Background: People with advanced illness usually want their healthcare where they live—at home—not in the hospital. Innovative models of palliative care that better meet the needs of seriously ill people at lower cost should be explored.Objectives: We evaluated the impact of a home-based palliative care (HBPC) program implemented within an Accountable Care Organization (ACO) on cost and resource utilization.Methods: This was a retrospective analysis to quantify cost savings associated with a HBPC program in a Medicare Shared Savings Program ACO where total cost of care is available. We studied 651 decedents; 82 enrolled in a HBPC program compared to 569 receiving usual care in three New York counties who died between October 1, 2014, and March 31, 2016. We also compared hospital admissions, ER visits, and hospice utilization rates in the final months of life.Results: The cost per patient during the final three months of life was $12,000 lower with HBPC than with usual care ($20,420 vs. $32,420; p = 0.0002); largely driven by a 35% reduction in Medicare Part A ($16,892 vs. $26,171; p = 0.0037). HBPC also resulted in a 37% reduction in Medicare Part B in the final three months of life compared to usual care ($3,114 vs. $4,913; p = 0.0008). Hospital admissions were reduced by 34% in the final month of life for patients enrolled in HBPC. The number of admissions per 1000 beneficiaries per year was 3073 with HBPC and 4640 with usual care (p = 0.0221). HBPC resulted in a 35% increased hospice enrollment rate (p = 0.0005) and a 240% increased median hospice length of stay compared to usual care (34 days vs. 10 days; p < 0.0001).Conclusion: HBPC within an ACO was associated with significant cost savings, fewer hospitalizations, and increased hospice use in the final months of life.
Of great importance to a wide variety of computer vision and image analysis problems is the ability to represent two-(2D) and three-dimensional (3D) data or objects. Implicit polynomial curves and surfaces are two of the most useful representations available. Their representational power is evidenced by their ability to smooth noisy data and to interpolate through sparse or missing data. Furthermore, their associated Euclidean and a ne invariants are powerful discriminators, making implicit polynomials a computationally attractive technology for recognizing objects in arbitrary positions with respect to cameras or range sensors. In this paper, we introduce a completely new approach to tting implicit polynomials to data. The algorithm represents a signi cant advancement of implicit polynomial technology for three important reasons. First, it is orders of magnitude faster than existing methods. Second, it has signi cantly better repeatability and numerical stability than current methods. Third, it can easily t polynomials of high, such as 14 th to 18 th , degree. In addition, this approach provides a completely new way of thinking about and handling implicit polynomials.
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