I’ve finally added a blog roll - links to other blogs. Actually, I have many more blogs saved under feeds, but I find that many of these feeds I don’t read thoroughly - more like a quick scan. The blogs listed here, however, I do read. In the field of healthcare, I have only one blog listed - The Covert Rationing Blog. I recommend it highly. At the end are two windsurfing blogs. The picture on the left is my view (literally) on windsurfing - from a helmet cam. For a longer version, go here or here.

EP22 was created as a means to use risk management to allow manufacturers to recommend the frequency of external quality control run by clinical laboratories. This was the so called option 4. Options 1-3 were part of the original CMS proposal to allow clinical laboratories to reduce the frequency of external quality control to once a month (provided certain conditions were met).

 

 

 

EP23 was the clinical laboratory follow on document to EP22.

Here’s my take on these two documents.

1.       Manufacturers won’t provide the information as suggested by EP22. (This information consists of experiments to demonstrate the efficacy of internal control measures). It would be a lot of work (e.g., cost) and there’s no regulatory requirement to do so. Moreover, if this information were provided, then it is labeling which would require FDA to review it. It is not clear that FDA has accepted this review task. 

Update on 8/4/08 - During a CLSI presentation at the AACC meeting in Washington, Alberto Gutierrez from the FDA gave a presentation. Afterwards, I asked him if FDA would review the material about internal control experiments that manufacturers might present as part of the package insert. He said that FDA would review this material - but from what was said it seemed that the review would be superficial and that only egregious problems would be flagged by the FDA.

2.       Clinical laboratory staff does not have the expertise to review this information, were it provided. This does not mean that clinical laboratory staff is incapable of reviewing it – they could acquire the expertise – it just seems unlikely.

3.       Should manufacturers provide this information and clinical laboratory staff review it, there would be no benefit with respect to improving QC. This is illustrated by an example in EP22 where the failure mode of “incorrect results due to low volume sample” is examined. After presenting the results of an experiment to show how an internal system control works, the user control measure is to “ensure that adequate volume of sample is presented to instrument.” But clinical laboratory staff would (or should) do this anyway. They don’t need EP22 and EP23 to know that one should follow the manufacturer’s instructions and to refrain from doing something stupid.

In clinical chemistry, risk management is “in.” But there are signs that its popularity is already starting to wane. This is unfortunate, as there is a great opportunity to use risk management tools to reduce both the risk and occurrence of laboratory errors. But one must focus not just on potential system errors, as EP22 and EP23 do, but on human errors as well.

In the June 2008 issue of Quality digest, there is an article by Jay Arthur entitled “Statistical Process Control for Healthcare” (1). After the usual boilerplate type of introduction, something caught my eye; namely, the so called good news that there is “inexpensive Excel based software to create control charts … .“ This made me go to the end of the article where sure enough the author just happens to sell such software. This may have been a good place for the author to introduce the term bias.

To understand a more serious problem with this article, consider a hospital process; namely analyzing blood glucose in a hospital laboratory. Because such a process has error, quality control samples are run. Say such a control has a target value of 100 mg/dL.  The values of the quality control samples are plotted by SPC software and rules are formulated. If the glucose control value is too high or too low, the process is said to be out of control and action is taken.

Now,  Mr. Arthur is trying to push SPC software not for a process but for errors in the process. For example, he uses the infection rate in a hospital. But the infection rate error is not a process that one wants to control – of course one does not want it to become worse - but its target is zero.

A more useful example than the hypothetical one provided by Mr. Arthur was published recently (2). Here, the authors were faced with an undesirable hospital infection error rate and set out to observe where errors occurred in the process of placing central lines. They then provided control measures and continued to track the error rate, which was reduced to zero. This is not SPC! It is much more like a FRACAS (Failure Reporting And Corrective Action System).

In another part of the article, Mr. Arthur suggests that “never events” can be tracked by SPC. Never events – a list of 28 such events have been put forth by the National Quality Forum – have as implied, targets of zero. Such an event is wrong site surgery. One should use something like FMEA (Failure Mode Effects Analysis) to reduce the risk of such events. It is silly to suggest SPC software for never events.

References

1.   See. http://www.qualitydigest.com/currentmag/articles/03_article.shtml

2.   An Intervention to Decrease Catheter-Related Bloodstream Infections in the ICU. Pronovost P, Needham D, Berenholtz S, Sinopoli D, Chu H, Cosgrove S, Sexton B, Hyzy R, Welsh R, Roth G, Bander J, Kepros J, Goeschel C N Engl J Med 355:2725, December 28, 2006

I just returned from the Westgard quality Control Workshop, where I was a speaker and have a few blogs worth of comments – this is the third.

EQC – Equivalent Quality Control

This is the CMS proposal (1) to allow clinical laboratories to reduce the frequency of quality control from twice per day to once a month given that 10 days of running QC shows no values that are out (and given some other conditions).

Let’s try to construct a hypothesis to base such a recommendation. For example:

given any possible error condition that could be detected by external quality control, internal quality control would detect the same error 100% of the time.

This is about the best I can think of, which would result in the recommendation:

Stop running external quality control.

What does running 10 days of external QC with no out of control results show? The answer is nothing. This is because one can assume that during these 10 days, there were either no errors or if there were errors, external QC was not able to detect them. (It is possible that internal QC detected errors during these 10 days). In fact, this experiment is guaranteed to be meaningless. To see this, one must realize that internal QC is always “on” and precedes external QC. So to see if external QC is redundant to internal QC for an error, would mean that internal QC would detect the error and either shut down the system or prevent the result – this being the external QC sample – from being reported. However, one can get different information by running external QC for a longer period because if internal QC misses an error but external QC detects the error, then one has proved that external QC is not redundant to internal QC. This was shown to me (2) as out of control results for a range of assays ranging from 1 to 10 per year, where these were real problems. Since controls are run twice per day, the number of affected patients samples is larger.

So a lab that reduces external QC to once a month is risking an even larger number of patient samples which is made worse since the clinician has probably acted on the erroneous results.

Rather than do the experiment suggested by CMS, a lab can simply examine its external QC records for a sufficient length of time.

References

1.       To review, see: See http://www.aacc.org/events/expert_access/2005/eqc/Pages/default.aspx

2.       Personal communication from Greg Miller of Virginia Commonwealth University

I just returned from the Westgard quality Control Workshop, where I was a speaker and have a few blogs worth of comments – this is the second.

How does one determine acceptable risk

This was one of the questions asked by a participant – are there any guidelines? I also commented recently, that in spite of all of talk about risk management and putting in place control measures until one has acceptable risk, no one knows what acceptable risk means. Here’s some more thoughts on this.

There are different risks (1). These can be enumerated. These include:

perception – complaints from either hospital or non hospital staff

performance – traditional quality, including errors that can affect patient safety

financial – errors that threaten the financial health of the service including lawsuits

regulatory – errors that threaten the accreditation status of the service

So first, one must say which risk one has in mind. One can envision an acceptable regulatory risk (we always pass inspections) but an unacceptable patient safety risk.  Note also, that the risks are not necessarily unique. One can have a patient safety failure with or without a lawsuit.

Assume the risk in question is the performance risk and specifically about patient safety. The Cadillac version of assessing risk would be to perform a quantitative fault tree and arrive at a numerical probability of patient risk. This is unlikely and one would probably have a qualitative assessment. Whether the assessment is quantitative or qualitative, this still hasn’t answered the acceptability question.

The problem is there is no easy answer to this question. If one had unlimited funds, one could lower the risk to whatever level was desired but funds are limited by the economic healthcare policy of the laboratory’s country (2). So one answer of acceptable risk is how this economic policy is translated into regulations. (e.g., one follows existing regulations and passes inspections). Yet, this is only a quasi legal way of stating acceptable risk.

Recommendation

I suggest that risk be assessed by traditional means (FMEA, fault tree) which includes a Pareto chart or table to rank the risks. Then, if one optimizes the money that one has in implementing control measures (mitigations) by a portfolio type means, then one has an acceptable risk under the imposed financial constraints.

References

1.       Managing risk in hospitals using integrated Fault Trees / FMECAs. Jan S. Krouwer, AACC Press, Washington DC, 2004.

2.       See http://covertrationingblog.com/

I just returned from the Westgard quality Control Workshop, where I was a speaker and have a few blogs worth of comments – this is the first.

What’s Missing from Clinical Laboratory Inspections

At the Westgard Workshop, most of the participants were from clinical laboratories and I was impressed with how smart these people are. I also got a sense of a tremendous regulatory burden. From the CAP CD, I obtained at the Workshop:

      The mission statement of the CAP Laboratory Accreditation Program is:

“The CAP Laboratory Accreditation Program improves patient safety by advancing the quality of pathology and laboratory services through education and standard setting, and ensuring laboratories meet or exceed regulatory requirements.”

I have had mixed feelings about inspections that certify quality and have previously reported my experience with an industry quality program – ISO 9001 (1).

Here’s my assessment of clinical laboratory inspections to certify laboratories. It would seem that the premise of these inspections is to ensure that specific policies and procedures are in place and executed as proven largely by documentation, which guarantees high quality. So what’s missing? As far as I can tell – and it is with great difficulty to read through these materials – that there is no measurement of error rates. Without such measurements, quality is unknown.

Recommendation

The regulatory bodies would describe a list of errors and their associated severities. The severities would be given numerical values such as the VA hospital system which uses 1-4. Every clinical laboratory would record each error (failure mode) that occurs in their laboratory, its severity, and its frequency (default frequency is of course 1).  They would multiply frequency x severity for each unique error (failure mode), add this up and get a rate by dividing by the number of tests reported per year.

Failing to count errors would be a serious violation.

This would be the start of a new premise for the regulatory bodies. Measure quality – if it’s unacceptable, the clinical laboratory would suggest and implement process changes. It’s a simple closed loop process. With emphasis on measurement, reliance on documentation should decrease and inspections should be less burdensome.

References

1.       Krouwer JS. ISO 9001 has had no effect on quality in the in-vitro medical diagnostics industry. Accred. Qual. Assur. 2004;9:39-43

Standards about risk management always talk about “acceptable risk.” This is a qualitative term. Unfortunately, for much of healthcare there is no matching quantitative assessment or goal. Consider two examples.

It is possible to estimate the probability of a severe adverse event and to have an associated goal for such a probability but no one in healthcare does this. So one will see things like, “with this mitigation we have reduced the risk of the adverse event to an acceptable level” but the reality is no one knows what this really means.

This has been considerable discussion about the National Quality Forum’s  so called 28 never events (1). Here are some problems with this concept.

Never is a poor goal – Adverse events can be considered within a risk management program. Risk is the combination of two items – severity and probability of occurrence. By their selection, one can gather than severity is high for the 28 events. However, probability can never be zero. Consider a simple example. The likelihood of performing wrong site surgery is X. One performs a double check to prevent wrong site surgery. Now the likelihood is 0.0001X. But the double check can fail. So one can perform a triple check. Now the probability is much lower but it is still not zero. And so on. Working with probabilities (as in fault trees), is one way to see that probabilities are never zero, nor is risk.

28 goals are too many – If one wants to manage anything, one needs a limited number of goals. There is no reason why one can’t combine events to give a single goal – the overall risk of an adverse event.

“largely preventable” is not the same as preventable – In the NQF site, the never events are said to be largely preventable. The problems with this are obvious.

References

1.       See http://216.122.138.39/projects/completed/sre/index.asp

This entry continues where the entry (Six Sigma can be dangerous to your health) left off. Given the problems with six sigma, what are some solutions to estimate the quality of an assay, using hCG as an example assay.

First, when total analytical error is calculated to estimate the values in zones A-C in an error grid, one should use conservative methods such as the empirical distributions suggested by the CLSI EP21A method, and where no data are deleted. Let’s say a clinical laboratory has done this evaluation with 40 patient samples for a new and reference method and found no results in zone C for an hCG assay. What can one conclude? Although there are 0% of the values in zone C, the 95% confidence interval extends to 7.2%. This means that for every million hCG results performed, up to 72,000 results could be in zone C. This is not very comforting and these types of evaluations don’t prove much, although one knows that the 7.2% rate is unlikely (because if this rate to occurred, it would be noticed).

FMEA is an approach that will provide an answer to the quality question but in its complete form, it requires considerable effort. To complete a FMEA analysis, one has to postulate all possible reasons why a result could fall into zone C. To get an idea of what is involved, take two possible failure modes, HAMA interference and a patient sample mix-up.

HAMA interference – To estimate the likelihood of a zone C result from HAMA interference, one needs to know the level of HAMA that will cause erroneous results in the assay and the probability of such levels in the population being sampled. Contacting the manufacturer might give one the level of HAMA to watch out for – I am not familiar with data about the distribution of HAMA in patient samples. Yet, one knows HAMA interference occurs (Clinical Chemistry. 2001;47:1332-1333).  

Patient sample mix-up – There are some data for patient sample mix-ups (Archives of Pathology and Laboratory Medicine: Vol. 130, No. 11, pp. 1662–1668). However, it seems that these cases are caught within the laboratory. One would need to determine how many cases actually are not caught within the laboratory. One could then model the likelihood of a zone C result by sampling from the empirical distribution of hCG results that are observed on the lab to see the likelihood of a mix-up causing a zone C result.

Because there are so many existing data in a clinical laboratory, one may also have the opportunity to perform FRACAS types of analyses. That is, in addition to modeling probabilities, once could use existing data to count actual failures.

One must then continue:

• with each other possible failure mode, calculate the probability of zone C results
• calculate the overall probability of zone C results (from all failure modes) and determine if that risk is acceptable
  • special software is typically used to perform these calculations
• construct a Pareto table if the overall probability of zone C results is too high and
• propose control measures to lower the overall risk to an acceptable level
  • the control measures must of course be affordable

At this point, one can get the idea that this level of effort is out of reach for clinical laboratories since the level of expertise and work need just to estimate the likelihood of a zone C result is huge. Even if a clinical laboratory could perform this task, it makes no sense to require every clinical laboratory to do so.

One possibility is to have a standards group tackle such a task., although this too has limitations as was shown for a (universal) control measure to prevent wrong site surgery.

Another possibility is to perhaps leverage resources beyond the clinical laboratory. For example, one could insist that before treatment for trophoblastic carcinoma, an hCG result should be confirmed either by performing a reference assay or perhaps by treating the sample and rerunning it. This requires an interaction between the clinical laboratory and clinicians.

So there are no easy answers to preventing severe, low frequency failures, (that cause patient harm) but as discussed before, coming up with a sigma estimate for an hCG assay, is also not the answer. Nor is doing nothing.

I recently spoke at the Quality in the Spotlight conference in Antwerp, Belgium and gratefully acknowledge being awarded the Westgard Quality Award. This award was presented by Jim Westgard himself. The Quality in the Spotlight conference is a two day conference in Antwerp, devoted each year to a quality theme. This year’s theme was quality tools. I spoke about FMEA on each of the two days. It wasn’t until the second day of the conference that I realized that some of the other presentations were bothering me – perhaps I had a case of brain jetlag. This is an interactive conference so had I been quicker I would have presented my concerns to the speakers. But this did not happen so my concerns are in the previous entry to this blog. Prof. Dr. Jean-Claude Libeer, who founded the conference and also spoke about me with respect to the award, said that it was my blog which impressed people. So perhaps my previous entry could be taken as an acceptance speech.

On the second day, per instructions, I attempted to do a “workshop”. This is in quotes because I had to involve the audience but was only given one hour. Had I to do this again, I would have given an award to one lady, who answered some of the questions I posed to the audience. One example – name a case of at risk behavior that you have experienced. Answer, a technician, who had trouble getting a barcode on a patient sample to register, scanned the barcode from another patient. So perhaps this is also an illustration of the need to perform a FMEA on a control measure (what can go wrong with implementing barcodes).

Another highlight of my trip was spending three days in Amsterdam and hearing that in spite of frequent mistakes, my Dutch is begrijpelijk (understandable).