Volume 11, Issue 4

Atrial fibrillation (AF) is the most frequent type of cardiac arrhythmia that affects over six million individuals in Europe. The incidence and prevalence of AF rises with age, and often occurs after cardiac surgery. Other risk factors correlated with AF comprise high blood pressure, diabetes mellitus, left atrial enlargement, ischemic heart disease, and congestive heart failure. Considering the high prevalence of AF in aging societies, strategies to prevent serious complications, such as stroke or heart failure, are important because they are correlated with high morbidity and mortality. The supplementation of sea-derived n-3 polyunsaturated fatty acids (PUFA) is widely discussed in this context, but the results of experimental and observational studies are in contrast to randomized placebo-controlled intervention trials (RCTs). Specifically, larger placebo-controlled n-3 PUFA supplementation studies with long follow-up showed a dose-dependent rise in incident AF. Daily n-3 PUFA doses of ≥1 g/d are correlated with a 50 % increase in AF risk, whereas a daily intake of <1 g/d causes AF in only 12 %. Individuals with a high cardiovascular risk (CVD) risk and high plasma-triglycerides seem particularly prone to develop AF upon n-3 PUFA supplementation. Therefore, we should exercise caution with n-3 PUFA supplementation especially in patients with higher age, CVD, hypertriglyceridemia or diabetes. In summary, existing data argue against the additive intake of n-3 PUFA for preventative purposes because of an incremental AF risk and lacking CVD benefits. However, more clinical studies are required to disentangle the discrepancy between n-3 PUFA RCTs and observational studies showing a lower CVD risk in individuals who regularly consume n-3 PUFA-rich fish.

Diagnostic errors comprise the leading threat to patient safety in healthcare today. Learning how to extract the lessons from cases where diagnosis succeeds or fails is a promising approach to improve diagnostic safety going forward. We present up-to-date and authoritative guidance on how the existing approaches to conducting root cause analyses (RCA’s) can be modified to study cases involving diagnosis. There are several diffierences: In cases involving diagnosis, the investigation should begin immediately after the incident, and clinicians involved in the case should be members of the RCA team. The review must include consideration of how the clinical reasoning process went astray (or succeeded), and use a human-factors perspective to consider the system-related contextual factors in the diagnostic process. We present detailed instructions for conducting RCA’s of cases involving diagnosis, with advice on how to identify root causes and contributing factors and select appropriate interventions.

Diagnostic errors in health care are a global threat to patient safety. Researchers have traditionally focused diagnostic safety efforts on identifying errors and their causes with the goal of reducing diagnostic error rates. More recently, complementary approaches to diagnostic errors have focused on improving diagnostic performance drawn from the safety sciences. These approaches have been called Safety-II and Safety-III, which apply resilience engineering and system safety principles, respectively. This review explores the safety science paradigms and their implications for analyzing diagnostic errors, highlighting their distinct yet complementary perspectives. The integration of Safety-I, Safety-II, and Safety-III paradigms presents a promising pathway for improving diagnosis. Diagnostic researchers not yet familiar with the various approaches and potential paradigm shift in diagnostic safety research may use this review as a starting point for considering Safety-I, Safety-II, and Safety-III in their efforts to both reduce diagnostic errors and improve diagnostic performance.

Clinical reasoning is considered one of the most important competencies but is not included in most healthcare curricula. The number and diversity of patient encounters are the decisive factors in the development of clinical reasoning competence. Physical real patient encounters are considered optimal, but virtual patient cases also promote clinical reasoning. A high-volume, low-fidelity virtual patient library thus can support clinical reasoning training in a safe environment and can be tailored to the needs of learners from different health care professions. It may also stimulate interprofessional understanding and team shared decisions. Implementation will be challenged by tradition, the lack of educator competence and prior experience as well as the high-density curricula at medical and veterinary schools and will need explicit address from curriculum managers and education leads.

Objectives Proper coding of heart failure (HF) in electronic health records (EHRs) is an important prerequisite for adequate care and research towards this vulnerable patient population. We set out to evaluate the accuracy of registration of HF diagnoses in primary care EHRs. Methods In a routine primary care database covering the Amsterdam Metropolitan Area, we identified all episodes of care with International Classification of Primary Care (ICPC) codes K77 (decompensatio cordis) or K84.03 (cardiomyopathy) up to 31/12/2021. We also performed two text-based searches to identify HF episodes without an appropriate ICPC-code. An expert panel evaluated all ICPC and text matches for congruence between the assigned codes and notes. Results From a database of 968,433 records we identified 19,106 patients (2.0 %) with a total of 24,011 ICPC-coded HF episodes. Removal of 1,324 episodes found to concern other or uncertain diagnoses and inclusion of 4,582 validated HF episodes identified through text search led to exclusion of 909 (overregistration: 4.8 %) and inclusion of 2,266 additional patients (underregistration: 11.1 %). The inclusion of miscoded HF episodes advanced the first known date of HF diagnosis in 3.9 % of records, with a median shift of 3.45 years. Episode-level underregistration decreased significantly over time, from 23.8 % in 2006 to 10.0 % in 2021. Conclusions While there is improvement over time, there are still substantial levels of over- and underregistration of HF, emphasizing the need for cautious interpretation of ICPC-coded data. The findings contribute to the understanding of HF registration issues in primary care and provide insights for improving registration practices.

Objectives Diagnostic disparities are preventable differences in diagnostic errors or opportunities to achieve diagnostic excellence. There is a need to summarize solutions with explicit considerations for addressing diagnostic disparities. We aimed to describe potential solutions to diagnostic disparities, organize them into an action-oriented typology with illustrative examples, and characterize these solutions to identify gaps for their further development. Methods During four human-centered design workshops composed of diverse expertise, participants ideated and clarified potential solutions to diagnostic disparities and were supported by environmental literature scan inputs. Nineteen individual semi-structured interviews with workshop participants validated identified solution examples and solution type characterizations, refining the typology. Results Our typology organizes 21 various types of potential diagnostic disparities solutions into four primary expertise categories needed for implementation: healthcare systems’ internal expertise, educator-, multidisciplinary patient safety researcher-, and health IT-expertise. We provide descriptions of potential solution types ideated as focused on disparities and compare those to existing examples. Six types were characterized as having diagnostic-disparity-focused examples, five as having diagnostic-focused examples, and 10 as only having general healthcare examples. Only three solution types had widespread implementation. Twelve had implementation on limited scope, and six were mostly hypothetical. We describe gaps that inform the progress needed for each of the suggested solution types to specifically address diagnostic disparities and be suitable for the implementation in routine practice. Conclusions Numerous opportunities exist to tailor existing solutions and promote their implementation. Likely enablers include new perspectives, more evidence, multidisciplinary collaborations, system redesign, meaningful patient engagement, and action-oriented coalitions.

Objectives Patients referred to general internal medicine (GIM) outpatient clinics may face a higher risk of diagnostic errors than non-referred patients. This difference in risk is assumed to be due to the differences in diseases and clinical presentations between referred and non-referred patients; however, clinical data regarding this issue are scarce. This study aimed to determine the frequency of diagnostic errors and compare the characteristics of referred and non-referred patients visit GIM outpatient clinics. Methods This study included consecutive outpatients who visited the GIM outpatient clinic at a university hospital, with or without referral. Data on age, sex, chief complaints, referral origin, and final diagnosis were collected from medical records. The Revised Safer Dx Instrument was used to detect diagnostic errors. Results Data from 534 referred and 599 non-referred patients were analyzed. The diagnostic error rate was higher in the referral group than that in the non-referral group (2.2 % vs. 0.5 %, p=0.01). The prevalence of abnormal test results and sensory disturbances was higher in the chief complaints, and the prevalence of musculoskeletal system disorders, connective tissue diseases, and neoplasms was higher in the final diagnoses of referred patients compared with non-referred patients. Among referred patients with diagnostic errors, abnormal test results and sensory disturbances were the two most common chief complaints, whereas neoplasia was the most common final diagnosis. Problems with data integration and interpretation were found to be the most common factors contributing to diagnostic errors. Conclusions Paying more attention to patients with abnormal test results and sensory disturbances and considering a higher pre-test probability for neoplasms may prevent diagnostic errors in patients referred to GIM outpatient clinics.

Objectives In the cognitive process of establishing a diagnosis, the performance of a diagnostician can be characterized in terms of sensitivity and specificity. The aims of the present study are to analyze in quantitative terms how cognitive bias affects the performance of a diagnostician, and how a diagnostician’s biased decision making is further influenced by personal cost-benefit considerations. Methods The test matrices of two sequential diagnostic tests are manipulated according to the rules of linear algebra, using multiplication of the second with the first test matrix to calculate their joint test characteristics. The decision tree and receiver operating characteristic (ROC) of a biased and unbiased diagnostician are used to calculate which combination of test characteristics maximizes the expected utility value. Results Biased diagnosticians cannot establish a diagnosis beyond their own limited or distorted level of understanding. An unbiased and a biased diagnostician alike adjust their choice of test characteristics according to their different cost-benefit estimation of the various test outcomes. From the perspective of an unbiased diagnostician, the choices made by a biased diagnostician appear to invert reality. However, the same appearance of inverted reality is perceived by the biased diagnostician, judging the choices made by the unbiased diagnostician. Conclusions As a general principle, human testers cannot test beyond their own level of understanding. They only see what they know. As they base their judgment on preconceived notions about the utilities associated with different test outcomes, human testers also tend to only know what they want to know.

Objectives Patients with a delayed diagnosis of diabetes are more likely to present in diabetic ketoacidosis (DKA). The objective of this study was to assess the prevalence, risk factors, and consequences of missed pediatric diabetes diagnoses in emergency departments (EDs) potentially leading to DKA. Methods Cases of children under 19 years old with a first-time diagnosis of diabetes mellitus presenting to EDs in DKA were drawn from the Healthcare Cost and Utilization Project database. A total of 11,716 cases were included. A delayed diagnosis of diabetes leading to DKA was defined by an ED discharge in the 14 days prior to the DKA diagnosis. The delayed diagnosis cases were analyzed using multivariate analysis to identify risk factors associated with delay, with the primary exposure being child opportunity index (COI) and secondary exposure being race/ethnicity. Rates of complications were compared across groups. Results Delayed diagnosis of new onset diabetes leading to DKA occurred in 2.9 %. Delayed diagnosis was associated with COI, with 4.5 , 3.5, 1.9, and 1.5 % occurring by increasing COI quartile (p<0.001). Delays were also associated with younger age and non-Hispanic Black race. Patients with a delayed diagnosis were more likely to experience complications (4.4 vs. 2.2 %, p=0.01) including mechanical ventilation, as well as more frequent intensive care unit admissions and longer length of stays. Conclusions Among children with new-onset DKA, 2.9 % had a delayed diagnosis. Delays were associated with complications. Children living in areas with lower child opportunity and non-Hispanic Black children were at higher risk of delays.

Objectives Monocyte distribution width (MDW) is a measure of monocyte anisocytosis. In this study, we assessed the role of MDW, in comparison to C-reactive protein (CRP), procalcitonin (PCT), and lactate, as a screening and prognostic biomarker of sepsis in intensive care unit (ICU) by longitudinally measuring it in the first 5 days of hospital stay. Methods We considered all consecutive patients admitted to the ICU. At admission, patients were classified as septic or not according to Sepsis-3 criteria. MDW, CRP, PCT, and lactate were measured daily in the first 5 days of hospitalization. ICU mortality was also recorded. Results We included 193 patients, 62 with sepsis and 131 without sepsis (controls). 58% and 26 % of the patients, with and without sepsis respectively, died during ICU stay. MDW showed the highest accuracy for sepsis detection, superior to CRP, PCT, and lactate (AUC of 0.840, 0.755, 0.708, 0.622, respectively). At admission, no biomarker predicts ICU mortality in patients with sepsis. The kinetic of all biomarkers during the first 5 days of hospitalization was associated with ICU mortality. Noteworthy, above all, the kinetic of MDW showed the best accuracy. Specifically, an increase or decrease in MDW from day 1–4 and 5 was significantly associated with mortality or survival, respectively. Conclusions MDW is a reliable diagnostic and prognostic sepsis biomarker, better than traditional biomarkers.

Objectives It has been recognized that shortened activated partial thromboplastin time (aPTT) may be caused by various preanalytical conditions. As coagulation Factor VIII is included in the in vitro intrinsic coagulation cascade measured by aPTT, we hypothesized that the shortened aPTT could be a result of elevated FVIII activity. We aimed to inspect the connection of elevated FVIII with shortened aPTT, and the possible effect inflammation has on routine laboratory parameters. Methods 40 patients from various hospital departments with aPTT measurement below the lower limit of the reference interval (<23.0 s) were included in the study. To compare the obtained results with aPTT measurements in the non-inflammatory state, samples from 25 volunteers (laboratory personnel) were collected. White blood cell count, C-reactive protein, aPTT, and FVIII values were measured in the control group. Results Only two samples among 40 patients with shortened aPTT (5 %) were clotted. Out of the remaining 38, 26 had FVIII activity above 150 % (upper limit of a reference interval), median value of 194 % (IQR: 143–243 %). Seven samples in the control group had shortened aPTT results (36 %). However, all coagulation samples were clot and hemolysis-free. Multiple regression identified only FVIII activity as an independent variable in predicting aPTT values (p=0.001). Conclusions Our results support the thesis that shortened aPTT is rarely a consequence of preanalytical problems. Elevated FVIII activity causes shortened aPTT, not only in the inflammatory state but also in individuals with concentration of inflammatory markers within reference intervals.

Objectives Since December 2019, worldwide public health has been exposed to a severe acute respiratory syndrome caused by Coronavirus-2. Serological testing is necessary for retrospective assessment of seroprevalence rates, and the determination of vaccine response and duration of immunity. For this reason, it was necessary to introduce a panel of tests able to identify and quantify Covid-19 antibodies. Methods As a Regional Reference Centre, the CRQ Laboratory (Regional Laboratory for the Quality Control) developed and conducted an External Quality Assessment (EQA) panel of assays, to evaluate the quality of various methods, that were used by 288 Sicilian laboratories, previously authorized on behalf of the Public Health Service. Results The performance test was based on pooled samples with different levels of concentration of antibodies. 97 , 98, and 95 % of the participating laboratories tested all samples correctly in 2020, 2021, and 2022 respectively. The best performance was observed in the test of total Ig. The general performance of laboratories improved over the years. Conclusions The incorrect diagnosis had and could still have important implications on vaccination cycles. Only through the effort of laboratory professionals, and the extension of the EQA scheme, a better harmonization of methods, protocols, and thus results, to guarantee a better healthcare system, will be possible.

Objectives Medical errors and complications pose a major threat to the safety of healthcare systems worldwide. This article was hence aimed at determining the current burden of complications of medical and surgical care in the US. Methods We searched the latest version of the US Centers for Disease Control and Prevention (CDC) WONDER online database (years 2018–2022) using ICD-10 codes Y40-Y84 (complications of medical and surgical care). Results The age-adjusted death rate for complications of medical and surgical care increased from 1.17 × 100,000 in 2018 to 1.49 × 100,000 in 2021, but then declined to 0.85 × 100,000 in 2022. The gender-specific analysis showed a similar trend, with the age-adjusted death rate values always being higher in men than in women. A clear age-dependent relationship was also found in the crude mortality rate for complications of medical and surgical care, as higher death rates were observed in older patients. Conclusions This analysis reveals that the burden of complications of medical and surgical care has increased over time, especially during the COVID-19 pandemic, but has then considerably declined in 2022. However, root cause analysis and actions are still needed for preventing the still noticeable consequences of medical complications.

Objectives This short communication explores the potential, limitations, and future directions of generative artificial intelligence (GAI) in enhancing diagnostics. Methods This commentary reviews current applications and advancements in GAI, particularly focusing on its integration into medical diagnostics. It examines the role of GAI in supporting medical interviews, assisting in differential diagnosis, and aiding clinical reasoning through the lens of dual-process theory. The discussion is supported by recent examples and theoretical frameworks to illustrate the practical and potential uses of GAI in medicine. Results GAI shows significant promise in enhancing diagnostic processes by supporting the translation of patient descriptions into visual formats, providing differential diagnoses, and facilitating complex clinical reasoning. However, limitations such as the potential for generating medical misinformation, known as hallucinations, exist. Furthermore, the commentary highlights the integration of GAI with both intuitive and analytical decision-making processes in clinical diagnostics, demonstrating potential improvements in both the speed and accuracy of diagnoses. Conclusions While GAI presents transformative potential for medical diagnostics, it also introduces risks that must be carefully managed. Future advancements should focus on refining GAI technologies to better align with human diagnostic reasoning, ensuring GAI enhances rather than replaces the medical professionals’ expertise.

Objectives Diagnostic excellence underscores the patient-centered diagnosis and patient engagement in the diagnostic process. In contrast to a patient-centered diagnosis, a doctor-centered diagnosis with a lack of patient engagement may inhibit the diagnostic process due to the lack of responsibility, disrupted information, and increased effect of cognitive biases, particularly in a situation where multiple physicians are involved. In this paper, we suggest a promising idea to enhance patient engagement in the diagnostic process by using written information by a patient about their perspective and experience, which can fill the gaps needed for diagnosis that doctors cannot find alone. Case presentation A 38-year-old woman developed chest pain, which gradually worsened during the following two years. For two years, she was evaluated in multiple departments; however, no definitive diagnosis was made, and her condition did not improve. During this evaluation, she searched her symptoms and image findings online. She reached a possible diagnosis of ‘esophageal achalasia.’ Still, she could not tell her concerns to any physicians because she felt that her concerns were not correctly recognized, although she showed her notes that her symptoms were recorded. She finally consulted the department of internal medicine, where her notes and previous test results were thoroughly reviewed. The final diagnosis of esophageal achalasia was confirmed. Conclusions Doctors must organize an environment where patients can freely express their thoughts, emotions, and ideas regarding their diagnosis. Cogenerating visit notes using patient input through written communication can be a promising idea to facilitate patient engagement in the diagnostic process.