National Health Spending Projections: The Estimated Impact Of Reform Through 2019

Office of the Actuary, Centers for Medicare and Medicaid Services, Baltimore, Maryland, USA.
Health Affairs (Impact Factor: 4.97). 10/2010; 29(10):1933-41. DOI: 10.1377/hlthaff.2010.0788
Source: PubMed


This paper presents updated national health spending projections for 2009-2019 that take into account recent comprehensive health reform legislation and other relevant changes in law and regulations. Relative to our February 2010 projections under prior law, average annual growth in national health spending over the projection period is estimated to be 0.2 percentage point higher than our previous estimate. The health care share of gross domestic product (GDP) is expected to be 0.3 percentage point higher in 2019. Within these net overall impacts are larger differences for trends in spending and spending growth by payer, attributable to reform's many major changes to health care coverage and financing.

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    • "It is possible, if not probable, that many of the same economic incentives observed in the ICU with managed care [6, 8] might emerge as PPACA initiatives are implemented, and the proposed structure of accountable care organizations (ACOs) [9] should recognize such incentives. Indeed, though much of the rationale behind PPACA centered around lowering the growth rate of health care spending, the ability of this legislation to favorably bend the cost curve may be limited [10]. Therefore, ancillary approaches to cost containment, particularly in resource-intensive areas such as the ICU, should be explored. "
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    ABSTRACT: The rising costs and suboptimal quality throughout the American health care system continue to invite critical inquiry, and practice in the intensive care unit setting is no exception. Due to their relatively large impact, outcomes and costs in critical care are of significant interest to policymakers and health care administrators. Measurement of potentially ineffective care has been proposed as an outcome measure to evaluate critical care delivery, and the Patient Protection and Affordable Care Act affords the opportunity to reshape the care of the critically ill. Given the impetus of the PPACA, systematic formal measurement of potentially ineffective care and its clinical, economic, and societal impact merits timely reconsideration.
    Critical care research and practice 04/2014; 2014:134198. DOI:10.1155/2014/134198
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    • "By 2020, the number of Americans age 65 and older will increase from 40 to 55 million [1]. The associated health care costs (Medicare) of this growing elderly population are projected to increase from $556 billion in 2011 to $922 billion in 2020 [2]. Emerging models of health care [3] suggest that the Information Technologies (IT) infrastructure of health systems will need to adapt to these changes and the shortage of nursing staff [4] [5]. "
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    ABSTRACT: The aim of this study is to describe the process of staff and patient adoption and compliance of a real-time locating system (RTLS) across two health care settings and present lessons learned. While previous work has examined the technological feasibility of tracking staff and patients in a health care setting in real-time, these studies have not described the critical adoption issues that must be overcome for deployment. The ability to track and monitor individual staff and patients presents new opportunities for improving workflow, patient health and reducing health care costs. A RTLS is introduced in both a long-term care and a polytrauma transitional rehabilitation program (PTRP) in a Veterans Hospital to track staff and patient locations and five lessons learned are presented from our experiences and responses to emergent technological, work-related and social barriers to adoption. We conclude that successful tracking in a health care environment requires time and careful consideration of existing work, policies and stakeholder needs which directly impact the efficacy of the technology.
    Technology and health care: official journal of the European Society for Engineering and Medicine 06/2013; 21(3):191-7. DOI:10.3233/THC-130738 · 0.70 Impact Factor
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    • "These include health reform and parity legislation and a substantial increase in federal funding flowing to safety net providers such as federally qualified health centers (FQHCs) and community health centers to promote the integration of behavioral health services and training and the implementation of EMR [77]. In the coming decade, over half of patients newly insured under health-care reform will be insured through Medicaid with the Children’s Health Insurance Program (CHIP) rolled into it [80], most of whom will receive services in private health systems and FQHCs. Our findings on barriers and facilitators of AOD screening of adolescents in primary care and the potential effectiveness of a systematic adolescent SBIRT model may help to inform this transformation of primary care practice. "
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    ABSTRACT: Objective This paper used data from a study of pediatric primary care provider (PCP) screening practices to examine barriers to and facilitators of adolescent alcohol and other drug (AOD) screening in pediatric primary care. Methods A web-based survey (N = 437) was used to examine the influence of PCP factors (attitudes and knowledge, training, self-efficacy, comfort with alcohol and drug issues); patient characteristics (age, gender, ethnicity, comorbidities and risk factors); and organizational factors (screening barriers, staffing resources, confidentiality issues) on AOD screening practices. Self-reported and electronic medical record (EMR)-recorded screening rates were also assessed. Results More PCPs felt unprepared to diagnose alcohol abuse (42%) and other drug abuse (56%) than depression (29%) (p < 0.001). Overall, PCPs were more likely to screen boys than girls, and male PCPs were even more likely than female PCPs to screen boys (23% versus 6%, p < 0.0001). Having more time and having other staff screen and review results were identified as potential screening facilitators. Self-reported screening rates were significantly higher than actual (EMR-recorded) rates for all substances. Feeling prepared to diagnose AOD problems predicted higher self-reported screening rates (OR = 1.02, p <0.001), and identifying time constraints as a barrier to screening predicted lower self-reported screening rates (OR = 0.91, p < 0.001). Higher average panel age was a significant predictor of increased EMR-recorded screening rates (OR = 1.11, p < 0.001). Conclusions Organizational factors, lack of training, and discomfort with AOD screening may impact adolescent substance-abuse screening and intervention, but organizational approaches (e.g., EMR tools and workflow) may matter more than PCP or patient factors in determining screening.
    Addiction science & clinical practice 08/2012; 7(1):13. DOI:10.1186/1940-0640-7-13
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