Medical Informatics Research Articles

Abstract B147: Avanzando Equidad de Salud: Latino Cancer Health Equity Research Center

Abstract South Texas (STX), spanning from San Antonio to the Texas-Mexico border, includes 25 rural counties, with 96% designated as primary care shortage areas, are notably the nation’s poorest and most medically underserved regions in the United States. Hispanic/Latinos (H/Ls) in the U.S. face challenges related to social determinants of health (SDoH), contributing to disparities in cancer prevention, diagnosis, treatment, and outcomes. H/Ls have higher risks of invasive cancers compared to peers, emphasizing the critical need to address SDoH to mitigate cancer disparities. Addressing these causes can improve access to healthcare, promote early detection, enhance patient education, and provide culturally competent care. The Avanzando Center (AC) aims to reduce health disparities across STX with community-engaged research, training, patient assessment, and advocacy that addresses the SDoH that deter Latinos from equitable access to cancer care, prevention, early detection, treatment, and improve patients’ cancer outcomes. To achieve its mission, the AC will 1) coordinate Research Scholars on SDoH-related cancer control projects, providing leadership, mentoring, and support in SDoH screening, clinical informatics, and outreach dissemination, 2) implement the SDoH Screener to screen patients at UT Health San Antonio’s Mays Cancer Center, Primary Care Center, and clinics across STX, ensuring they receive necessary support, 3) utilize the Salud America! (SA!) Latino health communications network for community education, empowering STX community members to advocate for local system and policy changes addressing structural SDoH, and 4) provide mentoring and career development in health equity (HE) and SDoH fundamentals to enhance cancer patient care and research advancements in the community. Research Scholars are actively employing interventions for 1) adolescent and young adult cancer survivors by establishing training programs for oncology providers to ensure proper guidance on vaccinations against HPV-related cancers, 2) using technology to improve treatment adherence among Latina breast cancer patients, and 3) addressing disparities in colorectal cancer screening and healthcare access among persistently impoverished populations. These studies will result in rigorous evaluation of intervention effectiveness by addressing the specific needs of cancer patients facing barriers related to SDoH and inform policy and practice initiatives to eliminate cancer as a major health concern. In addition, training and mentoring programs will enhance the knowledge and understanding of cancer care and research professionals, while the SA! Latino HE communication program to raise awareness about SDoH and cancer HE that includes blog posts featuring resources, inspirational stories, and promotional HE tools. The efforts focused on SDoH have the potential to significantly impact cancer outcomes and reduce disparities in Latino communities, ultimately advancing HE and improving overall well-being; specifically improving cancer outcomes, survivorship, and quality of life. Citation Format: Amelie G. Ramirez, Derek Rodriguez, Cliff Despres, Rebecca T. Jones, Ramon S. Cancino, Allison C. Grimes, Jonathan Gelfond, Byeongyeob Choi, Edgar Munoz, Patricia Chalela. Avanzando Equidad de Salud: Latino Cancer Health Equity Research Center [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B147.

Importance of variables from different time frames for predicting self-harm using health system data.

Self-harm risk prediction models developed using health system data (electronic health records and insurance claims information) often use patient information from up to several years prior to the index visit when the prediction is made. Measurements from some time periods may not be available for all patients. Using the framework of algorithm-agnostic variable importance, we study the predictive potential of variables corresponding to different time horizons prior to the index visit and demonstrate the application of variable importance techniques in the biomedical informatics setting. We use variable importance to quantify the potential of recent (up to three months before the index visit) and distant (more than one year before the index visit) patient mental health information for predicting self-harm risk using data from seven health systems. We quantify importance as the decrease in predictiveness when the variable set of interest is excluded from the prediction task. We define predictiveness using discriminative metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value. Mental health predictors corresponding to the three months prior to the index visit show strong signal of importance; in one setting, excluding these variables decreased AUC from 0.85 to 0.77. Predictors corresponding to more distant information were less important. Predictors from the months immediately preceding the index visit are highly important. Implementation of self-harm prediction models may be challenging in settings where recent data are not completely available (e.g., due to lags in insurance claims processing) at the time a prediction is made. Clinically derived variables from different time frames exhibit varying levels of importance for predicting self-harm. Variable importance analyses can inform whether and how to implement risk prediction models into clinical practice given real-world data limitations. These analyses be applied more broadly in biomedical informatics research to provide insight into general clinical risk prediction tasks.

Clinical informatics and molecular hybridization of established clinical DPP-4 inhibitors to generate next-level diabetes type 2 drugs

Clinical informatics and molecular hybridization of established clinical DPP-4 inhibitors to generate next-level diabetes type 2 drugs

Lessons Learned from the Zika Virus Epidemics: A Mounting Demand for Adopting Public Health Informatics Tools and Technologies for Epidemiological Surveillance and Outbreak Containment

Lessons Learned from the Zika Virus Epidemics: A Mounting Demand for Adopting Public Health Informatics Tools and Technologies for Epidemiological Surveillance and Outbreak Containment

Assessing the Capability of Advanced AI Models in Cardiovascular Symptom Recognition: A Comparative Study

The field of medical informatics has been significantly transformed in recent years with the emergence of Natural Language Understanding (NLU) and Large Language Models (LLM), providing new opportunities for innovative patient care solutions. This study aims to evaluate the effectiveness of publicly available LLMs as symptom checkers for cardiological diseases by comparing their diagnostic capabilities in real disease cases. We employed a set of 9 models, including ChatGPT-4, OpenSource models, Google PaLM 2, and Meta’s LLaMA, to assess their diagnostic accuracy, reliability, and safety across various clinical scenarios. Our methodology involved presenting these LLMs with symptom descriptions and test results in Spanish, requiring them to provide specialist diagnoses and recommendations in English. This approach allowed us to compare the performance of each model, highlighting their respective strengths and limitations in a healthcare context. The results revealed varying levels of accuracy, precision, and sensitivity among the models, demonstrating the potential of LLMs to enhance medical education and patient care. By analysing the capabilities of each model, our study contributes to a deeper understanding of artificial intelligence’s role in medical diagnosis. We argue for the strategic implementation of LLMs in healthcare, emphasizing the importance of balancing sensitivity and realism to optimize patient outcomes.

Recent advances in biometrics based on biomedical information (BioSMART)

Recent advances in biometrics based on biomedical information (BioSMART)

Modeling of joint extraction of entity relationships in clinical electronic medical records

The advancement of medical informatization necessitates extracting entities and their relationships from electronic medical records. Presently, research on electronic medical records predominantly concentrates on single-entity relationship extraction. However, clinical electronic medical records frequently exhibit overlapping complex entity relationships, thereby heightening the challenge of information extraction. To rectify the absence of a clinical medical relationship extraction dataset, this study utilizes electronic medical records from 584 patients in a hospital to create a compact clinical medical relationship extraction dataset. To address the pipelined relationship extraction model’s limitation in overlooking the one-to-many correlation problem between entities and relationships, this paper introduces a cascading relationship extraction model. This model integrates the MacBERT pre-training model, gated recurrent network, and multi-head self-attention mechanism to enhance the extraction of text features. Simultaneously, adversarial learning is incorporated to bolster the model’s robustness. In scenarios involving one-to-many relationships between entities, a two-phase task is employed. Initially, the main entity is predicted, followed by predicting the associated object and their correspondences. Employing this cascade-structured approach enables the model to flexibly manage intricate entity relationships, thereby enhancing extraction accuracy. Experimental results demonstrate the model’s efficiency, yielding F1-scores of 82.8%, 76.8%, and 88.2% for fulfilling relational extraction requirements and tasks on DuIE, CHIP-CDEE, and private datasets, respectively. These scores represent improvements over the benchmark model. The findings indicate the model’s applicability in practical domains, particularly in tasks such as biomedical information extraction.

Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.

Large language models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), potentially enabling new ways to analyze data, treat patients, and conduct research. This study aims to provide a comprehensive overview of LLM applications in BHI, highlighting their transformative potential and addressing the associated ethical and practical challenges. We reviewed 1698 research articles from January 2022 to December 2023, categorizing them by research themes and diagnostic categories. Additionally, we conducted network analysis to map scholarly collaborations and research dynamics. Our findings reveal a substantial increase in the potential applications of LLMs to a variety of BHI tasks, including clinical decision support, patient interaction, and medical document analysis. Notably, LLMs are expected to be instrumental in enhancing the accuracy of diagnostic tools and patient care protocols. The network analysis highlights dense and dynamically evolving collaborations across institutions, underscoring the interdisciplinary nature of LLM research in BHI. A significant trend was the application of LLMs in managing specific disease categories, such as mental health and neurological disorders, demonstrating their potential to influence personalized medicine and public health strategies. LLMs hold promising potential to further transform biomedical research and healthcare delivery. While promising, the ethical implications and challenges of model validation call for rigorous scrutiny to optimize their benefits in clinical settings. This survey serves as a resource for stakeholders in healthcare, including researchers, clinicians, and policymakers, to understand the current state and future potential of LLMs in BHI.

Special Issue on Informatics Education: Exploring the Impact of GitHub Copilot on Health Informatics Education.

The use of artificial intelligence-driven code completion tools, particularly the integration of GitHub Copilot with Visual Studio, has potential implications for Health Informatics education, particularly for students learning SQL and Python. This study aims to evaluate the effectiveness of these tools in solving or assisting with the solution of problems found in Health Informatics coursework, ranging from simple to complex. The study assesses the performance of GitHub Copilot in generating code for Health Informatics coding assignments from graduate classes, with a focus on the impact of detailed explanations on the tool's effectiveness. Findings reveal that GitHub Copilot can generate correct code for straightforward problems. The correctness and effectiveness of solutions decrease with problem complexity, and the tool struggles with the most challenging problems, although performance on complex problems improves with more detailed explanations. The study underscores the relevance of these tools to programming in Health Informatics education but also highlights the need for critical evaluation by students. It concludes with a call for educators to adapt swiftly to this rapidly evolving technology.

Results and implications for generative AI in a large introductory biomedical and health informatics course

Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50th and 75th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online.

Child Health and Mortality Prevention Surveillance (CHAMPS): Manhiça site description, Mozambique

The Manhiça Health Research Centre (Manhiça HDSS) was established in 1996 in Manhiça, a rural district at Maputo Province in the southern part of Mozambique with approximately 49,000 inhabited households, a total population of 209.000 individuals, and an annual estimated birth cohort of about 5000 babies. Since 2016, Manhiça HDSS is implementing the Child Health and Mortality Prevention Surveillance (CHAMPS) program aiming to investigate causes of death (CoD) in stillbirths and children under the age of 5 years using an innovative post-mortem technique known as Minimally Invasive Tissue sampling (MITS), comprehensive pathogen screening using molecular methods, clinical record abstraction and verbal autopsy. Both in-hospital and community pediatric deaths are investigated using MITS. For this, community-wide socio-demographic approaches (notification of community deaths by key informants, formative research involving several segments of the community, availability of free phone lines for notification of medical emergencies and deaths, etc.) are conducted alongside to foster community awareness, involvement and adherence as well as to compute mortality estimates and collect relevant information of health and mortality determinants. The main objective of this paper is to describe the Manhiça Health and Demographic Surveillance System (HDSS) site and the CHAMPS research environment in place including the local capacities among its reference hospital, laboratories, data center and other relevant areas involved in this ambitious surveillance and research project, whose ultimate aim is to improve child survival through public health actions derived from credible estimates and understanding of the major causes of childhood mortality in Mozambique.

Development of an Expert System for Diagnosing Musculoskeletal Disease

Musculoskeletal diseases (MSDs), encompasses various conditions affecting muscles, bones, tendons, ligaments, and joints, resulting to pain, inflammation, and limited mobility, significantly impacting individuals' quality of life. Diagnosing these diseases poses a challenge for healthcare professionals due to symptom similarities with other conditions. To address this, the development of expert systems tailored for musculoskeletal diagnosis has emerged as a promising approach to enhance clinical decision-making and improve patient outcomes. This study aims at developing and evaluating an expert system for musculoskeletal disease diagnosis, by leveraging a knowledge base containing information on common musculoskeletal diseases and symptoms. The system utilized a combination of rule-based and machine learning techniques to provide diagnostic recommendations to physicians. Comparative analysis with experienced physicians, using a dataset of patients with known musculoskeletal diseases, revealed the expert system’s diagnostic accuracy of 92%, recall of 98%, Precision of 91%, F1-Score of 94% and a quicker diagnosis compared to physicians. Additionally, the system demonstrated ease of use and user-friendliness. This project focuses on predictive algorithms, leveraging expert systems dating back to the 1970s, emulating human expert decision-making, particularly in disease diagnosis. The development of an expert system for musculoskeletal disease diagnosis symbolizes the convergence of medical expertise, computer science, and artificial intelligence. By integrating machine learning, natural language processing, and decision support systems, these expert systems have the potential to revolutionize musculoskeletal healthcare delivery. In conclusion, our results show that expert systems hold promise in transforming clinical practice and improving patient outcomes in musculoskeletal healthcare through interdisciplinary collaboration and continuous innovation.

Issue on Teaching and Training Future Health Informaticians:Partnering with Students to Develop a Capstone for a Graduate Health Informatics Program.

To assess the desirability, feasibility, and sustainability of integrating a project-based capstone course with the course-based curriculum of an interdisciplinary MSc health informatics program guided with a student-partnered steering committee and student-centered approach. We conducted an online cross-sectional survey (n=87) and three semi-structured focus groups (n=18) of health informatics students and alumni. Survey data was analyzed descriptively. Focus groups were audio-recorded and transcribed verbatim and then analyzed using a general inductive and classic analysis approach. Most students were supportive of including a capstone project but desired an option to work independently or within a group. Students perceived several benefits to capstone courses while concerned over perceived challenges to capstone implementation, evaluation, and managing group processes. Themes identified were: 1) professional development, identity, and career advancement; 2) emulating the real world and learning beyond the classroom, 3) embracing new, full circle learning, 4) anticipated course structure, delivery, and preparation, 5) balancing student choice, interests, and priorities, and 6) concerns over group dynamics, limitations, and support. This study demonstrates the value of having students as partners at each stage in the process from methods conception to course curriculum design. With the steering committee and the curriculum developer, we codeveloped a student-centered course that integrates foundational digital health-related project knowledge acquisition with an inquiry-based project which can be completed independently or in small groups. This study demonstrates the potential benefits and challenges that health informatics educators may consider when (re)-designing capstone courses.

Prompt Engineering Paradigms for Medical Applications: Scoping Review.

Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering-based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering-specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.

Enhancing Disease Detection in Electronic Medical Records: Integrating Human Expertise and Large Language Models with Application to Diabetes, Hypertension, and Acute Myocardial Infarction

ObjectiveElectronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs. MethodsWe developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts’ designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard. ResultsThe cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns. ConclusionThe proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs. ImplicationVariations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.

ISO Standards in Healthcare Organizations: Research Evolution and Trends from a Bibliometric Analysis

This study provides an overview of trends and evolution in International Organization for Standardization (ISO) implementation in healthcare over the past three decades, useful for researchers, practitioners, and policymakers in the sector. As an additional contribution, we propose the NU-index, which allows the evaluation of authors’ scientific output in a comprehensive way that will be valuable for researchers, institutions, and funding agencies seeking to assess researcher performance more accurately and understandably. A bibliometric analysis of 476 scientific articles from the Scopus Database was conducted, examining the scientific output, key authors, high-impact journals, and frequent keywords using VOSviewer, Bibliometrix library, and statistical analysis. Results reveal a significant increase in publications on ISO standards in healthcare since 2009, particularly in areas such as telemedicine, artificial intelligence, and information security. The emergence of ISO 11073 (health informatics) and ISO 13606 (electronic health records) highlights a growing trend towards information and communication technologies. Despite reported implementation barriers, such as lack of top management commitment and associated costs, adoption of these standards is increasing due to the demand for high-quality healthcare services and the need to comply with regulatory requirements.

Digital Competencies and Training Approaches to Enhance the Capacity of Practitioners to Support the Digital Transformation of Public Health: Rapid Review of Current Recommendations.

The COVID-19 pandemic highlighted gaps in the public health workforce's capacity to deploy digital technologies while upholding ethical, social justice, and health equity principles. Existing public health competency frameworks have not been updated to reflect the prominent role digital technologies play in contemporary public health, and public health training institutions are seeking to integrate digital technologies in their curricula. As a first step in a multiphase study exploring recommendations for updates to public health competency frameworks within the Canadian public health context, we conducted a rapid review of literature aiming to identify recommendations for digital competencies, training approaches, and inter- or transdisciplinary partnerships that can enhance public health practitioners' capacity to support the digital transformation of public health. Following the World Health Organization's (2017) guidelines for rapid reviews, a systematic search was conducted on Ovid MEDLINE, Ovid Embase, ERIC (Education Resources Information Center), and Web of Science for peer-reviewed articles. We also searched Google Scholar and various public health agency and public health association websites for gray literature using search terms related to public health, digital health, practice competencies, and training approaches. We included articles with explicit practice competencies and training recommendations related to digital technologies among public health practitioners published between January 2010 and December 2022. We excluded articles describing these concepts in passing or from a solely clinical perspective. Our search returned 2023 titles and abstracts, of which only 12 studies met the inclusion criteria. We found recommendations for new competencies to enable public health practitioners to appropriately use digital technologies that cut across all existing categories of the core competencies for public health framework of the Public Health Agency of Canada. We also identified a new competency category related to data, data systems management, and governance. Training approaches identified include adapted degree-awarding programs like combined public health and informatics or data science degree programs and ongoing professional certifications with integration of practice-based learning in multi- and interdisciplinary training. Disciplines suggested as important to facilitate practice competency and training recommendations included public health, public health informatics, data, information and computer sciences, biostatistics, health communication, and business. Despite the growth of digital technologies in public health, recommendations about practice competencies and training approaches necessary to effectively support the digital transformation of public health remain limited in the literature. Where available, evidence suggests the workforce requires new competencies that cut across and extend existing public health competencies, including new competencies related to the use and protection of new digital data sources, alongside facilitating health communication and promotion functions using digital media. Recommendations also emphasize the need for training approaches that focus on interdisciplinarity through adapted degree-awarding public health training programs and ongoing professional development.

Enhanced Pharmacy Service Delivery Through a Pharmacy Information System at a Medical Center: A Case Study.

A pharmacy information system (PIS) is a crucial tool for preventing errors and enhancing patient safety. This study aims to assess the influence of a PIS on workflow and medication errors (MEs) within the pharmacy department of a medical city in Saudi Arabia. The analysis encompasses an evaluation of the current workflow within pharmacy modules (in-patient, out-patient, and clinical pharmacy modules) and an exploration of associated challenges and inefficiencies. The study employed both qualitative and quantitative methodologies, incorporating structured interview techniques and the review of system records, such as logs, incident reports, and ME databases. Respondents recognized the benefits of the PIS, including the management of high-quality real-time data (26%), reduction of MEs (25%), acceleration of healthcare practices (23%), and absence of space-time constraints (14%). The study highlighted the role of PIS in addressing medication stock control and shortage issues, thereby improving overall medication availability. Furthermore, PIS significantly enhanced safety (93.3%), efficiency (80%), reliability, accuracy, ease of use (56.7%, 56.7%), and cost-effectiveness (53.7%). Following the implementation of the PIS, there was an increase in ME reporting, indicating improved error detection and management. The research revealed an increase in ME reporting rates in hospitals after PIS installation, attributed to improved medication management processes resulting in higher detection rates. As healthcare facilities increasingly embrace electronic medical records, the understanding of patient care quality will be significantly impacted. Consequently, further research is warranted to explore the role of PIS in reporting, detecting, analyzing, and mitigating MEs, as well as their influence on the work environment to promote a secure and efficient patient journey.

Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma

For the first time, mathematical models for evaluating biomedical images were obtained using fuzzy set methods on the basis of expert knowledge bases, thus making it possible to carry out complex qualitative diagnostics and increase the reliability and efficiency of diagnosis. A methodology for analyzing biomedical images was formed based on the fuzzy set apparatus, which allowed a fuller assessment of the level of disease for glaucoma patients. Aspects of the practical implementation of the optical–electronic system for biomedical information processing were considered. An algorithm and an optical–electronic system for biomedical image analysis are proposed, and they can be used to increase the informativeness and reliability in the diagnosis of eye pathologies and glaucoma in particular.

Healthcare Technology Management: Electronic Health Records and Informatics

The application of health informatics enables the creation of customised health interventions based on each person's requirements, preferences, and risk factors. Public health organisations can identify people who are at high risk for specific illnesses and provide tailored interventions to reduce risk factors and enhance outcomes by using data analytics and predictive modelling. Furthermore, via the use of social media platforms, digital health technologies, and personalised health interventions, health informatics enables public health organisations to interact with communities, encourage healthy lifestyles, and address health inequities. The importance of promoting teamwork, standardising data exchange procedures, and investing in workforce development is emphasised in this work. Public health management can meet these obstacles and fully utilise the advantages of health informatics to enhance population health outcomes.