Identifying Future High Cost Individuals within an Intermediate Cost Population

[wanghaisheng]

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874657/pdf/nihms755930.pdf

[wanghaisheng]

The goal should not only be to target the highest cost group but also aim interventions at preventing individuals from entering into this group in the first place. Predicting healthcare costs is an option to shape healthcare delivery programs and potentially improve health and control costs. Health cost prediction models often incorporate demographic information and information on clinical conditions based on data from medical records or claims databases 5.

Clinical conditions have been entered into prediction models as the diagnostic cost group (DCG), prevalent chronic conditions, or counts of chronic conditions. DCG models were originally developed to match HMO payments to the healthcare needs of enrollees. The system uses patients’ age, gender and medical diagnosis profiles to predict healthcare expenditures11–17. While these methods have been broadly used in Medicare databases, they are also applicable to private insurance and Medicaid databases 18. However, the system is limited by its requirement of special resources (e.g., commercial software and expertise), dependence on a common classification structure within ICD-9 codes, and accuracy of these codes.

As an alternative, the presence of certain chronic conditions can predict health expenditures in various settings 19–22. A third approach to predicting future costs is to use the count of chronic conditions. Several studies suggested that a simple count of chronic conditions can predict the length of hospital stay and mortality 23 and amount of health expenditures 19,22. The study by Farley et al 19 showed that not only the simple count of diagnose clusters but also the counts of prescriptions and physician visits were better predictors of future costs than the comorbidity measures (i.e. Charlson and Elixhauser indexes) with a count of diagnose clusters being the best predictor among all measurements examined. Similarly, Fleishman and Cohen 22, using data from the national Medical Expenses Survey, compared the ability of the DCG method, counts of chronic conditions, and the presence of ten prevalent chronic indicators to predict the top ten percent medical expenditure. The results showed that the count of chronic conditions significantly predicted future high-cost, controlled for DCG category, demographic characteristics and self-reported functional status. To advance our understanding of how population management principles should guide the structure of care delivery, we sought to determine what factors correlate with transitioning to the highest cost segment of the population within the VCC program 10. Using demographic data, diagnosis information, and medication utilization of healthcare utilization, we describe the factors that are associated with the transition of individuals from an intermediate cost segment to a high cost segment of the population

[wanghaisheng]

Our study, by systematically comparing the ability of the count of chronic conditions, the ten most prevalent chronic indicators, counts of prescriptions, and counts of total hospital visits to predict future high-costs, had results consistent with other studies 19,22. Our prediction models, however, combined clinical and administrative data without the complexity of DCG scores. Hence, the results derived from these models may be easier to implement. Moreover, our study focused on an uninsured adult population whose income was under 200% FPL. Our results may provide unique insights relevant to the newly insured populations under Medicaid expansion.

Noticeably, the model discrimination presented by this study is lower (c=0.68) than the results presented by other studies (c ranged from 0.81–0.85) 22,26,27. This difference may be a consequence of several factors. Our model included the counts of chronic conditions and presence of individual chronic conditions as the main predictors of high costs. These indicators are sensitive to information on severity of the conditions. For example, a study by Omachi and colleagues 28 showed that adding COPD severity measures significantly improved predictions of medical costs in the following year among a cohort of patients with COPD. Adding further information regarding the severity of each condition may increase the predictive power of the model. Additionally, the overall prevalence of chronic conditions were relative low in our dataset, likely due to the population being younger (mean age=43.3 years, standard deviation=12.6 years). Leaders and researchers should select the model that best fits the demographics of the population of interest.

This study has several limitations. Our dataset may not have contained all data on utilization or diagnoses for each patient. In addition, similar to the cost prediction results drawn from other administrative databases, we are unable to adjust the heterogeneities of disease conditions in the cost prediction model. Finally, the patient population included in this study is medically underserved and they are relatively younger and healthier, thus the results may not be generalizable to other patient population segments, such as those more affluent or individuals with better access to care, and those with older age and sicker conditions. Further model validation studies are needed to confirm our findings.

In conclusion, the results from this study show that a simple model including demographics and health utilization indicators is associated with high future costs. Specific medical diagnoses, such as congestive heart failure, and the count of chronic conditions were also associated with higher future utilization. Further validation of the models is recommended to confirm the predictive capacity of this approach. If confirmed, this approach could be used to identify high-risk individuals and target interventions that decrease utilization and improve health.

[wanghaisheng]

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[wanghaisheng]

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4194677/pdf/hcfr-21-3-093.pdf Principal Inpatient Diagnostic Cost Group Model for Medicare Risk Adjustment

https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Reports/downloads/pope_2000_2.pdf Diagnostic Cost Group HierarchicalCondition Category Models for Medicare Risk Adjustment

[wanghaisheng]

http://www.sciencedirect.com/science/article/pii/S109830151060575X http://ac.els-cdn.com/S109830151060575X/1-s2.0-S109830151060575X-main.pdf?_tid=ecef7918-8cad-11e7-bcd9-00000aab0f01&acdnat=1504006609_b34eced1e98439525f31ed487de5e0f6 The Estimation Power of Alternative Comorbidity Indices

[wanghaisheng]

Horizon Effects and Adverse Selection in Health Insurance Markets https://pdfs.semanticscholar.org/0447/5a4cff3f6cbc70635acc553efbf706f16a49.pdf

The idea of extending contract length interacts with a number of issues related to health insurance markets.First, two existing patterns of demand for insurance can influence the effect of contract length reform. Inertia in plan choice (as documented in Handel, 2013) implies that individ- uals might be slow to upgrade their coverage after moving to two-year contracts. However, one of the main results of the theoretical section is that the supply effect alone generates more coverage, even in the case in which the demand curve stays fixed. This result implies that inertia would reduce the efficiency gains of the reform, but not eliminate them.18 More- over, there is some evidence that consumers have different valuations for insurance contracts Bundorf et al., 2012), i.e., that there is some amount of horizontal differentiation. These findings imply that contract length reform is particularly attractive as it preserves consumer choice. In particular, the components of insurance contracts that display the most preference heterogeneity (i.e., plan network) are largely orthogonal to horizon. Another challenge to changing contract length in practice is commitment. In particu- lar, moving to longer contracts involves restricting the possibility for individuals to change plans after a year, even for such individuals that incur greater out-of-pocket costs as a result. Currently, individuals can change their coverage around the year when any one of several qualifying life events occurs (such as marriage or change of employment status). Such exemptions could be abused to circumvent coverage change restrictions. While practical en- forceability constraints are unlikely to be much more problematic for two-year contracts than they are with one-year contracts, they are important when considering contracts that extend further into the future. Handel et al. (2016) provides an in-depth discussion of long-term contracts with imperfect commitment.

This paper shows how extending the horizon of health insurance contracts impacts adverse selection in these markets. The main contribution of this paper is to show the implications of the dynamics of health risk over time, as opposed to simply its cross-sectional distribution. We show that increasing the contract horizon is another policy instrument that can be used to reduce selection. Conceptually, we argue that private information is endogenous to contract length because individual risk is harder to predict at longer horizons. This decrease in risk predictability is strongly borne by the data. Estimating a model of ACA-like exchanges, we find the effect of extending contracts to two years would be to expand coverage by about 6%. We also find positive, albeit moderate, welfare gains.

We study how increasing contract length affects adverse selection in health insur- ance markets. While health risks are persistent, private health insurance contracts in the U.S. have short, one-year terms. Short-term, community-rated contracts allow patients to increase their coverage only after risks materialize, leading to market unrav- eling. Longer contracts ameliorate adverse selection because both demand and supply exhibit horizon effects. Intuitively, longer horizon risk is less predictable, thus elevating demand for coverage and lowering equilibrium premiums. We estimate risk dynamics using data from 3.5 million U.S. health insurance claims and calculate counterfactual coverage and welfare in equilibrium with two-year contracts. We predict that such contracts would increase coverage by 6% from its initial level and yield average annual welfare gains of $100–$200 per person. Welfare gains from increased enrollment would partly offset by exposing those with low coverage to greater risk.