Hol ly S. Dav i s , M.Ed. , MBAHeal th Care Exce l

Are Your Data Pulling You Overboard Or Anchoring

You?

Conflicts of Interest Disclosures

I DO NOT have a financial interest/arrangement or affiliation with one or more organizations that could be perceived as a real or apparent conflict of interest in the context of the subject of this presentation.

I DO NOT anticipate discussing the unapproved/investigative use of a commercial product/device during this activity or presentation.

Objectives

At the end of the session, the participants will Understand the importance of data quality Examine the importance of variation and control Identify tools to more dynamically present data

The goal is to turn data into information, and information into insight.       -Carly Fiorina, Former CEO of HP

You may have “Dirty Data” to start with… or Blasting Barnacles

Start with the basics: do the numbers ADD UP? LOS Example

Use DESCRIPTIVE STATISTICS to check data accuracy: Date Calculations Example

http://www.ultimatewasher.com/articles/water-blasting-barnacles.htm

Variation

Common cause Inherent to the system and always present as long as

the process is not changed and is referred to as the natural variation in a process

Inherent part of the process design and affects all items

Effect of many small causes and cannot be totally eliminated

Requires the attention of management to change Accounts for 85-90% of quality problems in an

organization

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Variation

Special cause Mainly controllable by the “operator” Refers to problems that arise because something

unusual has occurred, not part of the process as designed, and does not affect all items

If variations fall outside the control limits or a non-random pattern is exhibited, special causes are assumed to exist and the process is said to be out of control

Joseph M. Juran, quality philosopher: Operator error is inadvertent, willful, or due to inadequate training or improper technique

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Control Charts

A graphical tool for monitoring the activity of an ongoing process

Three lines indicated on the control chart Center line: typically represents the average value of

the characteristic being plotted; indication of where the process is centered

Upper and Lower control limits: used to make decisions regarding the process; if points plot within the limits and do not exhibit any identifiable pattern, the process is said to be in statistical control

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Calculations

Sample mean (aka Average): Adding all observations in a sample and dividing by

the number of observations (n) in that samplePopulation mean:

Adding all data values in the population and dividing by the number of size of the population (N)

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Calculations

Sample standard deviation: = or Population standard deviation:

Example

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Control Charts indicate the following…

When to take corrective actionType of remedial action necessaryWhen to leave a process aloneProcess capabilityPossible means of quality improvement

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Analysis of Patterns in Control Charts

Rule 1: A process is assumed to be out of control if a single point plots outside the control limits.

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Analysis of Patterns in Control Charts

Rule 2: A process is assumed to be out of control if two out of three consecutive points fall outside the 2 warning limits on the same side of the center line.

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Analysis of Patterns in Control Charts

Rule 3: A process is assumed to be out of control if four out of five consecutive points fall beyond the 1 limit on the same side of the center line.

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Analysis of Patterns in Control Charts

Rule 4: A process is assumed to be out of control if nine or more consecutive points fall to one side of the center line.

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Analysis of Patterns in Control Charts

Rule 5: A process is assumed to be out of control if there is a run of six or more consecutive points steadily increasing or decreasing.

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Control Charts Are Used for Variables Such As…

Wait time of serviceTime to obtain an appointmentEffectiveness of medicines as indicated by

measures such as temperature or blood pressure

Response time for ambulancesAdmit time in emergency room service

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Not All Health Care Data Work in Control ChartsChart for Number of

Non-Conformities (c-chart) Number of errors in

blood or urine tests per ### samples

Number of billing errors per ### accounts

Number of adverse comments per week on nurses’ performance

Number of errors per week in deliveries to patients

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Not All Health Care Data Work in Control ChartsChart for Proportion

Non-Conforming (p-chart) Proportion of

Medicare/Medicaid cases in error

Proportion of payments in error

Proportion of tests performed incorrectly

Proportion of cases with inaccurate diagnosis

Proportion of cases with side effects of medication and/or treatment

Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition.(1998, 1993). Prentice-Hall, Inc.

Effective Ways to Show Data Graphically

Effective Ways to Show Data Graphically

Effective Ways to Show Data Graphically

Line Graph vs. Bar Graph

Line Graphs Track changes over

periods of time Better than bar graphs

when smaller changes exist

Compare changes over the same period for more than one group

Bar Graphs Used to compare

things between different groups or track changes over time

Better than line graphs when changes are larger when trying to measure change over time

Can you combine the two?

Questions?

For further information, please contact me atHolly.Davis@hcqis.org

Or by phone at (812) 234-1499 x.327 or (812) 243-3635

This material was prepared by Health Care Excel, the Medicare Quality Improvement Organization for Indiana, under contract with the Centers for Medicare & Medicaid Services (CMS), an agency of the U.S. Department of Health and Human Services. The contents presented do not necessarily reflect CMS policy. 10SOW-HCE-GENE-14-001 04/15/2014