Literature DB >> 21493407

Meaningful use of electronic health records in otolaryngology: recommendations from the American Academy of Otolaryngology--Head and Neck Surgery Medical Informatics Committee.

Subinoy Das1, Lee D Eisenberg, John W House, K J Lee, Rodney P Lusk, David R Nielsen, Milesh M Patel, Jayde M Steckowych, Edward B Ermini.   

Abstract

Under the Health Information Technology for Economic and Clinical Health (HITECH) Act, passed as a part of the American Recovery and Reinvestment Act of 2009, the US Congress implemented new regulations to encourage the adoption of electronic health records (EHRs). The federal government will expend up to $27 billion in incentive payments to physicians and hospitals to increase adoption and implement "meaningful use" of EHRs. Otolaryngologists may receive as much as $44,000 under Medicare or $63,750 under Medicaid as part of this law. In July 2010, the US Department of Health and Human Services announced final rules to support "meaningful use." This commentary discusses recommendations from the American Academy of Otolaryngology--Head and Neck Surgery Medical Informatics Committee for implementing "meaningful use" of EHRs to improve safety, quality, and efficiency of patient care and receiving incentive payments as defined by these new regulations.

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Year:  2011        PMID: 21493407     DOI: 10.1177/0194599810393441

Source DB:  PubMed          Journal:  Otolaryngol Head Neck Surg        ISSN: 0194-5998            Impact factor:   3.497


  6 in total

1.  Pilot study of meaningful use of electronic health records in radiation oncology.

Authors:  Xinglei Shen; Adam P Dicker; Laura Doyle; Timothy N Showalter; Amy S Harrison; Susan I DesHarnais
Journal:  J Oncol Pract       Date:  2012-03-20       Impact factor: 3.840

2.  Patulin induces colorectal cancer cells apoptosis through EGR-1 dependent ATF3 up-regulation.

Authors:  Osong Kwon; Nak Kyun Soung; N R Thimmegowda; Sook Jung Jeong; Jae Hyuk Jang; Dong-Oh Moon; Jong Kyeong Chung; Kyung Sang Lee; Yong Tae Kwon; Raymond Leo Erikson; Jong Seog Ahn; Bo Yeon Kim
Journal:  Cell Signal       Date:  2011-12-30       Impact factor: 4.315

3.  Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children.

Authors:  Haijun Zhai; Patrick Brady; Qi Li; Todd Lingren; Yizhao Ni; Derek S Wheeler; Imre Solti
Journal:  Resuscitation       Date:  2014-05-09       Impact factor: 5.262

4.  Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

Authors:  Qi Li; Kristin Melton; Todd Lingren; Eric S Kirkendall; Eric Hall; Haijun Zhai; Yizhao Ni; Megan Kaiser; Laura Stoutenborough; Imre Solti
Journal:  J Am Med Inform Assoc       Date:  2014-01-08       Impact factor: 4.497

5.  Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements.

Authors:  Ben Wellner; Joan Grand; Elizabeth Canzone; Matt Coarr; Patrick W Brady; Jeffrey Simmons; Eric Kirkendall; Nathan Dean; Monica Kleinman; Peter Sylvester
Journal:  JMIR Med Inform       Date:  2017-11-22

6.  Developing a Machine Learning System for Identification of Severe Hand, Foot, and Mouth Disease from Electronic Medical Record Data.

Authors:  Guangjian Liu; Yi Xu; Xinming Wang; Xutian Zhuang; Huiying Liang; Yun Xi; Fangqin Lin; Liyan Pan; Taishan Zeng; Huixian Li; Xiaojun Cao; Gansen Zhao; Huimin Xia
Journal:  Sci Rep       Date:  2017-11-27       Impact factor: 4.379

  6 in total

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