Literature DB >> 25006146

Leveraging the big-data revolution: CMS is expanding capabilities to spur health system transformation.

Niall Brennan1, Allison Oelschlaeger2, Christine Cox3, Marilyn Tavenner4.   

Abstract

As the largest single payer for health care in the United States, the Centers for Medicare and Medicaid Services (CMS) generates enormous amounts of data. Historically, CMS has faced technological challenges in storing, analyzing, and disseminating this information because of its volume and privacy concerns. However, rapid progress in the fields of data architecture, storage, and analysis--the big-data revolution--over the past several years has given CMS the capabilities to use data in new and innovative ways. We describe the different types of CMS data being used both internally and externally, and we highlight a selection of innovative ways in which big-data techniques are being used to generate actionable information from CMS data more effectively. These include the use of real-time analytics for program monitoring and detecting fraud and abuse and the increased provision of data to providers, researchers, beneficiaries, and other stakeholders. Project HOPE—The People-to-People Health Foundation, Inc.

Keywords:  Health Reform; Medicaid; Medicare; Research And Technology

Mesh:

Year:  2014        PMID: 25006146     DOI: 10.1377/hlthaff.2014.0130

Source DB:  PubMed          Journal:  Health Aff (Millwood)        ISSN: 0278-2715            Impact factor:   6.301


  6 in total

1.  Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation.

Authors:  David J Taber; Arun P Palanisamy; Titte R Srinivas; Mulugeta Gebregziabher; John Odeghe; Kenneth D Chavin; Leonard E Egede; Prabhakar K Baliga
Journal:  Transplantation       Date:  2015-02       Impact factor: 4.939

2.  Development of dynamic health care delivery heatmaps for end-of-life cancer care: a cohort study.

Authors:  Inas S Khayal; Gabriel A Brooks; Amber E Barnato
Journal:  BMJ Open       Date:  2022-05-19       Impact factor: 3.006

Review 3.  State of the art review: the data revolution in critical care.

Authors:  Marzyeh Ghassemi; Leo Anthony Celi; David J Stone
Journal:  Crit Care       Date:  2015-03-16       Impact factor: 9.097

Review 4.  Using Medical Claims Analyses to Understand Interventions for Parkinson Patients.

Authors:  Bastiaan R Bloem; Jan H L Ypinga; Allison Willis; Colleen G Canning; Roger A Barker; Marten Munneke; Nienke M De Vries
Journal:  J Parkinsons Dis       Date:  2018       Impact factor: 5.568

5.  Analytical Methods for a Learning Health System: 1. Framing the Research Question.

Authors:  Michael Stoto; Michael Oakes; Elizabeth Stuart; Lucy Savitz; Elisa L Priest; Jelena Zurovac
Journal:  EGEMS (Wash DC)       Date:  2017-12-07

6.  Assessing the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission or death.

Authors:  Yongkang Zhang; Yiye Zhang; Evan Sholle; Sajjad Abedian; Marianne Sharko; Meghan Reading Turchioe; Yiyuan Wu; Jessica S Ancker
Journal:  PLoS One       Date:  2020-06-25       Impact factor: 3.240

  6 in total

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