 To examine the costs of hospitalizations related to heart failure (HF) among patients aged 18 to 64 years by diagnosis status.

There are few reports on HF-related hospitalization costs in patients aged 18 to 64 years, although this group has had increased HF hospitalization rates in recent decades.

Using the 2005 MarketScan Commercial Claims and Encounters inpatient data set, we identified 23,216 hospitalizations for which HF was the primary or secondary diagnosis among patients aged 18 to 64 years who had a noncapitated health insurance plan. We used multivariate regression to analyze the association between patient characteristics and both hospitalization costs and length of stay (LOS).

For the entire population, the mean cost of hospitalization was $23,077; the cost was higher when HF was a secondary rather than the primary diagnosis ($25,325 vs $17,654; 

 <.001). After controlling for covariates, hospitalizations with HF as a secondary diagnosis resulted in $3944 higher costs than those with HF as the primary diagnosis (

 <.001). Among those with HF as a secondary diagnosis, the cost of hospitalization with the primary diagnosis of ischemic heart disease was $14,989 higher than it was when the primary diagnosis was noncardiovascular disease and nonrespiratory conditions (

 <.001). However, patients in the latter group had a longer LOS.

Information on the costs of HF-related hospitalizations can be used as inputs in economic evaluations such as cost-effectiveness analyses and as references for policy makers in making resource allocation decisions.

The costs of 23,216 hospitalizations related to heart failure (HF) among patients aged 18 to 64 years were analyzed.

The mean cost of HF-related hospitalizations was $23,077 and was higher when HF was a secondary rather than the primary diagnosis. 

For those with a secondary diagnosis of HF and the primary diagnosis of ischemic heart disease, the costs of hospitalizations were $14,989 higher than they were when the primary diagnosis was noncardiovascular disease and nonrespiratory conditions. 

Among all the categories of primary diagnoses, the primary diagnosis of noncardiovascular disease and nonrespiratory conditions resulted in the longest length of stay.

	In the United States, rates of hospitalization for heart failure (HF) increased substantially in the past 3 decades, and HF has become an epidemic.

 Although HF affects adults 65 years or older more than those below age 65 years, the younger group had a higher relative increase in hospitalization rates for HF, regardless of sex, than did the older group.

 In addition, the percent increase was greater for hospitalizations with HF as a secondary diagnosis than it was for hospitalizations with HF as the primary diagnosis.

 Nationally, approximately 70% of HF-related hospitalizations listed HF as a secondary diagnosis.

 From a public health perspective, promoting prevention and improving medical management based on an understanding of the costs for HF-related hospitalizations in people aged 18 to 64 years could have a large payoff in terms of containing the ever-increasing healthcare costs.

	For 2007, the medical cost of HF in the United States for those aged 65 years or older was estimated at $30.2 billion.

 The hospitalization cost for this age group was estimated at $20.1 billion in 2009.

 The average cost for an HF hospitalization more than doubled from about $7000 in the 1990s to $18,086 in recent years.

 On the national level, HF has an extraordinary impact, consuming 1% to 2% of the total healthcare resources in industrialized countries, including the United States, with this percentage likely to increase in the future.

 Moreover, HF is the fastest-growing cardiac disease entity in the United States, affecting 2% of the population, with nearly 1 million hospitalizations for acute decompensated congestive HF occurring annually.

	Although many researchers have investigated the medical costs related to HF, nearly all the cost studies have been for the population 65 years or older because of the high hospitalization rates for HF in this age group.

 Correspondingly, information is lacking on the costs of HF in adults under 65 years. Furthermore, although many previous studies had focused on hospitalization costs,

 the costs by detailed diagnosis codes and diagnosis status have not been well examined. Because the types of HF vary considerably and at least 70% of HF diagnoses are listed as a secondary diagnosis, it would be helpful to have cost analyses for this disease that consider these characteristics, especially as the information obtained could influence the cost-effectiveness of HF management programs. Thus, we investigated HF-related hospitalization costs among patients aged 18 to 64 years by diagnosis status.

	We identified HF-related hospitalizations from the 2005 MarketScan Commercial Claims and Encounters inpatient data set, which in that year had data on more than 1 million inpatient hospitalizations for privately insured patients 64 years or younger. The data contain comprehensive, highquality coding of medical services.

 Researchers have previously used this database for medical cost analyses of cardiovascular diseases (CVDs).

	We used the primary diagnosis and 14 secondary diagnoses to identify hospitalizations for patients aged 18 to 64 years with a diagnosis of HF; our analysis was restricted to the 

International Classification of Diseases, Ninth Revision 

) codes listed in the American College of Cardiology/American Heart Association HF performance measures (

). We identified 23,216 HF-related hospitalizations for patients with a noncapitated health insurance plan and with hospitalization costs between the 1st and 99th percentiles. We excluded the hospitalizations of patients with a capitated health insurance plan because their costs did not reflect the medical services they received; excluding hospitalizations with a cost below the 1st or above the 99th percentile reduced the influences of extreme values on the cost estimates. We further created 2 subsamples for the cost analyses: (1) HF as the primary diagnosis and (2) HF as a secondary diagnosis. Hospitalizations with both the primary diagnosis and a secondary diagnosis of HF were included in the first subsample.

	For hospitalizations with a secondary diagnosis of HF, we identified the primary diagnoses of (1) ischemic heart disease (IHD) (

 codes 410-414); (2) other diseases of the heart (

 codes 392-398, 402, 404, 415, 416, 420-427, and 429); (3) other CVDs (

 codes 390-459, excluding the codes for IHD and other diseases of the heart); (4) respiratory disease (

 codes 460-519), including pneumonia and chronic obstructive pulmonary disease; and (5) non-CVD and nonrespiratory conditions.

Outcome Measures and Other Control Variables

	The main outcome for this analysis, hospitalization costs (total payments to providers, not charges), represented the total costs per hospitalization, which included the costs for physician services, all diagnostic tests, therapeutics, supplies, and room fees during the stay. We excluded the hospitalizations with a cost below $1060 (cutoff point of the 1st percentile) and above $208,507 (cutoff point of the 99th percentile).

	Length of stay (LOS) was a second outcome measure. Because LOS is a major factor influencing the cost, reducing it might have important implications for managing HF and for cost containment.

 The LOS was categorized as 1 for hospital stays of fewer than 7 days and as 2 for hospital stays of 7 or more days. We used 7 days as the cutoff point because the average number of days for an HF-related hospitalization in the United States is about 6 to 8 days.

	We included age, sex, urbanization, and the Charlson Comorbidity Index as control variables. We used the Charlson Comorbidity Index, which measures the likelihood of death or serious disability in the subsequent year by diagnosis codes for up to 18 different diseases,

 to control for the effects of disease severity and comorbidities on the costs.

	Statistical analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, NC). Significance was assumed when the 2-tailed probability value was less than .001. For cost comparisons between hospitalizations with the primary diagnosis of HF and those with a secondary diagnosis of HF, the Wilcoxon 2-sample test was used. We used regression models to conduct multivariate analysis of the costs and LOS. Medical cost data were usually skewed and not normally distributed; thus, many researchers have used log-transformation in cost analyses. Many other studies, however, did not use the transformation because of the difficulties in interpreting the results.

 We excluded hospitalizations with total costs below the 1st or above the 99th percentile to avoid the effects of outliers, and our large sample sizes also made the analysis relatively robust to the violations of normality. Therefore, we did not transform the costs in our analysis. Because the study samples consisted of hospitalizations rather than patients, some patients might have had multiple hospitalizations during the year. To account for the possibility of the repeated measures in the analysis, we used the PROC MIXED procedure to estimate the impact of patient characteristics and other covariates on hospitalization costs, and we used the PROC GENMOD procedure to estimate the impact of these variables on LOS.

	For all 23,216 HF-related hospitalizations, the mean cost was $23,077; for the 6806 hospitalizations (29%) with HF listed as the primary diagnosis, the mean cost was $17,654, and for the 16,410 hospitalizations (71%) with HF listed as a secondary diagnosis, the mean cost was $25,325 (

). Hospitalized men incurred higher costs than hospitalized women, and among the 6 groups formed by combining sex and diagnosis status, men with HF as a secondary diagnosis had the highest cost ($27,277), whereas women with HF as a primary diagnosis had the lowest cost ($16,058). The costs across age groups varied slightly regardless of diagnosis status.

	The regression results indicated that sex significantly influenced costs, but age did not (

). In the entire sample, hospitalized men incurred costs that were $4317 (P <.001) higher than those of hospitalized women. Regional variations in the hospitalization costs were significant, with the highest cost in the West. Hospitalized patients with a LOS of 7 days or more incurred significantly higher costs than those with a LOS of less than 7 days. Hospitalized patients with a secondary diagnosis of HF incurred costs that were $3944 (P <.001) higher than hospitalized patients with the primary diagnosis of HF.

	Among patients with a secondary diagnosis of HF, 17% had a primary diagnosis of IHD; 14%, other diseases of the heart; 7%, other CVDs; 18%, respiratory diseases; and 44%, non-CVD and nonrespiratory conditions. Among these groups, hospitalized patients with IHD had the highest costs per hospitalization ($36,442). After controlling for other covariates and using hospitalizations with the primary diagnosis of non-CVD and nonrespiratory conditions as the reference, hospitalized patients with IHD incurred costs that were $14,989 higher (P <.001); those with other diseases of the heart had costs that were $9480 higher (P <.001) (Table 3).

	For both the entire sample and hospitalizations with a secondary diagnosis of HF, age 40 to 54 years, urbanization of hospitals, and the Charlson Comorbidity Index were significant predictors of LOS status (>7 days vs <7 days) (

). For example, patients aged 40 to 54 years were less likely to have a stay of 7 days or more than the reference population (those aged 55-64 years). Hospitalized male patients had a lower probability of having a LOS of 7 days or more than hospitalized female patients. For the entire sample only, having a secondary diagnosis of HF increased the probability of a longer stay. For the group of hospitalizations with a secondary diagnosis of HF, the primary diagnosis of other CVDs decreased the probability of having a longer LOS when compared with the primary diagnosis of non-CVD and nonrespiratory conditions. Hospitalized patients with the primary diagnosis of IHD, other diseases of the heart, or respiratory diseases might have a shorter LOS than the reference group.

	Our cost analysis provided new information in that it was based on all possible ICD-9 diagnoses of HF as well as diagnosis status. The detailed information we obtained could be useful in developing or evaluating the  cost-effectiveness of HF management programs as well as related policies, such as evaluating the impact on hospitalization costs of the classification of HF by the Agency for Healthcare Research and Quality as an ambulatory care—sensitive condition.

 This classification indicates that HF is a condition for which good outpatient care might prevent the need for hospitalization or for which early intervention might prevent the need for hospitalization or prevent complications or more severe diseases. Effective ambulatory care for HF will reduce hospitalization of patients with HF as the primary diagnosis, but will increase hospitalization of patients with HF as a secondary diagnosis. Moreover, improving survival of patients living with acute myocardial infarction (AMI) and the aging of the US population will increase the number of patients living with HF.

 Therefore, despite an overall decline in age-adjusted mortality from IHD in the United States, HF incidence and hospitalization rates have increased.

	According to Dr J. Fang, MD, an epidemiologist at the Centers for Disease Control and Prevention, as mortality from AMI and other IHDs continues to decline and the population continues to age, it is expected that the incidence and prevalence of HF will continue to increase. Management of HF in order to reduce hospitalizations with HF as the primary diagnosis was one of the Healthy People 2010 objectives. It also will be an objective in Healthy People 2020 (E-mail communication, July 2010).

	As we expected, we found that HF was more likely to be listed as a secondary diagnosis than as the primary diagnosis. We also found that the costs of hospitalizations were much higher with HF as a secondary diagnosis than as the primary diagnosis (the difference was $7671). After controlling for covariates, the difference still was substantial ($3944). Further study is needed to investigate how much of the hospitalization costs were for the medical management of HF, so the economic burden of HF can be quantified correctly.

	Earlier research found that hospitalization costs for HF management averaged $7000 in the early 1990s in patients 65 years or older and increased to an average of $18,086 in that group recently.

 However, it is unclear whether the HF in those studies was based on a primary or secondary diagnosis. If the most recent average was based on HF as the primary diagnosis, our results of $17,654 in 2005 for patients aged 18 to 64 years with HF as the primary diagnosis are quite comparable with the costs for patients 65 years or older.

	Among hospitalized adults with a secondary diagnosis of HF, we found that patients with IHD or with other diseases of the heart as the primary diagnosis incurred higher costs than those who had non-CVD and nonrespiratory conditions as the primary diagnosis. However, the hospitalized adults with IHD and other diseases of the heart were not more likely to have a LOS of 7 days or more than were those with non-CVD and nonrespiratory conditions. That may be because patients with IHD have a greater chance of getting percutaneous transluminal coronary angioplasty and coronary artery bypass grafting, which are very expensive, whereas treatment for acute myocardial infarction with revascularization reduced the total hospital LOS.

 The literature has shown that among HF-related hospitalizations in the United States, the percentage for patients with IHD, other diseases of the heart, and other CVDs as the primary diagnosis declined, while the proportion with a primary diagnosis of non-CVD increased.

 Because IHD causes reduced pump efficiency that may lead to HF, IHD is a major risk factor for HF. Primary prevention and appropriate management of IHD are critical steps in the reduction of HF admissions.

Articles

FOCUS ON HOSPITAL MEDICINE

Hospitalization costs associated with heart failure averaged $23,077 and were higher when heart failure was a secondary rather than the primary diagnosis.

Costs of Heart Failure-Related Hospitalizations in Patients Aged 18 to 64 Years

	We describe the initial experience with a first-to-market health insurance product design based on principles of both member and purchaser accountability. Two benefit levels were offered, enhanced and standard. Qualification for the enhanced benefit level was obtained through members' commitment to follow their physicians' recommended treatment plan. Employers were offered a discount of 10% in exchange for offering this new product and promoting a healthy work environment. Membership in the product grew beyond expectations, and several health improvements were noted.

	A new type of insurance product based on innovative financial incentives resulted in a higher level of both member and purchaser accountability for working toward and promoting a healthy lifestyle.

		Insurance products should be designed to promote wellness and a healthy lifestyle.

		Financial incentives can be used as a strong motivator for both members and employers to work toward a healthy lifestyle and a healthy work environment.

		Aligning incentives among members, employers, physicians, and the health plan is critical for achieving desired outcomes.

	Only about 3% of US healthcare expenditures are for prevention and wellness, versus the 97% that is spent on the treatment of acute and chronic illness.

 Although there is some debate about the extent to which focusing on health promotion, wellness, and prevention would ameliorate the continued escalation of healthcare costs, most believe that it is an approach worth  pursuing or, at the very least, an approach that merits further study.

 Underscoring the lack of attention to wellness and prevention is the issue of patient noncompliance. It is well-known that the behavioral patterns of many individuals reflect their tendency to care less about the future than the present (eg, the immediate gratification of eating outweighs the long-term, uncertain consequences of being overweight or obese).

 It also has been documented that adherence to treatment is  associated with better health outcomes, even when the treatment is a placebo.

 Medication noncompliance is an especially common and serious problem, and contributes to poor outcomes and substantially higher costs.

 This lack of attention to wellness, prevention, and compliance creates a spiral of increasingly poor outcomes and escalating costs.

	In October 2006, Blue Care Network of Michigan, the health maintenace organization subsidiary of Blue Cross Blue Shield of Michigan, launched a new product called Healthy 

. The product was designed to incorporate member accountability for following a healthy lifestyle (ie, personal responsibility) and employer commitment to wellness (ie, purchaser responsibility). It tested whether strong financial incentives, embedded directly in the product design and linked to the employees’ cost sharing and benefit coverage level, would result in improvements in both health and costs. The strategy was to create a partnership among 4 key players: the member, the member’s primary care physician, the employer, and the health plan. We report on the first 3 years of experience with this product.

Living was first offered in October 2006 after design work that included employer feedback, member and employer focus groups, and multiple conversations with physicians, agents, and other healthcare experts. Most constituents believed that members with unhealthy lifestyles should pay more for their healthcare than those who take care of themselves. This thinking was aligned with a 2006 national poll of US adults who thought it was fair to ask people with an unhealthy lifestyle to pay higher insurance premiums.

 The product had 2 benefit levels: enhanced and standard. The enhanced benefit level had 20% less employee cost sharing than the standard benefit level.

	Every enrollee received the enhanced benefit for the first 90 days. Within this first 90 days, members and their spouses, if applicable, had to complete 2 tasks. First, they had to take a health risk assessment. Second, they had to visit their primary care physician and complete a Healthy 

Living Qualification Form. The qualification form focused on 6 conditions: cigarette smoking, weight, blood pressure, blood sugar, cholesterol, and alcohol use (

). These 6 conditions were selected because they are leading causes of controllable healthcare costs, they are highly prevalent in the population, the downstream costs of inadequate control are considerable, and several of them have been linked to greater absenteeism, reduced productivity, increased workers' compensation costs, and higher disability expenses.

 The qualification form assigned 15 points to each condition with the exception of smoking, which was worth 25 points in recognition of the critical importance of smoking cessation in facilitating rapid reductions in morbidity and benefit costs. To continue to qualify for enhanced benefits beyond the first 90 days, a member had to receive 80 or more points on his or her qualification form. Failure to receive 80 or more points on the qualification form, failure to complete the qualification form, or failure to complete the health risk  assessment automatically moved the member and his or her family to the standard benefit level.

	To achieve the associated points, members had to commit to their physician’s recommended treatment plan, which typically included adhering to an appropriate diet, exercising, taking prescribed medications, and complying with other appropriate interventions. Members who committed to quit smoking were required to join Blue Care Network of Michigan’s Quit The Nic program, a phone-based smoking cessation program. Physicians determined when their patient had to return for follow-up.

	A critical feature of the product design was the conversation between the member and his or her physician. Accuracy in completing the qualification form was emphasized to the physicians during the orientation period. A second important component related to the employer. In exchange for receiving a 10% discount, employers had to have a smoke-free work site and agree to support their employees in their commitment to following a healthy lifestyle. Support included offering onsite exercise or nutrition classes and offering healthy foods in their vending machines or cafeteria. Employers who would not commit to a smoke-free environment or who would not support their employees in their health improvement efforts were not offered the product.

	Our initial efforts to measure the effectiveness of this new plan fell into 3 general categories: (1) attitudes about the plan as tracked by constituent reaction and sales, (2) effectiveness of the plan as tracked by the percentage of members qualifying for enhanced benefits, and (3) cost reductions as measured by claims cost and the number of people who demonstrated health improvements.

	Constituent reaction to the plan was significant and positive. Agents, employees, employer purchasing representatives, physicians, journalists, and legislators all endorsed it. This was evidenced by the rapid growth of the product, enthusiastic physician acceptance, supportive press coverage, and positive member and physician survey results. Membership exceeded expectations and was more than 60,000 in year 1 and more than 105,000 in year 3.

Enhanced Benefits and Promised Health Improvements

	Approximately 70% of people qualified for enhanced benefits, and 30% defaulted to standard benefits. The percentage varied by group size and whether or not the employer offered other health plan options. In general, larger employers took more steps to let their employees know about the need to complete the necessary office visits and paperwork. In addition, many large employers gave employees a choice of plans so that people who did not want to make a lifestyle change could enroll in another  plan. In these larger plans, a higher percentage of people qualified for enhanced benefits.

	With regard to promised health improvements, more than 90% of members who went to see their primary care physician committed to following the physician’s recommended treatment plan. Only alcohol use showed less than a 90% commitment level.

 Cost Reductions and Demonstrated Health Improvements

	To measure the extra cost associated with the 6 lifestyle factors targeted by the plan, we reviewed claims results.

	After adjusting for age, sex, and benefits, we observed extra cost associated with 5 of the 6 conditions. Only those people with high cholesterol did not have extra cost, which was probably related to the long latency period before high cholesterol is likely to impact health status. Those with diabetes and a body mass index of more than 30 had the highest extra morbidity (

	Four additional observations can be made based on Healthy

Living data: (1) People with multiple conditions generated  significantly higher costs than people who were working to improve only 1 lifestyle choice; (2) Individuals who retained enhanced benefits by taking steps to improve their health generated an average cost increase, whereas those who took the standard benefits generated very high cost increases; (3) Weight control was the area most resistant to change; and (4) Physicians were quite willing to accept a promise of improvement 3 years in a row, even though results fell short of expectations. That was demonstrated by the substantial number of members who continued with enhanced benefits through the first 3 years without meeting their targets. Numerous anecdotal comments from physicians indicated they do not want to play “insurance policeman” and are generally “lenient” with their patients.

	Experience with this new product revealed several themes. First, the product design, which links member accountability for improved health to the level of benefits, was critical for obtaining member engagement. More than two thirds of enrollees followed through and completed both the health risk assessment and the qualification form. In the first year of the program, approximately 70% of members who completed a qualification form indicated they would make at least 1 health improvement as a result of the product design.

	Second, although physicians supported the qualification form process, they were inclined to give their patients (ie, members with enhanced benefits) the benefit of the doubt, even when patients did not follow the recommended treatment plan. More than 99% of members who saw their physician for completion of the qualification form were “passed” by the physician with 80 or more points. Many physicians reported feeling pressured by some patients to make sure they received the highest level of benefits.

	Third, despite the leniency on the part of the physicians, the product resulted in improvements in member health. For example, 71% of members who agreed to get their blood pressure under control were able to do so, and 61% of members who were identified as having inadequate blood sugar control achieved an acceptable level of control. Members achieved these  improvements because the product design linked the level of benefits to their commitment to maintain or improve their health.  Some members clearly did not improve, which demonstrates the known difficulty of behavioral change and compliance.

	The product has proved enormously attractive for 3 reasons:

	1. Employers realized a 10% discount compared with plans that had a similar level of (enhanced) benefits and now believe that their future healthcare costs will be based on improved employee health habits.

	2. Most people believe their good habits should be rewarded and, to a lesser extent, that they are responsible for their unhealthy habits.

	3. Health improvements attributable to the plan have been encouraging to members, employers, and physicians.

Living product design shows that an insurance product that promotes wellness can drive important improvements in health. Products like this plan, which promote members’ commitment to improving their health, appear to have substantial potential to positively impact healthcare costs and quality.

	We thank Joe Bojman, FSA, MMAA; Jan Jennings, MS; Rami Garrett, BS; Udhay Ratnasamy, MS; and Pam Reinert, MSN, for their valuable comments and contributions. None of these individuals received direct compensation for their contributions.

 From Blue Cross Blue Shield of Michigan (DRW, DRN), Detroit, MI; Blue Care Network of Michigan (DRW), Southfield, MI. Funding Source: There was no external funding for this research.

 The authors (DRW, DRN) 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 (DRW, DRN); acquisition of data (DRW, DRN); analysis and interpretation of data (DRW, DRN); drafting of the manuscript (DRW, DRN); critical revision of the manuscript for important intellectual content (DRW, DRN); and statistical analysis (DRN).

WEB EXCLUSIVE

A new insurance product based on principles of member and purchaser accountability was adopted rapidly and resulted in several health improvements.

Initial Experience With a First-to-Market Member Accountability-Based Insurance Product

To assess whether health plan members who used retail clinics chose that setting for minor conditions and continued to see other providers for more complex conditions.

Retrospective analysis of claims data in a commercially insured population.

Health plan enrollment data were used to identify and describe the analysis population. Episode Treatment Groups were used to identify members with chronic conditions and to analyze reasons for retail clinic use, complexity of retail clinic visits, and care for chronic conditions in non—retail clinic settings. Logistic regression was used to study predictors of retail clinic use.

Retail clinic users differed significantly from nonusers. The most significant predictors of retail clinic use were age, sex, and proximity to a retail clinic. Episodes of care treated in the retail clinic appeared to be less complex than similar episodes treated in other settings. Chronically ill members who used the retail clinic saw another provider for their chronic condition at rates similar to or higher than those of members who did not use the retail clinic.

Individuals may be able to identify when conditions are minor enough to be treated in a retail clinic and serious enough to be treated by a traditional provider.

Appropriate use of retail clinics depends on an individual’s ability to identify when a condition is minor enough to be treated in that setting. We used claims data from a health plan to study the patterns of care for members who use retail clinics. We found that:

Episodes seen in the retail clinic setting appeared to be less complex than similar episodes seen in an office setting. 

 Members with chronic conditions who used retail clinics sought care for their chronic conditions from non—retail clinic providers at rates similar to those of members who did not use retail clinics.

	Retail clinics, also called convenience clinics, provide preventive care and treatment for minor, acute illnesses. They are located in retail stores, supermarkets, and pharmacies, and typically are staffed by certified physician assistants or nurse practitioners. For the consumer, retail clinics have advantages compared with traditional primary care, including lower cost and the availability of services without an appointment. The first retail clinics emerged in 2000 in Minnesota; the number has continued to grow nationally from 62 in 2006 to nearly 1000 in late 2008.

 Despite this growth, a 2007 survey found that only 1.2% of households had used a retail clinic in the previous year,

 even though a recent study estimated that more than one-fourth of the US population resides within a 10-minute drive of one,

 and a 2008 survey revealed that almost half of consumers would consider using a retail clinic.

	What does increasing use of retail clinics mean for consumers and for the healthcare system? These clinics offer convenience to the consumer and lower costs to the payer.

 Their appropriate use depends on the consumer’s ability to distinguish minor acute conditions that can be treated at a retail clinic from more serious conditions that would be better addressed by traditional providers. Given research suggesting that one-third of American adults have a health literacy measured at a “basic” or “below basic” level, this ability is not self-evident.

 Retail clinics have been criticized by the American Medical Association, the American Academy of Pediatrics, and the American Academy of Family Physicians, who claim that the clinics may create a conflict of interest (because of their relationships with retail pharmacies) and promote fragmented or substandard care.

 Providers have expressed concerns about use of retail clinics and the practice of waiving the copay for retail clinic visits.

 Critics also have cited concerns about the qualifications of clinic staff and their ability to diagnose serious medical problems,

 and about the disruption of existing physician—patient relationships, especially for those with chronic conditions.

	Research to date has focused on the purpose and cost of the visit, the quality of care provided,

 and the characteristics of retail clinic users.

 This research has not substantiated the concerns voiced about retail clinics, but rather suggests that minor conditions can be treated appropriately in that setting. A question that remains unanswered is whether individuals are adept at assessing the seriousness of their own conditions and are seeking care consistent with that assessment.

	The goal of this study was to extend the research to whether members choose retail clinics for minor concerns and whether they still choose non—retail clinic providers for less minor and chronic conditions. In particular, we estimated predictors of retail clinic use, compared complexity of visits for similar episodes in retail clinic and non–retail clinic settings, and assessed the degree to which members with chronic conditions who used a retail clinic also saw other providers for their chronic conditions.

	This study was set at Blue Cross and Blue Shield of Minnesota, a national health plan headquartered in Minnesota. Health plan claims and enrollment data were analyzed for a population of commercially insured members residing in Minnesota and continuously enrolled in 2007. The analysis included only services rendered by providers contracting with the health plan and located in Minnesota. This set included 72 separate retail clinic locations. A retail clinic is a unique category for contracting purposes and is defined as a clinic located in a retail or nonmedical setting with a limited scope of services. All retail clinics are covered by the same reimbursement arrangement. The services provided at retail clinics are limited by design to address noncomplex ailments that fall within the scope of practice of the provider staff. Some health systems in Minnesota have added clinics that function as retail clinics in terms of scope of practice and reimbursement, but are staffed by physicians in addition to nurse practitioners and physician assistants. These physician-staffed clinics were classified as retail clinics for the purposes of this analysis.

	To study whether members chose retail clinics for less complex concerns, we identified members with at least 1 visit to a retail clinic in 2007. To draw a comparison with members who did not use a retail clinic, we identified members with at least 1 office visit associated with diagnoses thought to be treatable in a retail clinic (see 

). We excluded members younger than 18 months (as of January 1, 2007) because the state of Minnesota requires that retail clinic patients be at least that old. We used geocoding to identify whether each member lived within 10 miles of a retail clinic.

 We then assessed whether members with at least 1 chronic condition who used a retail clinic continued to use traditional providers for chronic conditions. Episode Treatment Group (ETG) data from the prior years 2005 and 2006 were used to identify chronic conditions in the study population. Episode Treatment Groups use proprietary algorithms to group medical and pharmacy claims into episodes of care based on clinically similar conditions.

 Individuals with a chronic condition were identified using the following ETGs: diabetes (27-30), asthma (386-389), depression (95, 96), coronary artery disease (251-254, 256-265), congestive heart failure (267, 268), chronic obstructive pulmonary disorder (390-395), hyperlipidemia (47), and hypertension (278-281). We used episodes from the prior years to exclude new diagnoses of chronic conditions in the analysis year. For the comparison group of retail clinic nonusers with a chronic condition, we did not require an office visit as an inclusion criterion.

	We also used ETGs to describe the reason for the retail clinic visit and to describe the overall health of the members. Episode Risk Groups (ERGs) aggregate ETGs of similar clinical and risk characteristics.

 A person with multiple ETGs will have 1 or more ERGs. Each member was assigned a prospective ERG risk score, a weighted combination of those ERGs that predicts utilization. We defined high health risk as those members with a score of 7 or higher. This cutoff point captured those members incurring the top 0.5% of total claims spending.

	Demographic characteristics were compared between retail clinic users and nonusers using 

2 tests to assess whether clinic use was strongly dependent on sex, age, or type of health plan product. All available data describing an individual member were included in the analysis, including sex, age, health plan product, presence of a chronic condition, residence near a retail clinic, and ERG risk score. Health plan products were categorized as preferred provider organization (PPO), health maintenance organization (HMO), or consumer-directed health plan (CDHP). The PPO plans involve a network of preferred doctors and hospitals. Generally, services incurred with providers not in the network result in higher out-of-pocket expenses (ie, deductibles, copayments, and/or coinsurance). The HMO products require members to choose a primary care provider and require a copayment for most services. The CDHP products include major medical plans with high deductibles and out-of-pocket maximums, and some form of savings vehicle (eg, Health Reimbursement Arrangement, Health Savings Account) to fund expenses that are subject to the deductible. In the time period covered by the analysis, none of these products included an incentive for members to use retail clinics.

	We assessed whether members chose the retail clinic for minor conditions using the percentage of retail clinic episodes resolved in 1 visit. For people with chronic conditions, we analyzed whether they also saw a non—retail clinic provider for that condition. This study received no external funding.

	In 2007, among commercially insured members who were continuously enrolled, 40,686 members (2.8%) had at least 1 visit to a retail clinic. This constituted an increase of 71% over the previous year (results not shown). Among members who did not have a visit to a retail clinic, 651,514 members had at least 1 office visit for an immunization or for diagnosis of a condition that was potentially treatable in a retail clinic. 

compares the demographic characteristics of retail clinic users and nonusers. They differed significantly on nearly every measure analyzed. A higher percentage of retail clinic users were female, healthier, and younger compared with retail clinic nonusers. In both populations, a majority of members were enrolled in a PPO plan. A lower percentage of retail clinic users had chronic conditions and an ERG prospective risk score of 7 or higher. More than 90% of retail clinic users lived within 10 miles of a retail clinic, but fewer than 50% of nonusers did.

	We performed a logistic regression to identify the predictors of retail clinic use (

). All of the characteristics listed in Table 2 were significant predictors. This table indicates that compared with the reference group (members aged 25-44 years), all other age groups were less likely to use the retail clinic, after controlling for other characteristics. Members enrolled in CDHP plans, members enrolled in HMOs, and women were more likely to use retail clinics, whereas sicker members and members with chronic conditions were less likely. The most significant predictor in the model was whether the member lived within 10 miles of a retail clinic.

	We next sought to understand whether members appropriately chose the retail clinic for their care by analyzing the percentage of episodes of care resolved in 1 visit. We reasoned that if the episode that prompted the retail clinic visit resolved completely in that visit, the ailment was minor and using the retail clinic for its treatment was fitting. Overall, 94.8% of episodes with a retail clinic visit began with that visit. Moreover, 89.9% of episodes involved no other providers, and 88.3% were resolved in 1 visit, suggesting that the majority of visits were for minor conditions. 

 shows the percentage of episodes resolved in 1 visit for the top 10 episode types (as described by ETG and sub-ETG) seen in the retail clinic, and compares this percentage with that for episodes seen in a non—retail clinic setting. We excluded 2 of the most frequent episode types, routine inoculation (791, 0) and screenings and immunizations incidental to other services—immunizations (901, 7). As one would expect, for both of these episode types, nearly every episode was resolved in 1 visit, in both the retail clinic and the other settings. For all but 2 episode types, a greater percentage of episodes were resolved in 1 retail clinic visit than in 1 visit to other providers. This conclusion held when we applied the Bonferroni correction. For chronic sinusitis, there was no difference between the 2 settings. For routine exams, fewer retail clinic episodes were resolved in 1 visit. This may reflect the more open-ended nature of a routine exam, which could result in referrals for follow-up. We also performed this analysis separately for those members with and without chronic conditions, and separately for children aged 2 through 11 years, and the conclusions did not change (results not shown).

	Finally, we explored how often in the study year people with chronic conditions who were seen in a retail clinic also were seen by another provider for care of their chronic condition, which reflected a member’s ability to choose a care setting appropriate for more complex conditions. We compared that with the rate at which retail clinic nonusers with chronic conditions had provider visits for their chronic condition. For this comparison, we included all commercially insured members residing in Minnesota who had at least 1 chronic condition. When comparing the retail clinic users to the nonusers, we found that they differed in the same way as in the larger population (

). We limited this comparison to members 18 years and older, yielding 8859 retail clinic users and 386,600 nonusers with 1 or more chronic conditions. Retail clinic users with diabetes were less likely to see non—retail clinic providers for the care of their chronic conditions than were nonusers, and retail clinic users with hyperlipidemia or depression were more likely (

). There were no significant differences in these measures for the other conditions studied. The number of retail clinic users with chronic obstructive pulmonary disorder was too small to make a meaningful comparison. When we applied the Bonferroni correction, the difference for members with diabetes was no longer significant, although the differences for hyperlipidemia and depression remained significant.

	The number of retail clinics has increased significantly in the last decade. With growing acknowledgment of their ability to provide quality care at lower cost, insurers have developed strategies to encourage the use of such clinics. These policies have been criticized by those who worry that retail clinics will adversely affect the care received. In this study, we compared the characteristics of people who used retail clinics with the characteristics of those who did not, and estimated the predictors of retail clinic use. We explored whether members appeared to be making appropriate choices by examining the complexity of retail clinic visits and visits to non—retail clinic providers by members with chronic conditions.

	Overall, our findings of age and sex being significant predictors reaffirm the findings of previous research.

 We also found that proximity to a retail clinic was a significant predictor of retail clinic use. The percentage of care episodes resolved in 1 visit was higher for retail clinic users than for nonusers. This finding is consistent with an earlier study that found that retail clinic users were no more likely to have an “early return” visit.

 One interpretation of this finding is that the episodes seen in the retail clinic setting were less complex and were suitably treated there. Another interpretation is that non—retail clinic providers may have been more likely to schedule follow-up visits or to refer to other providers, and that retail clinics may discourage repeat visits. Claims data did not provide adequate information to judge if those episodes (whether occurring in a retail clinic or not) that included more than 1 visit were due to the proper referral of a patient with a more serious condition to a primary care provider or specialist, unnecessary referrals, or poor quality of care provided by the originating provider. For the retail clinic users, another possible explanation for the episodes requiring additional visits may be that the consumer underestimated the complexity of the condition and chose an inappropriate setting for treatment.

	Concerns about retail clinics disrupting the patient—provider relationship have not been fully explored. When we focused on members with chronic conditions, we found that retail clinic users were at least as likely as nonusers to have had a visit with another provider for their condition. No studies to date have addressed the impact of retail clinics on coordination of care. When a patient visits a retail clinic located in a clinic system with electronic health records, that record makes it possible for the patient’s primary care provider to know about the visit. Some retail clinics provide a record of the patient’s visit to the primary care provider at the patient’s request. In the absence of either of these options, a physician may not know about a patient’s retail clinic visit, which may affect coordination of care.

	This study has several limitations. Some chronic conditions (eg, depression) may have been overidentified because of limitations of the episode grouper logic. Missing episodes and orphan records resulting from the logic may have slightly affected the results by under- or overidentifying retail clinic visits. We could not ascertain how many members attempted to use the retail clinic but were turned away because their presenting condition was not appropriate for treatment there. One study suggested 2% of retail clinic patients were triaged to an emergency department or a primary care provider.

 In our analysis of why people with chronic conditions went to the retail clinic, we did not control for clinically significant combinations of acute and chronic conditions such as asthma and bronchitis. Finally, claims data presented an incomplete picture of the decision-making process that led the member to the retail clinic. The member’s stated symptoms may not match the diagnosis eventually coded on the claim by the retail clinic provider. Furthermore, we cannot know from the claims whether additional diagnoses (beyond the presenting complaint) were uncovered during the course of the retail clinic visit and resulted in follow-up visits to other providers. These new diagnoses may have resulted in a subsequent episode of care that was not captured by this analysis.

	Previous research on retail clinics focused on the purpose of the visits, the characteristics of the patients, cost, and quality of care. In this study, we explored whether members could ascertain when the severity of their condition indicated that a retail clinic would be an appropriate treatment setting. For the most common acute and preventive conditions, the choice of a retail clinic does not appear to have been associated with problematic self-triage, with more than 88% of episodes resolved in 1 visit. Sicker members appeared to be less likely to use retail clinics. Retail clinic use was not associated with a lesser frequency of ongoing visits to other providers for the treatment of chronic conditions. The degree to which patients with chronic conditions who use the retail clinic receive high-quality care for those conditions from their regular provider should be further studied. Another area for future study is whether healthier patients using retail clinics for routine and minor acute care affect the patient mix in primary care settings.

 From Blue Cross and Blue Shield of Minnesota (ARW, XTZ, WS, HR, EPB, NAG, TJS), Eagan, MN. Dr Wilson is now with the Medical University of South Carolina, Charleston, SC.

This study was funded by Blue Cross and Blue Shield of Minnesota.

MANAGERIAL

Care episodes treated in retail clinics appeared to be less complex than those treated in office settings.

Retail Clinic Versus Office Setting: Do Patients Choose Appropriate Providers?

 To describe rates of scheduled and unscheduled readmissions among midlife and older patients with diabetes and to examine associated socioeconomic and clinical factors.

Using the 2006 California State Inpatient Dataset, we identified 124,967 patients 50 years or older with diabetes who were discharged from acute care hospitals between April and September 2006 and examined readmissions in the 3 months following their index hospitalizations.

About 26.3% of patients were readmitted within the 3-month period following their index hospitalizations, 87.2% of which were unscheduled readmissions. Patients with unscheduled readmissions were more likely to have a higher comorbidity burden, be members of racial/ethnic minority groups with public insurance, and live in lower-income neighborhoods. Having a history of hospitalization in the 3 months preceding the index hospitalization was also a strong predictor of unscheduled readmissions. Almost one-fifth of unscheduled readmissions (constituting approximately 27,500 inpatient days and costing almost $72.7 million) were potentially preventable based on definitions of Prevention Quality Indicators by the Agency for Healthcare Research and Quality. Scheduled readmissions were less likely to occur among patients 80 years or older, the uninsured, and those with an unscheduled index hospitalization.

The predictors of scheduled and unscheduled readmissions are different. Transition care to prevent unscheduled readmissions in acutely ill patients with diabetes may help reduce rates, improving care. Further studies are needed on potential disparities in scheduled readmissions.

This study used a population-based data set to examine socioeconomic and clinical factors associated with scheduled and unscheduled readmissions after hospital discharge among older patients with diabetes.

A substantial percentage (12.8%) of readmissions were scheduled at least 24 hours in advance. 

The causes and predictors of scheduled and unscheduled readmissions were distinct, suggesting that scheduled readmissions should be separated from unscheduled readmissions in studies of readmission. 

The lower likelihood of scheduled readmissions among older and uninsured patients implies less access to advanced coordinated healthcare services for these groups.

Diabetes mellitus affects about 23.6 million individuals in the United States and is the seventh leading cause of death.

 Because of the complexity of the disease and its management, patients with diabetes are more likely to use healthcare services than the general population,

 with a total direct medical cost of about $116 billion and a mean cost that is 2.3 times higher than that of individuals without diabetes.

 Moreover, almost 6.3 million hospital stays in 2004 were by patients with diabetes as a principal or coexisting condition, costing almost $57.8 billion, or about 20% of total hospital costs, while hospitalizations principally for diabetes cost about $3.9 billion.

 Many of these hospital stays included readmissions of the same patients, and hospital readmission has been a major quality concern in diabetes care. However, there are few population-based studies on readmission among patients with diabetes and its risk factors. Jiang et al

 reported that more than 30% of older patients with diabetes were readmitted during the same year as their index hospitalization in 5 states in 1999. Readmissions accounted for 55.2% of total hospital stays, and the mean total hospital cost for patients with readmissions was 2.5 times higher than that for patients without readmissions. They also reported a high risk of readmission among racial/ethnic minority groups and variations in readmission rates by insurance type.

 observed racial/ethnic disparities among hospital readmissions in Pennsylvania hospitals between 1994 and 2001 and noted the positive effect of public health clinics in reducing hospital readmissions among low-income patients with diabetes.

 A gap in the literature on hospital readmissions among patients with diabetes has been the lack of differentiation between scheduled and unscheduled readmissions.

 Readmissions are often assumed to be unscheduled, but this may not be true, especially for patients with existing complex conditions such as diabetes. The need for attention to the type of readmission has been raised,

 have explicitly focused on unscheduled readmissions only. No study seems to have assessed what the reasons for and the factors associated with scheduled readmissions are, and whether they are similar to those associated with unscheduled readmissions.

 Another gap in the literature is that studies have often focused on readmissions within 1 month of hospital discharge

 or on readmissions among Medicare patients.

 This short observation window is because hospital readmission has been widely studied in terms of its relationship to hospital performance.

 However, this may be too narrow an approach to evaluate readmission risk from the perspective of patients, for whom avoiding a later readmission is as important as avoiding an early readmission. Investigations focusing on patient risk for readmission have often used a 3-month window following hospital discharge,

 The objectives of this study were to examine and compare the demographic, socioeconomic, and clinical factors associated with scheduled and unscheduled readmissions in midlife and older patients with diabetes using a population-based data set from California. We also examined common reasons for scheduled and unscheduled readmissions and estimated the cost and extent of potentially preventable readmissions using Prevention Quality Indicators (PQIs) of the Agency for Healthcare Research and Quality (AHRQ).

 We analyzed the 2006 California State Inpatient Dataset (SID), developed as part of the Healthcare Cost and Utilization Project (HCUP) sponsored by the AHRQ.

 The publicly available California SID includes hospital discharge abstract data (such as demographic characteristics, clinical information, resource use, and payers) from short-term, nonfederal, general, and specialty hospitals. California was selected for the analysis because it has a large and ethnically/racially diverse population

 and provides in its SID an encrypted unique patient identifier (UPI) that is used to link hospital readmissions to a distinct patient. The California Office of Statewide Health Planning and Development (OSHPD)

 routinely monitors the quality of hospital inpatient data and corrects or edits the data if necessary. They also ask reporting institutions to make corrections when their reports do not meet the error tolerance level (now 2%) that the state has established.

 Our sample included patients 50 years or older with diabetes as the principal or a secondary diagnosis, identified using the AHRQ’s Clinical Classifications Software (CCS 45 [diabetes mellitus without complications] and CCS 50 [diabetes mellitus with complications]),

 all of whom were admitted to California hospitals at least once between April and September 2006. We defined the first admission as the index hospitalization and identified readmissions within 3 months of the index hospitalization using the encrypted UPI. As did Jencks et al,

 we counted no more than 1 readmission for each patient and focused on the characteristics of the first readmission (ie, scheduled or unscheduled) in the analysis. Similar to the approach by Jiang et al,

 we excluded patients with a missing UPI, patients with the same UPI but with an inconsistent age (differing by >1 year) or sex, and patients with a missing admission or discharge month.

 The outcome variables of this study were scheduled and unscheduled readmissions. We used the SCHED variable in the California SID,

 which refers to whether an admission is scheduled at least 24 hours in advance. Therefore, we defined unscheduled readmissions as readmissions that were not scheduled at least 24 hours in advance and scheduled readmissions as those that were scheduled at least 24 hours in advance. The SCHED variable is derived from the type of admission in the California hospital inpatient data report collected by the California OSHPD, from which the California SID is developed for the HCUP. The California OSHPD

 provides detailed guidelines for each variable in the report, including the type of admission, and conducts multistep validation checks and edits.

 We examined patient sociodemographic and clinical characteristics that were potentially associated with readmission variables, representing the 3 major categories of the Behavioral Model of Health Services Use.

 This model posits that potential and realized health services use is determined by interactions among predisposing, enabling, and need characteristics of individuals and the healthcare systems in the communities where they reside. Predisposing factors are individual characteristics existing before illness that determine the likelihood of seeking care and are measured by age, sex, and race/ethnicity. Enabling factors provide the means to use health services and include individual-level and community-level factors measured by primary payer type, resident location, and neighborhood median income. The California SID does not provide the patient’s exact income level, so we used as a proxy variable the median household income for the patient’s zip code in 2006. Perceived health status and evaluated health status determine the need for health services measured by comorbidities, prior hospitalization history, and variables related to index hospitalizations. The severity of conditions was measured by the number of chronic conditions in the 18 body systems using the Chronic Condition Indicator developed by the AHRQ.

This population-based study examines socioeconomic and clinical factors associated with scheduled and unscheduled readmissions after discharge among older patients with diabetes.

October 2010

Costs of Heart Failure-Related Hospitalizations in Patients Aged 18 to 64 Years

Initial Experience With a First-to-Market Member Accountability-Based Insurance Product

Retail Clinic Versus Office Setting: Do Patients Choose Appropriate Providers?

Scheduled and Unscheduled Hospital Readmissions Among Patients With Diabetes

Variation in Prescription Use and Spending for Lipid-Lowering and Diabetes Medications in the Veterans Affairs Healthcare System

Shared Savings Program for Accountable Care Organizations: A Bridge to Nowhere?

Assessing the Accuracy of Drug Profiles in an Electronic Medical Record System of a Washington State Hospital

Effect of a Patient Panel-Support Tool on Care Delivery

Hospital Readmission Among Participants in a Transitional Case Management Program

Mental Health Services Utilization Among Women Experiencing Intimate Partner Violence