Krouwer Consulting
 

 

Home
Up
Modeling
Freq. of Med. Errors
Frequency of Medical Errors II
Six Sigma
Six Sigma II
Six Sigma III
Outliers
Equivalent QC
Equivalent QC2
Sys. not People
Near Miss
Detection
Recovery
Risk Management
FMEA FRACAS
Fault Tree Example
Beware the Technical Administrator
Discrepant Analysis
FMEA Validation
Proficiency Testing and Six Sigma
It's Up To the Lab Director
Bland Altman Plots
Risk Management II
Unit Use QC
The reverse Pareto – not a good
Risk Management III
Pedigree of Approaches to Reduce Error
What it Takes to Get New Ideas Accepted
FMEA FRACAS FTs Pareto
When to conduct FMEA
Error Reporting Systems for Clin
Customer Misuse
Fault Isolation
Uniformity of Claims
Outliers in QC and Proficiency Testing
The quality of quality initiativ
Publishing in journals or on web
FDA Waiver Guidance
Pay for Performance
Pay for performance II
Performance vs. Attribute Goals
ISO Terminology
Latent Errors
Preventable medical errors
Troponin I
GUM Comment
More on GUM
Standards Issues
Slow Qual. Progress
More QC?
Report Writing
Software Validation
Dump Data

Frequency of Medical Errors II - Where’s the Data?

In virtually any tutorial about quality improvement, one is likely to encounter something like Figure 1, which describes a “closed loop” process. The way this works is simple. One has a goal which one wishes to meet. One measures data appropriate to the goal. If the results from this measure fall short, one enters the “closed loop” where one revises the process and measures progress and continues this cycle until the goal is met. Then, one enters into a different phase, (not shown in Figure 1), where one ensures that the goal will continue to be met.

Figure 1 – The Closed Loop Process

Two deficiencies in the patient safety movement are: 1) the lack of clear, quantitative goals; and 2) the data from which one can measure progress. A list of problems the way goals are often stated is available (1).

An interesting paper that appeared recently discuses wrong site surgery (2). Given the visibility of wrong site surgery, one notable aspect of this paper is that it is one of the few sources which has wrong site surgery rates. The wrong site surgery rate was 1 in 112,994, or 8.85 wrong site surgeries per million opportunities. To recall, a 6 sigma process has 3.4 errors per million opportunities, so this rate is about 5.8 sigma. The authors state that the rate is equivalent to an error occurrence once every 5 to 10 years. This corresponds to the lowest frequency ranking in the Veterans Administration scheme of an error occurrence once or less every 5 to 30 years (3).

Another interesting aspect of the paper is the discussion of the Universal Protocol, which is a series of steps incorporated into the surgical process and designed to prevent wrong site surgery. One of the conclusions of the paper is that the Universal Protocol does not prevent all wrong site surgeries. The Universal Protocol was implemented as the solution to prevent wrong site surgeries. The problem is that where one would hope that a process change might be sufficient to remedy an issue, often this is not the case. Thus, one must continue to collect data and to add remedies and or change existing ones until the goal has been met, or in other words, continue with the cycle shown in figure 1. So one criticism of the patient safety movement is the mandated, static nature of corrective actions. The dynamic nature implied in figure 1 seems to have been bypassed.

The authors lament that the public is likely to overreact to wrong site surgery relative to other surgical errors such as retained foreign bodies. There are several points to be made here.

In classifying the severity of an error, one must examine the effect of the error, which means looking at the consequences of downstream events connected to the error (often facilitated by using a fault tree). Based on the authors discussion from actual data, retained foreign bodies is a more severe error than wrong site surgery. This is somewhat of a surprise, but is understandable.

Given one has classified all error events for criticality (which is severity and frequency of occurrence), one has the means to construct a Pareto chart. Since organizations have limited resources and cannot fix all problems, based on the Pareto chart, retained foreign bodies is likely to be higher on the Pareto chart than wrong site surgery and deserves more attention.

Proposed process changes need to be evaluated with respect to cost and effectiveness. The “portfolio” of proposed process changes can be viewed as a decision analysis problem whereby the “basket” of process changes selected represent the largest cumulative reduction in medical errors (e.g., reduction in cost associated with medical errors) for the lowest cumulative cost. See the essay on preventability.

I discuss (4) a hypothetical case where two events have identical criticality with respect to patient safety but one is high profile and the other isn’t. Should the high profile event get more attention? The answer is yes, because besides patient safety, there are other error categories for which the high profile event will be more important, such as customer complaints, threat to accreditation, and threat to financial health.

There are other comments that could be made but perhaps the most important comment is that studies such as those conducted by these authors are extremely valuable and are the heart of figure 1; namely, examining error events and currently implemented corrective actions and deciding how to make further improvements.

References:

  1. Assay Development and Evaluation: A Manufacturer’s Perspective. Jan S. Krouwer, AACC Press, Washington DC, 2002. pp 33-44.
  2. Kwaan MR, Studdert DM, Zinner MJ, Gawande AA Incidence, patterns, and prevention of wrong-site surgery. Arch Surg. 2006;141:353-7; discussion 357-8, available at http://archsurg.ama-assn.org/cgi/content/full/141/4/353
  3. Healthcare Failure Mode and Effect Analysis (HFMEA) VA National Center for Patient Safety http://www.va.gov/ncps/SafetyTopics/HFMEA/HFMEAmaterials.pdf
  4. Managing risk in hospitals using integrated Fault Trees / FMECAs. Jan S. Krouwer, AACC Press, Washington DC, 2004. pp 17-18.