Decision Support Research Articles

Artificial intelligence applications in ophthalmic surgery.

Technologies in healthcare incorporating artificial intelligence tools are experiencing rapid growth in static-image-based applications such as diagnostic imaging. Given the proliferation of artificial intelligence (AI)-technologies created for video-based imaging, ophthalmic microsurgery is likely to experience significant benefits from the application of emerging technologies to multiple facets of the care of the surgical patient. Proof-of-concept research and early phase clinical trials are in progress for AI-based surgical technologies that aim to provide preoperative planning and decision support, intraoperative image enhancement, surgical guidance, surgical decision-making support, tactical assistive technologies, enhanced surgical training and assessment of trainee progress, and semi-autonomous tool control or autonomous elements of surgical procedures. The proliferation of AI-based technologies in static imaging in clinical ophthalmology, continued refinement of AI tools designed for video-based applications, and development of AI-based digital tools in allied surgical fields suggest that ophthalmic surgery is poised for the integration of AI into our microsurgical paradigm.

Decision Support Framework for the Choice of Delay Analysis Techniques Used in Extension of Time Claims

Decision Support Framework for the Choice of Delay Analysis Techniques Used in Extension of Time Claims

Ethical considerations for large language models in ophthalmology.

This review aims to summarize and discuss the ethical considerations regarding large language model (LLM) use in the field of ophthalmology. This review of 47 articles on LLM applications in ophthalmology highlights their diverse potential uses, including education, research, clinical decision support, and surgical assistance (as an aid in operative notes). We also review ethical considerations such as the inability of LLMs to interpret data accurately, the risk of promoting controversial or harmful recommendations, and breaches of data privacy. These concerns imply the need for cautious integration of artificial intelligence in healthcare, emphasizing human oversight, transparency, and accountability to mitigate risks and uphold ethical standards. The integration of LLMs in ophthalmology offers potential advantages such as aiding in clinical decision support and facilitating medical education through their ability to process queries and analyze ophthalmic imaging and clinical cases. However, their utilization also raises ethical concerns regarding data privacy, potential misinformation, and biases inherent in the datasets used. Awareness of these concerns should be addressed in order to optimize its utility in the healthcare setting. More importantly, promoting responsible and careful use by consumers should be practiced.

Precision medicine in hepatology: harnessing IoT and machine learning for personalized liver disease stage prediction

<span>In this research, we used a dataset from Siksha ‘O’ Anusandhan (S’O’A) University Medical Laboratory containing 6,780 samples collected manually and through internet of things (IoT) sensor sources from 6,780 patients to perform a thorough investigation into liver disease stage prediction. The dataset was carefully cleaned before being sent to the machine learning pipeline. We utilised a range of machine learning models, such as Naïve Bayes (NB), sequential minimal optimisation (SMO), K-STAR, random forest (RF), and multi-class classification (MCC), using Python to predict the stages of liver disease. The results of our simulations demonstrated how well the SMO model performed in comparison to other models. We then expanded our analysis using different machine learning boosting models with SMO as the base model: adaptive boosting (AdaBoost), gradient boost, extreme gradient boosting (XGBoost), CatBoost, and light gradient boosting model (LightGBM). Surprisingly, gradient boost proved to be the most successful, producing an astounding 96% accuracy. A closer look at the data showed that when AdaBoost was combined with the SMO base model, the accuracy results were 94.10%, XGBoost 90%, CatBoost 92%, and LightGBM 94%. These results highlight the effectiveness of proposed model i.e. gradient boosting in improving the prediction of liver disease stage and provide insightful information for improving clinical decision support systems in the field of medical diagnostics.</span>

Computerized Decision Support Systems Informing Community-Acquired Pneumonia Surveillance, France, 2017-2023.

We show the value of real-time data generated by a computerized decision support system in primary care in strengthening pneumonia surveillance. The system showed a 66% (95% CI 64%-67%) increase in community-acquired pneumonia from 2018 to 2023 for the population of France, 1 month before a national alert was issued.

Uso del framework de ISTQB para aplicaciones desarrolladas en una entidad bancaria: Caso de uso “Portabilidad de Nómina”

This paper refers to quality control through software testing, by managing the fundamental elements that involve the International Software Testing Qualifications Board (ISTQB), using the Extreme Programming (XP) methodology. The purpose of this paper is to implement a test plan strategy for the verification and validation of the processes that are being carried out for Payroll Portability in the Financial Institution, which allows the identification of artifacts and/or documents that support decision-making for senior management, obtaining as a product in the test control process both individually and as a development team.

Machine learning techniques for diagnosis of rare diseases from medical images

Introduction/Background: Rare diseases are life-threatening or chronically debilitating conditions that affect a small percentage of the population. Early and accurate diagnosis of rare diseases is challenging due to their complexity and limited understanding. Conventional diagnostic methods are often inadequate, time-consuming, and expensive. Machine learning (ML) has emerged as a promising technique for the analysis of medical images to support the diagnosis of various diseases including rare disorders. ML algorithms can learn complex patterns from large medical imaging datasets to aid clinicians in disease diagnosis, prognosis, and detection of complications. Materials and Methods: A structured search was conducted in electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar to identify peer-reviewed articles published between 2013 to 2023 related to ML-based diagnosis of rare diseases from medical images. A total of 187 articles were identified after removing duplicates. The titles and abstracts of retrieved articles were screened to determine their relevance based on the research objective. Finally, 51 full-text articles were selected for the final review. The key data extracted from selected studies included diseases, imaging modalities, ML algorithms, performance metrics, datasets, and limitations. Results: Most studies evaluated deep learning-based ML techniques, with convolutional neural networks (CNN) being the most applied algorithm. CNNs were mainly used to classify rare diseases based on specific imaging features in X-rays, CT, and MRI scans. Diseases frequently investigated included cardiovascular, neurological, and rare genetic disorders. Publicly available datasets such as Deep Lesion, MSD, and ChestX-ray14 were commonly utilized. Most studies reported high classification accuracy ranging from 80-95% on test datasets. However, limited generalizability due to small private datasets and lack of external validation were limitations. Discussion: The results demonstrate the potential of deep learning for the image-based diagnosis of rare diseases. Features learned from large imaging data sets enabled CNNs to distinguish subtle abnormalities. Hybrid models combining CNNs with other techniques like recurrent neural networks further improved performance. Overall, ML showed promise as a decision support tool. However, more multi-center validation studies are needed using larger and diverse datasets before clinical adoption. Standardization of performance metrics and reporting is also required to establish reliability and generalizability. Conclusion: This comprehensive review provided insights into ongoing research applying ML to medical images for rare disease diagnosis. While initial results are encouraging, further work is still necessary before ML can be reliably used in clinical decision making. Larger collaborative efforts and open-source datasets are needed to advance the field. Standardization of ML frameworks can also promote reproducible research and comparison of different methodologies.

Advancements in health information technology and their role in enhancing cancer care: Innovations in early detection, treatment, and data privacy

The rapid advancement of Health Information Technology (HIT) has revolutionized cancer care, introducing new capabilities in early detection, individualized treatment, and data security. This paper reviews the key HIT innovations that have reshaped oncology, including the widespread adoption of Electronic Health Records (EHRs), the use of artificial intelligence (AI) in diagnostic processes, and the application of telemedicine and predictive analytics to improve treatment planning. These technologies have enabled earlier and more accurate diagnoses, expanded access to care, and facilitated data-driven, personalized treatment strategies. In parallel, this review addresses the importance of safeguarding sensitive patient data, focusing on encryption, anonymization techniques, and the regulatory frameworks like HIPAA and GDPR that ensure data privacy. Finally, the paper discusses future directions for HIT, particularly the potential of AI-driven decision support systems, real-time data analytics, and privacy-enhancing algorithms to further advance cancer care, creating a more secure, efficient, and patient-centered healthcare system.

Utilization of big data analytics to identify population health trends and optimize healthcare delivery system efficiency

Big data analytics has emerged as an incredibly valuable tool in understanding population health trends and improving the effectiveness of healthcare delivery systems. By leveraging big data sources from various domains, including the patients’ electronic health records, claims data, wearables, and social media accounts, healthcare organizations can obtain novel and rich insights regarding population health characteristics, predisposing factors, and disease progression. The arrival of big data has transformed healthcare organizations by providing a scientific population health management and health system enhancement tool. Using superior analytical tools, healthcare entrepreneurs and policymakers can discover relations, rates, and patterns that are concealed in large data sets. Such knowledge can be used for early detection of possible interventions, management of resources, and prevention measures, thus leading to better health and less spending on health issues. Moreover, big data analytics helps in early diagnostics and the development of management strategies for high-risk groups, which in turn improves the functioning and effectiveness of healthcare systems. It is also important to note that big data solutions in healthcare are not limited to population health management, but also include functional aspects of healthcare organizations. Additionally, using data about patient movements, resource consumption, and clinical activity, it is possible to determine inefficiencies and opportunities to improve processes in healthcare organizations. This approach of collecting and analyzing data helps in decision making thus reducing time and improving patient flow and experience. However, incorporating real-time data into the clinical decision support systems can improve diagnostic capabilities, treatments offered, and patient tracking resulting in improved quality of services delivered.

Design and Implementation of a 7-Person Voting Device based on Quartus

As the complexity of digital system design continues to increase, efficient and precise decision support systems have become increasingly important. This paper presents the design and implementation of a 7-voter decision system based on Altera’s Quartus software. The voter is applied in the core decision-making process to ensure the accuracy and efficiency of decisions. The study begins by outlining the design principles of the 7-voter decision system, using a design philosophy that minimizes timing delays and optimizes hardware resource utilization. A complex majority voting logic circuit is implemented using high-level hardware description languages (VHDL/Verilog), ensuring the advanced nature and applicability of the voting circuit. In terms of the logical implementation of the voting circuit, the paper conducts an in-depth analysis of the optimization of combinational and sequential logic and standardizes the input and output to enhance the signal stability and the system’s resistance to interference. On the Quartus platform, the design process of the voting system is elaborated in detail, with careful consideration given to boundary conditions and hardware compatibility. The implementation of the voter has undergone rigorous simulation and testing. Strict parameter setting for simulations and waveform analysis confirm the correctness of the circuit, and fault simulation tests further confirm its reliability. Performance indicators such as response time and percentage of resource utilization have been quantitatively analyzed and meet the design objectives set beforehand. Overall, this research not only realizes a highperformance 7-voter decision system but also provides an effective case study for applications on the high-performance Quartus design platform. Future research can build on the current work to further optimize algorithms and hardware, explore the potential for the application of the voter in a broader range of fields, and adapt to the new features of the Quartus platform to achieve more powerful decision support functions.

Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG

This work reports an efficient method to detect SARS-CoV-2 antibodies in blood samples based on SERS combined with a machine learning tool. For this purpose, gold nanoparticles directly conjugated with spike protein were used in human blood samples to identify anti-SARS-CoV-2 antibodies. The comprehensive database utilized Raman spectra from all 594 blood serum samples. Machine learning investigations were carried out using the Scikit-Learn library and were implemented in Python, and the characteristics of Raman spectra of positive and negative SARS-CoV-2 samples were extracted using the Uniform Manifold Approximation and Projection (UMAP) technique. The machine learning models used were k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Decision Trees (DTs), logistic regression (LR), and Light Gradient Boosting Machine (LightGBM). The kNN model led to a sensitivity of 0.943, specificity of 0.9275, and accuracy of 0.9377. This study showed that combining Raman spectroscopy and a machine algorithm can be an effective diagnostic method. Furthermore, we highlighted the advantages and disadvantages of each algorithm, providing valuable information for future research.

Strengths and weaknesses of computerized clinical decision support systems: insights from a digital control center (C3 COVID-19) for early and personalized treatment for COVID-19.

Clinical Decision Support Systems (CDSS) are computer-based tools that leverage the analysis of large volumes of health data to assist healthcare professionals in making clinical decisions, whether preventive, diagnostic, or therapeutic. This review examines the impact of CDSS on clinical practice, highlighting both their potential benefits and their limitations and challenges. We detail the experience of clinical medical professionals in the development of a virtual control center for COVID-19 patients (C3 COVID-19) in Spain during the SARS-CoV-2 pandemic. This tool enabled real-time monitoring of clinical data for hospitalized COVID-19 patients, optimizing personalized and informed medical decision-making. CDSS can offer significant advantages, such as improving the quality of inpatient care, promoting evidence-based clinical and therapeutic decision-making, facilitating treatment personalization, and enhancing healthcare system efficiency and productivity. However, the implementation of CDSS presents challenges, including the need for physicians to become familiar with the systems and software, and the necessity for ongoing updates and technical support of the systems.

The Investigation of the Application of RAG Technology in the Field of EHR

Electronic Health Records (EHR) play a crucial role in contemporary medical information systems, capturing extensive data in multiple scenarios. This review explores the impact of Retrieval Augmented Generation (RAG) on managing EHR. By integrating retrieval and generation processes, RAG significantly enhances data handling, clinical decision support, and patient management. EHRs contain extensive data like patient histories and diagnostic information, which are becoming increasingly complex. The RAG improves the accuracy and richness of this data processing by using a retrieval module to extract relevant text fragments and a generation module to create precise outputs. Despite its potential, RAG faces challenges such as data inconsistency, privacy concerns, and the need for efficient training. Recent studies highlight the RAG’s effectiveness in summarizing clinical notes and enhancing prediction accuracy, suggesting a promising future in healthcare. However, ongoing research is necessary to optimize the RAG’s application and address these challenges, aiming to transform healthcare delivery effectively.

Health Catch-UP!: a realist evaluation of an innovative multi-disease screening and vaccination tool in UK primary care for at-risk migrant patients

BackgroundMigrants to the UK face disproportionate risk of infections, non-communicable diseases, and under-immunisation compounded by healthcare access barriers. Current UK migrant screening strategies are unstandardised with poor implementation and low uptake. Health Catch-UP! is a collaboratively produced digital clinical decision support system that applies current guidelines (UKHSA and NICE) to provide primary care professionals with individualised multi-disease screening (7 infectious diseases/blood-borne viruses, 3 chronic parasitic infections, 3 non-communicable disease or risk factors) and catch-up vaccination prompts for migrant patients.MethodsWe carried out a mixed-methods process evaluation of Health Catch-UP! in two urban primary healthcare practices to integrate Health Catch-UP! into the electronic health record system of primary care, using the Medical Research Council framework for complex intervention evaluation. We collected quantitative data (demographics, patients screened, disease detection and catch-up vaccination rates) and qualitative participant interviews to explore acceptability and feasibility.ResultsNinety-nine migrants were assessed by Health Catch-UP! across two sites (S1, S2). 96.0% (n = 97) had complete demographics coding with Asia 31.3% (n = 31) and Africa 25.2% (n = 25), the most common continents of birth (S1 n = 92 [48.9% female (n = 44); mean age 60.6 years (SD 14.26)]; and S2 n = 7 [85.7% male (n = 6); mean age 39.4 years (SD16.97)]. 61.6% (n = 61) of participants were eligible for screening for at least one condition and uptake of screening was high 86.9% (n = 53). Twelve new conditions were identified (12.1% of study population) including hepatitis C (n = 1), hypercholesteraemia (n = 6), pre-diabetes (n = 4), and diabetes (n = 1). Health Catch-UP! identified that 100% (n = 99) of patients had no immunisations recorded; however, subsequent catch-up vaccination uptake was poor (2.0%, n = 1). Qualitative data supported acceptability and feasibility of Health Catch-UP! from staff and patient perspectives, and recommended Health Catch-UP! integration into routine care (e.g. NHS health checks) with an implementation package including staff and patient support materials, standardised care pathways (screening and catch-up vaccination, laboratory, and management), and financial incentivisation.ConclusionsClinical Decision Support Systems like Health Catch-UP! can improve disease detection and implementation of screening guidance for migrant patients but require robust testing, resourcing, and an effective implementation package to support both patients and staff.

Development and implementation of a guideline-based decision support system for etiological diagnosis of Sustained Monomorphic Ventricular Tachycardia (SMVT)

Abstract Background Guideline (GL)-based decision support systems (DSSs) play a significant role in supporting physicians in their decision-making processes by providing structured clinical guidance. While these systems have shown significant success in enhancing practitioner performance and patient outcomes in general practice, their application in the context of ventricular arrhythmias remains limited. Moreover, the clinical etiological diagnosis of ventricular arrhythmias presents various challenges for healthcare professionals, such as adherence to updated diagnostic criteria and understanding of complex arrhythmia mechanisms. Purpose In this study, we aim to design, implement, and evaluate a guideline-based decision support systems for etiological diagnosis of sustained monomorphic ventricular tachycardia (SMVT), which could potentially address critical challenges associated with the disease diagnosis and management. Methods To comprehensively capture the semantics embedded in clinical guidelines, we first developed a dedicated knowledge graph tailored to the clinical decision-making. This involved extracting entities and relationships pertinent to key decision procedures from ESC guidelines for ventricular arrhythmias, clinical textbooks or literatures. Subsequently, we translated instances within the knowledge graph into archetypes within openEHR, amplifying the interoperability of clinical information. To enhance the interpretability of the DSS, we established executable diagnostic and treatment processes to offer visual representation of clinical steps in diagnosis and treatment. Ultimately, we constructed a decision support engine to implement the GL-DSS, and its effectiveness was evaluated with anonymous real-world data. Results The proposed GL-DSS was found to be quite feasible and could provide etiological diagnosis for SMVT. The knowledge base for the constructed GL-DDS included more than 2000 entities and relations, which played a vital role in representing disease pathogenesis. The proposed system can map the clinical entities to a set of openEHR archetypes, thus mitigate the interoperability issues of the hospital information systems and CDSS data integration. Also, this module improved system usability and ensured that guidelines could be easily translated and updated into decision support tools. Real-word medical records from 20 patient were collected for model evaluation, the results showed that the GL-DSS covered most of the entities and relations between diseases and diagnostic evidence recorded in clinical texts and achieved a diagnosis accuracy of 80%. Conclusions The results demonstrated that our proposed GL-DSS could completely and accurately capture the diagnostic information under the concrete EHR data. Thus, the implement decision support system could have the potential to improve clinical decision-making, patient care, and outcomes in this specialized medical domain.

Predicting High-Flow Nasal Cannula Oxygen Therapy Failure in Patients With Acute Hypoxaemic Respiratory Failure Using Machine Learning: Model Development and External Validation.

To develop and validate a prediction model for high-flow nasal cannula (HFNC) failure in patients with acute hypoxaemic respiratory failure (AHRF). AHRF accounts for a major proportion of intensive care unit (ICU) admissions and is associated with high mortality. HFNC is a non-invasive respiratory support technique that can improve patient oxygenation. However, HFNC failure, defined as the need for escalation to invasive mechanical ventilation, can lead to delayed intubation, prolonged mechanical ventilation and increased risk of mortality. Timely and accurate prediction of HFNC failure has important clinical implications. Machine learning (ML) can improve clinical prediction. Multicentre observational study. This study analysed 581 patients from an academic medical centre in Boston and 180 patients from Guangzhou, China treated with HFNC for AHRF. The Boston dataset was randomly divided into a training set (90%, n = 522) and an internal validation set (10%, n = 59), and the model was externally validated using the Guangzhou dataset (n = 180). A random forest (RF)-based feature selection method was used to identify predictive factors. Nine machine learning algorithms were selected to build the predictive model. The area under the receiver operating characteristic curve (AUC) and performance evaluation parameters were used to evaluate the models. The final model included 38 features selected using the RF method, with additional input from clinical specialists. Models based on ensemble learning outperformed other models (internal validation AUC: 0.83; external validation AUC: 0.75). Important predictors of HFNC failure include Glasgow Coma Scale scores and Sequential Organ Failure Assessment scores, albumin levels measured during HFNC treatment, ROX index at ICU admission and sepsis. This study developed an interpretable ML model that accurately predicts the risk of HFNC failure in patients with AHRF. Clinicians and nurses can use ML models for early risk assessment and decision support in AHRF patients receiving HFNC. TRIPOD checklist for prediction model studies was followed in this study. Patients were involved in the sample of the study.

Multi-attribute-based procedure for seismic loss scenario in a historical area

AbstractSeismic events have shown to be exceedingly damaging to structures over time, with serious social and economic consequences. As a result, large-scale seismic risk assessments are essential for reducing the potential damage from future earthquakes. Therefore, the proposed study attempts to examine the vulnerability and risk of unreinforced masonry buildings (URM) placed in aggregate conditions in a historical area of the city centre of Lisbon. To this purpose, a comprehensive exposure model was developed combining satellite remote sensing, GIS software, and census data. Subsequently, seismic hazard was evaluated in the area, considering both Peak Ground Acceleration (PGA) and macro-seismic intensity for different return periods (i.e. 2-50-100-475-975-2500-5000 years). Vulnerability was assessed by introducing a novel approach to earthquake risk assessment using Multi-Criteria Decision-Making methodologies. Specifically, the method employs the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) computational methods to evaluate parameter weights and vulnerability index. Damage scenarios, fragility curves and mean damage ratios curves were provided to offer an overview of the vulnerability of the assets exposed to risk. Finally, the expected consequences were evaluated in terms of direct economic losses showing an economic loss of 595 M€ for a 475-years return period, while 1108 M€ for 5000-years. This study significantly enhances seismic analysis for urban areas by introducing a Multi-Criteria Approach. This method simplifies vulnerability assessment, ensuring ease of application and reproducibility. Its insights offer valuable support for disaster risk management decisions, facilitating the implementation of resilience and risk-reduction strategies.

GPT models classify free text from emergency department accesses in children with medical complexity

Abstract Background Emergency Department (ED) management software often collects clinical information as free text, but its unstructured nature limits routine use. While Machine Learning (ML) techniques have been employed to classify such information, they demand substantial data and computational resources. GPT-based models, using direct text prompts, offer a solution without requiring retraining, potentially simplifying clinical practice. Aims To assess OpenAI GPT’s performance in automating data extraction from ED medical records of complex pediatric patients. Methods Electronic admission records of children and young adults referring to the pediatric ED of Padova University Hospital (2007-2023) were analyzed. Principal variables of interest were discharge diagnosis, reason for ED access, required medical procedures, color code at triage, color code at discharge/hospitalization, outcome of the access. GPT-4 classification, via OpenAI API, was compared with manual classification by an expert pediatrician on sample of 791 records. Results Preliminary results of this study analyzed 107 records, showing a correct classification of discharge diagnosis in 95% of records. The reason for access to the ED was correctly identified in 97% of cases, while outcome of admission and color code, both at triage and discharge/hospitalization, were correctly classified in all given cases. Misclassification occurred mainly in cases where classification made by the model was incomplete (e.g., in the case of a trauma, abbreviated site of lesion was not identified by the model). Conclusions GPT-based models demonstrate feasibility, accuracy, and efficiency in transforming unstructured data into structured formats. Ultimately, GPT-based models could provide aid to the medical personnel in decision-making processes. Key messages • Emergency departments often collect clinical data in unstructured free text format, resulting in usability limitations. • GPT-powered models efficiently convert unstructured data to structured formats, offering potential support for medical decision-making.

Prediction of catchment efficiency in direct energy deposition using dimensional analysis and machine learning

Direct Energy Deposition (DED) is an additive manufacturing process used to fabricate thin/thick-walled structures and high-value component restorations. As blown powder-based DED gains prominence, catchment efficiency becomes imperative for enhancing material utilization and process effectiveness. This study focuses on predicting catchment efficiency within a DED process by investigating four significant process parameters: laser power, powder feed rate, carrier gas flow rate, and deposition speed. The primary objective is to devise a dimensionless number capable of predicting catchment efficiency based on a combination of these parameters, categorizing efficiency into low, medium, and high regions. This approach reduces the need for resource-intensive experimentation. The experimental validation of the proposed dimensionless number was conducted, showing an error rate of around 8%. Additionally, six machine learning classifier algorithms – Support Vector Machines, K-Nearest Neighbours, Decision Tree, Random Forest, Linear Regression, and Gaussian Naive Bayes – were employed to categorize catchment efficiency zones. Support Vector Machine demonstrated the best performance with a predictive accuracy of 0.98. This study provides a methodology for catchment efficiency prediction, enhancing decision-making and resource optimization, and can be used to optimize process parameters for thin-wall or relatively thin-wall fabrication.

Pharmacologic treatment of hypertension guided by non-invasive haemodynamics in primary care improves blood pressure control

Abstract Background Current guidelines on hypertension (HTN) provide blood pressure (BP) targets and therapeutic algorithms but ignore the potential to treat individual patients according to their haemodynamic profile. We have shown that use of non-invasive haemodynamic information obtained by impedance cardiography (ICG) can improve BP control in the primary care setting. Purpose To investigate whether improved rates of BP control by matching patients’ non-invasive haemodynamics to specific pharmacologic treatments and increasing patient engagement translates into improved clinical outcome measures. Methods Electronic medical records (EMR) were obtained for 14,698 patients from two US primary care groups between 2014 and 2023. ICG was done when BP exceeded 140/90 mmHg. Haemodynamic measurements included mean arterial pressure (MAP), cardiac index (CI), cardiac power index (CPI), total peripheral resistance index (TPRI), stroke index (SI) and heart rate (HR) (Figure 1). Haemodynamic ICG profiles were classified as: Vasoconstriction, Hyperdynamic, or Mixed pattern. Printed clinical decision support (CDS) tools and later an EMR integrated CDS application were used to communicate recommendations to physicians. Printed reports were shared with patients to increase their engagement. Pharmacological therapy was tailored based on haemodynamic information. Rates of successful BP treatment and the occurrence of myocardial infarction (MI) or stroke were tracked through the EMR. Results Of 14,698 patients, 50% were women, 17% were Black and 17% were aged 18-49 years, 45% aged 50-69 years, and 38% age ≥ 70 years. A total of 21,246 ICGs were performed classifying patients as Vasoconstriction 47%, Hyperdynamic with high heart rate (HR) or high stroke index (SI) 24%, and Mixed Haemodynamic (combination of Vasoconstriction and Hyperdynamic) 29% (Figure 1). Demographic features were not strongly associated with haemodynamic classification. Within 2-3 years of inclusion, >85% of patient achieved BP control (<140/90 mmHg), which was sustained during follow-up. Over a median (IQR) follow-up of 8 (2016-2023) years, there were 14 (5) MI and 34 (16) strokes. These event rates appear to be lower than those reported in other large cohorts of patients with HTN. Conclusions Treating patients with HTN according to their individual haemodynamic profile is associated with high rates of BP control. Further analysis will determine whether the rates of MI, stroke and death are lower than expected based on the patients baseline risk profile.Haemodynamic Status (ICG Test Results)