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Journal Article

Modified Early Warning Scorecard: The Role of Data/Information Quality within the Decision Making Process  pp100-109

John O Donoghue, Tom O Kane, Joe Gallagher, Garry Courtney, Abdur Aftab, Aveline Casey, Javier Torres, Philip Angove

© Jan 2011 Volume 14 Issue 1, ECIME 2010 Special Issue, Editor: Miguel de Castro Neto, pp1 - 166

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Abstract

Presented in this paper is the Patient Assessment‑Data Quality Model (PA‑DQM). It is designed to assess how patient datasets which are poor in composition can impact on the decision processes following patient assessment. The PA‑DQM in particular examin es four key Data Quality (DQ) dimensions: timeliness, accuracy, consistency and completeness. This DQ model is generic in nature as any number of decision making processes can be substituted to reflect the medical scenario under consideration. For examp le, Intensive Care Unit (ICU) admissions, Emergency Room (ER) triage systems or Modified Early Warning Scorecards (MEWS). The PA‑DQM presented is evaluated using the MEWS process as an exemplar. Paper based MEWS are utilised to assist medical staff identify at risk patients with a declining health status. The calculated MEWS score is designed to trigger earlier medical interventions to avoid or reduce the potential impact of catastrophic events. In particular the existing MEWS system which (i.e. a paper based approach) is evaluated alongside an electronic‑Modified Early Warning Scorecard (e‑MEWS) system, which is designed and developed to reduce the number of DQ issues which continue to persist with the paper based process. To validate the asse rtions presented in this paper a workshop (participation of 51 medical staff) was held in St. LukeĀs Hospital, Kilkenny, Ireland, where the paper based MEWS has been adopted for the last 3 years. It is clear from our initial findings that the proposed e ‑MEWS system has the ability to greatly enhance the levels of DQ over its existing paper based counterpart.

 

Keywords: Information Quality, MEWS, Health Informatics and Body Area Networks.

 

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