Are Your Data Pulling You Overboard Or Anchoring You?

Are Your Data Pulling You Overboard Or Anchoring You? Holly S. Davis, M.Ed., MBA Health Care Excel

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 outsidethe 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 sample • Population 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 action Type of remedial action necessary When to leave a process alone Process capability Possible 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 service Time to obtain an appointment Effectiveness of medicines as indicated by measures such as temperature or blood pressure Response time for ambulances Admit 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 Charts Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition. (1998, 1993). Prentice-Hall, Inc. • Chart 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

Not All Health Care Data Work in Control Charts Mitra, A. Fundamentals of Quality Control and Improvement. Second Edition. (1998, 1993). Prentice-Hall, Inc. • Chart 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

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 at Holly.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

Are Your Data Pulling You Overboard Or Anchoring You?