Literature DB >> 28685304

Perioperative and ICU Healthcare Analytics within a Veterans Integrated System Network: a Qualitative Gap Analysis.

Seshadri Mudumbai1,2, Ferenc Ayer3, Jerry Stefanko4.   

Abstract

Health care facilities are implementing analytics platforms as a way to document quality of care. However, few gap analyses exist on platforms specifically designed for patients treated in the Operating Room, Post-Anesthesia Care Unit, and Intensive Care Unit (ICU). As part of a quality improvement effort, we undertook a gap analysis of an existing analytics platform within the Veterans Healthcare Administration. The objectives were to identify themes associated with 1) current clinical use cases and stakeholder needs; 2) information flow and pain points; and 3) recommendations for future analytics development. Methods consisted of semi-structured interviews in 2 phases with a diverse set (n = 9) of support personnel and end users from five facilities across a Veterans Integrated Service Network. Phase 1 identified underlying needs and previous experiences with the analytics platform across various roles and operational responsibilities. Phase 2 validated preliminary feedback, lessons learned, and recommendations for improvement. Emerging themes suggested that the existing system met a small pool of national reporting requirements. However, pain points were identified with accessing data in several information system silos and performing multiple manual validation steps of data content. Notable recommendations included enhancing systems integration to create "one-stop shopping" for data, and developing a capability to perform trends analysis. Our gap analysis suggests that analytics platforms designed for surgical and ICU patients should employ approaches similar to those being used for primary care patients.

Entities:  

Keywords:  Gap analysis; Health information technology; Healthcare analytics; Intensive care unit; Operating room; Perioperative information management system

Mesh:

Year:  2017        PMID: 28685304     DOI: 10.1007/s10916-017-0762-z

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  30 in total

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Review 5.  Anesthesia information management systems.

Authors:  Stanley Muravchick
Journal:  Curr Opin Anaesthesiol       Date:  2009-12       Impact factor: 2.706

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Journal:  J Adv Nurs       Date:  1994-02       Impact factor: 3.187

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8.  Implementing electronic health care predictive analytics: considerations and challenges.

Authors:  Ruben Amarasingham; Rachel E Patzer; Marco Huesch; Nam Q Nguyen; Bin Xie
Journal:  Health Aff (Millwood)       Date:  2014-07       Impact factor: 6.301

9.  Big data in health care: using analytics to identify and manage high-risk and high-cost patients.

Authors:  David W Bates; Suchi Saria; Lucila Ohno-Machado; Anand Shah; Gabriel Escobar
Journal:  Health Aff (Millwood)       Date:  2014-07       Impact factor: 6.301

10.  Deployment of Analytics into the Healthcare Safety Net: Lessons Learned.

Authors:  David Hartzband; Feygele Jacobs
Journal:  Online J Public Health Inform       Date:  2016-12-28
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