Literature DB >> 17304783

Evidence-based management assessment of return on investment from anesthesia information management systems.

Cormac T O'Sullivan1, Franklin Dexter, David A Lubarsky, Michael M Vigoda.   

Abstract

A systematic and comprehensive review of the scientific literature revealed 4 evidence-based methods that contribute to a positive return on investment from anesthesia information management systems (AIMS): reducing anesthetic-related drug costs, improving staff scheduling and reducing staffing costs, increasing anesthesia billing and capture of anesthesia-related charges, and increased hospital reimbursement through improved hospital coding. There were common features to these interventions. Whereas an AIMS may be the ideal choice to achieve these cost reductions and revenue increases, alternative existing systems may be satisfactory for the studied applications (i.e., the incremental advantage to the AIMS may be less than predicted from applying each study to each facility). Savings are likely heterogeneous among institutions, making an internal survey using standard accounting methods necessary to perform a valid return on investment analysis. Financial advantages can be marked for the anesthesia providers, although hospitals are more likely to purchase the AIMS.

Mesh:

Year:  2007        PMID: 17304783

Source DB:  PubMed          Journal:  AANA J        ISSN: 0094-6354


  4 in total

1.  Anesthesia recordkeeping: accuracy of recall with computerized and manual entry recordkeeping.

Authors:  Thomas Corey Davis; Jeffrey A Green; Alexander Colquhoun; Brenda L Hage; Chuck Biddle
Journal:  J Clin Monit Comput       Date:  2012-03-17       Impact factor: 2.502

2.  The state of adoption of anesthesia information management systems in Canadian academic anesthesia departments: a survey.

Authors:  Pooya Kazemi; Francis Lau; Allan F Simpao; R J Williams; Clyde Matava
Journal:  Can J Anaesth       Date:  2021-01-29       Impact factor: 5.063

Review 3.  Anesthesia information management systems: a review of functionality and installation considerations.

Authors:  Jesse M Ehrenfeld; Mohamed A Rehman
Journal:  J Clin Monit Comput       Date:  2010-08-24       Impact factor: 2.502

4.  Opal: an implementation science tool for machine learning clinical decision support in anesthesia.

Authors:  Andrew Bishara; Andrew Wong; Linshanshan Wang; Manu Chopra; Wudi Fan; Alan Lin; Nicholas Fong; Aditya Palacharla; Jon Spinner; Rachelle Armstrong; Mark J Pletcher; Dmytro Lituiev; Dexter Hadley; Atul Butte
Journal:  J Clin Monit Comput       Date:  2021-11-27       Impact factor: 1.977

  4 in total

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