Literature DB >> 23888641

Return on investment for vendor computerized physician order entry in four community hospitals: the importance of decision support.

Eyal Zimlichman1, Carol Keohane, Calvin Franz, Wendy L Everett, Diane L Seger, Catherine Yoon, Alexander A Leung, Bismarck Cadet, Michael Coffey, Nathan E Kaufman, David W Bates.   

Abstract

BACKGROUND: In-hospital adverse events are a major cause of morbidity and mortality and represent a major cost burden to health care systems. A study was conducted to evaluate the return on investment (ROI) for the adoption of vendor-developed computerized physician oder entry (CPOE) systems in four community hospitals in Massachusetts.
METHODS: Of the four hospitals, two were under one management structure and implemented the same vendor-developed CPOE system (Hospital Group A), while the other two were under a second management structure and implemented another vendor-developed CPOE system (Hospital Group B). Cost savings were calculated on the basis of reduction in preventable adverse drug event (ADE) rates as measured previously. ROI, net cash flow, and the breakeven point during a 10-year cost-and-benefit model were calculated. At the time of the study, none of the participating hospitals had implemented more than a rudimentary decision support system together with CPOE.
RESULTS: Implementation costs were lower for Hospital Group A than B ($7,130,894 total or $83/admission versus $19,293,379 total or $113/admission, respectively), as were preventable ADE-related avoided costs ($7,937,651 and $16,557,056, respectively). A cost-benefit analysis demonstrated that Hospital Group A had an ROI of 11.3%, breaking even on the investment eight years following implementation. Hospital Group B showed a negative return, with an ROI of -3.1%.
CONCLUSIONS: Adoption of vendor CPOE systems in community hospitals was associated with a modest ROI at best when applying cost savings attributable to prevention of ADEs only. The modest financial returns can beattributed to the lack of clinical decision support tools.

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Year:  2013        PMID: 23888641     DOI: 10.1016/s1553-7250(13)39044-8

Source DB:  PubMed          Journal:  Jt Comm J Qual Patient Saf        ISSN: 1553-7250


  5 in total

1.  Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.

Authors:  G Segal; A Segev; A Brom; Y Lifshitz; Y Wasserstrum; E Zimlichman
Journal:  J Am Med Inform Assoc       Date:  2019-12-01       Impact factor: 4.497

Review 2.  Personalization and Patient Involvement in Decision Support Systems: Current Trends.

Authors:  S Quaglini; L Sacchi; G Lanzola; N Viani
Journal:  Yearb Med Inform       Date:  2015-08-13

3.  A qualitative study identifying the cost categories associated with electronic health record implementation in the UK.

Authors:  Sarah P Slight; Casey Quinn; Anthony J Avery; David W Bates; Aziz Sheikh
Journal:  J Am Med Inform Assoc       Date:  2014-02-12       Impact factor: 4.497

4.  Cost of running a full-service receiving office at a centralised testing laboratory in South Africa.

Authors:  Naseem Cassim; Neeshan Ramdin; Sadhaseevan Moodly; Deborah K Glencross
Journal:  Afr J Lab Med       Date:  2022-07-13

Review 5.  Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review.

Authors:  Philip von Wedel; Christian Hagist
Journal:  J Med Internet Res       Date:  2020-11-18       Impact factor: 5.428

  5 in total

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