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Background: In response to high cost and inadequate quality, the healthcare system is in the midst of a transition from paying for volume to paying for value. Billions of dollars could be saved through more effective medication use, and evidence supports the role of the community pharmacist in lowering healthcare cost and improving healthcare quality through medication optimization. Despite this, value-based payment models for community pharmacies are rare, and those that do exist have not been critically evaluated and implementation in a commercially insured population is rare. Objective: The first objective was to design and test a conceptual model of pharmacy value. The second objective was to evaluate variation in the value community pharmacies provide a commercial insurer by assessing the relationship between attributed patients’ healthcare quality and cost. Methods: This study used prescription and medical claims data for 2012 and 2013 from a large commercial insurer in Iowa and South Dakota. Patients were attributed to the pharmacy filling the majority of their prescriptions. Pharmacies’ weekly prescription volume and Sunday prescription filling behavior were used as structural measures of healthcare quality. Percent of days covered (PDC) metrics for beta-blockers, statins, renin-angiotensin system antagonists and non-insulin diabetes agents were used as process metrics. Pharmacies were excluded if the denominator for any PDC metric was less than 15. Outcome metrics consisted of a non-trauma, non-cancer, unplanned hospitalization rate and a non-trauma ED visit rate. Cost impact was categorized into pharmaceutical, medical, and total cost of care. High quality pharmacies with typical or low associated costs or low cost pharmacies with typical to high quality were identified as high value and vice versa for low value. All metrics were risk-adjusted using mixed effect models with a random pharmacy intercept. The ratio between observed and expected quality scores was used for quality scoring. Quality outliers were identified by comparing the 95% CI around pharmacies’ risk-adjusted scores to the all-pharmacy risk-adjusted score mean. A t-test was used to assess variation in pharmacy value. Results: There were 171 pharmacies and 74,581 patients eligible for scoring on all quality metrics. Mixed effects models observed a small but significant impact of pharmacy on process and outcome healthcare quality. No relationship between structures and processes, processes and outcomes was detected. Ten pharmacies were scored as high quality and nine as low quality. Similar numbers were identified for cost outliers, and significant variation in value was detected. Implications/conclusions: Results support the hypothesis that high and low value pharmacies exist. A well-designed value-based payment model could be used to create incentives for pharmacists to enhance care for commercially insured patients, but validation is needed to ensure that incentives are aligned appropriately.

In most markets, competition induces efficiency by ensuring that goods are priced according to their marginal cost. This is not the case in health insurance markets. This is due to the fact that the cost of a health insurance policy depends on the characteristics of the consumer purchasing it, and asymmetric information or regulation often precludes an insurer from matching the price an individual pays to her expected cost. This disconnect between cost and price causes inefficiency: When the premiums paid by consumers do not match their expected costs, consumers may sort inefficiently across plans. In this dissertation, I study the effects of policies used to alleviate selection problems. In Chapter 1, I develop a model to study the effects of risk adjustment on equilibrium prices and sorting. I simulate consumer choice and welfare with and without risk adjustment in the context of a Health Insurance Exchange. I find that when there is no risk adjustment, the market I study unravels and everyone enrolls in the less comprehensive plan. However, diagnosis-based risk adjustment causes over 80 percent of market participants to enroll in the more comprehensive plan. In Chapter 2, we study an unintended consequence of risk adjustment: upcoding. When payments are risk adjusted based on potentially manipulable risk scores, insurers have incentives to maximize those risk scores. We study upcoding in the context of Medicare, where private Medicare Advantage plans are paid via risk adjustment but Traditional Medicare is not. We find that when the same individual enrolls in a private plan her risk score is 5% higher than if she would have enrolled in Traditional Medicare. In Chapter 3, we study two forms of insurance for insurers: Reinsurance and risk corridors. Protecting insurers from risk can lower prices and improve competition by inducing entry into risky markets. It can also induce inefficiencies by causing insurers to manage risk less carefully. We use simulations to compare the power of reinsurance and risk corridors to protect insurers against risk while limiting efficiency losses. We find that risk corridors are always able to limit insurer risk with the lowest efficiency cost.

Angiotensin Converting Enzyme (ACE) inhibitors are one of the recommended prevention therapy for patients with acute myocardial infarction (AMI) in the clinical guidelines. Two types (tissue and non-tissue) of ACE inhibitors are available with huge cost difference but the comparative treatment benefit and risk between them are unclear. The objective of this study was to investigate the comparative treatment effectiveness and safety between tissue and non-tissue ACE inhibitors among elderly patients after AMI. This is a retrospective cohort study with intention to treatment design using Medicare service claims files from 2007 to 2009 with Medicare beneficiaries 65 years or older after the index AMI hospitalization and who survived to discharge between January 1 2008 to December 31 2008 and received ACE inhibitors (N=34,679). Risk adjustment and instrumental variable (IV) analyses were used to investigate comparative treatment effectiveness including AMI, stroke, heart failure requiring hospitalization, all-cause mortality and a composite of the endpoints during the follow-up and the comparative treatment safety - a composite of hyperkalemia and acute renal failure requiring hospitalization during the follow-up. Both the risk adjustment and IV analyses showed no significant differences between tissue and non-tissue ACE inhibitors for the investigated outcomes of the comparative treatment effectiveness and safety in the study cohort. However, subgroup analyses from the IV models showed that tissue ACE inhibitors as compared to non-tissue ACE inhibitors increased the hazard risk by approximately 30% to 60% (p < 0.05) for heart failure requiring hospitalization among the patients with heart failure and reduced hazard risk by approximately 30% to 40% (p <0.05) for AMI among patients without heart failure. In conclusion, though this study did not find significant difference between tissue and non-tissue ACE inhibitors for the comparative treatment effectiveness and safety in the study cohort, considerable comparative treatment effectiveness may exist in the subgroup of patients with and without heart failure in the elderly patients after AMI.

Risk adjustment models are used to predict health care costs of insurees and represent an important part of mechanisms for redistribution of funds among insurance companies. In the Czech Republic, pharmacy-based cost groups (PCGs) were introduced into the risk adjustment model in 2018, reflecting the costs of chronic diseases in addition to age and gender. The thesis reviews the model for the most expensive chronic disease - renal failure. Using the sample of General Health Insurance fund (GHI) insurees reported with typical health care consumption for kidney disease in years 2015-2018, we tested the current model and subsequently modified the classification criteria for PCG "renal failure". The classification based on the number of dialysis procedures proved to be much better indicator of costs than the currently used consumption of typical drugs. The incorporation of dialysis-based approach into the PCG model improved the explained variation from 26 % to 49 %, and the predictive power increased substantially. The study suggests improvements of the Czech risk adjustment model and proposes a fairer fund redistribution among insurance companies, while no additional data collection is needed.

BACKGROUND: There is continued attention towards using patient demographic and clinical characteristics available in health administrative data when case mix adjusting the measurement of length of stay (LOS) for hospitalized children. However, little is known about what proportion of children’s LOS is explained by these characteristics. OBJECTIVES: The objectives of the study were to quantify the amount of variation in LOS within and across hospitals that is explained by demographic and clinical factors of hospitalized pediatric patients. METHODS: A retrospective cohort analysis was completed of 818,848 hospitalizations for any reason occurring from 1/1/2014 to 12/31/2014 in one of 44 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. A generalized linear model was derived to simultaneously regress demographic factors [age, race/ethnicity, payer, rural residence, health professional shortage area (HPSA) residence, income, and distance traveled], and clinical factors (severity of illness, type and number of chronic conditions) on LOS. The percentage of LOS attributable to each characteristic within each hospital was quantified using the covariance test of the hospital random effect. RESULTS: The factors with the greatest impact on LOS were severity of illness and chronic condition type and number, with a median (interquartile range) of 16.8% (IQR 15.0%-19.4%) and 4.0% (IQR 2.9%-4.5%) of LOS, respectively, explained by these characteristics across hospitals. LOS varied significantly (p<0.05) with both severity of illness and chronic condition type and number for all 44 hospitals in the cohort. All patient demographic factors, (age, race/ethnicity, payer, rural residence, HSPA residence, income, and distance traveled) had minimal impact on LOS, with <0.1% of LOS explained by each characteristic. Across hospitals, 78.3% (IQR 75.8-80.2%)] of LOS remained unexplained by the patient characteristics under study. CONCLUSIONS: Patients’ clinical characteristics ascertained from administrative data account for approximately one-fifth of LOS whereas their demographic characteristics account for a negligible amount. Efforts to optimize the efficiency of inpatient care for hospitalized children might benefit from uncovering how much of the vast amount of unexplained LOS is due to modifiable aspects of care quality. / 2018-06-16T00:00:00Z

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第18191号 / 社医博第52号 / 新制||社医||8(附属図書館) / 31049 / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 中山 健夫, 教授 佐藤 俊哉, 教授 木村 剛 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

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Risk-adjustment schemes are used to monitor hospital performance, on the assumption that excess mortality not explained by case mix is largely attributable to suboptimal care. We have developed a model to estimate the proportion of the variation in standardised mortality ratios (SMRs) that can be accounted for by variation in preventable mortality. The model was populated with values from the literature to estimate a predictive value of the SMR in this context-specifically the proportion of those hospitals with SMRs among the highest 2.5% that fall among the worst 2.5% for preventable mortality. The extent to which SMRs reflect preventable mortality rates is highly sensitive to the proportion of deaths that are preventable. If 6% of hospital deaths are preventable (as suggested by the literature), the predictive value of the SMR can be no greater than 9%. This value could rise to 30%, if 15% of deaths are preventable. The model offers a 'reality check' for case mix adjustment schemes designed to isolate the preventable component of any outcome rate.

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