Volume 10, Issue 4

Objectives To assess the usefulness of case reports as sources for research on diagnostic errors in uncommon diseases and atypical presentations. Content We reviewed 563 case reports of diagnostic error. The commonality of the final diagnoses was classified based on the description in the articles, Orphanet, or epidemiological data on available references; the typicality of presentation was classified based on the description in the articles and the judgment of the physician researchers. Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC), and Generic Diagnostic Pitfalls (GDP) taxonomies were used to assess the factors contributing to diagnostic errors. Summary and outlook Excluding three cases in that commonality could not be classified, 560 cases were classified into four categories: typical presentations of common diseases (60, 10.7 %), atypical presentations of common diseases (35, 6.2 %), typical presentations of uncommon diseases (276, 49.3 %), and atypical presentations of uncommon diseases (189, 33.8 %). The most important DEER taxonomy was “Failure/delay in considering the diagnosis” among the four categories, whereas the most important RDC and GDP taxonomies varied with the categories. Case reports can be a useful data source for research on the diagnostic errors of uncommon diseases with or without atypical presentations.

Background Early stages of hepatitis B virus (HBV) infection usually involve inflammation of the liver. Patients with chronic infection have an increased risk of progressive liver fibrosis, cirrhosis, and life-threatening clinical complications of end-stage hepatocellular carcinoma (HCC). Content Early diagnosis of hepatic fibrosis and timely clinical management are critical to controlling disease progression and decreasing the burden of end-stage liver cancer. Fibrosis staging, through its current gold standard, liver biopsy, improves patient outcomes, but the clinical procedure is invasive with unpleasant post-procedural complications. Routine blood test markers offer promising diagnostic potential for early detection of liver disease without biopsy. There is a plethora of candidate routine blood test markers that have gone through phases of biomarker validation and have shown great promise, but their current limitations include a predictive ability that is limited to only a few stages of fibrosis. However, the advent of machine learning, notably pattern recognition, presents an opportunity to refine blood-based non-invasive models of hepatic fibrosis in the future. Summary In this review, we highlight the current landscape of routine blood-based non-invasive models of hepatic fibrosis, and appraise the potential application of machine learning (pattern recognition) algorithms to refining these models and optimising clinical predictions of HBV-associated liver disease. Outlook Machine learning via pattern recognition algorithms takes data analytics to a new realm, and offers the opportunity for enhanced multi-marker fibrosis stage prediction using pathology profile that leverages information across patient routine blood tests.

The increasing prevalence of multimorbidity requires new theoretical models and educational approaches to develop physicians’ ability to manage multimorbidity patients. The Health Issues Network (HIN) is an educational approach based on a graphical depiction of the evolutions over time of the concurrent health issues of a patient and of their interactions. From a theoretical point of view, the HIN approach is rooted in Prigogine’s vision of the “becoming” of the events and in the concept of knowledge organization, intended as the process of storing and structuring of information in a learner’s mind. The HIN approach allows to design clinical exercises to foster learners’ ability to detect evolutionary paths and interactions among health issues. Recent findings of neuroscience support the expectation that interpreting, completing, and creating diagrams depicting complex clinical cases improves the “sense of time”, as a fundamental competence in the management of multimorbidity. The application of the HIN approach is expected to decrease the risk of errors in the management of multimorbidity patients. The approach is still under validation, both for undergraduate students and for the continuous professional development of physicians.

In February 2023, the first national conference on Diagnostic Excellence was held in Japan. As the conference covered a wide range of academic areas, we placed particular emphasis on topics related to excellence in physicians’ clinical reasoning. This focus reflected the culture of Japanese medical professionals, especially of physicians, who have shown passion for clinical diagnosis for decades, having held non-profit voluntary multi-institutional conferences during off-duty hours. Of the over 1,400 participants who attended the two-day conference, 80% were generalist physicians and residents, 10% were medical students, and the remainder were healthcare professionals and participants from other academic areas. Given the background of Japanese physicians’ passion for clinical diagnosis, the conference organizers believed that focusing on the diagnostic thinking of physicians as part of the Diagnostic Excellence concept would have strong appeal to participants. Simultaneously, the organizers believed that it was important to target participants in their 20s–40s, and made efforts to utilize social networking services and advertising strategies, including creating individual posters with the help of professional designers. On reflection after the conference, consideration of the local characteristics of the population, particularly their interest in the target population, and the expansion of the conference’s focus to include younger participants may have served as drivers of the success of the conference. This outcome of the conference in Asia is an intriguing step in the world deployment strategy of Diagnostic Excellence, and is expected to promote cooperation among Asia and the United States, Europe, and Oceania.

Growing awareness of the genetic basis of disease is transforming the opportunities for improving patient care by accelerating the development, delivery and uptake of personalised medicine and diseases diagnostics. This can mean more precise treatments reaching the right patients at the right time at the right cost. But it will be possible only with a coherent European Union (EU) approach to regulation. For clinical and biological data, on which the EU is now legislating with its planned European Health Data Space (EHDS), it is crucial that the design of this new system respects the constraints also implicit in the testing which generates data. The current EHDS proposal may fail to meet this requirement. It risks being over-ambitious, while taking insufficient account of the demanding realities of data access in daily practice and current economics/business models. It is marred by imprecision and ambiguity, by overlaps with other EU legislation, and by lack of clarity on funding. This paper identifies key issues where legislators should ensure that the opportunities are not squandered by the adoption of over-hasty or ill-considered provisions that jeopardise the gains that could be made in improved healthcare.

Objectives Achieving diagnostic excellence on medical wards requires teamwork and effective team dynamics. However, the study of ward team dynamics in teaching hospitals is relatively underdeveloped. We aim to enhance understanding of how ward team members interact in the diagnostic process and of the underlying behavioral, psychological, and cognitive mechanisms driving team interactions. Methods We used mixed-methods to develop and refine a conceptual model of how ward team dynamics in an academic medical center influence the diagnostic process. First, we systematically searched existing literature for conceptual models and empirical studies of team dynamics. Then, we conducted field observations with thematic analysis to refine our model. Results We present a conceptual model of how medical ward team dynamics influence the diagnostic process, which serves as a roadmap for future research and interventions in this area. We identified three underexplored areas of team dynamics that are relevant to diagnostic excellence and that merit future investigation (1): ward team structures (e.g., team roles, responsibilities) (2); contextual factors (e.g., time constraints, location of team members, culture, diversity); and (3) emergent states (shared mental models, psychological safety, team trust, and team emotions). Conclusions Optimizing the diagnostic process to achieve diagnostic excellence is likely to depend on addressing all of the potential barriers and facilitators to ward team dynamics presented in our model.

Objectives Current autopsy practice guidelines do not provide a mechanism to identify potential causes of diagnostic error (DE). We used our autopsy data registry to ask if gender or race were related to the frequency of diagnostic error found at autopsy. Methods Our autopsy reports include International Classification of Diseases (ICD) 9 or ICD 10 diagnostic codes for major diagnoses as well as codes that identify types of error. From 2012 to mid-2015 only 2 codes were used: UNDOC (major undocumented diagnoses) and UNCON (major unconfirmed diagnoses). Major diagnoses contributed to death or would have been treated if known. Since mid-2015, codes included specific diagnoses, i.e. undiagnosed or unconfirmed myocardial infarction, infection, pulmonary thromboembolism, malignancy, or other diagnosis as well as cause of death. Adult autopsy cases from 2012 to 2019 were assessed for DE associated with reported sex or race (nonwhite or white). 528 cases were evaluated between 2012 and 2015 and 699 between 2015 and 2019. Results Major DEs were identified at autopsy in 65.9 % of cases from 2012 to 2015 and in 72.1 % from 2015 to 2019. From 2012 to 2015, female autopsy cases showed a greater frequency in 4 parameters of DE, i.e., in the total number of cases with any error (p=0.0001), in the number of cases with UNDOC errors (p=0.0038) or UNCON errors (p=0.0006), and in the relative proportions of total numbers of errors (p=0.0001). From 2015 to 2019 undocumented malignancy was greater among males (p=0.0065); no other sex-related error was identified. In the same period some DE parameters were greater among nonwhite than among white subjects, including unconfirmed cause of death (p=0.035), and proportion of total error diagnoses (p=0.0003), UNCON diagnoses (p=0.0093), and UNDOC diagnoses (p=0.035). Conclusions Coding for DE at autopsy can identify potential effects of biases on diagnostic error.

Objectives To derive a method of automated identification of delayed diagnosis of two serious pediatric conditions seen in the emergency department (ED): new-onset diabetic ketoacidosis (DKA) and sepsis. Methods Patients under 21 years old from five pediatric EDs were included if they had two encounters within 7 days, the second resulting in a diagnosis of DKA or sepsis. The main outcome was delayed diagnosis based on detailed health record review using a validated rubric. Using logistic regression, we derived a decision rule evaluating the likelihood of delayed diagnosis using only characteristics available in administrative data. Test characteristics at a maximal accuracy threshold were determined. Results Delayed diagnosis was present in 41/46 (89 %) of DKA patients seen twice within 7 days. Because of the high rate of delayed diagnosis, no characteristic we tested added predictive power beyond the presence of a revisit. For sepsis, 109/646 (17 %) of patients were deemed to have a delay in diagnosis. Fewer days between ED encounters was the most important characteristic associated with delayed diagnosis. In sepsis, our final model had a sensitivity for delayed diagnosis of 83.5 % (95 % confidence interval 75.2–89.9) and specificity of 61.3 % (95 % confidence interval 56.0–65.4). Conclusions Children with delayed diagnosis of DKA can be identified by having a revisit within 7 days. Many children with delayed diagnosis of sepsis may be identified using this approach with low specificity, indicating the need for manual case review.

Objectives Paper mills, companies that write scientific papers and gain acceptance for them, then sell authorships of these papers, present a key challenge in medicine and other healthcare fields. This challenge is becoming more acute with artificial intelligence (AI), where AI writes the manuscripts and then the paper mills sell the authorships of these papers. The aim of the current research is to provide a method for detecting fake papers. Methods The method reported in this article uses a machine learning approach to create decision trees to identify fake papers. The data were collected from Web of Science and multiple journals in various fields. Results The article presents a method to identify fake papers based on the results of decision trees. Use of this method in a case study indicated its effectiveness in identifying a fake paper. Conclusions This method to identify fake papers is applicable for authors, editors, and publishers across fields to investigate a single paper or to conduct an analysis of a group of manuscripts. Clinicians and others can use this method to evaluate articles they find in a search to ensure they are not fake articles and instead report actual research that was peer reviewed prior to publication in a journal.

Objectives Existing computerized diagnostic decision support tools (CDDS) accurately return possible differential diagnoses (DDx) based on the clinical information provided. The German versions of the CDDS tools for clinicians (Isabel Pro) and patients (Isabel Symptom Checker) from ISABEL Healthcare have not been validated yet. Methods We entered clinical features of 50 patient vignettes taken from an emergency medical text book and 50 real cases with a confirmed diagnosis derived from the electronic health record (EHR) of a large academic Swiss emergency room into the German versions of Isabel Pro and Isabel Symptom Checker. We analysed the proportion of DDx lists that included the correct diagnosis. Results Isabel Pro and Symptom Checker provided the correct diagnosis in 82 and 71 % of the cases, respectively. Overall, the correct diagnosis was ranked in 71 , 61 and 37 % of the cases within the top 20, 10 and 3 of the provided DDx when using Isabel Pro. In general, accuracy was higher with vignettes than ED cases, i.e. listed the correct diagnosis more often (non-significant) and ranked the diagnosis significantly more often within the top 20, 10 and 3. On average, 38 ± 4.5 DDx were provided by Isabel Pro and Symptom Checker. Conclusions The German versions of Isabel achieved a somewhat lower accuracy compared to previous studies of the English version. The accuracy decreases substantially when the position in the suggested DDx list is taken into account. Whether Isabel Pro is accurate enough to improve diagnostic quality in clinical ED routine needs further investigation.

Objectives Within a Hospital Medicine division at a large academic institution, an innovative program has been developed to offer third year medical learners enrolled in their Internal Medicine Clerkship the opportunity to deepen their diagnostic reasoning skills with a weekly case-based learning activity entitled “Hospital Medicine’s Mystery Case of the Week”. The mystery case curriculum has four primary learning objectives (1): To understand the value of employing a multisystem approach to a common presenting problem (2); To apply a diagnostic schema to a clinical problem (3); To apply knowledge of cost appropriate workups to a clinical case and (4) To identify cognitive biases that can influence decision making in the context of treating a clinical problem. Methods A process evaluation of the mystery case curriculum was conducted over a 6-month implementation period. The process evaluation was guided by eight domains of focus: program acceptability, demand, implementation, practicality, adaptation, integration, expansion and limited-efficacy testing. Measures included a pre/post mixed-methods survey, attendance log and timesheet. Pre/post survey data was analyzed via paired samples t-tests and open-ended responses were analyzed via a deductive content analysis. Results Process evaluation findings demonstrated the program had high levels of acceptability and demand. Survey findings suggest the curriculum may increase feelings of preparedness for Internal Medicine exams as well as student interest, comfort and confidence related to diagnostic processes. Conclusions Future evaluations should work to understand the influence this curriculum has on diagnostic accuracy within a clinical setting.

Objectives The gastrointestinal (GI) physical exam provides critical information about underlying disease states. However, since assessment of physical examination skills is rarely conducted as part of internal medicine residency training, little is known about resident performance on the GI physical exam. Methods During a clinical skills assessment that took place between November 2019 and February 2020, internal medicine interns examined the same patient with chronic liver disease while being observed by faculty preceptors. We compared the exam maneuvers performed with those expected by the faculty evaluators. We noted which maneuvers were performed incorrectly, whether physical exam technique correlated with identification of physical exam findings, and if performance on the physical exam was associated with building an appropriate differential diagnosis. This four-hour assessment was required for internal medicine interns within two different residency programs in the Baltimore area. Results More than half of the 29 participating interns (n=17, 58.6 %) received a “needs improvement” score on their physical exam technique. Technique was highly correlated with identifying the correct physical signs (r=0.88, p<0.0001). The most commonly excluded maneuvers were assessing for splenomegaly and hepatomegaly. The most commonly missed findings were splenomegaly and hepatomegaly. Most interns included chronic liver disease as part of their differential diagnosis even if they received “needs improvement” scores on physical exam technique or identifying physical signs. Conclusions Internal medicine interns would benefit from learning an organized approach to the gastrointestinal exam. This would likely lead to increased identification of important gastrointestinal findings.

Objectives The transition from the intensive care unit (ICU) to the medical ward is a high-risk period due to medical complexity, reduced patient monitoring, and diagnostic uncertainty. Standardized handoff practices reduce errors associated with transitions of care, but little work has been done to standardize the ICU to ward handoff. Further, tools that exist do not focus on preventing diagnostic error. Using Human-Centered Design methods we previously created a novel EHR-based ICU-ward handoff tool (ICU-PAUSE) that embeds a diagnostic pause at the time of transfer. This study aims to explore barriers and facilitators to implementing a diagnostic pause at the ICU-to-ward transition. Methods This is a multi-center qualitative study of semi-structured interviews with intensivists from ten academic medical centers. Interviews were analyzed iteratively through a grounded theory approach. The Sittig-Singh sociotechnical model was used as a unifying conceptual framework. Results Across the eight domains of the model, we identified major benefits and barriers to implementation. The embedded pause to address diagnostic uncertainty was recognized as a key benefit. Participants agreed that standardization of verbal and written handoff would decrease variation in communication. The main barriers fell within the domains of workflow, institutional culture, people, and assessment. Conclusions This study represents a novel application of the Sittig-Singh model in the assessment of a handoff tool. A unique feature of ICU-PAUSE is the explicit acknowledgement of diagnostic uncertainty, a practice that has been shown to reduce medical error and prevent premature closure. Results will be used to inform future multi-site implementation efforts.

Objectives To describe patterns observed in antibody titer trendlines in patients with mast cell activation syndrome (MCAS, a prevalent but underrecognized chronic multisystem inflammatory disorder of great clinical heterogeneity) and offer clinical lessons learned from such pattern recognition. Methods The available records of 104 MCAS patients drawn from the authors’ practices were reviewed, including all antibody tests therein. Results All patients had positive/elevated antibodies of various sorts at various points, but for most of the antibodies which were found to be positive at least some points, the diseases classically associated with those antibodies were not present, marking such antibodies as clinically insignificant mimickers (likely consequent to inflammatory effects of MCAS on the immune system itself driving spurious/random antibody production) rather than “on-target” and pathogenic antibodies reflecting true disease warranting treatment. We also observed two distinct patterns in trendlines of the titers of the mimickers vs. the trendline pattern expected in a true case of an antibody-associated disease (AAD). Conclusions Our observations suggest most positive antibody tests in MCAS patients represent detection of clinically insignificant mimicking antibodies. As such, to reduce incorrect diagnoses of AADs and inappropriate treatment in MCAS patients, caution is warranted in interpreting positive antibody tests in these patients. Except in clinically urgent/emergent situations, patience in determining the trendline of a positive antibody in an MCAS patient, and more carefully assessing whether the AAD is truly present, is to be preferred.

Objectives Heart failure (HF) is a prevalent syndrome with considerable disease burden, healthcare utilization and costs. Timely diagnosis is essential to improve outcomes. This study aimed to compare the diagnostic performance of B-type natriuretic peptide (BNP) and N-terminal proBNP (NT-proBNP) in detecting HF in primary care. Our second aim was to explore if personalized thresholds (using age, sex, or other readily available parameters) would further improve diagnostic accuracy over universal thresholds. Methods A retrospective study was performed among patients without prior HF who underwent natriuretic peptide (NP) testing in the Amsterdam General Practice Network between January 2011 and December 2021. HF incidence was based on registration out to 90 days after NP testing. Diagnostic accuracy was evaluated with AUROC, sensitivity and specificity based on guideline-recommended thresholds (125 ng/L for NT-proBNP and 35 ng/L for BNP). We used inverse probability of treatment weighting to adjust for confounding. Results A total of 15,234 patients underwent NP testing, 6,870 with BNP (4.5 % had HF), and 8,364 with NT-proBNP (5.7 % had HF). NT-proBNP was more accurate than BNP, with an AUROC of 89.9 % (95 % CI: 88.4–91.2) vs. 85.9 % (95 % CI 83.5–88.2), with higher sensitivity (95.3 vs. 89.7 %) and specificity (59.1 vs. 58.0 %). Differentiating NP cut-off by clinical variables modestly improved diagnostic accuracy for BNP and NT-proBNP compared with a universal threshold. Conclusions NT-proBNP outperforms BNP for detecting HF in primary care. Personalized instead of universal diagnostic thresholds led to modest improvement.

Objectives Because there is little published evidence on the effects of incomplete filling of K 2 EDTA evacuated blood tubes on routine hematological testing, this original study aimed to provide updated information on this preanalytical aspect. Methods The study population consisted of 17 ostensibly healthy volunteers. Blood was drawn by venipuncture with a 10 mL syringe and dispensed in varying amounts (0.2, 0.5, 1.0, 2.0, and 3.0 mL) into 3.0 mL blood tubes containing spray-dried 5.4 mg K 2 EDTA. All tubes were gently mixed and used to perform routine hematology tests on the Sysmex XN-10. Clinically significant variations were defined when the limits of desirable specifications of bias derived from biologic variation were exceeded. Results The desirable bias was exceeded in 33 % filled tubes (1.0 mL) for hematocrit and MCV (increased values) and for MCHC (decreased values), while it was exceeded in 17 % filled tubes (0.5 mL) for hemoglobin, hematocrit and MCV (increased values), and for MCHC (decreased values). Finally, the variation of values was higher than the desirable bias for RBC, hemoglobin, hematocrit and MCV (increase), and for MCHC and MPV (decrease) in 7 % filled tubes (0.2 mL). No clinically significant variations were observed in tubes filled up to 67 % of their nominal volume (i.e., 2.0 mL). Conclusions Consideration should be given to reject spray-dried K 2 EDTA blood tubes that contain a blood volume <67 % of the nominal fill volume, as biased laboratory data in these samples may interfere with clinical decision making and care management.