Assessing the Impact of the Center for Medicare and Medicaid Services Policy Guidance on Part D Prescriptions Among Hospice Patients.

Hospice care facilities are required to provide prescription drugs related to a hospice patient's terminal illness. From October 2010 to present, the Center for Medicare and Medicaid Services (CMS) has issued a series of communications regarding Medicare paying for hospice patients' prescription drugs under Part D that should be covered under the hospice Medicare Part A benefit. On April 4, 2011, CMS issued specific policy guidance to providers aimed at preventing inappropriate billing. While CMS has documented Part D prescription decreases in hospice patients, no research exists that connects these decreases and the policy guidance. This study aims to evaluate the effect of the April 4, 2011, policy guidance on hospice patients' Part D prescriptions. This study employed generalized estimating equations to assess (1) total monthly average prescriptions of all medications and (2) four categories of commonly prescribed hospice medications in pre-and-post policy guidance. This research used the Medicare claims of 113,260 Part D-enrolled Medicare male patients aged 66 and older between April 2009 and March 2013, including 110,547 non-hospice patients and 2713 hospice patients. Hospice patients' monthly average total Part D prescriptions decreased from 7.3 pre-policy guidance to 6.5 medications following the issuing of the guidance, while the four categories of hospice-specific medications decreased from .57 to .49. The findings of this study show that CMS's guidance issued to providers to prevent the inappropriate billing of hospice patients' prescriptions to the Part D benefit may lead to Part D prescription decreases as observed in this sample.

Family structure and children's unmet health-care needs.

This study assessed children's unmet health-care needs within different family types (two-parent biological/adoptive, two-parent stepfamily, and single-mother family type) using data from the 2011/2012 National Survey of Children's Health. Findings indicate that 10.4% of children in single-mother family types had unmet health-care needs compared to 8.7% of children from a two-parent stepfamily and 5.3% for those from two-parent biological/adoptive families. Further analyses revealed racial/ethnic disparities with Black children from two parent-biological/adoptive families being 1.54 (95% confidence interval 1.13, 2.05) times more likely to have unmet health-care needs, while Hispanic children were less likely to have unmet health-care needs relative to their white counterparts. Children from lower income two-parent families had a higher likelihood of unmet health-care needs. The noncontinuous insurance coverage was a risk factor for increasing unmet health-care needs across all three different family types. These findings show major differences in unmet health-care needs among children living in different family structure types. It is recommended that interventions for increasing access to care need to be tailored differently across various family types in order to achieve continuous and sufficient health-care services for our children.

Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology.

BACKGROUND: Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. RESULTS: This retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient's cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient's race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P < 0.1) than methods without ontologies. CONCLUSIONS: This study demonstrated that bio-ontologies can be harnessed to provide medical knowledge for ML algorithms. The presented method demonstrates that encoding specific types of hierarchical relationships to guide rule learning is possible, and can be extended to other types of semantic relationships present in biomedical ontologies. The ontology-guided ML method achieved better performance than the method without ontologies. The presented method can also be used to promote the effectiveness and efficiency of ML in healthcare, in which use of background knowledge and consistency with existing clinical expertise is critical.