Strategies to reduce diagnostic errors: a systematic review

Abimanyi-Ochom, Julie; Mudiyanselage, Shalika Bohingamu; Catchpool, Max; Firipis, Marnie; Dona, Sithara Wanni Arachchige; Watts, Jennifer J.

doi:10.1186/s12911-019-0901-1

Cited by 61 publications

(48 citation statements)

References 54 publications

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“…Developers seem to think that they individually should stop making errors that get through to production. Other disciplines, for example, health [41], have shown that a…”

Section: Discussionmentioning

confidence: 99%

How Software Developers Mitigate Their Errors When Developing Code

Nagaria

Hall

2022

IIEEE Trans. Software Eng.

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Code remains largely handmade by humans and, as such, writing code is prone to error. Many previous studies have focused on the technical reasons for these errors and provided developers with increasingly sophisticated tools. Few studies have looked in detail at why code errors have been made from a human perspective. We use Human Error Theory to frame our exploratory study and use semi-structured interviews to uncover a preliminary understanding of the errors developers make while coding. We look particularly at the Skill-based (SB) errors reported by 27 professional software developers. We found that the complexity of the development environment is one of the most frequently reported reasons for errors. Maintaining concentration and focus on a particular task also underpins many developer errors. We found that developers struggle with effective mitigation strategies for their errors, reporting strategies largely based on improving their own willpower to concentrate better on coding tasks. We discuss how using Reason's Swiss Cheese model may help reduce errors during software development. This model ensures that layers of tool, process and management mitigation are in place to prevent developer errors from causing system failures.

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“…Developers seem to think that they individually should stop making errors that get through to production. Other disciplines, for example, health [41], have shown that a…”

Section: Discussionmentioning

confidence: 99%

How Software Developers Mitigate Their Errors When Developing Code

Nagaria

Hall

2022

IIEEE Trans. Software Eng.

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“…There is only one strategy that can be applied to decrease and nullify the effect of systematic bias: Review, criticize and modify testing procedure. (Abimanyi-Ochom et al, 2019). The authors would like to stress upon the fact that further reviewing, critical appraisal and modi ication in the testing procedure of Modi ied Jaffe's kinetic method is the need of an hour amongst the cohort of laboratory scientists and technicians.…”

Section: Figure 1: Scatter Diagram Using Pearson's Correlation Coef Imentioning

confidence: 99%

Analytical performance comparisons of Modiϐied Jaffe’s kinetic method and Enzymatic Trinder method for creatinine along with risk zone identification

Yadav

Sawant

Shelke

2021

ijrps

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The aim of this study is to compare analytical performance characteristics and also the patient results obtained from both Modified Jaffe’s kinetic and Enzymatic Trinder methods for serum creatinine so as to identify risk zone, if present, within the measurement range. Serum creatinine was measured on 206 left-over serum samples by Modified Jaffe’s kinetic and Enzymatic Trinder methods. For analytical performance comparisons, limit of detection(LOD), limit of quantification(LOQ), linearity, measuring range, intra and inter-assay CV were measured and compared. Statistical comparisons were done by Pearson‘s correlation coefficient and Bland-Altman tests. For Enzymatic Trinder and Modified Jaffe’s kinetic methods, LODs for serum creatinine were 0.01 & 0.02 mg/dl respectively; LOQs were 0.04 & 0.06 mg/dl respectively; linearity were upto 55 mg/dl & 30 mg/dl respectively. Correlation coefficient was high (r=0.99); intra and inter-assay CV measurements were acceptable. However, CV was lower for Enzymatic Trinder method. Bland-Altman plot showed that more than 95% data points lie within ± 1.96 SD limit of mean difference value (0.16). Average discrepancy (ie. bias) was 0.16 mg/dl across whole measurement range. However, at low concentrations, Modified Jaffe’s kinetic method gave higher values indicating systematic bias, thereby forming a “risk zone” in measurement range. Analytical performance requirements were met by both methods for routine use and good agreement exists between them. However, better performance was not shown by Modified Jaffe’s kinetic method at low concentrations. Such a “risk zone” needs to be identified by laboratories for accurate reporting of creatinine results.

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“…Diagnostic errors are common and it is estimated that everyone will experience at least one diagnostic error in their lifetime 1 . Although there is general agreement on co-or multi-morbidities complicating the diagnostic procedure resulting in higher risk for erroneous diagnoses, definitions for erroneous diagnoses lack consistency and several terms are used to refer to flawed diagnoses [2][3][4][5] . The terms misdiagnosis and overdiagnosis are often used interchangeably and they can be difficult to distinguish 3 .…”

Section: Introductionmentioning

confidence: 99%

Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis

Jørgensen

Brunak

2021

npj Digit. Med.

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Diagnostic errors are common and can lead to harmful treatments. We present a data-driven, generic approach for identifying patients at risk of being mis- or overdiagnosed, here exemplified by chronic obstructive pulmonary disease (COPD). It has been estimated that 5–60% of all COPD cases are misdiagnosed. High-throughput methods are therefore needed in this domain. We have used a national patient registry, which contains hospital diagnoses for 6.9 million patients across the entire Danish population for 21 years and identified statistically significant disease trajectories for COPD patients. Using 284,154 patients diagnosed with COPD, we identified frequent disease trajectories comprising time-ordered comorbidities. Interestingly, as many as 42,459 patients did not present with these time-ordered, common comorbidities. Comparison of the individual disease history for each non-follower to the COPD trajectories, demonstrated that 9597 patients were unusual. Survival analysis showed that this group died significantly earlier than COPD patients following a trajectory. Out of the 9597 patients, we identified one subgroup comprising 2185 patients at risk of misdiagnosed COPD without the typical events of COPD patients. In all, 10% of these patients were diagnosed with lung cancer, and it seems likely that they are underdiagnosed for lung cancer as their laboratory test values and survival pattern are similar to such patients. Furthermore, only 4% had a lung function test to confirm the COPD diagnosis. Another subgroup with 2368 patients were found to be at risk of “classically” overdiagnosed COPD that survive >5.5 years after the COPD diagnosis, but without the typical complications of COPD.

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Strategies to reduce diagnostic errors: a systematic review

Cited by 61 publications

References 54 publications

How Software Developers Mitigate Their Errors When Developing Code

How Software Developers Mitigate Their Errors When Developing Code

Analytical performance comparisons of Modiϐied Jaffe’s kinetic method and Enzymatic Trinder method for creatinine along with risk zone identification

Time-ordered comorbidity correlations identify patients at risk of mis- and overdiagnosis

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