Medical Logic Research Articles

Paradoxical bodies: negotiating biomedical fix, responsibility, and care in a weight-loss surgery clinic

AbstractThis ethnographic project explores how inherent discursive and social tensions are expressed and worked out within obesity care at a weight-loss surgical (WLS) clinic. First, complicated doctor and patient encounters occur because WLS follows a medical logic of intervention and is presented as a “biomagical” procedure that miraculously alters the body. Surgeons, however, explain that the surgery’s success depends on patients’ long and hard work. Second, the clinic’s interdisciplinary approach adds more complexity. While expanding the scope of treatment, it blurs the division of labor among professionals and the distinction between body and mind. It also diffuses the responsibility for patients’ lifestyles and recovery among professionals. Third, alongside notions about patients’ autonomy and free choice, their wishes and bodies are constantly channeled, problematized, and negotiated with various family and peer support. While crucial for the surgery’s success, such interconnectedness raises questions about how decision making and social pressures affect the patients’ journey. Thus, WLS is refracted among webs of multiple actors and contradictory perspectives. This complexity invites a reflection on how such tensions and paradoxes destabilize medical power in modifying the body, challenge personal responsibility assumptions, and impact the quality of care.

Evaluating the Bias in Hospital Data: Automatic Preprocessing of Patient Pathways Algorithm Development and Validation Study.

The optimization of patient care pathways is crucial for hospital managers in the context of a scarcity of medical resources. Assuming unlimited capacities, the pathway of a patient would only be governed by pure medical logic to meet at best the patient's needs. However, logistical limitations (eg, resources such as inpatient beds) are often associated with delayed treatments and may ultimately affect patient pathways. This is especially true for unscheduled patients-when a patient in the emergency department needs to be admitted to another medical unit without disturbing the flow of planned hospitalizations. In this study, we proposed a new framework to automatically detect activities in patient pathways that may be unrelated to patients' needs but rather induced by logistical limitations. The scientific contribution lies in a method that transforms a database of historical pathways with bias into 2 databases: a labeled pathway database where each activity is labeled as relevant (related to a patient's needs) or irrelevant (induced by logistical limitations) and a corrected pathway database where each activity corresponds to the activity that would occur assuming unlimited resources. The labeling algorithm was assessed through medical expertise. In total, 2 case studies quantified the impact of our method of preprocessing health care data using process mining and discrete event simulation. Focusing on unscheduled patient pathways, we collected data covering 12 months of activity at the Groupe Hospitalier Bretagne Sud in France. Our algorithm had 87% accuracy and demonstrated its usefulness for preprocessing traces and obtaining a clean database. The 2 case studies showed the importance of our preprocessing step before any analysis. The process graphs of the processed data had, on average, 40% (SD 10%) fewer variants than the raw data. The simulation revealed that 30% of the medical units had >1 bed difference in capacity between the processed and raw data. Patient pathway data reflect the actual activity of hospitals that is governed by medical requirements and logistical limitations. Before using these data, these limitations should be identified and corrected. We anticipate that our approach can be generalized to obtain unbiased analyses of patient pathways for other hospitals.

신문기사에 대한 의미연결망 분석을 통한 의료 시장화 논리와 의료 보장성 논리 분석

There is a lack of research analyzing the formation and change of institutional logics in the Korean healthcare sector. This study examines the major institutional changes that occurred from 1990 to 2018 and explores institutional logics by conducting semantic network analysis on Korean newspaper articles. The analysis identified the 'medical marketization logic' and the 'medical coverage logic' as the main competing logics, and six groups of institutional vocabularies were found to support these two logics. The medical marketization logic has been strengthened primarily during conservative governments, while the medical coverage logic has been strengthened during liberal governments, and large-scale infectious disease epidemics have been shown to be important catalysts for changes in institutional logics. This study contributes to understanding the complexity and dynamics of institutional logics, suggests prospects and possibilities for institutional logic change in healthcare, and suggests directions for future research.

AI-Driven Diagnostic Assistance in Medical Inquiry: Reinforcement Learning Algorithm Development and Validation.

For medical diagnosis, clinicians typically begin with a patient's chief concerns, followed by questions about symptoms and medical history, physical examinations, and requests for necessary auxiliary examinations to gather comprehensive medical information. This complex medical investigation process has yet to be modeled by existing artificial intelligence (AI) methodologies. The aim of this study was to develop an AI-driven medical inquiry assistant for clinical diagnosis that provides inquiry recommendations by simulating clinicians' medical investigating logic via reinforcement learning. We compiled multicenter, deidentified outpatient electronic health records from 76 hospitals in Shenzhen, China, spanning the period from July to November 2021. These records consisted of both unstructured textual information and structured laboratory test results. We first performed feature extraction and standardization using natural language processing techniques and then used a reinforcement learning actor-critic framework to explore the rational and effective inquiry logic. To align the inquiry process with actual clinical practice, we segmented the inquiry into 4 stages: inquiring about symptoms and medical history, conducting physical examinations, requesting auxiliary examinations, and terminating the inquiry with a diagnosis. External validation was conducted to validate the inquiry logic of the AI model. This study focused on 2 retrospective inquiry-and-diagnosis tasks in the emergency and pediatrics departments. The emergency departments provided records of 339,020 consultations including mainly children (median age 5.2, IQR 2.6-26.1 years) with various types of upper respiratory tract infections (250,638/339,020, 73.93%). The pediatrics department provided records of 561,659 consultations, mainly of children (median age 3.8, IQR 2.0-5.7 years) with various types of upper respiratory tract infections (498,408/561,659, 88.73%). When conducting its own inquiries in both scenarios, the AI model demonstrated high diagnostic performance, with areas under the receiver operating characteristic curve of 0.955 (95% CI 0.953-0.956) and 0.943 (95% CI 0.941-0.944), respectively. When the AI model was used in a simulated collaboration with physicians, it notably reduced the average number of physicians' inquiries to 46% (6.037/13.26; 95% CI 6.009-6.064) and 43% (6.245/14.364; 95% CI 6.225-6.269) while achieving areas under the receiver operating characteristic curve of 0.972 (95% CI 0.970-0.973) and 0.968 (95% CI 0.967-0.969) in the scenarios. External validation revealed a normalized Kendall τ distance of 0.323 (95% CI 0.301-0.346), indicating the inquiry consistency of the AI model with physicians. This retrospective analysis of predominantly respiratory pediatric presentations in emergency and pediatrics departments demonstrated that an AI-driven diagnostic assistant had high diagnostic performance both in stand-alone use and in simulated collaboration with clinicians. Its investigation process was found to be consistent with the clinicians' medical investigation logic. These findings highlight the diagnostic assistant's promise in assisting the decision-making processes of health care professionals.

Customer Healthcare Complaints in Brazil Are Seldom about Medical Errors.

This study reviewed different country studies and noted that complaints in Brazil are more concentrated in complaints about being attended to and receiving access to services, rather than about clinical quality and safety issues. This paper explores the possible explanations for these differences based on the institutional logics theory and which logics actors privilege, and how they may play out in the healthcare field. To accomplish this undertaking, this study makes use of the healthcare complaint categorization developed by Reader and colleagues, which has been used by various studies. Next, a set of studies about healthcare complaints in different countries was examined to analyze the issues most common in the complaints and compare this information with the Brazilian data. This study identified three explanations why complaints about medical errors seldom occur. One group of studies highlights the hardships of local health systems. Another focuses on patient behavior. Finally, the third kind focuses on the issue of power to determine health orientation. The studies about a lack of resources do not directly explain why fewer complaints about clinical quality occur, thus helping to stress the management issues. Patient behavior studies indicate that patients may be afraid to point out medical errors or may be unaware of the procedures of how to do so, suggesting that family logic is left out of the decisions in the field. The third group of work highlights the prominence of the medical professional logic, both in terms of regulation and medical exercise.

Precision oncology and AI: The ACMA system for personalized treatment matching using real-world data.

e23134 Background: Precision oncology promises tailored cancer treatments, yet current methods often overlook individual patient variability. The AI-powered Case Matching Assistant (ACMA) system addresses this by utilizing real-world data (RWD) from over 100,000 clinical cases to personalize treatment strategies, considering genetic profiles, cancer types, treatment stages, and drug interactions. Methods: ACMA enhances the Large Language Model (LLM) with oncology-specific pretraining that encompasses both molecular and clinical data, ensuring a holistic understanding of patient profiles. It employs cosine similarity for multi-faceted case comparisons, integrating genetic, biomarker, and vital clinical information. Context-aware learning prompts fine-tune the LLM, generating recommendations that reflect the full spectrum of patient-specific data, from genetic variations to clinical histories and treatment responses, facilitating truly personalized treatment pathways. Results: Preliminary results show ACMA's tailored recommendations improve patient outcomes, with increased median survival rates and cost-effective treatment options. The system's 90% relevance match for clinical trial enrollment also expands access to novel therapies. In the absence of a standard treatment protocol for BRCA germline mutations in advanced lung cancer, ACMA provides crucial insights. For instance, a 36-year-old female with BRCA1 c.527del mutation achieved complete remission with a PFS of 38 months after a combination therapy trial. A 56-year-old male with a BRCA2 S1722Yfs*mutation saw partial remission and over 6 months PFS with olaparib. Another male patient, a smoker with concurrent BRCA2 G1712X and TP53 mutations, experienced partial remission following apatinib therapy. ACMA analyzed treatment data from similar genetic profiles and identified a targeted therapy that had shown efficacy in comparable cases. The oncologist used this information as a reference to adjust the treatment plan, resulting in a positive clinical outcome for the patient. This example underscores the potential of ACMA to provide actionable insights for personalized cancer care. Conclusions: Surpassing traditional single-feature matching, ACMA rapidly aligns multi-dimensional characteristics such as cancer type, disease stage, molecular data, and treatment phases. It customizes the interplay of these features with medical logic, delivering tailored outputs through the LLM. This approach offers an economical, convenient, and timely solution, setting a new benchmark in personalized oncology and establishing ACMA as a leader in the field. This distinctive advantage not only enhances patient outcomes but also establishes a formidable barrier to entry for competitors, solidifying ACMA's position at the forefront of oncology practice.

Infection Control Through Clinical Pipelines Built with Arden Syntax MLM Building Blocks.

Healthcare-associated infections (HAIs) may have grave consequences for patients. In the case of sepsis, the 30-day mortality rate is about 25%. HAIs cost EU member states an estimated 7 billion Euros annually. Clinical decision support tools may be useful for infection monitoring, early warning, and alerts. MONI, a tool for monitoring nosocomial infections, is used at University Hospital Vienna, but needs to be clinically and technically revised and updated. A new, completely configurable pipeline-based system for defining and processing HAI definitions was developed and validated. A network of data access points, clinical rules, and explanatory output is arranged as an inference network, a clinical pipeline as it is called, and processed in a stepwise manner. Arden-Syntax-based medical logic modules were used to implement the respective rules. The system was validated by creating a pipeline for the ECDC PN5 pneumonia rule. It was tested on a set of patient data from intensive care medicine. The results were compared with previously obtained MONI output as a suitable reference, yielding a sensitivity of 93.8% and a specificity of 99.8%. Clinical pipelines show promise as an open and configurable approach to graphically-based, human-readable, machine-executable HAI definitions.

Evaluation of the Proposed Arden Syntax v3.0 to Represent Query Data Mappings Using FHIR.

Arden Syntax is a standard that encodes knowledge as Medical Logic Modules but lacks a standard query data model and terminology. Assess FHIR-enabled data access in a proposed version of Arden Syntax. Queries in 340 MLMs were tabulated. FHIR can serve as the Arden Syntax data model, but nonstandard terminologies still impede interoperability. The proposed Arden Syntax v3.0 FHIR-enabled data access is adequate as a query data model for MLMs.

BIM-based method for post-earthquake medical functionality assessment of hospital departments considering functional coupling

Hospitals play an important role in post-earthquake emergency response and recovery, and the assessment of post-earthquake medical functionality is important for ensuring hospital functionality after earthquakes. Such assessments of hospital departments currently face the following challenges: (1) lack of unified modeling rules and integrated platforms to manage the associated data; (2) the complex functional coupling relationships between different components and medical equipment are not often considered; (3) lack of unified communication platforms, which hinders collaboration among researchers from different fields. Therefore, this paper proposes a BIM-based method for post-earthquake medical functionality assessment (PEMFA) of hospital departments considering functional coupling. Specifically, first, multi-level of detail (LOD) BIM modeling rules for structural components, nonstructural components, and medical equipment oriented towards PEMFA are suggested. Thereafter, a conversion method for the medical functionality assessment model and PEMFA method based on BIM are proposed. Subsequently, the PEMFA method for departments is proposed, emphasizing functional coupling. Finally, a typical medical building is selected as an example to demonstrate the proposed method. The results indicate the following: (1) the fragility data for various components and equipment of medical buildings can be integrated using the proposed multi-LOD modeling rules; (2) the mapping relationship between medical functional logic and BIM has been established, through which the functional coupling relationship between different components and equipment can be fully considered, thus providing an important method for the PEMFA of hospital departments and displaying results.

Socialisation of children to nurse and nursing images: AGoffman-inspired thematic analysis of children's picture books in a Swedish context.

Picture books are often part of children's socialisation processes, contributing to the children forming images of the world, including ideas about (categories of) people, such as nurses. The study aims to explore how nurses/nursing are portrayed in children's picture books in a Swedish context. Through a systematic search, 44 books were included for analysis using thematic analysis and a theoretical lens inspired by Goffman. The results were presented in three themes: 'The costume characterised and designated nurses', 'Nurses and nursing were defined through specific activities and accessories', and 'Nurses' role as caregivers and decency practitioners'. The results showed that nurses were depicted/described in varied contexts, performing their roles mainly front stage in 'hands-on work' in close contact with patients and relatives. Nurses were attributed different accessories and personality characteristics. Nurses/nursing were generally set within a healthcare context, oftentimes within an overarching medical logic. Historical depictions of nurses'uniforms still appeared as a signifier for nurses/nursing. The presentation of nurses/nursing might have potential implications on children's understanding of and for reputations of nurses/nursing in the long term as primary socialisation and related understandings of the (sub)world(s) are deeply rooted in humans.

Work like a doctor: Unifying scan localizer and dynamic generator for automated computed tomography report generation

Computed Tomography Report Generation (CTRG) aims to generate medical reports towards a series of radiological images, which is an advancement of the conventional X-ray report generation (generating one medical description only based on a single X-ray snapshot). Beyond the difficulties faced in the traditional task, CTRG requires the model to filter out the lesion regions from sequential scans, producing a fine-grained report that conforms to medical logic and common sense. Limited to available datasets, there are few methods trying to tackle this task. Besides, although densely aggregating sequential features may be beneficial, it introduces extra noise. Moreover, radiology reports are long narratives composed of abnormal descriptions and template sentences, but most studies ignore this hierarchical nature and generate the entire reports uniformly. This paper aims to bridge the gap from three distinct perspectives: first, we develop two large-scale clinical datasets termed CTRG-Brain-263K and CTRG-Chest-548K, which contain 263670 brain CT scans and 548696 chest CT scans with authoritative diagnosis reports, respectively. Second, we design a self-attention-based Scan Localizer (SL) that captures a representation most reflective of the lesion area. And a reconstruction loss is introduced to minimize the distance between focused and original scans. Finally, we propose a Dynamic Generator (DG) that decouples the decoder into abnormal and template branches, with produced proposals dynamically aggregated for the final generation. Experimental results confirm the proposed SL-DG outperforms existing methods, i.e., about ＋5.2% and ＋0.4% CIDEr points on CTRG-Brain-263K and CTRG-Chest-548K, respectively.

Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt.

Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically. This study addresses the long-tail challenge by transforming this multi-label classification task into an autoregressive generation task. Specifically, we first introduce a novel pretraining objective to generate free text diagnoses and procedures using the SOAP structure, the medical logic physicians use for note documentation. Second, instead of directly predicting the high dimensional space of ICD codes, our model generates the lower dimension of text descriptions, which then infers ICD codes. Third, we designed a novel prompt template for multi-label classification. We evaluate our Generation with Prompt (GPsoap) model with the benchmark of all code assignment (MIMIC-III-full) and few shot ICD code assignment evaluation benchmark (MIMIC-III-few). Experiments on MIMIC-III-few show that our model performs with a marco F130.2, which substantially outperforms the previous MIMIC-III-full SOTA model (marco F1 4.3) and the model specifically designed for few/zero shot setting (marco F1 18.7). Finally, we design a novel ensemble learner, a cross-attention reranker with prompts, to integrate previous SOTA and our best few-shot coding predictions. Experiments on MIMIC-III-full show that our ensemble learner substantially improves both macro and micro F1, from 10.4 to 14.6 and from 58.2 to 59.1, respectively.

Inclusion and Participation in a Support Programme for Bereaved Adolescents - Relational Perspectives From an Ethnographic Field Study in a Swedish Context.

The death of a parent is a life-changing event, and different programmes are developed to support children. This study explored how parental bereaved adolescents were included and (inter)acted in a Swedish support programme. The conducted ethnographic field study included six adolescents, their parents, and eight volunteers. The empirical material was thematically analysed through a theoretical lens inspired by Bourdieu. Three themes emerged: 'Different strategies for adolescents' inclusion in the programme,' 'Medico-psychological understanding of grief and suffering,' and 'Reproduction of the logic of the school.' Adolescents were included in the programme through different strategies, where adults functioned as gatekeepers. The programme reproduced the school logic and was based on a medico-psychological grief/bereavement understanding. Volunteers had pedagogic authority and concomitant symbolic power, ruling adolescents to do what they must do in the meetings, silently socialising them into the medical logic. The adolescents only interacted and communicated with each other during breaks.

Challenges in preserving the "good doctor" norm: physicians' discourses on changes to the medical logic during the initial wave of the COVID-19 pandemic.

The COVID-19 pandemic was a tremendous challenge to the practice of modern medicine. In this study, we use neo-institutional theory to gain an in-depth understanding of how physicians in Sweden narrate how they position themselves as physicians when practicing modern medicine during the first wave of the pandemic. At focus is medical logic, which integrates rules and routines based on medical evidence, practical experience, and patient perspectives in clinical decision-making. To understand how physicians construct their versions of the pandemic and how it impacted the medical logic in which they practice, we analyzed the interviews from 28 physicians in Sweden by discursive psychology. The interpretative repertoires showed how COVID-19 created an experience of knowledge vacuum in medical logic and how physicians dealt with clinical patient dilemmas. They had to find unorthodox ways to rebuild a sense of medical evidence while still being responsible for clinical decision-making for patients with critical care needs. In the knowledge vacuum occurring during the first wave of COVID-19, physicians could not use their common medical knowledge nor rely on published evidence or their clinical judgment. They were thus challenged in their norm of being the "good doctor". One practical implication of this research is that it provides a rich empirical account where physicians are allowed to mirror, make sense, and normalize their own individual and sometimes painful struggle to uphold the professional role and related medical responsibility in the early phases of the COVID-19 pandemic. It will be important to follow how the tremendous challenge of COVID-19 to medical logic plays out over time in the community of physicians. There are many dimensions to study, with sick leave, burnout, and attrition being some interesting areas.

Examining the Dilemma Between Artificial Intelligence Techniques and Professional Medical Service: A Hybrid Balancing Perspective

Artificial intelligence (AI), which is reshaping the resource landscape by detecting patterns and structuring new rules within the medical industry, has been recognized as the latest disruptive innovation wave in the health-care industry. However, that has also caused a contradiction, which is between quantificationism logic and medical professionalism logic. To combine co-existing logics within a health-care organization, this research delivers a practical-oriented hybrid balancing model with multidimensional mechanisms to tackle dual institutional logics, including valuable data standard establishment, securable information interface design, and dynamic intelligent service development. This study extends our understanding of intelligent hospital development by examining the balance between AI techniques and professional medical service.

Использование МИС для контроля качества лечения

The article presents an approach to quality control of treatment at the present stage. A trend of today – the use of a medical information system for quality control of the medical and diagnostic process (with special attention paid not to reporting to higher authorities, but to the organization of internal control in a medical organization). Possible tools and benefits are considered. P u r p o s e . Determining the capabilities of Hospital Information System for quality control of medical care. M a t e r i a l s a n d m e t h o d s . We analyzed the requirements of regulatory acts for the control of medical care, as well as the real needs of HIS users. An experiment on the formation of a set of tools for quality control of treatment was carried out using the example of Hospital Information System Interin PROMIS Alpha. We considered the prospects taking into account the results of the research of Ailamazyan Program Systems Institute of RAS in the field of working with clinical data banks. R e s u l t s . General requirements for internal quality control include 5 levels of control. The use of HIS is most in demand in the following areas: compliance with medical care standards, internal control tools (analysis of compliance with clinical recommendations, expert assessment of the quality of treatment, quality and safety of medical activities, format and logic control, etc.) and ensuring patient safety. The use cases of the HIS in these areas are illustrated by the example of the Hospital Information System Interin PROMIS Alpha. The prospects of the development of the direction consisting in transferring the main load from a person to an HIS and in attracting AI technologies and neural networks are considered. F i n d i n g s . The main problems of quality control are the need for the participation of highly qualified specialists and the high complexity of the process. These problems can be solved by shifting the control regulations from «after the fact» to «proactive» and «in real time» by using HIS, as well as involving knowledge-intensive technologies in this process with the involvement of artificial intelligence and neural networks

‘Disability is not a word we use’: Social workers' professional judgements about support for disabled young people leaving care

Disabled young people leaving care often experience a more complex transition to adulthood than other youths. Still, policy and services can fail to recognize the intersection between a young person's care experiences and disability. Drawing on data from a qualitative interview study with 14 social workers who work with aftercare in the Norwegian child welfare services, we investigate social workers' professional judgements about support for this subgroup of the leaving care population. Our analysis uses the theoretical construct of institutional logics and shows that social workers did not include concepts of disability in their judgements about support for these young people. Instead, the social workers' considerations were guided by three other organizing principles: a ‘medical logic’, an ‘activation logic’ and an ‘aftercare logic’. We discuss these findings in light of critical disability studies and argue for a more nuanced understanding of disability in social work practice with care leavers. Highlighting disability rights and going beyond diagnosis and categorisations of disabled people can challenge a medical model approach to service provision.

Auxiliary signal-guided knowledge encoder-decoder for medical report generation

Medical reports have significant clinical value to radiologists and specialists, especially during a pandemic like COVID. However, beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists’ working patterns. Specifically, the auxiliary patches are explored to expand the widely used visual patch features before fed to the Transformer encoder, while the external linguistic signals help the decoder better master prior knowledge during the pre-training process. Our approach performs well on common benchmarks, including CX-CHR, IU X-Ray, and COVID-19 CT Report dataset (COV-CTR), demonstrating combining auxiliary signals with transformer architecture can bring a significant improvement in terms of medical report generation. The experimental results confirm that auxiliary signals driven Transformer-based models are with solid capabilities to outperform previous approaches on both medical terminology classification and paragraph generation metrics.

MMKP: A mind mapping knowledgebase prototyping tool for precision medicine.

BackgroundWith significant advancements in the area of precision medicine, the breadth and complexity of the relevant knowledge in the field has increased significantly. However, the difficulty associated with dynamic modelling and the disorganization of such knowledge hinders its rapid development potential.ResultsTo overcome the difficulty in using the relational database model for dynamic modelling, and to aid in the organization of precision medicine knowledge, we developed the Mind Mapping Knowledgebase Prototyping (MMKP) tool. The MMKP implements a novel design that we call a “polymorphic foreign key”, which allows the establishment of a logical linkage between a single table field and a record from any table. This design has advantages in supporting dynamic changes to the structural relationships in precision medicine knowledge. Knowledge stored in MMKP is presented as a mind map to facilitate human interaction. When using this tool, medical experts may curate the structure and content of the precision knowledge in a flow that is similar to the human thinking process.ConclusionsThe design of polymorphic foreign keys natively supports knowledge modelling in the form of mind mapping, which avoids the hard-coding of medical logic into a rigid database schema and significantly reduces the workload that is required for adapting a relational data model to future changes to the medical logic. The MMKP tool provides a graphical user interface for both data management and knowledgebase prototyping. It supports the flexible customization of the data field constraints and annotations. MMKP is available as open-source code on GitHub: https://github.com/ZjuLiangsl/mmkp.

A new threat is on the horizon: The monkeypox virus. Should we worry about it or just keep an eye on it?

Monkeypox is a rare viral disease that is endemic in many African countries. However, within days, dozens of cases of monkeypox were confirmed in at least 12 non-African countries. It first appeared in the UK and was reported on 7 May 2022. The discovery of the virus in many patients and in different populations around the world within a few days raised the issue of scientific, medical, managerial, and social logic. The cases reported to date have no established travel links to endemic areas, but a recent mass event may have served as a focus for amplification. Epidemiological investigations are ongoing. With increased surveillance in non-endemic countries, it is likely that more cases of monkeypox will be identified and confirmed. With all this, we must ask ourselves, as we did with the advent of COVID-19, whether the population is seriously threatened and whether or not we should be concerned about the new virus in the future.