Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach\'s alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran\'s health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to \"depict the epidemic curve during outbreaks of infectious diseases.\" Conversely, a notable weakness was the \"insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study\'s findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems.

OBJECTIVE: High quality data entry in clinical trial databases is crucial to the usefulness, validity, and replicability of research findings, as it influences evidence-based medical practice and future research. Our aim is to assess the quality of self-reported data in trial registries and present practical and systematic methods for identifying and evaluating data quality.
METHODS: We searched ClinicalTrials.Gov for interventional total knee arthroplasty(TKA) trials between 2000-2015. We extracted required and optional trial information elements and used the CTG\'s variables\' definitions. We performed a literature review on data quality reporting on frameworks, checklists, and overviews of irregularities in healthcare databases. We identified and assessed data quality attributes: consistency, accuracy, completeness, and timeliness.
RESULTS: We included 816 interventional TKA trials. Data irregularities varied widely: 0% to 100%. Inconsistency ranged from 0% to 36%, most often non-randomized labeled allocation were combined with a \"single group\" assignment trial design. Inaccuracy ranged from 0% to 100%. Incompleteness ranged from 0% to 61%: 61% finished TKA trials did not report their outcome. As regard to irregularities in timeliness: 49% of the trials were registered more than 3 months after the start date.
CONCLUSIONS: We found significant variations in the data quality of registered clinical TKA trials. Trial sponsors should be committed to ensuring that the information they provide is reliable, consistent, up-to-date, transparent and accurate. CTG\'s users need to be critical when drawing conclusions based on the registered data. We believe this awareness will increase well-informed decisions about published articles and treatment protocols, including replicating and improving trial designs.

BACKGROUND: Healthcare faces challenges due to the advancements of Industry 4.0 as large volumes of data are generated within healthcare facilities that, combined with the complex nature of healthcare environments, make it difficult to utilise and interpret this data effectively.
OBJECTIVE: A novel holonic approach to clinical pathway data analysis is presented and implemented as a clinical pathway digital twin. A holon is here taken to be an autonomous and co-operative building block of a software system for transforming, transporting, storing and/or validating information. The digital twin\'s aim is to ingest, structure and analyse the information associated with a clinical pathway to support healthcare professionals in making informed decisions, for example monitoring and predicting the duration from admission to discharge for individual patients.
METHODS: Real world observations and a review of literature led to the identification of a generic set of clinical pathway analysis needs and, derived therefrom, a set of design requirements. A proof-of-concept clinical pathway analysis digital twin was implemented using a holonic approach derived from the ARTI reference architecture. The holonic approach is evaluated in a hip and knee replacement pathway case study. The evaluation includes automated statistical analyses and machine learning predictions.
RESULTS: The evaluation demonstrates that the holonic approach provides an intuitive and extensible means to aggregate and disaggregate information tactically, and to derive context-tailored analysis features. The holonic approach enhances checking for data completion and handling data anomalies. The evaluation also demonstrates on-demand report generation, which reduces repetitive manual tasks for healthcare professionals.
CONCLUSIONS: The novel holonic data analysis approach facilitates context-rich analyses tailored to specific clinical pathway activities, with effective tailoring of data ingestion and analysis. Healthcare professionals can use the data analysis approach to extract valuable insights for decision-making related to clinical pathways.

BACKGROUND: Historically, diseases and non-battle injuries (DNBI) typically stand for 70%‒95% of all medical events during military missions. There is, however, no comprehensive compilation of medical statistics for Swedish soldiers during deployment.
METHODS: During United Nations Multidimensional Integrated Stabilization Mission in Mali, climate data and medical outpatient health surveillance data were compiled for Swedish soldiers deployed to Timbuctoo, between 2015 and 2019. Correlations between climate data and medical outpatient health surveillance data were analysed.
RESULTS: Battle injuries accounted for 0.4% of the visits to healthcare, while diseases accounted for 53.6%, and non-battle injuries for 46%, the majority being musculoskeletal injuries. The combination of high temperature, humidity, sun radiation and good visibility, during summer rotation weeks, caused more events of injuries and heat stress than any other period.
CONCLUSIONS: Musculoskeletal injuries were the major cause for visits to the Swedish camp hospital. Injuries and heat stress increased during periods of high temperature, humidity, sun radiation and good visibility. Lack of medical data, i.e. unknown number of unique patients seeking healthcare, cause codes not always connected to a primary diagnosis, and revisits not being connected to a diagnose, complicated interpretation of health risk factors.

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We introduce the disclosure-outcomes management model. The model views disclosure in intelligence interviews as a behaviour interviewees use to profitably navigate self-interest dilemmas. We theorized that interviewees compare the potential outcomes of disclosing to their self-interests. They evaluate the extent to which disclosure will facilitate or impede those self-interests: an interviewee\'s self-interest dilemma elicits cooperation with respect to some information but not other information. A Preliminary Study (N = 300) supported the model\'s predictions. We proposed a Replication Study (N = 369) to examine the model further. Participants assumed the role of an intelligence source undergoing an interview. They decided what information to disclose, contending the typical dilemma in an intelligence interview wherein disclosure could jeopardize or advance their self-interests. The results from the Preliminary and Replication studies were broadly in line with our proposition: perceived benefits positively influenced the likelihood of disclosing. However, a negative interaction between costs and benefits observed in the Preliminary Study did not replicate. That finding may be due to power constraints, not evidence against the existence of an interaction effect. Our proposal that-generally speaking-interviewees are likelier to disclose information units that seem less versus more risky requires further examination. Individual-level sensitivity to benefits, costs and their co-occurrence varied substantially in our studies. We discuss avenues for future research.

BACKGROUND: Health care in developing countries often lacks adequate bookkeeping and national cancer registries, means of information that have proven to impact disease research and care. The true burden of brain tumors therefore remains unchecked and so does the extent of the problem. Therefore, this study aims to explore the challenges and potential strategies related to information management of brain tumors in low- and middle-income countries (LMICs).
METHODS: A comprehensive literature search conducted using databases such as PubMed, Scopus, Google Scholar, and Cumulated Index in Nursing and Allied Health Literature, without any language restrictions, from inception to October 20, 2022. Following screening and extraction of data, themes were generated using the information management domain of the National Surgical, Obstetric, and Anesthesia Plan framework.
RESULTS: The final analysis includes 23 studies that highlighted the challenges to managing information to the surgical care given to brain tumors in LMICs, including lack of proper hospital record system (43%), lack of national brain tumor registry (67%), lack of local management guidelines (10%), and low research output (33%). Some of the proposed strategies in the literature to address these barriers include improving data management systems (45%), developing a population-based brain tumor registry (64%), and formulating local treatment guidelines (9%) for the management of brain tumors.
CONCLUSIONS: In LMICs, improving patient outcomes and quality of life postneurosurgical intervention for brain tumors requires coordinated efforts to enhance information systems. The support of the government and public health professionals is vital in implementing strategies to achieve this goal.

Efficient information management is pivotal for the effective execution of spatial planning projects, yet many encounter hurdles stemming from disjointed information systems and processes. In this article, authors consider the intricacies of information management within the realm of spatial planning, leveraging insights from the multifaceted MAKING-CITY project to elucidate common challenges and propose innovative solutions. The proposed conceptual model for an integrated information management system offers a holistic approach, aiming to eliminate weaknesses inherent in existing information landscapes by seamlessly integrating diverse information components into a unified framework. By facilitating robust communication channels and synchronizing the actions of all project participants and stakeholders, this model enhances collaboration, streamlines workflows, and fosters informed decision-making. Importantly, the adaptability and versatility of developed model ensure its applicability across various spatial planning initiatives, offering a scalable solution to address the evolving demands of urban development projects. Through a meticulous examination of real-world challenges and practical solutions, this article contributes to the advancement of information management practices in spatial planning, laying the groundwork for more efficient, sustainable, and inclusive urban development processes in today\'s dynamic landscape.

Internationally, the quality of death certification is poor although there are multiple efforts underway to improve the process. In England, a new medical certification system has been proposed to improve the quality of data. We surveyed general practitioners (n = 95) across the West Yorkshire area of England to appraise their views regarding whether further possible changes to the death certification system could promote their quality.

The impact of contracting coronavirus on healthcare providers (HCPs) affects their ability to combat the infection. The virus can be transmitted through droplets from sneezing, coughing, and yelling, making it essential for HCPs to plan ahead when dealing with patients with respiratory symptoms. The need to assess healthcare providers\' perceived adherence to COVID-19 Prevention and Control Practices (PCP) in Health Records and Information Management is vital for optimizing healthcare operations and ensuring the safety of both patients and providers. This study assesses healthcare providers\' perceived adherence to COVID-19 PCP in Health Records and Information Management. Subjects and Method. A cross-sectional survey was conducted to collect data from 1268 HCPs working in eight randomly selected hospitals across five regions in Ghana. The survey was carried out from May 15, 2022, to August 13, 2022. Simple random sampling was used to choose these eight facilities from a total of 204 hospitals. Within each facility, HCPs from various departments were selected using simple random sampling. The EpiInfo 7 software\'s StatCalc tool was used to choose a total sample size of 1268 from an estimated 4482 HCP-PR from the eight hospitals. Compliance with COVID-19 PCP was assessed using a 3-point scale, ranging from one (Yes always) to three (No). Cronbach\'s alpha reliability coefficient was used to examine the statistical reliability of the variables in the dataset. Cronbach\'s alpha was 0.73 overall, suggesting strong reliability. Bartlett\'s test for equal variances was used for comparative analysis of health facility and overall mean COVID-19 PCP in different areas of health facilities. IBM SPSS (version 23) statistical software was used for the data analysis process.
A total of 1268 HCP-PR participated in the survey, resulting in a 99.6% response rate. Findings reveal that 760 healthcare professionals who handle patients\' records (HCP-PR), constituting 60%, consistently followed COVID-19 protocols in the registration and clinic preparation zones. Another 390 individuals (30.7%) adhered to these protocols occasionally, while 119 (9.4%) failed to comply. Similarly, in the filing area, 739 respondents (58.3%) consistently adhered to COVID-19 protocols, 358 (28.3%) occasionally did so, and 170 (13.4%) did not follow the protocols at all. Regarding handling health records cautiously, 540 participants (42.5%) always did, 448 (35.3%) did so sometimes, and 280 (22.2%) neglected these precautions. Additionally, 520 respondents (41.0%) consistently followed COVID-19 precautions when handling computers and other equipment, 393 (31.0%) did so occasionally, and 355 (28.0%) did not adhere to these precautions.
The majority of respondents showed good compliance with COVID-19 protocol in the registration and clinic preparation areas. However, in the filing area, just over four out of every seven respondents consistently adhered to COVID-19 PCP. Additionally, four out of every seven participants did not comply with COVID-19 PCP when handling patients\' records. Analysis reveals diverse adherence to COVID-19 PCP, and statistical tests show variable performance, highlighting standout health facilities.