Integrating Bayesian reasoning into medical education using smartphone apps

Ultrasonographic cervical length screening at 20-24 weeks of gestation in twin pregnancies for prediction of spontaneous preterm birth: A 10-year Taiwanese cohort

BACKGROUND

Shortened cervical length is one of the primary predictors for spontaneous preterm deliveries in twin pregnancies. However, there is lack of consensus regarding cut-off values. Recent evidence highlights that established cut-offs for cervical length screening might not always apply across different populations. This study aims to present the distribution of cervical length in Taiwanese twin pregnancies and to assess its predictive value for spontaneous preterm birth during mid-trimester screening.

MATERIALS AND METHODS

This retrospective analysis of cervical length screening in Taiwan evaluated 469 twin pregnancies between 20-24 weeks of gestation. Outcome data were obtained directly from the medical records of the delivery hospital. The study explored the predictive value of cervical length screening for spontaneous preterm birth and the characteristics of cervical length distribution in Taiwanese twin pregnancies.

RESULTS

The average gestational age at screening was 22.7 weeks. Cervical length values displayed a non-normal distribution (p-value <0.001). The median, 5th and 95th centiles were 37.5 mm 25.1 mm, and 47.9 mm, respectively. Various cut-off values were assessed using different methods, yielding positive [negative] likelihood ratios for spontaneous preterm births between 32-37 weeks of gestational age (GA) (1.3-30.1 and [0.51-0.92]) and for very preterm births between 28-32 weeks GA (5.6-51.1 and [0.45-0.64]).

CONCLUSIONS

The findings from our analysis of Taiwanese twin pregnancies uphold the moderate predictive potential of cervical length screening, consistent with prior investigations. The presented likelihood ratios for predicting preterm birth at different gestational ages equip clinicians with valuable tools to enhance their diagnostic rationale and resource utilization. By fine-tuning screening parameters according to the spontaneous preterm birth prevalence and clinical priorities of the particular population, healthcare providers can enhance patient care. Our data implies that a cervical length below 20 mm might provide an optimal balance between minimizing false negatives and managing false positives when predicting spontaneous preterm birth.

A procedure for predicting, illustrating, communicating, and optimizing patient-centered outcomes of epilepsy surgery using nomograms and Bayes' theorem

Clinicians have an ethical obligation to obtain and convey relevant information about possible treatment outcomes in a manner that can be comprehended by patients. This contributes to the processes of informed consent and shared prospective decision-making. In epilepsy neurosurgery, there has historically been an emphasis on studying clinician-centered (e.g., seizure- and cognition-related) outcomes and using these data to inform recommendations and, by extension, to frame pre-surgical counseling with respect to patients' decisions about elective neurosurgery. In contrast, there is a relative dearth of available data related to patient-centered outcomes of epilepsy neurosurgery, such as functional (e.g., employment) status, and there is also a lack of methods to communicate these data to patients. Here, illustrated using a hypothetical case scenario, we present a potential solution to the latter of these problems using principles of evidence-based neuropsychology; published data on patient employment status before and after epilepsy neurosurgery; and Bayes' theorem. First, we reviewed existing literature on employment outcomes following epilepsy neurosurgery to identify and extract data relevant to our hypothetical patient, clinical question, and setting. Then, we used the base rate (prior probability) of post-surgical unemployment, contingency tables (to derive likelihood ratios), and Bayes' theorem to compute the conditional (posterior) probability of post-surgical employment status for our hypothetical patient scenario. Finally, we translated this information to an intuitive visual format (Bayesian nomogram) that can support evidence-based pre-surgical counseling. We propose that the application of our patient-centered decision-support process and visual aid will improve clinician-patient communication about prospective risks and benefits of epilepsy neurosurgery and will empower clinicians and patients to make informed decisions about whether or not to pursue elective neurosurgery with a greater degree of confidence and with more realistic and concrete expectations about possible outcomes. We further propose that clinicians and patients would benefit from incorporating this evidence-based framework into a broader sequence of function-focused epilepsy treatment that includes pre-surgical assessments and interventions ("prehabilitation"), neurosurgery, and post-surgical cognitive/vocational rehabilitation.

Diagnostic Error in Pediatrics: A Narrative Review

A priority topic for patient safety research is diagnostic errors. However, despite the significant growth in awareness of their unacceptably high incidence and associated harm, a relative paucity of large, high-quality studies of diagnostic error in pediatrics exists. In this narrative review, we present what is known about the incidence and epidemiology of diagnostic error in pediatrics as well as the established research methods for identifying, evaluating, and reducing diagnostic errors, including their strengths and weaknesses. Additionally, we highlight that pediatric diagnostic error remains an area in need of both innovative research and quality improvement efforts to apply learnings from a rapidly growing evidence base. We propose several key research questions aimed at addressing persistent gaps in the pediatric diagnostic error literature that focus on the foundational knowledge needed to inform effective interventions to reduce the incidence of diagnostic errors and their associated harm. Additional research is needed to better establish the epidemiology of diagnostic error in pediatrics, including identifying high-risk clinical scenarios, patient populations, and groups of diagnoses. A critical need exists for validated measures of both diagnostic errors and diagnostic processes that can be adapted for different clinical settings and standardized for use across varying institutions. Pediatric researchers will need to work collaboratively on large-scale, high-quality studies to accomplish the ultimate goal of reducing diagnostic errors and their associated harm in children by addressing these fundamental gaps in knowledge.

Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review

BACKGROUND

As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education.

OBJECTIVE

With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness.

METHODS

After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career).

RESULTS

Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective.

CONCLUSIONS

This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.