Internal Process Model Monitor Role Competency: Managing Collective Performance

Agenda Brainstorm check-in improvement options See demonstration of control chart program Complete remaining scenarios Review as a class

Learning Goals Be able to: Distinguish between special and common causes of variation Describe how you would use control charts as a tool of TQI –What their purpose is –When you use them –How they help distinguish type of cause

Patient Check-In Process Using Fishbone Diagram to Develop Improvement Ideas

Every Process Has Variation Time to feed nursing home patients Frequency of nosocomial infections Days in accounts receivable

Two Basic Types of Variation Common Cause: Due to interactions of variables within processes –Inherent in the process as it now occurs Special Cause: Due to specific causes –“Assignable” –May be attributable to an individual

Two Frequent Mistakes Mistaking common cause variation for special Mistaking special cause variation for common

Special Cause Variation May not need to fix at all May be fixed by adjusting one or two things –Just involves 1-2 people

Common Cause Variation Won’t change unless you change one or more factors in the process Best fixed by all the process ‘owners’

The Rule is… First remove special causes and then change the fundamental process

We Use Control Charts to See how variable the process is Determine if special or common cause Find out what effects changes have made

Run chart: Display of data in the order in which they appear Control Chart: Run Chart with upper and lower control limits

Upper and Lower Control Limits UCL = x-bar + 3*(r-bar/1.128) LCL= x-bar - 3*(r-bar/1.128) Where R-bar = sum of ranges/# ranges is a statistically derived constant

How Many Data Points Do You Need? At least 25 This provides context

Control Charts: What Type of Chart to Use for Different Types of Data

Attribute Data: Defects (Counts) # times an event of interest occurs in a given period Number of failures –# infections/1000 patients –# service interruptions in a given time –# complaints/month U Charts (variable sample size)

Attribute Data: Defective (Proportion) How many events failed out of a given total What % went wrong –Bad x-rays –Phone calls where caller hung up –Patient treatments interrupted –Proportion of staff calling in sick P charts (variable sample size)

Variable Data (Continuous) Can theoretically assume infinitely variable values –Amount of substance present in sample –Time to complete a task –Dimensions of a wound –Flow rate of a liquid X charts

How to Interpret Control Charts Three lines: Median (average) Upper Control Limit Lower Control Limit

How To Interpret Control Charts 1: Unusually large or small values 2c: Shifts in the middle value 2d: Trends 2e: Zigzags