Statistical Thinking to Improve Quality

This posting illustrates the use of model building to study cause and effect and reduce common-cause variation.  One approach to model building is to build a model such as a regression model based on either results from an experimental design or observed process data.  Another approach illustrated in this posting is to construct a simulation model based on the system flow chart or process map.    One application of a simulation model is to predict flow times or service times for complex systems.   In service or health system applications customer service or wait times could be useful quality measures.   One uses the simulation model by varying input variables such as the number of servers to predict their effect on customer service times.

Davies (2007) describes a case study involving the treatment of minor injuries and medical problems in an emergency department in England.   Receptionists route arriving patients with minor injuries or medical conditions are routed to the “Minors” department.   The standard processing procedure has receptionists in the Minors department assign patients to a queue for triage nurses who assess the patient condition and needs.   Then the triage nurse routes the patients to a doctor or nurse for treatment.   The nurses are qualified to assess and treat minor injuries but not to handle minor medical conditions which are handled by doctors.   These nurses are Emergency Nurse Practitioners (EPNs).  Call this procedure “See” and “Treat”.   The UK national health service recommended that emergency departments skip the triage nurse step.   The health service recommended that receptionists route patients to a doctor or ENP for diagnosis and treatment.  Call this procedure “See & Treat”.   The intent was to reduce patient system time by eliminating a step and its associated queuing time.   The following figure depicts the “See & Treat” patient flow.

Davies describes a simulation model for comparing the two procedures.   This model represents the processing of individual patients, their waiting times, and individual task processing times.   Inputs to the model would include distributions for task times, distributions for times between patient arrivals, and the numbers of doctors and EPNs.  The following figure presents some of the simulation results.   The new procedure “See & Treat” that eliminates the triage step gives the lowest system time.

References

1. Davies, R. (2007). "See and Treat" or "See" and "Treat" in an Emergency Department. 2007 Winter Simulation Conference. Washington, DC.
   

 

Posted by Gordon M. Clark at 06:54 PM | Permalink | Comments (0)

This posting continues the discussion of the seventh step, Study Cause and Effect, of the Hoerl-Snee Process Improvement Strategy.  Tools that might be used in this step that were not summarized in the previous posting are: 

• Experimental Design.  A systematic planned variation of input factors for an actual process.   The experimenter observes the effect of these variations on important quality characteristics.   The 1/30/2008 posting mentions the use of designed experiments by an OEM manufacturer to determine an improved raw material composition.  The 2/28/2008 posting discusses the effort by a company to reduce the rejection rate at one of its machine shops.   Based on a Cause & Effect diagram, project members selected four factors for further analysis based on designed experiments.   These factors were Feed Rate, Wheel Speed, Work Speed, and Wheel Grade.   Analysis of the experimental results identified “optimum” levels for the four factors.
• Model Building.  One could construct a model of a process that predicts quality performance based on input variables.   The 2/18/2008 posting describes the actions of a Midwest manufacturing firm to reduce time delays experienced by customers contacting their order processing center.  They constructed a simulation model of the order-taking process.  Using the simulation model they determined the staffing level of customer service representatives by the hour of a work day to meet time-delay objectives.  Why don’t software companies use simulation models to specify technical support personnel requirements?

Subsequent postings will illustrate the use of experimental design and model building to Study Cause and Effect.

Posted by Gordon M. Clark at 03:57 PM | Permalink | Comments (0)