How Does Managed Care Improve the Quality of Breast Cancer Care Among Medicare-Insured Minority Women?

Smith-Gagen, Julie; Loux, Travis; Drake, Chris; Pérez‐Stable, Eliseo J.

doi:10.1007/s40615-015-0167-y

Cited by 2 publications

(2 citation statements)

References 59 publications

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“…A similar study reported that ethnic minorities in European countries reported less satisfaction with health care than native majority patients that their health care was also disadvantaged, thus revealing diminished quality of care to minority patients (Hanssens, Detollenaere, Hardyns, & Willems, 2016). A secondary analysis study in the United States with a large dataset suggested that patient-centered care and managed care for ethnic minority patients may reduce medical disadvantages and increase quality of care to those populations (Smith-Gagen, Loux, Drake, & Pérez-Stable, 2016).…”

Section: Discussionmentioning

confidence: 93%

Nurses’ Barriers to Care of Ethnic Minorities: A Qualitative Systematic Review

Joo

Liu

2019

West J Nurs Res

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Several studies have reported nurses’ challenges when caring for ethnic minority patients from diverse cultures and who have language differences. This qualitative systematic review aims to identify and describe barriers to nursing practices and to understand the common perceptions that nurses in Western countries have when providing culturally competent care to ethnic minority patients. In this review, eight qualitative, peer-reviewed studies published from 2010 to 2018 are synthesized to identify barriers to nurses’ practice with ethnic minority patients. Qualitative systematic review protocols with thematic synthesis as a methodology were employed, and five common themes were identified: communication issues; unclear, missing, or culturally inappropriate care information and resources; insufficient cultural training and education; challenging therapeutic relationships with patients; and concern about quality of care. These results suggest that future studies should explore the cost-effectiveness of cultural competency training.

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Section: Discussionmentioning

confidence: 93%

Nurses’ Barriers to Care of Ethnic Minorities: A Qualitative Systematic Review

Joo

Liu

2019

West J Nurs Res

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“…Health outcomes for special populations are improved when management is modified to fit their specific needs. This finding has been duplicated in disabled (Burns, 2009;Hall, Kurth, & Chapman, 2015), homeless (Levitt et al, 2013;McCormack, Hoffman, Wall, & Goldfrank, 2013;Patterson, Nochajski, & Wu, 2014), minority (Eberly, Davidoff, & Miller, 2010;Smith-Gagen, Loux, Drake, & Perez-Stable, 2016), female (Bierman & Clancy, 2000;Kneipp et al, 2013), pediatric (Morris, Schettine, Roohan, & Gesten, 2011), elderly (Ahmed, Taylor, McDaniel, & Dyer, 2012;Black, 2011;Kwak, Kramer, Lang, & Ledger, 2013), and hospice (Brink & Smith, 2008) populations.…”

Section: Interpretation Of the Review Resultsmentioning

confidence: 94%

Informatics strategies for risk stratification in population health management

Sheets¹

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Risk analysis and population health management can improve health outcomes, but improved risk stratification is needed to manage healthcare costs. Analysis of 157 publications on translational implementations of "risk stratification in population health management of chronic disease" showed a consensus that population health management and risk stratification can improve outcomes, but found uncertainty over best methods for risk prediction and controversy over the cost savings. The consensus of another 85 publications on the methodologies of "data mining for predictive healthcare analytics" was that clinically interpretable machine learning techniques are more appropriate than "black box" techniques for structured big data sources in healthcare, and the "area under the curve" of a prediction model's sensitivity versus one-minus-specificity is a standard and reliable way to measure the model's discrimination. This study used clinically interpretable machine-learning algorithms, combined with simple but powerful data analytic techniques such as cost analysis and data visualization, to evaluate and improve risk stratification for a managed patient population. This study retrospectively observed 10,000 mid-Missouri Medicare and Medicaid patients between 2012 and 2014. Cost and utilization analyses, statistical clustering, contrast mining, and logistic regression were used to identify patients within a managed population at risk for higher healthcare costs, demonstrate longitudinal changes in risk stratification, and characterize detailed differences between high-risk and low-risk patients. The two highest risk stratification tiers comprised only 21% of patients but accounted for 43% of prospective charges. Patients in the most expensive sub-cluster of the most expensive risk tier were nearly twice as costly as high-risk patients on average. Combining contrast mining with logistic regression predicted the most expensive 5% of patients with 84% accuracy, as measured by area under the curve. All the strategies used in this study, from the simplest to the most sophisticated, produced useful insights. By predicting the small number of patients who will incur the majority of healthcare expenses in terms that are clinically interpretable, these methods can support population health managers in focusing preventive and longitudinal care more effectively. These models, and similar models developed by integrating diverse informatics strategies, could improve health outcomes, delivery, and costs.

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How Does Managed Care Improve the Quality of Breast Cancer Care Among Medicare-Insured Minority Women?

Cited by 2 publications

References 59 publications

Nurses’ Barriers to Care of Ethnic Minorities: A Qualitative Systematic Review

Nurses’ Barriers to Care of Ethnic Minorities: A Qualitative Systematic Review

Informatics strategies for risk stratification in population health management

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