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The American Journal of Managed Care April 2015
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Persistent High Utilization in a Privately Insured Population
Wenke Hwang, PhD; Michelle LaClair, MPH; Fabian Camacho, MS; and Harold Paz, MD, MS
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Persistent High Utilization in a Privately Insured Population

Wenke Hwang, PhD; Michelle LaClair, MPH; Fabian Camacho, MS; and Harold Paz, MD, MS
This study describes the small number of individuals responsible for larger shares of healthcare cost persistently over a 3-year period.
The frequency of emergency department (ED) use also increased as persistency increased. This was true for ED visits resulting in subsequent hospital admissions, as well as ED visits resulting in outpatient discharge. For ED visits resulting in outpatient discharge—indicative of potentially avoidable ED visits—the frequency of use for persistent high users (0.8 visits per enrollee per year) was more than 5 times higher than for never high users (0.1 visits per enrollee per year). However, the ω2 associated with both categories of ED visits indicated that neither were strong predictors of persistent high users.


The comorbidity conditions of each group were characterized using ACE-27 to determine the types of conditions present (Table 3). Comorbidities with moderate association with persistence were: hypertension (φc = 0.36), respiratory disorders (φc = 0.31), diabetes (φc = 0.28), congestive heart failure (φc = 0.27), and arrhythmia (φc = 0.25). The number of comorbidities from 2008 to 2011 (φc = 0.36) and comorbidity severity from 2008 to 2011 (φc = 0.30) were also associated with the persistent high-user group. The presence of at least 2 conditions significantly increased the likelihood of being in the persistent high-user group when compared with having only 1 condition, no condition, or if any conditions present have mild overall severity.

The variations in comorbidities across the 4 groups of users exhibit a consistent dose-response pattern. The most common conditions in all user groups, in decreasing order, were hypertension, diabetes, and respiratory illnesses. The prevalence of these 3 conditions among persistent high users was 67%, 36%, and 31%, respectively; additionally, the prevalence was much lower in the never high-user group, at 14%, 4%, and 2%, respectively. Notably, there was also high prevalence of renal disease (17%), arrhythmia (17%), congestive heart failure (17%), neuromuscular disorder (17%), and solid tumor (14%) among persistent high users. A trend of higher prevalence of conditions with increasing persistence was consistent across nearly all conditions. Among never high users, 4% of enrollees were classified as having moderate to severe comorbidity burden, compared with incidental high users (25%), frequent high users (45%), and persistent high users (55%).

Exploratory Predictive Model

A multivariate logistic regression model was employed to explore the predictive power of using year 1 data (2008) to determine if an individual would be classified into the persistent high-user group for the following 3 years (2009-2011). In the test sample, the area under curve statistic (c-value), a measure of classification accuracy, was 0.923; this indicates a very high level of accuracy, with 1.00 being the highest possible level. An optimal sensitivity and specificity can be chosen by selecting a point along the receiver operating characteristic curve for the logistic model. In this case, the sensitivity, specificity, and positive predictive value results in values of 0.80, 0.90, and 0.19, respectively.


The persistent high-user group in this study had a high prevalence of several common chronic conditions (eg, the aforementioned hypertension, diabetes, and respiratory illness), and members of this group were also more likely to have multiple conditions than other groups. The highest expenditure categories for persistent high users were professional services and medication; they also had many more outpatient primary and specialty care visits than any other group. Collectively, this indicates that persistent high users have multiple common chronic conditions and, compared with other groups, a higher overall disease burden, more frequent visits to outpatient primary and specialty care, and they also take multiple medications at a cost nearly 18 times that of never high users.

The predictive model suggests the expenditure and comorbidity data in prior years may be sufficient to predict membership in a high-user group in later years with a high degree of accuracy, indicating a real potential for an organization to project persistent high-cost enrollees based on prior data captured in this study. However, high accuracy did not translate into high model performance in some aspects. For example, the positive predictive value of the best model was close to 20%, indicating that only 1 in 5 patients identified as persistent high users will actually fall in the high persistence group. Whether any other classifier may be able to improve on this baseline positive predictive value is an open question. Total expenditure may be a main determinant of future persistence, or a strong surrogate for its underlying drivers.

Due to the limitations of working with insurance claims data, this study was not able to include certain data that might have been valuable to this analysis, such as socioeconomic variables and location of services. Without such information, it is impossible to determine if certain enrollees utilize more or less expensive locations for services. Additionally, the study data set only includes data from a single employer in Pennsylvania, limiting generalizability of results. However, the time frame (4 years) and large enrollee population (employer-insured population) provide unique insight into understanding persistent high users of healthcare. This insight will benefit those seeking ways to improve the health and utilization patterns of persistent high users in employer-based populations.

One method to improve outcomes for high-cost patients is to address the prevention and care of chronic conditions. Charlson and colleagues found that patients with multiple chronic conditions incurred significantly more healthcare costs than those with just 1 chronic condition.8 In the employer-based insurance population in this study, having chronic conditions and having multiple conditions were much more prevalent among persistent high users. Provision of medical home care has been shown to be an effective approach to address such conditions. 9,10 The use of nurses for a home telecare intervention for congestive heart failure patients has also shown positive results.11 In recent years, the use of paramedics for such home visits has also been considered. Community paramedicine has the potential to fill in many roles, including patient education, patient adherence to care plans, and patient self-care.12

Value-based insurance design (V-BID) is another potential option for improving outcomes of patients with persistent high cost. V-BID has gained increasing popularity among employers and is included as an allowable form of coverage by the Affordable Care Act.13 The hope is that improved patterns of care will increase the quality of care received, indirectly decreasing healthcare costs. V-BIDs can focus on common chronic conditions, such as those prevalent in the persistent high-user group in our study population, in an attempt to improve outcomes and control costs. Evidence-based treatment guidelines exist for managing these chronic conditions. One study assessing the effectiveness of a V-BID that adjusted cost-sharing for chronic medications showed an increase in medication adherence and increased participation in a care management program.14 Another study focused on a V-BID that targeted high users of care by incentivizing enrollment in a disease management program,15 which resulted in an increase in medication adherence and a decrease in total healthcare costs. Other studies utilizing various V-BIDs have shown improved medication adherence, but little change in healthcare expenditures.16 While these benefit designs may not always result in immediate cost savings, they often show an improvement in outcomes for the patients enrolled. This may be an effective way to address the needs of patients with persistent high utilization and costs, with the potential for cost savings in the long term.


This study highlights the need to proactively engage employees and their dependents for primary and secondary prevention of common chronic diseases before an individual’s health status, healthcare utilization, and medical cost become difficult to manage.

Author Affiliations: Department of Public Health Services (WH, ML, FC, HP), and Department of Medicine (HP), Penn State University College of Medicine, Hershey, PA.

Source of Funding: None.

Author Disclosures: At the time this study was conducted, Dr Paz was a board member and CEO of the Penn State Hershey Medical Center and was the Dean of the Penn State University College of Medicine. Dr Hwang, Ms LaClair, and Mr Camacho report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (WH, ML, FC, HP); acquisition of data (WH); analysis and interpretation of data (WH, ML, FC, HP); drafting of the manuscript (WH, ML); critical revision of the manuscript for important intellectual content (WH, ML, FC, HP); statistical analysis (WH, FC); administrative, technical, or logistic support (ML); and supervision (WH, HP).

Address correspondence to: Wenke Hwang, PhD, Penn State University College of Medicine, Department of Public Health Sciences, 90 Hope Dr, Ste 2200, Hershey, PA 17033. E-mail:
1. Berk ML, Monheit AC. The concentration of health care expenditures, revisited. Health Aff (Millwood). 2001;20(2):9-18.

2. Coughlin TA, Long SK. Health care spending and service use among high-cost Medicaid beneficiaries, 2002-2004. Inquiry. 2009;46(4):405-417.

3. Shenkman E, Knapp C, Sappington D, Vogel B, Schatz D. Persistence of high health care expenditures among children in Medicaid. Med Care Res Rev. 2007;64(3):304-330.

4. Monheit AC. Persistence in health expenditures in the short run: prevalence and consequences. Med Care. 2003;41(7 suppl):III53-III64.

5. Naessens JM, Stroebel RJ, Finnie DM, et al. Effect of multiple chronic conditions among working-age adults. Am J Manag Care. 2011;17(2):118-122.

6. Janicki H. Employment-based Health Insurance: 2010. Washington, DC: US Department of Commerce, Economics and Statistics Administration, US Census Bureau; 2013.

7. Fleming ST, Sabatino SA, Kimmick G, et al. Developing a claim-based version of the ACE-27 comorbidity index: a comparison with medical record review. Med Care. 2011;49(8):752-760.

8. Charlson M, Charlson RE, Briggs W, Hollenberg J. Can disease management target patients most likely to generate high costs? the impact of comorbidity. J Gen Intern Med. 2007;22(4):464-469.

9. Sherman BW, Webber A, McSwain C. Employer perspectives on the patient-centered medical home. Med Care Res Rev. 2010;67(4):485-491.

10. Rosenthal TC. The medical home: growing evidence to support a new approach to primary care. J Am Board Fam Med. 2008;21(5):427-440.

11. Jerant AF, Azari R, Nesbitt TS. Reducing the cost of frequent hospital admissions for congestive heart failure: a randomized trial of a home telecare intervention. Med Care. 2001;39(11):1234-1245.

12. Crawford CZ. The Feasibility and Role of Community Paramedicine in Nebraska. Nebraska EMS/Trauma Program; 2011.

13. Fendrick AM, Martin JJ, Weiss AE. Value-based insurance design: more health at any price. Health Serv Res. 2012;47(1, pt 2):404-413.

14. Clark B, DuChane J, Hou J, Rubinstein E, McMurray J, Duncan I. Evaluation of increased adherence and cost savings of an employer value-based benefits program targeting generic antihyperlipidemic and antidiabetic medications. J Manag Care Pharm. 2014;20(2):141-150.

15. Kim YA, Loucks A, Yokoyama G, Lightwood J, Rascate K, Serxner SA. Evaluation of value-based insurance design with a large retail employer. Am J Manag Care. 2011;17(10):682-690.

16. Lee JL, Maciejewski M, Raju S, Shrank WH, Choudhry NK. Value-based insurance design: quality improvement but no cost savings. Health Aff (Millwood). 2013;32(7):1251-1257.
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