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

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 examines 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 example, 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 assertions presented in this paper a workshop (participation of 51 medical staff) was held in St. Lukes 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.