Running Head: QUALITY IMPROVEMENT PROPOSAL

Quality Improvement Proposal: Diagnostic Errors

Name

Institutional Affiliation
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Quality Improvement Proposal: Diagnostic Errors

Overview of the Problem

Diagnostic error is one of the major problems encountered in my current workplace. A

recent review of internal records revealed that about 12% of the patients have received a wrong

diagnosis resulting in admission after subsequent hospital visits. Furthermore, the review showed

that almost 30% of hospital visitors receive medication without a laboratory diagnosis. This trend

shows that physicians rely on their experience to prescribe medications without necessarily

carrying out a laboratory test. Diagnosis errors have a significant effect on treatment outcomes,

and thus their prevention is of utmost importance. Various factors hinder the prevention of

diagnosis errors. For instance, the errors cannot be identified in real-time. As such, most

processes of identifying diagnostic errors involve a retrospective review of practice data.

Furthermore, diagnosis is a complex cognitive process, which relies on individual abilities.

Diagnostic errors are a major cause of poor health care outcomes including readmission rates and

mortality. Furthermore, diagnostic errors result in a high cost of care because of worsening

symptoms as well as the double cost of medications.

The Need for a Quality Improvement Initiative

Diagnosis can come from various sources. For instance, cognitive bias has been shown to

increase diagnostic errors because physicians rely on heuristic assumptions to make a provisional

diagnosis. Heuristic assumptions include experience with similar cases, reliance on initial

diagnostic impression, reliance on subtle cues, and reliance on test results. In addition to

cognitive biases, healthcare system issues can also lead to delayed or missed diagnoses. For

instance, poor communication between a physician and laboratory technician can result in
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diagnostic errors. Inappropriate triage frameworks and a lack of follow-up tests can also increase

the probability of diagnostic errors. Considering the systematic nature of the diagnostic errors,

supportive tools could be instrumental in reducing the errors. The focus of such tools is to

address the underlying causes of the errors so that their rates can reduce. In this case, diagnostic

checklists are proposed as effective tools for facilitating diagnostic decision-making. Checklists

can reduce diagnostic errors by providing a framework for identifying cognitive biases,

consideration of alternative diagnoses, and critical examination of the data processing process.

Supporting Research

Research related to addressing diagnostic issues has increased in the past decade with a focus on

system-based or instructional interventions. The underlying assumptions of research are that

most diagnostic errors emerge from cognitive biases (Dahm, Williams, & Crock, 2021; Fawver

et al., 2020). In experimental research involving radiologists, Fawver et al. (2020) found that

most of the diagnostic errors were judgmental. In this case, the physician made a premature

diagnosis without spending sufficient time extracting critical information. In some cases,

decisions were based on patient history. Other studies were based on the premise that diagnostic

errors are caused by knowledge deficits. For instance, research by Zwaan, Monteiro, Sherbino,

Ilgen, Howey, and Norman (2017) involving a retrospective chart review of chronic obstructive

pulmonary disease (COPD) cases revealed that knowledge deficit was a major cause of

suboptimal clinical acts.

Researchers have proposed that checklists are safe interventions, which can be used to

improve clinical outcomes including peri-operative communication, surgical errors, and

diagnostic errors. Boyle et al. (2017) identified the need for research regarding the development

of a standard checklist for identifying child psychiatric disorder. Building on early research, the
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authors suggested that self-completed checklists could produce similar outcomes to standard

diagnostic interviews. Wiggins et al. (2019) carried out research to evaluate the effectiveness of

the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). DSM-5 is a

diagnostic checklist, which is used to evaluate the severity and presence of autism spectrum

disorder (ASD). The results of the research showed that the DSM-5 checklist had high specificity

and sensitivity in identifying ASD. This research demonstrates the potential of checklists to

improve diagnostic accuracy.

Research has also been carried out to examine the significance of the impact of checklists

in improving diagnostic accuracy. For instance, Reihani, Azarfardian, Ebrahimi, and Foroughian

(2019) carried out cross-sectional research to examine the effectiveness of checklists on

interpreting electrocardiogram. Using a sample of medical interns, the research showed that the

tool was effective in diagnostic confirmation and review. However, the research showed that the

use of a checklist did not improve interpretation accuracy in comparison to an object-oriented

system. Similarly, Sibbald et al. (2019) compared knowledge retrieval and de-biasing checklists

in reducing errors in electrocardiogram interpretation. The results of the research showed that,

although checklists did not reduce diagnostic errors, they were helpful for novices. These studies

demonstrate that checklists could be useful in addressing diagnostic errors.

Implementation of the Quality Improvement Initiative

The goal of the quality improvement initiative is to implement a checklist to reduce

diagnostic errors. The first step towards implementation involves the development of the

checklist. The checklist will focus on three aspects; disease-specific features, cognitive bias, and

knowledge deficit. Disease-specific features include symptoms of various ailments and their

interrelationships. Cognitive bias includes instructions for physicians to identify common biases
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in the diagnosis process. Finally, knowledge deficit includes additional information related to

specific ailments. After the development of the checklist, it will be disseminated to a sample of

physicians (about 30%) while the rest will be used as the control population. The sample size is

representative and could provide valuable insights into the effectiveness of the checklist tool. To

eliminate research bias, the physicians involved in the evaluation of the checklist will not be

notified of the intended purpose. Rather, the physicians will only be briefed about the intention

to introduce the checklists and the importance of regularly using them. The tools will be used for

six months before conducting a summative evaluation.

Evaluation of the Quality Improvement Initiative

Evaluation of the initiative will involve a comparison of diagnostic errors between the

physicians using the checklist with those using the conventional approach. The guiding

hypothesis is as follows:

H0: The application of a checklist will reduce diagnostic errors related to cognitive bias

and knowledge deficit.

Data for the evaluation will consist of diagnostic errors including incorrect, delayed, or

missed diagnoses. The errors will be counted for all physicians in our facility to provide a

comprehensive dataset. In addition to the data, the diagnostic process will be documented

including the number of patients subject to laboratory tests and the accuracy of the tests.

Analysis of the data will be completed through repeated measures analysis of the variance

(ANOVA). A t-test will also be used to compare the diagnostic errors between the physicians

using a checklist and those using conventional methods. A qualitative assessment will also be

carried out to determine the persistence of cognitive bias and knowledge deficit in both groups.
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All quantitative analyses will be conducted with IBM SPSS. The primary outcome of the

research is diagnostic errors while the secondary outcomes are cognitive bias and knowledge

deficit.

Conclusion

Diagnostic errors are a major problem in most healthcare facilities. In our facility,

diagnostic errors account for a significant amount of admission after subsequent hospital visits.

The problem is mainly attributed to cognitive bias, system challenges, and knowledge deficit.

Diagnostic errors can result in devastating effects on the quality of care including readmission

rates and mortality. As such, it is important to minimize the errors to improve the quality of care.

In this case, checklists are proposed as a quality improvement initiative to reduce diagnostic

errors. There is sufficient evidence in the literature, which support the effectiveness of checklists

in improving healthcare outcomes including reducing diagnostic errors. However, some studies

have found no evidence of improving diagnostic performance. In this case, an evaluation is

proposed to evaluate the effectiveness of checklists in reducing diagnostic errors. The evaluation

includes qualitative and quantitative analysis of the error rates and their sources. The evaluation

will provide insights into improving the quality of care at our facility.
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References

Boyle, M. H., Duncan, L., Georgiades, K., Bennett, K., Gonzalez, A., Van Lieshout, R. J., ... &

Janus, M. (2017). Classifying child and adolescent psychiatric disorder by problem

checklists and standardized interviews. International Journal of Methods in Psychiatric

Research, 26(4), e1544.

Dahm, M. R., Williams, M., & Crock, C. (2021). ‘More than words’-Interpersonal

Communication, Cognitive Bias and Diagnostic Errors. Patient Education and

Counseling.

Fawver, B., Thomas, J. L., Drew, T., Mills, M. K., Auffermann, W. F., Lohse, K. R., &

Williams, A. M. (2020). Seeing isn’t necessarily believing: Misleading contextual

information influences perceptual-cognitive bias in radiologists. Journal of Experimental

Psychology: Applied.

Reihani, H., Azarfardian, N., Ebrahimi, M., & Foroughian, M. (2019). The Effects of Using

Checklists on Electrocardiogram Interpretation: A Cross-Sectional Study on Medical

Interns. Advances in medical education and practice, 10, 1089.

Sibbald, M., Sherbino, J., Ilgen, J. S., Zwaan, L., Blissett, S., Monteiro, S., & Norman, G.

(2019). Debiasing versus knowledge retrieval checklists to reduce diagnostic error in

ECG interpretation. Advances in Health Sciences Education, 24(3), 427-440.

Wiggins, L. D., Rice, C. E., Barger, B., Soke, G. N., Lee, L. C., Moody, E., ... & Levy, S. E.

(2019). DSM-5 criteria for autism spectrum disorder maximizes diagnostic sensitivity

and specificity in preschool children. Social psychiatry and psychiatric

epidemiology, 54(6), 693-701.

Zwaan, L., Monteiro, S., Sherbino, J., Ilgen, J., Howey, B., & Norman, G. (2017). Is bias in the

eye of the beholder? A vignette study to assess recognition of cognitive biases in clinical

case workups. BMJ quality & safety, 26(2), 104-110.