Errors patient identification

Hospital work areas and staff asked to complete the survey When the Hospital SOPS was developed, it was not specifically designed and tested for use with non-clinical staff like those in honsekeeping, facilities, or human resources. Yet once the snrvey was released, it became very clear that hospitals wanted to survey all staff from all units and departments, with the understanding that every staff member plays an important role in ensuring patient safety. By being attentive and aware of patient safety risks, in an environment that encourages open communication and learning, even non-clinical staff can help prevent medication errors, patient identification errors and many other types of errors. Since one of the uses of the survey is as an education and awareness tool, it makes sense for hospitals to conduct the survey in a broad way across units and staff positions. 

Specimen mix-up Incorrect volume of specimen Interfering substance present Instrument precision problem Wrong patient identification Report not posted in chart Report not legible Report delayed Transcription error Interfering substances not recognized... 

Correct identification of patients and specimens is a major concern for laboratories. The highest frequency of errors occurs with the use of handwritten labels and request forms. One method for checking identification is to compare identifiers such as the patient s name and his or her unique hospital number. The identification on the specimen label should also correspond with the identification on the requisition form. The use of plastic embossed patient identification cards to imprint the patient s name on test request forms and on blood collection labels can eliminate transcription and identification errors but does not guarantee that tiie patient name on the labels correctly identifies the donor of the specimen. The integration of bar code technology into the analytical systems that are used by clinical laboratories has significantly reduced identification problems (see Chapter 11). 

Many modern analyzers u.se a closed-tube technology to minimize exposure to biohazards and to reduce manual manipulations. Samples and reagents arc dispensed automatically, the measurements made by photometry or ion-selective electrodes, and the results computed. Most have bar-coding capabilities to reduce errors from incorrect patient identification, A typical chemistry-immunochemisiry automated analyzer is shown in Figure 3.3-18. 

Patient identification wristbands patients should notify staff at any stage of hospitaliation if the information on their wristband is incorrect or if they have no wristband to prevent errors related to patient misidentification. 

Unknown compounds are detected frequently, and laboratories eventually develop some level of comfort in recognizing them as artifacts that are not significant clinically, at least for the purpose of ruling out a possible inborn error of metabolism [21]. However, there are instances when an unknown compound is found in multiple specimens from the same patient and cannot be associated with ongoing drug and known dietary intake. This was the set of circumstances that led to the identification of 2-octenylsuccinic acid as the compound referred to by some laboratories as pseudo-orotic acid [11, 18]. On the other hand, the spectrum shown in Fig. 3.1.10, tentatively identified as 4-hydroxy 2-hexenoic acid by GC-MS/MS, belongs to a compound that appears in the urine of patients with disorders of propionate me-... 

Hemolytic post-transfusion reactions (50) are most often caused by clerical and administrative errors involving misidentification (incorrect labeling of blood samples, erroneous identification of patients) rather than by mistakes in the laboratory (2,51). However, scrupulous care is demanded at all stages in a review of hemolytic and other transfusion reactions, the importance of competent handling of whole blood and red cell suspensions (including the avoidance of freezing or osmotic damage) has been stressed (52). 

Certain errors, particularly errors in specimen identification, have been detected by comparing laboratory test results with values obtained on previous specimens from the same patient. The expected variability of test results depends on both tlie analyte and the time interval between determinations. Ladenson has defined delta check limits based on a 3-day mterval in terms of a percentage change from the initial value. His check limits for some common tests are shown in Table 19-6. 

Identification is a major problem in clinical laboratories, and serious untoward events can occur with misidentified specimens. Unambiguous identification is possible today with bar-coding and similar machine-labeling techniques (31). The model discussed here is for testing patient-derived materials in a clinical laboratory the model can, of course, be extended to other applications. A machine-readable label on every specimen is the contemporary standard of modern equipment. Keying in identifiers to an instrument is less desirable owing to the inevitable human errors. In our experience with bar-code readers, they read the label correctly or don t work at all. The topic, automated specimen identifications, is described in more detail in Section 8.2 here (32). 

If we now look at the ability of various types of clinical trial to resolve total variation, then the situation is as illustrated in Table 25.5, which is also taken from Senn (2001). We can see that, as discussed above, a single cross-over trial is not capable of distinguishing between sources of variation C and D. This is the situation discussed in Section 25.2.1. A slight caveat must be entered here. If more than two treatments are being studied in a cross-over trial, then some partial identification of treatment-by-patient interaction is possible. This is because in a cross-over trial in n and t treatments in t periods the nt —1 degrees of freedom resolve into n — for patients and t— for each of treatments and periods. This leaves nt — n — lt + liot error. If r = 2 the degrees of freedom reduce ton-2, which is fewer than the number of patients. However, if r > 3 then the residual degrees of freedom for even moderately sized trials exceed the number of patients and this means that some partial identification is possible (Senn, 2002 Senn and Hildebrand, 1991). Nevertheless, full identification requires full replication of patient-treatment combinations. 

Even if identification errors can be detected automatically, the problem remains that correcting them takes quite some time and may even become a source of new errors - of a kind that cannot be detected automatically. In addition, typing the patient and exam IDs at the modality takes time. Thus, blindly replacing the film-based organization by the modern digital one actually lowers productivity. 

Figure 9.1 provides a conceptual framework for risk identification. There are various sources of risk of medical products that can result in injury or death, including known side effects, medication and device errors, product defects, and other remaining uncertainties. These risks are influenced by many factors, including patient characteristics (such as demographics, comorbidities, concomitant medications, and health service utilization), health system factors (such as utilization practice and provider behavior). 

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