Clinical Knowledge Management Research Articles

Success of ChatGPT, an AI language model, in taking the French language version of the European Board of Ophthalmology examination: A novel approach to medical knowledge assessment

The purpose of this study was to evaluate the performance of ChatGPT, a cutting-edge artificial intelligence (AI) language model developed by OpenAI, in successfully completing the French language version of the European Board of Ophthalmology (EBO) examination and to assess its potential role in medical education and knowledge assessment. ChatGPT, based on the GPT-4 architecture, was exposed to a series of EBO examination questions in French, covering various aspects of ophthalmology. The AI's performance was evaluated by comparing its responses with the correct answers provided by ophthalmology experts. Additionally, the study assessed the time taken by ChatGPT to answer each question as a measure of efficiency. ChatGPT achieved a 91% success rate on the EBO examination, demonstrating a high level of competency in ophthalmology knowledge and application. The AI provided correct answers across all question categories, indicating a strong understanding of basic sciences, clinical knowledge, and clinical management. The AI model also answered the questions rapidly, taking only a fraction of the time needed by human test-takers. ChatGPT's performance on the French language version of the EBO examination demonstrates its potential to be a valuable tool in medical education and knowledge assessment. Further research is needed to explore optimal ways to implement AI language models in medical education and to address the associated ethical and practical concerns.

Graduating Student Nurses’ Preparedness for Nursing Practice amid COVID-19

Student nurses must be prepared professionally to provide nursing care, especially during a critical time such as COVID-19. Regardless of undergraduate nursing programmes focusing on preparedness for practice, the concept of preparedness for nursing practice is not well understood. In this descriptive quantitative study, we determined the extent of the nursing practice preparedness of graduating student nurses amid COVID-19 as perceived by these nurses and nurse educators and the possible interventions to enhance the preparedness for practice of these nurses. In the study, we used an adapted Nursing Practice Readiness Tool, employed the complete enumeration survey method and analysed through frequency, percentages and weighted means. A total of 118 student nurses and 51 nurse educators from a selected university completed the questionnaire. The data were analysed through SPSS version 25. The findings revealed that the scales, which ranked from greatest to lowest preparedness for practice, were: professionalism (4.83), communication (4.65), management of responsibilities (4.61), critical thinking (4.36), clinical knowledge (4.26), and technical skills (4.02). We concluded that the students’ performance in the nursing competency items under clinical knowledge, critical thinking, and especially technical skills, demand increased attention to be better equipped as they transition into nursing practice. Specifically, of the 36 competency items, they were least prepared in using clinical and information technologies, and performing clinical procedures. Finally, as the top intervention, we proposed the development of contingency plans for unusual situations such as a pandemic (75.74%) to enhance the student nurses’ preparedness for practice.

Natural Language Processing Basics.

Natural Language Processing Basics.

Clinical leadership and knowledge management: Essential role in patient safety culture?

The subject of patient safety culture has attracted international interest. However, implementing these patient safety policies and practices is not easy, as significant challenges remain. This study analyzes the influence of clinical leadership effectiveness on patient safety culture by distributing questionnaires to 237 nurses in Hospital “X” in West Nusa Tenggara. Structural Equation Modeling (SEM) based on Partial Least Square (PLS) was used for data analysis and knowledge management was placed as a mediating variable in this study. It was found that the effectiveness of clinical leadership has a significant effect on patient safety culture. Thus, this study opens the scope of clinical leadership research in hospitals by exploring the effectiveness of leadership on patient safety culture. The role of clinical leaders in creating a patient safety culture with the mediation of knowledge management was also examined.

Nursing Informatics Integration into Mainstream Health Informatics.

Nursing Informatics emerged in Australia during the early 1980s and drove the Professional development and acceptance of Health Informatics. Milestones achieved include the development of a national journal, the establishment of the Health Informatics Society of Australia and the Australasian College of Health Informatics (now collectively the Australasian Institute of Digital Health), nursing participation in Health Informatics standards development activities, adoption of the HL7 messaging standard, the delivery of numerous workshops, an annual national health informatics conference since 1993, hosting international conferences, the development and delivery of Health Informatics post graduate programs and establishing a research centre where the first prototype for an archetype repository was developed. This became the openEHR Clinical Knowledge Manager. The most recent milestone was the establishment of a private company that became a Registered Training Organisation. Continuing challenges include workforce capacity building to address the poor understanding of the need for improved data and IT governance at every level, the need to comply with proven scientific and technical principles and a need to transform national and international traditional infrastructures no longer fit for purpose to enable adequately support for global sustainable digital health ecosystems. Desired personal and aggregate data supply chains must be taken seriously and be supported by the best available technologies. Our collective biggest challenge is to improve multidisciplinary and intersectoral collaboration, semantic interoperability and optimum digital support to maintain global public health.

Risk factors associated with severe hospital burden of COVID-19 disease in Regione Lombardia: a cohort study

BackgroundUnderstanding the risk factors associated with hospital burden of COVID-19 is crucial for healthcare planning for any future waves of infection.MethodsAn observational cohort study is performed, using data on all PCR-confirmed cases of COVID-19 in Regione Lombardia, Italy, during the first wave of infection from February-June 2020. A multi-state modelling approach is used to simultaneously estimate risks of progression through hospital to final outcomes of either death or discharge, by pathway (via critical care or not) and the times to final events (lengths of stay). Logistic and time-to-event regressions are used to quantify the association of patient and population characteristics with the risks of hospital outcomes and lengths of stay respectively.ResultsRisks of severe outcomes such as ICU admission and mortality have decreased with month of admission (for example, the odds ratio of ICU admission in June vs March is 0.247 [0.120–0.508]) and increased with age (odds ratio of ICU admission in 45–65 vs 65 + age group is 0.286 [0.201–0.406]). Care home residents aged 65 + are associated with increased risk of hospital mortality and decreased risk of ICU admission. Being a healthcare worker appears to have a protective association with mortality risk (odds ratio of ICU mortality is 0.254 [0.143–0.453] relative to non-healthcare workers) and length of stay. Lengths of stay decrease with month of admission for survivors, but do not appear to vary with month for non-survivors.ConclusionsImprovements in clinical knowledge, treatment, patient and hospital management and public health surveillance, together with the waning of the first wave after the first lockdown, are hypothesised to have contributed to the reduced risks and lengths of stay over time.

Representation of Diagnosis and Nursing Interventions in OpenEHR Archetypes.

The study aimed to represent the content of nursing diagnosis and interventions in the openEHR standard. This is a developmental study with the models developed according to ISO 18104: 2014. The Ocean Archetype Editor tool from the openEHR Foundation was used. Two archetypes were created; one to represent the nursing diagnosis concept and the other the nursing intervention concept. Existing archetypes available in the Clinical Knowledge Manager were reused in modeling. The representation of nursing diagnosis and interventions based on the openEHR standard contributes to representing nursing care phenomena and needs in health information systems.

Risk Factors for Severe Hospital Burden During the First Wave of COVID-19 Disease in Regione Lombardia

Background: Understanding the risk factors associated with hospital burden of COVID-19 is crucial for healthcare planning for any future waves of infection.Methods: An observational cohort study is performed, using data on all RT-PCR confirmed cases of COVID-19 in Regione Lombardia, Italy, during the first wave of infection from February-June 2020. A multi-state modelling approach is used to simultaneously estimate risks of progression through hospital to final outcomes of either death or discharge, by pathway (via critical care or not) and the times to final events (lengths of stay). Logistic and time-to-event regressions are used to quantify the association of patient and population characteristics with the risks of hospital outcomes and lengths of stay respectively.Findings: Risks of severe outcomes such as ICU admission and mortality have decreased with month of admission and increased with age. Care home residents aged 65+ are at increased risk of hospital mortality and decreased risk of ICU admission. Being a healthcare worker appears to have a protective effect on mortality risk and length of stay. Lengths of stay decrease with month of admission for survivors, but do not appear to vary with month for non-survivors.Interpretation: Improvements in clinical knowledge, treatment, patient and hospital management and public health surveillance, together with the waning of the first wave after the first lockdown, are hypothesised to have contributed to the reduced risks and lengths of stay over time.Funding: This work has been funded by the Medical Research Council (De Angelis, Jackson, Presanis: Unit programme number MC UU 00002/11; Kunzmann: Unit programme number MC_UU_00002/10); and the UKRI-MRC COVID-19 Rapid Call (Presanis, De Angelis, grant no MC_PC_19074).Declaration of Interests: None to declare.

OpenEHR Archetype Use and Reuse Within Multilingual Clinical Data Sets: Case Study.

BackgroundDespite electronic health records being in existence for over 50 years, our ability to exchange health data remains frustratingly limited. Commonly used clinical content standards, and the information models that underpin them, are primarily related to health data exchange, and so are usually document- or message-focused. In contrast, over the past 12 years, the Clinical Models program at openEHR International has gradually established a governed, coordinated, and coherent ecosystem of clinical information models, known as openEHR archetypes. Each archetype is designed as a maximal data set for a universal use-case, intended for reuse across various health data sets, known as openEHR templates. To date, only anecdotal evidence has been available to indicate if the hypothesis of archetype reuse across templates is feasible and scalable. As a response to the COVID-19 pandemic, between February and July 2020, 7 openEHR templates were independently created to represent COVID-19–related data sets for symptom screening, confirmed infection reporting, clinical decision support, and research. Each of the templates prioritized reuse of existing use-case agnostic archetypes found in openEHR International's online Clinical Knowledge Manager tool as much as possible. This study is the first opportunity to investigate archetype reuse within a range of diverse, multilingual openEHR templates.ObjectiveThis study aims to investigate the use and reuse of openEHR archetypes across the 7 openEHR templates as an initial investigation about the reuse of information models across data sets used for a variety of clinical purposes.MethodsAnalysis of both the number of occurrences of archetypes and patterns of occurrence within 7 discrete templates was carried out at the archetype or clinical concept level.ResultsAcross all 7 templates collectively, 203 instances of 58 unique archetypes were used. The most frequently used archetype occurred 24 times across 4 of the 7 templates. Total data points per template ranged from 40 to 179. Archetype instances per template ranged from 10 to 62. Unique archetype occurrences ranged from 10 to 28. Existing archetype reuse of use-case agnostic archetypes ranged from 40% to 90%. Total reuse of use-case agnostic archetypes ranged from 40% to 100%.ConclusionsInvestigation of the amount of archetype reuse across the 7 openEHR templates in this initial study has demonstrated significant reuse of archetypes, even across unanticipated, novel modeling challenges and multilingual deployments. While the trigger for the development of each of these templates was the COVID-19 pandemic, the templates represented a variety of types of data sets: symptom screening, infection report, clinical decision support for diagnosis and treatment, and secondary use or research. The findings support the openEHR hypothesis that it is possible to create a shared, public library of standards-based, vendor-neutral clinical information models that can be reused across a diverse range of health data sets.

Nutrition Information in Oncology - Extending the Electronic Patient-Record Data Set.

Electronic health records (EHRs) present extensive patient information and may be used as a tool to improve health care. However, the oncology context presents a complex content that increases the difficulties of EHR application. This study aimed at developing openEHR-archetypes representing clinical concepts in cancer nutrition-care, as well as to develop an openEHR-template including the aforementioned archetypes. The study involved the following stages: 1) a thorough literature review, followed by an expert’s (nutrition guideline authors) survey, aiming to identify the main statements of published clinical guidelines on nutrition in cancer patients that were not included on the Clinical Knowledge Manager (CKM) repository; 2) modelling of the archetypes using the Ocean Archetype Software and submission to the CKM repository; 3) creating an example template with Template Designer; and 4) automatic conversion of the openEHR-template into a readily usable EHR using VCIntegrator. The clinical concepts (among 17 clinical concepts not yet available in the CKM repository) chosen for further development were: body composition, diet plan, dietary nutrients, dietary supplements, dietary intake assessment, and Malnutrition Screening Tool (MST). So far, four archetypes were accepted for review in the CKM repository and a template was created and converted into an EHR. This study designed new openEHR-archetypes for nutrition management in cancer patients. These archetypes can be included in EHR. Future studies are needed to assess their applicability in other areas and their practical impact on data quality, system interoperability and, ultimately, on clinical practice and research.Electronic supplementary materialThe online version of this article (10.1007/s10916-020-01649-9) contains supplementary material, which is available to authorized users.

Development of an openEHR Template for COVID-19 Based on Clinical Guidelines.

BackgroundThe coronavirus disease (COVID-19) was discovered in China in December 2019. It has developed into a threatening international public health emergency. With the exception of China, the number of cases continues to increase worldwide. A number of studies about disease diagnosis and treatment have been carried out, and many clinically proven effective results have been achieved. Although information technology can improve the transferring of such knowledge to clinical practice rapidly, data interoperability is still a challenge due to the heterogeneous nature of hospital information systems. This issue becomes even more serious if the knowledge for diagnosis and treatment is updated rapidly as is the case for COVID-19. An open, semantic-sharing, and collaborative-information modeling framework is needed to rapidly develop a shared data model for exchanging data among systems. openEHR is such a framework and is supported by many open software packages that help to promote information sharing and interoperability.ObjectiveThis study aims to develop a shared data model based on the openEHR modeling approach to improve the interoperability among systems for the diagnosis and treatment of COVID-19.MethodsThe latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. First, the guideline was analyzed and the data items used for diagnosis and treatment, and management were extracted. Second, the data items were classified and further organized into domain concepts with a mind map. Third, searching was executed in the international openEHR Clinical Knowledge Manager (CKM) to find the existing archetypes that could represent the concepts. New archetypes were developed for those concepts that could not be found. Fourth, these archetypes were further organized into a template using Ocean Template Editor. Fifth, a test case of data exchanging between the clinical data repository and clinical decision support system based on the template was conducted to verify the feasibility of the study.ResultsA total of 203 data items were extracted from the guideline in China, and 16 domain concepts (16 leaf nodes in the mind map) were organized. There were 22 archetypes used to develop the template for all data items extracted from the guideline. All of them could be found in the CKM and reused directly. The archetypes and templates were reviewed and finally released in a public project within the CKM. The test case showed that the template can facilitate the data exchange and meet the requirements of decision support.ConclusionsThis study has developed the openEHR template for COVID-19 based on the latest guideline from China using openEHR modeling methodology. It represented the capability of the methodology for rapidly modeling and sharing knowledge through reusing the existing archetypes, which is especially useful in a new and fast-changing area such as with COVID-19.

Monitoring Asynchrony During Invasive Mechanical Ventilation.

Mechanical ventilation in critically ill patients must effectively unload inspiratory muscles and provide safe ventilation (ie, enhancing gas exchange, protect the lungs and the diaphragm). To do that, the ventilator should be in synchrony with patient's respiratory rhythm. The complexity of such interplay leads to several concerning issues that clinicians should be able to recognize. Asynchrony between the patient and the ventilator may induce several deleterious effects that require a proper physiological understanding to recognize and manage them. Different tools have been developed and proposed beyond the careful analysis of the ventilator waveforms to help clinicians in the decision-making process. Moreover, appropriate handling of asynchrony requires clinical skills, physiological knowledge, and suitable medication management. New technologies and devices are changing our daily practice, from automated real-time recognition of asynchronies and their distribution during mechanical ventilation, to smart alarms and artificial intelligence algorithms based on physiological big data and personalized medicine. Our goal as clinicians is to provide care of patients based on the most accurate and current knowledge, and to incorporate new technological methods to facilitate and improve the care of the critically ill.

Can openEHR Represent the Clinical Concepts of an Obstetric-Specific EHR - ObsCare Software?

ObsCare is an obstetric-specific Electronic Health Record in use in nine Portuguese obstetric departments. Like other EHRs, it faces major challenges related to semantic interoperability and data quality. openEHR is proposed to address those needs. This study aimed to describe a summary representation of Obscare workflow and to validate whether archetypes in the openEHR Clinical Knowledge Manager repository can represent ObsCare clinical concepts. The study included the phases: a) ObsCare form selection; b) Description of the workflow care process; c) Detailed data extraction; and d) CKM models analysis. 379 variables were analyzed: 219 were fully represented in CKM repository; 99 were partially represented and needed archetype modification; and 61 were not represented and need new archetypes. To conclude, our study showed that the openEHR CKM repository requires further enhancements to be able to fully answer to the needs of an obstetric-specific EHR, the ObsCare software.

Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR.

BackgroundClinical information models (CIMs) enabling semantic interoperability are crucial for electronic health record (EHR) data use and reuse. Dual model methodology, which distinguishes the CIMs from the technical domain, could help enable the interoperability of EHRs at the knowledge level. How to help clinicians and domain experts discover CIMs from an open repository online to represent EHR data in a standard manner becomes important.ObjectiveThis study aimed to develop a retrieval method to identify CIMs online to represent EHR data.MethodsWe proposed a graphical retrieval method and validated its feasibility using an online CIM repository: openEHR Clinical Knowledge Manager (CKM). First, we represented CIMs (archetypes) using an extended Bayesian network. Then, an inference process was run in the network to discover relevant archetypes. In the evaluation, we defined three retrieval tasks (medication, laboratory test, and diagnosis) and compared our method with three typical retrieval methods (BM25F, simple Bayesian network, and CKM), using mean average precision (MAP), average precision (AP), and precision at 10 (P@10) as evaluation metrics.ResultsWe downloaded all available archetypes from the CKM. Then, the graphical model was applied to represent the archetypes as a four-level clinical resources network. The network consisted of 5513 nodes, including 3982 data element nodes, 504 concept nodes, 504 duplicated concept nodes, and 523 archetype nodes, as well as 9867 edges. The results showed that our method achieved the best MAP (MAP=0.32), and the AP was almost equal across different retrieval tasks (AP=0.35, 0.31, and 0.30, respectively). In the diagnosis retrieval task, our method could successfully identify the models covering “diagnostic reports,” “problem list,” “patients background,” “clinical decision,” etc, as well as models that other retrieval methods could not find, such as “problems and diagnoses.”ConclusionsThe graphical retrieval method we propose is an effective approach to meet the uncertainty of finding CIMs. Our method can help clinicians and domain experts identify CIMs to represent EHR data in a standard manner, enabling EHR data to be exchangeable and interoperable.

An openEHR Approach to Detailed Clinical Model Development: Tobacco Smoking Summary Archetype as a Case Study.

Data modeling for electronic health records (EHRs) is complex, requiring technological and cognitive sophistication. The openEHR approach leverages the tacit knowledge of domain experts made explicit in a model development process aiming at interoperability and data reuse. The purpose of our research was to explore the process that enabled the aggregation of the tacit knowledge of domain experts in an explicit form using the Clinical Knowledge Manager (CKM) platform and associated assets. The Tobacco Smoking Summary archetype is used to illustrate this. Three methods were used to triangulate findings: (1) observation of CKM discussions by crowdsourced domain experts in two reviews, (2) observation of editor discussions and decision-making, and (3) interviews with eight domain experts. CKM discussions were analyzed for content and editor discussions for decision-making, and interviews were thematically analyzed to explore in depth the explication of tacit knowledge. The Detailed Clinical Model (DCM) process consists of a set of reviews by domain experts, with each review followed by editorial discussions and decision-making until an agreement is reached among reviewers and editors that the DCM is publishable. Interviews revealed three themes: (1) data interoperability and reusability, (2) accurate capture of patient data, and (3) challenges of sharing tacit knowledge. The openEHR approach to developing an open standard revealed a complex set of conditions for a successful interoperable archetype, such as leadership, maximal dataset, crowdsourced domain expertise and tacit knowledge made explicit, editorial vision, and model-driven software. Aggregated tacit knowledge that is explicated into a DCM enables the ability to collect accurate data and plan for the future. The process based on the CKM platform enables domain experts and stakeholders to be heard and to contribute to mutually designed standards that align local protocols and agendas to international interoperability requirements.

Cartera de servicios y cuadro básico de indicadores de calidad para las unidades de cefalea: estudio de consenso

To develop a proposal for a portfolio of services and a set of indicators for Headache Units. Qualitative study that applied techniques of consensus with the participation of 15 professionals who are experts in the treatment of headaches (experience of 15 years) and in quality assurance. The description of the indicators included: statement, definition, standard, type of indicator, dimension, source, level of evidence, and clarification of terms. The proposal for a portfolio of services included the following areas: clinical management, patient-centered care, community involvement, knowledge management, translational research, and social dissemination. The proposal converged in 13 indicators exploring five dimensions. This proposal contributes to ensure and assess the level of quality of a Headache Unit or outpatient clinic.

Modeling EHR with the openEHR approach: an exploratory study in China

BackgroundThe openEHR approach can improve the interoperability of electronic health record (EHR) through two-level modeling. Developing archetypes for the complete EHR dataset is essential for implementing a large-scale interoperable EHR system with the openEHR approach. Although the openEHR approach has been applied in different domains, the feasibility of archetyping a complete EHR dataset in a hospital has not been reported in academic literature, especially in a country where using openEHR is still in its infancy stage, like China. This paper presents a case study of modeling an EHR in China aiming to investigate the feasibility and challenges of archetyping a complete EHR dataset with the openEHR approach.MethodsWe proposed an archetype modeling method including an iterative process of collecting requirements, normalizing data elements, organizing concepts, searching corresponding archetypes, editing archetypes and reviewing archetypes. Two representative EHR systems from Chinese vendors and the existing Chinese EHR standards have been used as resources to identify the requirements of EHR in China, and a case study of modeling EHR in China has been conducted. Based on the models developed in this case study, we have implemented a clinical data repository (CDR) to verify the feasibility of modeling EHR with archetypes.ResultsSixty four archetypes were developed to represent all requirements of a complete EHR dataset. 59 (91%) archetypes could be found in Clinical Knowledge Manager (CKM), of which 35 could be reused directly without change, and 23 required further development including two revisions, two new versions, 18 extensions and one specialization. Meanwhile, 6 (9%) archetypes were newly developed. The legacy data of the EHR system in hospitals could be integrated into the CDR developed with these archetypes successfully.ConclusionsThe existing archetypes in CKM can faithfully represent most of the EHR requirements in China except customizations for local hospital management. This case study verified the feasibility of modeling EHR with the openEHR approach and identified the fact that the challenges such as localization, tool support, and an agile publishing process still exist for a broader application of the openEHR approach.

Designing Clinical Concept Models for a Nationwide Electronic Health Records System For Japan

Objectives: We developed an electronic health records (EHR) system for regional healthcare in 2000. This EHR stores the health records of more than 6,300 patients in two regions of Japan; however, clinical updates and improved interoperability with other clinical standards, such as HL7 or others are needed. In 2015, this EHR system was upgraded to create a nationwide-scale healthcare data repository to improve the interoperability of clinical data with openEHR technology. Methods: The clinical data in our EHR system has 16 components constructed with Medical Markup Language (MML) standards and periodic mass screening for employees and students. Therefore, we constructed mindmaps of the clinical MML and surveillance data to analyse the concept models. Based on mindmap analysis, we designed archetypes of the concepts identified using Ocean Archetype Editor. The artefacts were mainly quoted from the openEHR clinical knowledge manager (CKM). As the archetypes on CKM did not include all MML semantics, the archetypes were newly designed to complement the semantics of the MML. Results: We developed clinical information models by archetypes that semantically equalled the EHR system. Twenty-one MML components/modules and concept models using 99 archetypes were constructed for periodic mass screening services. Most of the archetypes were quoted from CKM; however, 22 archetypes were specialised, and eight archetypes were newly designed. The reasons for specialisation were to adjust the demographics to Japanese and to extend the archetypes to the dental domain. Conclusion: We constructed concept models with archetypes semantically equivalent to conventional data and developed new archetypes for mass screening by archetype technology. The suggested archetype technology improved the flexibility of the EHR system to cover the existing standards.

Clinical reasoning and knowledge management in final year medical students: the role of Student-led Grand Rounds.

ObjectivesThe development of clinical reasoning and decision-making skills is often limited in medical school curricula. In reality, medical graduates acquire these skills during their first few years of residency. For many, this can be stressful as they may be working under limited supervision as a part of their rotations.MethodsStudent-led Grand Rounds was developed to transfer both explicit and tacit knowledge to final year medical students. This pilot project was to apply the principles of knowledge management to allow students to be exposed to the reasoning and decision making of common clinical presentations. Student feedback through questionnaires was collated at the end of the program.ResultsBased on feedback and focus groups, modifications were made to produce a stable program in subsequent clinical rotations. Formal feedback was collated from all the 76 students who participated in the first year. This represented 100% of the cohort for this clinical school for that year. There was a 100% response rate as the feedback forms were given and collected at the end of the last session per block. The student responses were both in ratings defined in a feedback forms and in written comments. A total of 74 of the 76 students rated the program highly. They enjoyed the nonthreatening interactions. The remaining two students preferred more didactic teaching.ConclusionThis initiative allows an efficient transfer and utilization of knowledge. This could maximize the acquisition of practical knowledge by medical students as they finish their course in the transition to graduate medical practice.

Effectiveness of a clinical knowledge support system for reducing diagnostic errors in outpatient care in Japan: A retrospective study

Clinical evidence has indicated the effectiveness of computer-based systems for preventing and reducing diagnostic errors. Our study aimed to evaluate the effectiveness of UpToDate, a computer-based clinical knowledge management system, for reducing diagnostic errors. We retrospectively identified 100 patients who visited an outpatient department in a community-based hospital from July 2014 to June 2015. Fifty patients (exposure group) were seen by UpToDate-equipped physicians and another 50 (control group) were seen by UpToDate-unequipped physicians. We extracted data on patient sex, age, primary diagnosis, and case difficulty that could potentially affect diagnostic outcomes. We compared the two groups regarding diagnostic error rate and performed logistic regression analysis to analyze the concurrent effects of various factors affecting diagnostic error. The diagnostic error rate was 2% in the exposure group, while the error rate was 24% in the control group. Multivariate logistic regression analysis showed that error rate reduction was significantly associated with exposure to UpToDate with an odds ratio of 15.21 (95% CI 1.86–124.36). Our results demonstrated the effectiveness of UpToDate for the prevention and reduction of diagnostic error.