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Which EMR vendors are best for meaningful use?

April 13, 2011 | Healthcare IT News Staff

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Although nearly 80 percent of ambulatory providers that have purchased an EMR are confident they will qualify for meaningful use (MU) in 2011, a closer look at what functionalities they have actually implemented reveals that most still have significant holes to fill.

Over two-thirds of the surveyed providers are not sharing medical records electronically with patients, and nearly half have not implemented clinical decision support (CDS) rules, two key MU requirements, according to a KLAS report.

[Related: Panel wants MU to 'shine spotlight' on providers health data safeguards.]

The report presents the results of interviews with 597 ambulatory providers using 25 different EMR systems. The study measured vendor performance in eight key MU areas: progress notes, drug-drug and drug-allergy alerts, CPOE, data mining tools, e-prescribing, CDS rules, patient electronic access to medical records, and viewing active medication, allergy, and problem lists.

KLAS' report, "Ambulatory EMR: A KLAS Guide to Meaningful Use Success," notes that Epic and Greenway customers appear best-prepared overall to qualify for MU incentives this year, with high usage and high clinician satisfaction in the key MU areas measured. Providers using Praxis, SRSsoft, simplifyMD, and LSS, however, appear the least prepared to qualify for MU in 2011.

[Related: Standards panel explores path to stage 2 meaningful use. See also: CMS starts Medicare incentive attestation on April 18.]

The report also outlines several best practices for ambulatory providers looking to make an EMR purchase in the near future.

Other vendors covered in the report include AdvancedMD, Allscripts, Amazing Charts, Aprima, athenahealth, Cerner, e-MDs, eClinicalWorks, GE Healthcare, Healthport, Henry Schein, Ingenix, McKesson, MedPlus, NextGen, Sage and Sevocity.

 

Related Topics:
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  • Healthport
  • GE Healthcare
  • Greenway
  • Henry Schein
  • McKesson
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  • e-prescribing
  • mining
  • data mining
  • Medicare

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