A Map for Clinical Laboratories Management Indicators in the Intelligent Dashboard

Abstract

Introduction:management challenges of clinical laboratories are more complicated for educational hospital clinical laboratories. Managers can use tools of business intelligence (BI), such as information dashboards that provide the possibility of intelligent decision-making and problem solving about increasing income, reducing spending, utilization management and even improving quality. Critical phase of dashboard design is setting indicators and modeling causal relations between them. The paper describes the process of creating a map for laboratory dashboard.Methods:the study is one part of an action research that begins from 2012 by innovation initiative for implementing laboratory intelligent dashboard. Laboratories management problems were determined in educational hospitals by the brainstorming sessions. Then, with regard to the problems key performance indicators (KPIs) specified.Results:the map of indicators designed in form of three layered. They have a causal relationship so that issues measured in the subsequent layers affect issues measured in the prime layers.Conclusion:the proposed indicator map can be the base of performance monitoring. However, these indicators can be modified to improve during iterations of dashboard designing process.

Clinical laboratory services for primary healthcare centers in urban cities: a pilot ACO model of ten primary healthcare centers

“…In any Clinical laboratory, the total testing process of a laboratory (TTS) is divided into three phases: pre-analytical, analytical, and post-analytical [22]. Laboratories should be actively involved in the design, monitor and control of all the process phases [23].…”

“…The regular review of these KPI measures helps identify and minimize significant variations and trends. The selection of which KPI's to monitor was left to the laboratory management, provided it included the pre-analytical, analytical, and postanalytical phases of the testing process, with an emphasis on the pre-and post-analytical phases [22,25,26].…”

“…In general, TAT, critical result reporting, and amendment of results are three crucial KPIs covering the analytical and post-analytical phases of the TTS [22]. In this project we focused on the TAT as the KPI to monitor the analytical phase of the whole TTS and it will give us a clear indication on how effective the centralization of all tests to a reference hospital is.…”

Failure rate prediction of equipment: can Weibull distribution be applied to automated hematology analyzers?

“…However, the MTBF of group A was still much longer than group B, as the MTBF predicts the probability of equipment failure when applied to a group of similar equipment. Though the MTBF cannot predict the failure of a particular analyzer accurately, the failure prediction model may be incorporated into the intelligent dashboard of a laboratory information system for the real time monitoring of equipments [15]. This, however, requires further study.…”

Framework Development for Dynamic System Validation

“…Each type of test has a different but standard cycle time, and patients are informed about expected report delivery time based on a testing procedure's standard time. In many CLs, delays on test delivery time are considered major issue [29]. Delays in required/standard TAT are accepted as a major problem leading to performance deficiency [30].…”