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BackgroundGiven that the quinolones is one of the antibacterial classes most frequently used to treat patients with bacterial infections in the United States, any change in prescribing patterns of quinolones will impact Medicaid medical expenditures.ObjectivesThis study was undertaken to examine trends in utilization, reimbursement, and prices of quinolone antibacterials for the US Medicaid population.MethodsThe publicly available Medicaid State Drug Utilization outpatient pharmacy files were used for this study. Quarterly and annual prescription counts and reimbursement amounts were calculated for each of the quinolones reimbursed by Medicaid from quarter 1, 1991 through quarter 2, 2015. Average per-prescription reimbursement, as a proxy for drug price, was calculated as the drug reimbursement divided by the number of prescriptions.ResultsThe total annual number of quinolone prescriptions increased 402%, from 247,395 in the first quarter of 1991 to 1.2 million in the second quarter of 2015, peaking at 1.3 million in the first quarter of 2005. Similarly, the total reimbursement for quinolone agents increased by 245.5% over the same period. More than 80% of quinolone prescriptions reimbursed by Medicaid were for the second-generation agent, ciprofloxacin, and the third-generation agent, levofloxacin. The average payment per prescription for quinolones increased from US$43.8 in the first quarter of 1991 to US$87.6 in the second quarter of 2015.ConclusionsA substantial rise in Medicaid expenditures on quinolones was observed during the 25-year study period, which was mainly because of rising utilization. Therefore, there is a need for additional research that has access to clinically relevant data with which to measure the rate of inappropriate quinolone use among the Medicaid population and associated clinical outcomes and healthcare costs.Electronic supplementary materialThe online version of this article (doi:10.1007/s41669-016-0007-y) contains supplementary material, which is available to authorized users.

rare diseases, off-label drug use, pediatrics, infants, children.Many pediatric patients with rare diseases use drugs off-label due to limited data in pediatric patients. Off-label treatment remains an important public health issue for neonates, infants, children, and adolescents, especially for pediatric patients with rare diseases. For patients with rare diseases, the majority of medications have no or limited information in labelling for pediatric use. Children present unique considerations in clinical trials due to ethical and clinical concerns, which have limited and even discouraged testing of drugs in the pediatric population. Numerous legislative measures have been enacted to address barriers in pediatric drug testing. This research reviewed off-label medication use in rare pediatric diseases, evaluated recent medication uses in pediatric clinical practice, discussed key regulations for rare pediatric diseases, and summarized recent drug approvals for rare pediatric diseases. This study demonstrates the ongoing medical need for newly approved medications to treat pediatric rare diseases and revealed the positive impact of regulations from the Orphan Drug Act of 1983 to the Research to Accelerate Cures and Equity (RACE) for Children Act on drug development and off-label medication practice in rare pediatric disease management. This article provides informative historical background and current considerations of off-label use of medications in neonates, infants, children, and adolescents with rare diseases.

Objective: This study estimated nationally representative medical expenditures of gynecologic cancers, described treatment patterns and assessed key risk factors associated with the economic burden in the United States. Methods: A retrospective repeated measures design was used to estimate the effect of gynecologic cancers on medical expenditures and utilization among women. Data were extracted from the Medical Expenditure Panel Survey (weighted sample of 609,787 US adults) from 2007 to 2014. Using the behavioral model of health services utilization, characteristics of cancer patients were examined and compared among uterine, cervical, and ovarian cancer patients. Multivariable linear regression models were conducted on medical expenditure with a prior logarithmic transformation.

Background Patient portals tethered to electronic health records systems have become attractive web platforms since the enacting of the Medicare Access and Children’s Health Insurance Program Reauthorization Act and the introduction of the Meaningful Use program in the United States. Patients can conveniently access their health records and seek consultation from providers through secure web portals. With increasing adoption and patient engagement, the volume of patient secure messages has risen substantially, which opens up new research and development opportunities for patient-centered care. Objective This study aims to develop a data model for patient secure messages based on the Fast Healthcare Interoperability Resources (FHIR) standard to identify and extract significant information. Methods We initiated the first draft of the data model by analyzing FHIR and manually reviewing 100 sentences randomly sampled from more than 2 million patient-generated secure messages obtained from the online patient portal at the Mayo Clinic Rochester between February 18, 2010, and December 31, 2017. We then annotated additional sets of 100 randomly selected sentences using the Multi-purpose Annotation Environment tool and updated the data model and annotation guideline iteratively until the interannotator agreement was satisfactory. We then created a larger corpus by annotating 1200 randomly selected sentences and calculated the frequency of the identified medical concepts in these sentences. Finally, we performed topic modeling analysis to learn the hidden topics of patient secure messages related to 3 highly mentioned microconcepts, namely, fatigue, prednisone, and patient visit, and to evaluate the proposed data model independently. Results The proposed data model has a 3-level hierarchical structure of health system concepts, including 3 macroconcepts, 28 mesoconcepts, and 85 microconcepts. Foundation and base macroconcepts comprise 33.99% (841/2474), clinical macroconcepts comprise 64.38% (1593/2474), and financial macroconcepts comprise 1.61% (40/2474) of the annotated corpus. The top 3 mesoconcepts among the 28 mesoconcepts are condition (505/2474, 20.41%), medication (424/2474, 17.13%), and practitioner (243/2474, 9.82%). Topic modeling identified hidden topics of patient secure messages related to fatigue, prednisone, and patient visit. A total of 89.2% (107/120) of the top-ranked topic keywords are actually the health concepts of the data model. Conclusions Our data model and annotated corpus enable us to identify and understand important medical concepts in patient secure messages and prepare us for further natural language processing analysis of such free texts. The data model could be potentially used to automatically identify other types of patient narratives, such as those in various social media and patient forums. In the future, we plan to develop a machine learning and natural language processing solution to enable automatic triaging solutions to reduce the workload of clinicians and perform more granular content analysis to understand patients’ needs and improve patient-centered care.