision.

	Of the 70 plans in the sample, 2 were not eligible to participate—1 did not offer full medical coverage prod-ucts and 1 served only Medicaid beneficiaries. Of the remaining 68, the participation rate was 36% (25 plans), representing 51% of all commercial members in the sam-ple because larger plans were more likely to participate. 

) summarize the sampling frame and characteristics of surveyed plans.

	Participating plans were surveyed by telephone using a structured instrument. The survey was administered by a 2-person team and lasted 90 minutes. Respondents typically included the health plan’s medical director or chief medical officer, or they designated a plan representative(s). The survey focused on the commercial segment of the plan’s enrollment, with questions about the health plan, the range of its chronic care management programs, interactions with patients and providers, and factors affecting the operating environment.

	For the case studies, 6 health plans from the first phase were purposefully selected: 2 regional and 2 statewide plans in different parts of the country, and 2 national plans, as described in 

. The case studies entailed 1- to 2-day visits by a 2-person team to conduct semi-structured interviews with staff members, including management, medical directors, and chronic care program staff, as well as reviews of plan documents (eg, program materials, evaluation reports, publications). The surveys and case study site visits were conducted between 2011 and 2012.

Key Components of Chronic Care Management Programs

We found that chronic care management programs were offered by all plans in our sample regardless of size, location, or ownership status. The programs were included as standard components of fully insured and self-insured plans as well as under integrated delivery systems, but our case studies show that some health plans allowed self-insured employers to opt out

	Our results suggest there is a similarity in the overall structure of health plan chronic care management programs. This “typical” structure is depicted schematically in 

. The first component of this structure is the identification of all members with chronic conditions, usually based on diagnoses recorded on claims data or on direct referral from providers or other plan programs. The second component is risk stratification of those members to match interventions and resources to patient need and risk for example, using in-person interactions for members with repeated hospital admissions. Most typically, stratification is based on a combination of proprietary criteria for utilization (eg, number of office visits, hospital admissions, prescription drugs), care gaps (eg, lack of medication adherence and missed preventive services), and electronic lab data (eg, cholesterol and A1C level) when available.

	Based on risk stratification, members are then assigned to 1 of 3 programs: health promotion/wellness, disease management, and case management. Health promotion and wellness programs focus on primary prevention and address unhealthy lifestyles (eg, smoking, lack of exercise) and risk factors (eg, high cholesterol, family history) through behavioral interventions that involve educational materials, individual and group coaching, and often workplace health promotion events. Disease management programs aim to improve clinical care of chronic conditions and patient self-management. Typically, call center-based nurses educate patients about their conditions and encourage them to take an active role in their management. In addition, providers are informed about gaps in care, such as overdue tests or lack of medication adherence. Finally, case management sometimes described as a more intense extension of disease management targets the members at highest risk, no matter what their underlying conditions are. After assignment, members are recruited for program participation, and upon consent, they are enrolled. As members “graduate” (ie, achieve the goals of their programs [eg, members in case management stabilizing their conditions to a level at which self-management suffices]), the plan may assign them to a different chronic care management program or even move them back to the general membership pool.

	Despite the similarities across plans in the overall program structure, implementation of each of the components can vary significantly. With respect to member identification and risk stratification, plans use proprietary algorithms to determine which members are in need of chronic care management, and additionally, use different numbers of risk strata to stratify these members. We also found differences in the boundaries among programs. One case study plan, for example, is in the process of merging disease and case management into a unified program that offers in-person counseling for higher-risk patients. Plans also differ in their integration of chronic care management with other programs, such as utilization review, behavioral health, and pharmacy benefits management.

	The intensity of services provided by health plans varies based on patient risk and needs. Survey data show that plans offer responses to patient- and caregiver-initiated questions by e-mail or phone (96%), patient tools to make decisions about treatment options (83%), and a 24-hour hotline available for urgent healthcare questions (75%). In-person coaching by nonclinicians is only offered by 13% of plans none of which are large plans while in-person counseling by clinically trained staff is offered by 33% of plans, of which 2 are small, 1 is mid-sized, and 5 are large.

 shows the additional services that members in case management receive. These services range from coordination with social care services (eg, home health agencies and meals-on-wheels), to coordination of care among different providers (eg, primary care, specialty care, and rehabilitation services). More than half of the plans provide in-home services to these high-risk patients.

Patient Engagement in Chronic Care Management

Once members have been identified as eligible for chronic care management and matched to programs, an enrollment specialist will contact them for program recruitment. Our survey data show, however, that approximately 42% of health plans report substantial challenges to recruitment because of missing or invalid contact information or members not responding to repeated outreach attempts. In response, health plans have started using a wide variety of channels and touch points for their initial outreach, including mailings, online support tools, calls from trained recruitment specialists or chronic care management nurses, and tailored interactive voice response calls. Also, the timing of outreach matters, as calls immediately after hospital discharge are viewed as more successful than those unrelated to hospitalization. Several case study plans in our study place nurses in hospitals for recruitment into case management, where in addition to participating in the patient’s discharge planning, they refer members to the plan’s case management program (if appropriate) and obtain valid contact information for future communication.

	Even if plans reach members, not all are willing to join a program. Those who are moderately ill are typically the hardest to engage because they are commonly asymptomatic and do not perceive a need to improve their health. For members who do join a program, surveyed plans report limited patient engagement (83%), health literacy (67%), and readiness to change (58%) as the main obstacles to improving chronic care. In response, 71% of surveyed plans state that they have learned that the “flexibility to tailor interventions to patient needs and readiness” is key to successful chronic care management. Following these lessons, plans are experimenting with new initiatives. One case study plan reported success with prescripted text messages that are sent every week (similar to a “tip of the week”) to members with diabetes to help with adherence and lifestyle change. Another case study plan is transitioning toward high-touch personalized interactions in its chronic care management programs, making its programs as accessible as possible by extending outreach hours, putting case managers into provider practices and emergency departments, and experimenting with technology (eg, chat rooms for younger members).

	Finally, incentives are viewed to be an important way to engage patients. Almost half of the surveyed health plans (48%) use incentives within their chronic care management programs. 

 shows the types of incentives used by plans; these incentives are primarily tied to enrollment into or completion of a program and less often to actual health outcomes.

Provider Engagement in Chronic Care Management

The need for coordination with providers was named by 38% of plans as the most important factor to make chronic care management programs work. Interestingly, small plans (less than 200,000 members) were more likely than larger plans (more than 1 million members) to name it as a critical factor (71% vs 17%, respectively).

	Figure 3 depicts the most common ways in which health plans work together with providers in managing patients with chronic disease. Survey data show that almost all plans send reports on care gaps, such as failure to refill chronic medications regularly, and the frequency of these care gap reports varies across plans. Among plans that offer such reports, 84% provide them at regular intervals, 16% on demand only, and 12% both at regular intervals and on demand. Over half of plans are actively working with their contracted providers on practice redesign models, such as patient-centered medical homes.

	About two-thirds of plans (63%) in the survey stated that provider reluctance to change affects chronic care manage-ment program operations; many providers, they note, prefer to focus on their traditional encounter-based approach as opposed to population management. During one case study, we were told that smaller practices and providers close to retirement are especially reluctant to change their established practice models, as are those that had negative experiences with capitation models in the 1990s. Sixty seven percent of surveyed plans reported that limited provider capabilities, such as overextension of staff and lack of patient registries and electronic medical records (EMRs), present barriers to effective chronic care management.

Emerging Practices in Chronic Care Management

Articles

This study shows that health plans use chronic care management programs as standard components of the overall approach to manage the health of their members.

Results From a National Survey on Chronic Care Management by Health Plans

To analyze the impact of worksite wellness programs on health and financial outcomes, and the effect of incentives on participation.

Sources were PubMed, CINAHL & EconLit, Embase, Web of Science, and Cochrane for 2000-2011. We examined articles with comparison groups that assessed health-related behaviors, physiologic markers, healthcare cost, and absenteeism. Data on intervention, outcome, size, industry, research design, and incentive use were extracted.

 A total of 33 studies evaluated 63 outcomes. Positive effects were found for threefourths of observational designs compared with half of outcomes in randomized controlled trials. A total of 8 of 13 studies found improvements in physical activity, 6 of 12 in diet, 6 of 12 in body mass index/weight, and 3 of 4 in mental health. A total of 6 of 7 studies on tobacco and 2 of 3 on alcohol use found significant reductions. All 4 studies on absenteeism and 7 of 8 on healthcare costs estimated significant decreases. Only 2 of 23 studies evaluated the impact of incentives and found positive health outcomes and decreased costs.

The studies yielded mixed results regarding impact of wellness programs on healthrelated behaviors, substance use, physiologic markers, and cost, while the evidence for effects on absenteeism and mental health is insufficient. The validity of those findings is reduced by the lack of rigorous evaluation designs. Further, the body of publications is in stark contrast to the widespread use of such programs, and research on the effect of incentives is lacking.

Because of the emphasis the Affordable Care Act places on worksite health promotion, employers have increasingly offered worksite wellness programs, but little research has evaluated the current impact of these programs.

		We analyzed a total of 33 studies published since 2000 that evaluated the characteristics, impact, and incentives of worksite wellness programs.

		 Studies suggest mixed impact on health-related behaviors and cost, with insufficient evidence for effects on absenteeism and mental health.

		 Lack of rigorous evaluation designs reduces the internal validity of these findings.

Employers have increasingly offered worksite wellness programs to employees and their families to decrease their cost of providing healthcare coverage and improve their employees’ productivity. The goals of these programs are to promote healthy lifestyles and prevent disease with educational (eg, diet counseling) and motivational (eg, provision of incentives for lifestyle changes) approaches.

 In 2009, 58% of US employers offered at least 1 wellness program.

 In 2010, consumer participation in programs rose from 19% to 22%.

 This trend is likely to accelerate, as the Patient Protection and Affordable Care Act emphasizes prevention.

 The law provides wellness program start-up grants for small firms, establishes a 10-state demonstration program to reward program participation, and establishes technical assistance for evaluating programs. The law also gives employers greater latitude in rewarding staff for healthy lifestyles by raising the rewards for program participation. The limit, set at 20% of the cost of coverage, will increase to 30% in 2014, and the secretaries of Health and Human Services, Labor, and the Treasury will jointly have the authority to raise it as high as 50%.

Reflecting their growing importance, several reviews of worksite wellness programs and their components have been published. Baicker et al

assessed the impact of 32 programs on medical costs and absenteeism that were published since 1982 and found that programs typically return 3 dollars for every dollar invested, which is consistent with other research suggestive of savings.

Other studies have found positive effects as well,

 including evidence for certain components of wellness programs (eg, health risk assessment).

Despite these findings, a review is needed on the current impact of wellness programs to examine how employers have responded to current policy and programmatic priority changes. We conducted a systematic review of wellness programs to understand:

• What are the characteristics of the worksite wellness

• What impact do programs have on outcomes?

• What types of incentives are provided for program uptake and what is their impact?

This review contributes to the evidence base in several ways. We considered outcomes beyond medical cost and absenteeism to include behavior change and health effects. Only studies with a comparison strategy were included, to maximize validity of the findings. We also limited our scope to studies about comprehensive programs that were published after 2000 to yield a more accurate reflection of current programs. Finally, we looked at the use of incentives for program uptake.

We conducted a keyword search covering PubMed, CINAHL & EconLit (EBSCO), Embase, Web of Science, and Cochrane from January 2000 through June 2011 (see the 

). Additional articles were identified through reference searches of recent literature reviews or meta-analyses.

Articles were included if they had a control or other comparison group and evaluated outcomes of comprehensive worksite wellness programs (ie, multiple wellness components focused on health promotion or disease prevention). We excluded opinion and theory articles, reviews, articles without a comparison group, non-English language and non-US articles, articles published before 2000, and articles that focused exclusively on disease management.

Two investigators (KCO, KVB) independently evaluated articles for inclusion based on title and abstract review, and then full text review. A third investigator (SM or CS) served as tie breaker in case of discrepancy.

We extracted type of intervention, setting, and research design from each study. Programs and worksites were classified by type, size, and industry.

We categorized the quality of the design using methods adapted from previous meta-analyses

: controlled trials with random assignment, prospective studies with nonrandomly assigned comparison groups, and observational designs with internal comparison groups (eg, participants vs nonparticipants).

We identified 1546 articles from our search and 9 through bibliography searches of review articles (

). We excluded 1492 upon title and abstract review. A total of 62 full-text articles were assessed for eligibility; 29 were not eligible (eg, noncomprehensive programs, international, no comparison), yielding a final sample of 33 articles.

Of the 33 studies, 22 reported company size (

); 8 studies were done in medium-sized companies, while only 1 reported on a small worksite. Of the 33 studies, 29 reported the industry. About half were conducted in companies that provided services, which is comparable to the distribution of industries in the overall economy.

Program modality varied substantially, as 31 studies reported multiple delivery methods (23 reported 3 or more). The most common modality was self-help or educational materials and/or individual coaching or counseling.

A total of 63 outcomes were evaluated between the 33 studies (

). The most common were exercise (n = 13), diet (n = 12), and physiologic markers (n = 12). Others reported on healthcare cost (n = 8), smoking (n = 7), alcohol use (n = 3), absenteeism (n = 4), and mental health (n = 4). The majority of studies (64%) used self-reported data for at least 1 outcome. About three-fourths of the observational designs reported beneficial outcomes compared with about half of the randomized trials.

Analysis of studies of worksite wellness programs suggested mixed impact on health-related behaviors and cost, with insufficient evidence regarding absenteeism and mental health.

Systematic Review of the Impact of Worksite Wellness Programs

To quantify rates of stand-alone e-prescribing (SEP) adoption and use among primary care physicians (PCPs) participating in a SEP initiative and to determine which physician and patient characteristics were associated with higher rates of each.

Using records from an insurer-led SEP initiative, we compared the characteristics of 297 PCPs who adopted SEP through the initiative with the characteristics of 1892 eligible PCPs who did not. Among 297 adopters, we studied the extent of SEP use.

Dependent variables included each physician’s adoption of SEP and his or her e-prescribing use ratio (the ratio of electronic prescriptions to pharmacy claims in the same period). Independent variables included characteristics of PCPs (specialty, practice size, and prescribing volume) and their patients (patient age, sex, race/ethnicity, and household income).

Solo practitioners, pediatricians, and physicians with more patients from predominantly African American zip codes were underrepresented among SEP adopters. The mean (SD) e-prescribing use ratio among adopters was 0.23 (0.28). Twenty percent of physicians maintained e-prescribing use ratios above 0.50. Available physician characteristics explained little of the variance in use, but physicians in smaller practices had greater use (P = .02).

Certain categories of physicians may need more tailored incentives to adopt SEP. On average, adopters used the SEP system for only about one-quarter of their prescriptions. Some adopters achieved high levels of SEP use, and further research is needed to elucidate the factors that enabled this.

e-Prescribing is seen as a critical technology for improving medication use.

In a health plan—sponsored e-prescribing initiative, the mean e-prescribing rate of participating primary care physicians (PCPs) was 1 prescription per 4 pharmacy claims, but some PCPs achieved high use.

Given this low use, future initiatives may need to consider more resources to increase e-prescribing use.

Efforts should be made to ensure that all patient demographics benefit from e-prescribing.

Higher e-prescribing use among physicians in smaller practices suggests that e-prescribing may be an appropriate manner of extending health information technology to these physicians, who traditionally are reluctant users of such technology. 

There is evidence that in some settings health information technology (HIT) can improve patient outcomes and reduce healthcare costs.

 Most of this evidence comes from 4 healthcare organizations at which academic physicians and employees are usually required to use homegrown electronic medical records (EMRs).

 However, few physicians practice in these types of environments. More than 75% of physicians practice in groups of 5 or fewer.

 Unfortunately, the structure of these small community private practices is not conducive to providing the financial and time investment necessary for EMR adoption.

 As a result, only 9% to 14% of these practices have adopted EMRs compared with 23% to 50% of larger practices.

Stand-alone e-prescribing (SEP) has been proposed as a possible method of transitioning community physicians toward EMR functionality without the initial investments required for a full EMR system.

 Indeed, recent legislation promises to increase Medicare reimbursement for e-prescribing physicians in the short term and to decrease Medicare reimbursement for paper prescribers in the long term.

We are aware of only 1 prior study that evaluates adoption and use of commercial SEP systems by community physicians. Fischer et al

 examined use and adoption of the PocketScript system, which was offered without cost to high-volume outpatient prescribers in Massachusetts. A striking finding of their analysis was the low use of e-prescribing, which (although increasing throughout the period studied) was less than 30% of all eligible prescriptions 1 year after adoption. The authors cite anecdotal evidence of increased e-prescribing since that period but present data only through early 2005. 

To further characterize experiences with SEP from other states using another e-prescribing system and in a more recent period, we quantified the rates of e-prescribing adoption and use that occurred when Horizon Blue Cross Blue Shield of New Jersey (Horizon BCBSNJ) offered SEP to community physicians participating in their health maintenance organization (HMO) and preferred provider organization network. Our primary study objectives were to quantify rates of SEP adoption and use and to determine which physician and patient characteristics were associated with higher rates of each.

Horizon BCBSNJ, New Jersey’s largest health insurer, provides coverage for 3.2 million members. In late 2004, Horizon BCBSNJ launched an initiative offering its physicians Caremark’s iScribe SEP software (Caremark is Horizon BCBSNJ’s pharmacy benefits manager). The program installed SEP systems for individual physicians rather than for practices as a whole. All features of the program, including the target population, recruitment, and incentives provided, were determined by Horizon BCBSNJ for purposes of improving care delivery. Our analysis of physicians’ SEP adoption and use was subsequently designed to use secondary data from the program and from other sources.

Of approximately 14,250 physicians in the Horizon BCBSNJ provider network, about 5890 were eligible for the program based on prescribing activity that resulted in at least 500 Horizon BCBSNJ pharmacy claims annually (this cutoff was determined by Horizon BCBSNJ for purposes of program feasibility). Eighty-seven percent of these eligible physicians were in practices containing 5 or fewer physicians. An initial wave of recruitment focused on the highest-volume prescribers (>2500 filled prescriptions per year), and subsequent phases targeted incrementally lower-volume prescribers. By the time the allocated resources were expended, 4457 physicians had received the e-prescribing offer (

Study Population and Dependent Variable for Physician Adoption Analysis

We retrospectively constructed 2 cohorts, one of physicians who adopted the offered SEP system and another of physicians who did not. Physician adoption was then used as the dependent variable in our adoption analysis. Physicians were also characterized based on their specialty, practice size, prior Horizon BCBSNJ pharmacy claims volume, and assigned primary care patient panel. Horizon BCBSNJ ensured that all patients in their HMO and point-of-service insurance plans had an assigned primary care physician (PCP), whereas all patients with other Horizon BCBSNJ insurance plans did not. To focus our analysis on PCPs, physicians without any assigned Horizon BCBSNJ primary care patients were excluded from the analysis. However, physicians with few Horizon BCBSNJ—assigned primary care patients were included in the analysis, with the expectation that they probably also provided primary care to many non–managed care patients insured by Horizon BCBSNJ.

Dependent Variable for e-Prescribing Use Analysis

The second major goal of our project was to study physician use of SEP among adopters. We calculated an “e-prescribing use ratio” (range, 0-1) by dividing the number of SEP prescriptions the physician wrote for Horizon BCBSNJ patients by the number of Horizon BCBSNJ pharmacy claims attributable to the physician during the same quarter. Each physician’s ratio was calculated for each quarter of the e-prescribing use evaluation period (January 1, 2006, to June 30, 2006). Because pharmacy claims may be generated for prescriptions written before a given quarter, it was possible for a PCP’s ratio to exceed 1; this occurred in particular when total prescription denominators were low. Therefore, all eprescribing use ratios were capped at 1 for the analysis. The ratio numerator included all prescriptions generated through the SEP system whether printed or electronically transmitted. Because we wanted to understand physician behavior rather than patient behavior, electronic renewals (physician behavior) were counted in the ratio numerator, and renewal claims were counted in the ratio denominator. Refills (patient behavior) of existing prescriptions were excluded from the ratio numerator and denominator.

After all PCPs were assigned e-prescribing use ratios, some physicians were also classified as “never having used” the system if records did not show any electronic prescriptions after the day of activation (when test prescriptions were often transmitted). Other PCPs were classified as having “quit e-prescribing” if they had initially used the system but later stopped synchronizing their personal digital assistant and sending any electronic prescriptions by the last quarter of the use evaluation period.

Independent Variables for PCP Characteristics and Patient Panel Data

Caremark provided physician specialty and practice size information, which was available only in previously determined groupings (1, 2-5, 6-10, and >10 physicians). Each physician’s total pharmacy claims volume was provided by Horizon BCBSNJ for 2003 (the calendar year before the start of program recruitment) in categories (0-250, 251-500, etc), which we aggregated into approximate “high,” “medium,” and “low” tercile categories. Physicians were categorized as low-volume prescribers (<1750 Horizon BCBSNJ pharmacy claims in 2003, which represented the 35th percentile), mid-volume prescribers, and high-volume prescribers (>3500 Horizon BCBSNJ pharmacy claims in 2003, which represented the 71st percentile).

We used zip codes to estimate the racial/ethnic makeup of the neighborhoods from which the PCPs’ patients were drawn. First, patients living in zip codes with more than 50% African American residents (per 2000 US Census data) were categorized as living in majority African American neighborhoods; those from zip codes with more than 40% Hispanic residents were categorized as living in Hispanic plurality neighborhoods. An analysis of the studied zip codes showed that these predominantly African American and Hispanic neighborhood categorizations were mutually exclusive more than 99% of the time. The PCPs were then categorized based on having at least 10% of their patients living in majority African American and Hispanic plurality neighborhoods (which represented the 80th and 87th percentiles, respectively).

Our analysis consisted of 2 components. In the first component (SEP adoption analysis), we compared the physician characteristics of adopting PCPs versus control PCPs using t test, χ2 test, and multivariate logistic regression. The second component measured use among adopting PCPs via a 2-part model. The first part was a logistic regression model in which the dependent variable was “never having used” or “quit e-prescribing” (as already described) versus having some evidence of e-prescribing use into the last quarter of the observation period. The objective was to identify factors that predispose physicians to stop e-prescribing. The second part was a multivariate linear regression model that examined the association between physician characteristics and extent of SEP use among the subsample of physicians who had started e-prescribing and did not quit. We excluded “never having used” and “quit eprescribing” physicians to examine ongoing use barriers among those physicians who continued to try to use SEP.

Both regression models were constructed by beginning with all available predictor variables included, and then model fit was manually reassessed after elimination of each nonassociated (P >.05) predictor variable. Model fit was assessed using R2 for the linear regression and C statistic for the logistic regression. Colinearity was assessed using correlation coefficients between all variables remaining in each of the final models. Only complete cases were analyzed in the regression models. All analyses were performed using statistical software (SAS, release 9.1; SAS Institute, Inc, Cary, NC).

PCP Characteristics Associated With SEP Adoption

Of 4457 physicians invited to join the SEP program, 427 adopted the SEP system during 2005 (Figure 1). An additional 64 physicians who adopted the SEP system in 2006 (part way through the use evaluation period) were excluded from our analyses. Of 427 adopting physicians, 297 (69.6%) were classified as PCPs based on their having any primary care patients assigned from a Horizon BCBSNJ managed care plan as of January 1, 2006. There were 3966 control physicians; 1892 (47.7%) had primary care patients assigned to them. Therefore, PCPs participated at a higher rate than non-PCPs. The control and adoption cohorts had medians of 68 and 69 assigned primary care patients, respectively.

 compares the adoption and control cohorts of PCPs and their patients. Practice size distribution differed between the cohorts (

 <.001). Pharmacy claims volume and specialty of the cohorts were not significantly different, nor were sex, age, and household income of the PCPs’ patients. The PCPs in the adoption cohort were less likely to have more than 10% of their patient panel live in majority African American neighborhoods (

Multivariate logistic regression analysis showed that the association of SEP adoption with practice size and neighborhood racial/ethnic makeup persists after controlling for other PCP characteristics (

). No association was found between SEP adoption and PCP pharmacy claims volume or patient panel age, sex, or household income. χ2 Test for physician specialty showed no statistically significant difference between the 2 groups of physicians. When adjustments were made for practice size and race/ethnicity, the regression model indicated that pediatricians were significantly less likely to adopt e-prescribing (odds ratio, 0.61; 95% confidence interval, 0.41-0.92; P = .02). None of the distinct variables in the final model had a correlation coefficient exceeding 0.15.

PCP Characteristics Associated With Extent of e-Prescribing Use

Among 297 SEP-adopting PCPs, the mean (SD) e-prescribing use ratio was 0.23 (0.28) (interquartile range, 0.00-0.39), which represents 23 electronic prescriptions per 100 pharmacy claims (

). Thirty-four PCPs (11.4%) never used the system after it was installed, and another 22 PCPs (7.4%) quit using it after at least some initial use. Among the remaining 241 PCPs, e-prescribing use ratios were less than 0.25 in 141 (58.5%), 0.25 to 0.50 in 51 (21.2%), and greater than 0.50 in 49 (20.3%).

In the first part of our 2-part model, we used logistic regression to estimate the association of physician and patient characteristics with being in the “never having used” or “quit e-prescribing” categories. However, these classifications were not significantly associated with any of our independent variables. In the second part of our 2-part model (

), physician practice size was significantly associated with e-prescribing use ratio (omnibus 

 = .02), although this factor accounted for little of the variance (R2 = 0.01). Among those using e-prescribing at all, PCPs in larger practices (6-10 and >10 physicians) tended to use their e-prescribing systems for fewer of their prescriptions. None of the distinct variables in the final model had a correlation coefficient exceeding 0.15.

SEP has been proposed as an entry-level HIT accessible to most US physicians who work in small practices. Because small practices have been slow to adopt other forms of HIT,

 several initiatives are promoting SEP to this group of physicians.

 Despite the interest, there are limited data about which physicians actually adopt SEP and which patient populations might be affected.

 Furthermore, levels of use are particularly important to understand for SEP because physicians can easily revert to paper prescribing.

Our adoption analysis found that PCPs who adopted SEP were more likely to be in practices with 2 to 10 physicians and were less likely to be pediatricians or have patients living in majority African American neighborhoods. A prior study

 of SEP adoption reported the practice size and specialty of adopting physicians, but these physicians were not compared with nonadopters. Other studies

 of EMR adoption found that adoption increases steadily with increasing practice size.  In contrast, we found that physicians in practices of 2 to 5 physicians and 6 to 10 physicians were equally likely to adopt SEP and that both groups had 1.8 times the odds of adopting SEP compared with solo practice physicians. Because our sample underrepresented large group practices, our confidence interval for this group is large, and we cannot draw any conclusions about the relative likelihood of these physicians to adopt SEP. Nonetheless, our results from small and midsize physician groups suggest that SEP is being adopted by physicians who might otherwise have been unlikely to use HIT.

In a health plan–sponsored e-prescribing initiative, participating PCPs' mean e-prescribing rate was 1 prescription per 4 pharmacy claims, but some PCPs achieved high use.

Adoption and Use of Stand-Alone Electronic Prescribing in a Health Plan-Sponsored Initiative

 Integrated health management programs combining disease prevention and disease management services, although popular with employers, have been insufficiently researched with respect to their effect on costs.

: To estimate the overall impact of a population health management program and its components on cost and utilization.

Study Design, Setting, and Participants: 

Observational study of 2 employer-sponsored health management programs involving more than 200,000 health plan members.

 We used claims data for the first program year and the 2 preceding years to calculate cost and utilization metrics, and program activity data to determine program uptake. Using an intent-to-treat approach and regression-based risk adjustment, we estimated whether the program was associated with changes in cost and utilization. Data on program fees were unavailable.

 Overall, the program was associated with a nonsignificant cost increase of $13.75 per member per month (PMPM). The wellness component alone was associated with a significant increase of $20.14 PMPM. Case and disease management were associated with a significant decrease in hospital admissions of 4 and 1 per 1000 patientyears, respectively.  Conclusions: Our results suggest that the programs did not reduce medical cost in their first year, despite a beneficial effect on hospital admissions. If we had been able to include program fees, it is likely that the overall cost would have increased significantly. Although this study had important limitations, the results suggest that a belief that these programs will save money may be too optimistic and better evaluation is needed.   (Am J Manag Care. 2009;15(2):113-120)

Purchasers are increasingly relying on population health management programs to contain costs; however, whether these programs can reduce costs has been insufficiently researched.

Using data from 2 large employers, our evaluation shows that integrated health management programs that combine disease prevention and disease management services do not seem to reduce healthcare spending in their first year. 

 No information on program fees was available. It is likely that overall cost would have increased significantly if we had been able to include them. 

Purchasers of disease prevention and disease management programs should be skeptical of vendors that claim substantial savings in the short run.

As major purchasers of healthcare in the United States, employers are increasingly using population health management programs to constrain the growth of healthcare costs. According to a recent survey, 65% of employers with 500 or more employees and 75% of those with 20,000 or more employees provided 1 or more disease management programs in 2006, up from 58% in 2004.

 Population health management programs combine interventions that focus on patients with specific chronic conditions (disease management) or very high cost irrespective of the cause (case management) with socalled wellness components that are aimed at using health risk assessments (HRAs) to identify unhealthy behaviors (eg, smoking, lack of exercise) or risk factors (eg, elevated blood pressure) and at helping employees and dependents address these health risks in order to prevent the development of chronic diseases.

Although integrated population health management is conceptually appealing, there is limited scientific evidence that it can actually deliver on its promises. This is in part because evaluations are typically conducted by the program vendors themselves using proprietary methods and are not submitted to peer-reviewed journals. Those methods are typically based on observational designs and thus prone to bias.

 As the industry is increasingly aware that skepticism about the impact of these programs impedes market growth, more rigorous and transparent evaluation standards are beginning to emerge. Most notably, the Disease Management Association of America (DMAA) has published national consensus recommendations for disease management evaluation.

 Although these guidelines represent an important step toward transparency, they have 2 major limitations. First, although use of an equivalent comparison group is encouraged, the guidelines do not explicitly recommend individual-level statistical analysis to account for differences between intervention and comparison groups.

 Second, they are specifically designed for stand-alone disease management programs, whereas many employers are now implementing and attempting to measure integrated programs across the care continuum.

We used data from the population health management programs of 2 large employers to analyze the impact of the program on overall medical cost and on utilization of inpatient and emergency department (ED) care. We conducted sensitivity  analyses to assess the impact of methodologic choices on our findings. To our knowledge, we are the first to publish an evaluation of this novel program type.

We analyzed the population health management programs of 2 large employers in the consumer goods industry. Both employers had offered a case management program for high-cost patients, a disease management program for employees and dependents with selected chronic conditions, and a medical advice line staffed by nurses for several years; both added a wellness program, consisting of HRA and lifestyle management, in July 2004 and September 2004, respectively. All program components are operated by a variety of external vendors. For disease and case management, the vendors would use proprietary algorithms to screen the claims data of employees and dependents. If their diagnoses and resource use patterns met the vendors’ eligibility criteria, members would be offered support through written and/or Web-based materials and call center—based care managers. For the wellness program, all employees and their spouses, but not children, were given the opportunity to take a voluntary HRA survey. Respondents were offered coaching if the survey response revealed relevant risk factors. Participation was voluntary (ie, members could opt out of any offerings).

Although the companies advertised the 2004 program expansion to their employees, no incentives or penalties were introduced to encourage program participation. Likewise, program participation had no effect on a member’s health plan benefits (ie, no additional services or modified cost-sharing arrangements were offered in exchange for participation).

We used health plan claims and enrollment data as well as program participation and activity data from 2001 to 2005. No information on program fees was available during this evaluation. Our data covered more than 200,000 employees and dependents in the firms’ self-insured health plans. We used the first year of the expanded program as the intervention year (ie, July 1, 2004, through June 30, 2005, for Employer A; and September 1, 2004, through August 31, 2005, for Employer B). The 2 prior years were used as baseline. This study was approved by RAND’s institutional review board.

Our dependent variables of interest were total paid health insurance claims (medical plus prescription drugs) per member per month (PMPM) and information on hospital admissions and ED visits for all causes. Because we did not have access to information on program fees, we could not calculate the return on investment. Age, sex, the presence of comorbidities (asthma, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, depression, diabetes, and cerebrovascular disease) and an overall risk score derived from the predictive modeling systems each company used to predict healthcare spending were used as covariates for risk adjustment. For each of the 4 program components (case management, disease management, nurse advice line, and wellness), our intervention variables were based on separate determinations of whether members were eligible for that component and whether they opted to participate. 

Our primary analysis used a difference-in-differences approach that estimated the differential change from the baseline years to the intervention year for the program participants and nonparticipants, adjusted for observable differences in risk. This comparison strategy is based on recent DMAA recommendations to use the trend in members who are not program-eligible, often referred to as the “nonpurchased trend,” as a benchmark for the trend in the program members.

 It should be emphasized that “nonpurchased” does not necessarily mean healthy, because health management services usually are procured for specifically defined conditions and not all members with the condition are eligible for the program (only those who meet the program vendors’ eligibility criteria). The implicit assumption behind this comparison strategy is that the risk-adjusted trends in the comparison and intervention groups would be similar in the absence of the intervention.

Following the intent-to-treat principle, we labeled all members who had been identified as eligible for 1 of the program components as participants in that component. To even out seasonal variation in care utilization, only members with 12 months of continuous enrollment in 1 of the firms’ selfinsured plans were included in the analysis. We used ordinary least-squares regressions to estimate the effect of program participation on cost and logistic regression to determine the effect of the program on the probability of hospital admissions and ED visits. Costs were logarithmically transformed to account for their nonnormal distribution. Standard errors were adjusted to account for repeated observations by member. All data extraction was done with SAS 9.1 (SAS Institute Inc, Cary, NC); all statistical analyses were done with Stata 8.0 (StataCorp LP, College Station, TX).

We conducted various analyses to assess how sensitive our findings on the program’s cost impact were to variations in the analytic design. First, we used only 1 baseline year instead of the available 2-year period. Second, we included only members who were consecutively enrolled over all 3 years of the analysis. Third, we included members who were enrolled for at least 6 months in both the baseline and the intervention periods, weighting for the number of months each member contributed to the analysis. Fourth, we added interaction terms between program components for members with overlapping participation to test for multiplicative effects.

Lastly, we repeated our primary analysis, replacing the actually paid claims for office visits, ED visits, and hospital admissions with unit cost estimates. For office and ED visits, unit costs were calculated as the average paid claims for those events in the first baseline year. For hospital admissions, unit cost estimates were calculated based on estimates specific to all patient-refined diagnosis-related groups (APR-DRGs). We used the 2005 Nationwide Inpatient Sample (NIS) and corresponding cost-to-charge ratio file to estimate average cost in each APR-DRG. We merged the cost-to-charge ratio file onto the NIS file based on the unique hospital identifier, multiplied the cost for each admission by the cost-to-charge ratio, and calculated samplewide averages of the resulting charges by APR-DRG. Each inpatient stay in our data was assigned to an APRDRG using 3M Core Grouping Software version 5.3.0 (3M Health Information Systems, Salt Lake City, UT), and the associated charges were replaced by the estimated average charges in the corresponding APR-DRG. 

 presents a comparison of demographic characteristics of the 158,962 members in the comparison group and the 39,809 members in the intervention group during the baseline period. Statistical tests revealed that the intervention group members were on average older and less likely to be female. They had higher average spending and risk scores. The prevalence of various common conditions, with the exception of stroke, also was higher in the intervention group.

Almost 55,000 members were identified or volunteered for a component of the population health management program in the intervention period. By far the most commonly used program was the wellness program with more than 43,000 members, followed by disease management with 18,497 members. The nurse advice line and the case management program accounted for 1774 and 2020 members, respectively. Of the members that the vendors had identified as eligible for case and disease management, 59% and 44%, respectively, agreed to participate (ie, did not opt out of the program). A total of 14,032 members participated in more than 1 program and the most common overlap was between wellness and disease management, with 8953 members. 

 shows the health management programs in which members participated.

 summarizes the relative changes in overall medical cost, ED visits, and hospital utilization for program participants and nonparticipants. On average, cost increased $9.83 PMPM faster for participants than for nonparticipants. Although the faster increase was observed for all program components, it was most prominent for case management ($137.34 PMPM) and smallest for the wellness component ($5.26 PMPM). Effects on utilization were mixed, with the most relevant effect being a reduction of hospital admissions in the disease management component.

 shows, the risk-adjusted estimates of program impact suggest that the integrated program, as well as all individual intervention components except case management, is associated with higher PMPM cost. However, the change is only statistically significant for the wellness program, which showed a $20.14 PMPM cost increase. There was a small but statistically significant decrease in the probability of all-cause hospital admissions of 4 events per 1000 patient-years for case management members, and 1 event per 1000 patientyears for disease management members. The probability of an ED visit increased significantly for disease management participants (7 per 1000 patient-years) and users of the nurse advice line (67 per 1000 patient-years).

The variations run on our sample selection and analytic design typically resulted in small changes to the estimates and would not have affected the interpretation of the results substantially (

). The most consistent finding was that using 12 months of baseline data instead of 24 months led to a more favorable assessment of the program effect. Adding interaction terms between program components changed the relative assessment of the wellness program. Whereas the wellness program was associated with a significant $20.14 increase in PMPM cost in the model without interaction terms, it dropped to $8.37 PMPM and lost its statistical significance in the model with the interaction term. However, this model also showed that members who used both disease management and wellness programs experienced a statistically significant cost increase of $19.09 PMPM. Of note, basing the evaluation on unit cost estimates rather than actual cost did not alter the results substantially. 

Population health management programs have become very popular, particularly in the employer community, but so far little objective information on their impact is publicly available. To our knowledge, ours is the first independent evaluation of an example of an integrated program that offers support for members with manifest conditions as well as for those with risk factors and unhealthy lifestyles. Our results suggest that, at least in the first year of the expansion from a disease and case management program to an integrated population health management model, participants and nonparticipants had similar increases in medical cost, adjusting for observable differences in case mix. In other words, we could not detect a positive impact on medical cost and utilization. To the contrary, some of our findings point toward faster cost increases for program participants than for nonparticipants, even though these estimates were not statistically significant. As we did not have access to information about program fees paid to the vendors, we could only speculate as to whether adding those fees to the cost for the program members would have made the increase statistically significant. Consistently, we did not find that the program significantly reduced the probability of highcost events (eg, ED visits, hospital admissions), which is the mechanism through which such programs reduce cost.

The question now becomes whether it is surprising that integrated population health management had no measurable impact on cost in the first year; we think the answer is no. Such programs try to improve health-related behavior, reduce risk factors, and encourage the use of preventive services. Although these goals would imply increased use of medical services in the short term, the assumption is that the resulting improvements in health and disease control can help avoid high-cost events (eg, ED visits, hospital admissions) in the future enough to offset the higher short-term cost and the cost of the program. As working-age populations tend to have a relatively low baseline rate of hospital admissions, however, it is challenging to achieve enough of a reduction to generate the necessary savings in the short run.

Past research is consistent with this intuition. Although integrated population health management programs as a whole have not been sufficiently evaluated, more information is available on the impact of their components, in particular on the impact of disease management and worksite wellness programs. A recent RAND review of the disease management literature showed solid evidence for a positive impact on quality of care and disease control, but very little support for the hypothesis that disease management can decrease cost.

 Most notably, there was very limited evidence for the effect of large-scale, population-based programs that are administered by health plans or third-party vendors, which are the type of programs typically used as part of the health management interventions. Better support exists for worksite wellness programs, as reviews by Pelletier concluded that these programs reduce risk factors, improve clinical outcomes, and decrease cost, even though the underlying research varies in its methodologic rigor.

 Positive effects typically take time to materialize though, as suggested by a recent study that showed a wellness program yielded net savings only after 4 years.

An important result of this study is that the estimates of program impact were quite robust against changes in the definition of the sample and the analytic design. Our findings suggest that using a longer baseline period (24 months instead of 12 months) led to a more conservative assessment of the program’s effect, lending support to a recently published DMAA recommendation to use at least 24 months of baseline data.

 The use of unit cost rather than actual charges, which has been advocated as being less sensitive to unrelated changes in prices for medical services, did not affect our results substantially.

 However, it has to be kept in mind that most of our unit cost estimates were derived as sample averages at baseline; true unit cost derived from an external sample might have had a different effect. The use of interaction terms to account for program overlap did influence the relative effect of the disease management and wellness components.

Clearly, our comparison strategy is not optimally protected against bias. Although the difference-in-differences approach can account for baseline differences, it rests on the implicit assumption that the trend in the program and nonprogram groups would have been the same in the absence of the intervention. As program participants were on average sicker than nonparticipants, it is reasonable to assume that cost in the program group would increase faster, which would bias our results against finding a positive program effect. We tried to minimize this bias by adjusting for observable variables, but unobservable differences may have influenced the results, as our statistical model has only limited explanatory power. 

Our data also did not provide for a clean baseline period without any program exposure. Therefore, we may have misestimated the effect of the components that started prior to 2004 (case management, disease management, and nurse advice line).

Although our study had important design limitations, its results suggest that a belief that these programs will save money in the short run may be too optimistic and better evaluation is needed. However, the finding that broadening a case and disease management program to include wellness programs does not have a measurable impact on healthcare cost in the first year does not disprove that this approach to improving health works in the long run. As such programs differ substantially in scope, content, and execution, one should be careful about generalizing the findings from one evaluation to other programs.

 But until more definite evidence is available, purchasers of such programs, private and public, should view vendor claims about the prospect of short-term savings with caution and insist on a valid and transparent evaluation strategy.

Our findings also show that estimates from a multivariate statistical model differ substantially from those based on an unadjusted comparison of means, which is the most commonly used evaluation approach in the industry. This is particularly true when it comes to the effect of individual components of an integrated program. Because the statistical model does differ substantially and, by including more information, is better able to adjust for differences in risk and the effect of multiple program participation, these findings support an evaluation strategy based on statistical modeling instead of the current industry-standard unadjusted comparison of means.

We are indebted to the 3M Corporation for providing us with their DRG Groupers for the unit cost analysis and Dr Norbert Goldfield (3M) for his advice on how to utilize the grouper software. We acknowledge the use of the Healthcare Cost & Utilization Project data files for the unit cost analysis.

From the RAND Corporation (SM, SLZ, AKJ), Arlington, VA; and Mercer Human Resource Consulting (SAS, DBG), Los Angeles, CA. Dr Mattke is now with Bain & Company, Boston, MA. Ms Zakowski is now with Mercer Human Resource Consulting, San Francisco, CA.

The study was funded by contributions from the 2 employers who provided data, and Mercer Human Resource Consulting.

 Dr Mattke conducts research and consults on projects for purchasers and operators of disease management programs. Dr Serxner, Ms Zakowski, and Dr Gold are employees of Mercer Human Resource Consulting, a firm that conducts research and provides consulting services on projects for purchasers and vendors of disease management programs. Mr Jain reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Portions of this paper were presented at the Disease Management Leadership Forum, Las Vegas, NV, September 18, 2007.   Authorship Information: Concept and design (SM, SAS, SLZ, DBG); analysis and interpretation of data (SM, SLZ, AKJ, DBG); drafting of the manuscript (SM, SAS, SLZ, AKJ, DBG); critical revision of the manuscript for important intellectual content (SAS, SLZ, DBG); statistical analysis (SM, SLZ, AKJ); obtaining funding (SM); administrative, technical, or logistic support (SM); and supervision (SM, SAS).

 Soeren Mattke, MD, DSc, Bain & Company, 131 Dartmouth St, Boston, MA 02116. E-mail: soeren.mattke@bain.com.

1. Mercer Health & Benefits LLC. Mercer’s 2006 National Survey of Employer-Sponsored Health Plans

 Working Towards Wellness: Accelerating the Prevention of Chronic Disease. 2007. http://www.weforum.org/pdf/Wellness/report.pdf. Accessed November 10, 2008. 

 New Levers and New Strategic Choices. July 2007. http://www.bcg.com/impact_expertise/publications/files/Medical_Management_New_Levers_Jul_2007.pdf. Accessed November 10, 2008. 

Integrated health management programs combining disease prevention and disease management services, although popular with employers, may not save money, at least in their first year.

Soeren Mattke, MD, DSc

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