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Multi-class Influential Article Multi-class queuing for improving ED times LWOT is noted as most significant factor Used routing matrices for patient.

Dec 14, 2015

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Page 1: Multi-class Influential Article Multi-class queuing for improving ED times LWOT is noted as most significant factor Used routing matrices for patient.
Page 2: Multi-class Influential Article Multi-class queuing for improving ED times LWOT is noted as most significant factor Used routing matrices for patient.

Multi-class Influential Article

•Multi-class queuing for improving ED times

•LWOT is noted as most significant factor

•Used routing matrices for patient flow patterns; we’d simplify

•Use statistical software and use preexisting, comparative numbers

•Split patient flow is factored; puts lower-acuity patients in a separate monitoring queue from high-acuity

Page 3: Multi-class Influential Article Multi-class queuing for improving ED times LWOT is noted as most significant factor Used routing matrices for patient.

Six Sigma Influential Article

•Hospital saw LWOT times drop from 14% to 1.8%

•Sigma cert. staff bring specific skill sets to their work areas

•At Doctors, Sigmas could see how skills can help staffs save time

•Sigma staff go to workers and gather their input

•Workers every step is identified and quantified; control teams then implement rapid-cycle testing to eliminate waste

•We should find what % of staff at Doctors are Six Sigma

Page 4: Multi-class Influential Article Multi-class queuing for improving ED times LWOT is noted as most significant factor Used routing matrices for patient.

Data Presentation

•Primarily to enhance captured tabular data

•Introduces our stakeholders to the power of pivot tables

•Pivot tables a part of Excel since 1995

•Breakdowns allow tabulated statistics to display as part of our elaboration analysis

•This analysis expands any data subgroups we deem relevant

Page 5: Multi-class Influential Article Multi-class queuing for improving ED times LWOT is noted as most significant factor Used routing matrices for patient.

Data Display

•We will place the data into a correlation matrix

•Highlighting and charting closely matched variables

•Goal is showing the similarities/differences to values found at other hospitals through other studies

•Administrators should note close comparisons/contrasts and then make determinations as to actions to take