Academic literature on the topic 'Critical care medicine Decision making Data processing'
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Journal articles on the topic "Critical care medicine Decision making Data processing"
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Eby, D., and J. Woods. "P052: The importance of structured ambulance radio patches during termination of resuscitation calls." CJEM 19, S1 (May 2017): S95. http://dx.doi.org/10.1017/cem.2017.254.
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Introduction: Pre-hospital telecommunication (patches) requires a special type of conversation. Receiving and processing correct information is critical when making clinical decisions, such as a termination of resuscitation (ToR). In a study of radio patches, a common patch structure emerged from the data analysis. Use of this standard structure resulted in shorter and less confusing patches. We sought to understand patch structure to be able to target interventions to improve the quality and efficiency of communication needed for critical clinical decisions. Methods: We undertook a retrospective analysis of all ToR patches between physicians and paramedics from 4 paramedic services, recorded by the Ambulance Dispatch Centre between Jan 01-Dec 31, 2014. Four services used Primary Care Paramedics and 1 service also used Advanced Care Paramedics. MP3 patch recording files were anonymized, transcribed, and read multiple times by the authors. Transcripts were coded and analyzed using mixed methods-quantitative descriptive statistics and qualitative thematic framework analysis. Results: The data set was 127 ToR patches-466 pages of transcripts. 116 patches (91.3%) had a standard structure (SS): participant introduction, clinical data presentation, clarification of data, making the decision, exchange of administrative information, and sign off. Paramedics used a mean of 81 words (95CI 74,88) to present the ‘clinical data’. Enough data was presented to meet ToR rule criteria in 52 cases (44.8%). Before making a decision to terminate resuscitation, physicians sought clarification in 100 cases (78.7%). After making the ToR decision, some physicians needed to justify their decision by seeking more data in 17 cases (13.4%). Exchange of non-clinical information (numbers, times, name spellings) took a mean of 200 words (95CI 172,228) and averaged 84 seconds or 35% of the average patch time. SS patches used a mean of 558 words, and lasted 234 sec (95CI 215,252). Non-SS patches used a mean of 654 words and lasted 286 sec (95CI 240,332). Conclusion: The most common patch structure consisted of participant introduction, data presentation, clarification of data, making the clinical decision, exchange of administrative information, and a sign off. Deviation from this SS resulted in longer patches. When a non-SS patch structure was used, the patching paramedic was tied up 25% longer and unavailable to provide patient care.
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Hueso, Miguel, Lluís de Haro, Jordi Calabia, Rafael Dal-Ré, Cristian Tebé, Karina Gibert, Josep M. Cruzado, and Alfredo Vellido. "Leveraging Data Science for a Personalized Haemodialysis." Kidney Diseases 6, no. 6 (2020): 385–94. http://dx.doi.org/10.1159/000507291.
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<b><i>Background:</i></b> The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed. <b><i>Summary:</i></b> Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis. <b><i>Key messages:</i></b> Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.
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Shah, Adnan Muhammad, Wazir Muhammad, and KangYoon Lee. "Examining the Determinants of Patient Perception of Physician Review Helpfulness across Different Disease Severities: A Machine Learning Approach." Computational Intelligence and Neuroscience 2022 (February 26, 2022): 1–15. http://dx.doi.org/10.1155/2022/8623586.
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(1) Background. Patients are increasingly using physician online reviews (PORs) to learn about the quality of care. Patients benefit from the use of PORs and physicians need to be aware of how this evaluation affects their treatment decisions. The current work aims to investigate the influence of critical quantitative and qualitative factors on physician review helpfulness (RH). (2) Methods. The data including 45,300 PORs across multiple disease types were scraped from Healthgrades.com. Grounded on the signaling theory, machine learning-based mixed methods approaches (i.e., text mining and econometric analyses) were performed to test study hypotheses and address the research questions. Machine learning algorithms were used to classify the data set with review- and service-related features through a confusion matrix. (3) Results. Regarding review-related signals, RH is primarily influenced by review readability, wordiness, and specific emotions (positive and negative). With regard to service-related signals, the results imply that service quality and popularity are critical to RH. Moreover, review wordiness, service quality, and popularity are better predictors for perceived RH for serious diseases than they are for mild diseases. (4) Conclusions. The findings of the empirical investigation suggest that platform designers should design a recommendation system that reduces search time and cognitive processing costs in order to assist patients in making their treatment decisions. This study also discloses the point that reviews and service-related signals influence physician RH. Using the machine learning-based sentic computing framework, the findings advance our understanding of the important role of discrete emotions in determining perceived RH. Moreover, the research also contributes by comparing the effects of different signals on perceived RH across different disease types.
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Bylone, Mary. "Effective Decision Making: Data, Data, and More Data!" AACN Advanced Critical Care 21, no. 2 (April 1, 2010): 130–32. http://dx.doi.org/10.4037/15597768-2010-2003.
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Goldman, Gilbert M., and Thyyar M. Ravindranath. "The Contextual Nature of Critical Care Judgment." Journal of Intensive Care Medicine 9, no. 2 (March 1994): 58–63. http://dx.doi.org/10.1177/088506669400900202.
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Critical care decision-making involves principles common to all medical decision-making. However, critical care is a remarkably distinctive form of clinical practice and therefore it may be useful to distinguish those elements particularly important or unique to ICU decision-making. The peculiar contextuality of critical care decision-making may be the best example of these elements. If so, attempts to improve our understanding of ICU decision-making may benefit from a formal analysis of its remarkable contextual nature. Four key elements of the context of critical care decisions can be identified: (1) costs, (2) time constraints, (3) the uncertain status of much clinical data, and (4) the continually changing environment of the ICU setting. These 4 elements comprise the context for the practice of clinical judgment in the ICU. The fact that intensivists are severely constrained by teh context of each case has important ramifications both for practice and for retrospective review. During retrospective review, the contextual nature of ICU judgment may be unfairly neglected by ignoring one or more of the key elements. Such neglect can be avoided if intensivists demand empathetic evaluation from reviewers.
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Saravanan, Pratima, and Jessica Menold. "Developing an Evidence-Based Clinical Decision-Support System to Enhance Prosthetic Prescription." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 10, no. 1 (June 2021): 121–25. http://dx.doi.org/10.1177/2327857921101116.
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With the rapid increase in the global amputee population, there is a clear need to assist amputee care providers with their decision-making during the prosthetic prescription process. To achieve this, an evidence-based decision support system that encompasses existing literature, current decision-making strategies employed by amputee care providers and patient-specific factors is proposed. Based on an extensive literature review combined with natural language processing and expert survey, the factors influencing the current decision-making of amputee care providers in prosthetic prescription were identified. Following that, the decision-making strategies employed by expert and novice prosthetists were captured and analyzed. Finally, a fundamental understanding of the effect gait analysis has on the decision-making strategies of prosthetists was studied. Findings from this work lay the foundation for developing a real-time decision support system integrated with a portable gait analysis tool to enhance prescription processes. This is critical in the low-income countries where there is a scarcity of amputee care providers and resources for an appropriate prescription.
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Larionova, Regina. "INTELLIGENT DECISION-MAKING SUPPORT ALGORITHMS FOR HEALTH-CARE INSTITUTIONS." Applied Mathematics and Control Sciences, no. 1 (April 14, 2021): 81–94. http://dx.doi.org/10.15593/2499-9873/2021.1.05.
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The aim of the article is to develop algorithms of intellectual support for managerial decision-making in a preventive medical institution by medical staff on loading/reserving the number of patients to be served. The article substantiates the relevance of improving the mechanisms of management of non-stationary processes of medical services by health care institution (HCP) on the basis of subject-oriented modeling of its activities as a system of mass service. The article offers tools for an integrated assessment of the current state of LPI as a socio-economic system for the substantiation of necessary measures to ensure the required level of readiness of LPI. The article touches upon the problem of determining the functional completeness of LFU, and in this connection, the range of seasonal planning is considered. The paper highlights the issues of the coordination procedure in the formation of a comprehensive assessment of LPF on the reserve/loading issues. The author proposes a mechanism for processing the data coming from LPFs according to the predicate, on the basis of which an automated data processing procedure can be built as an addition in the formation of a comprehensive assessment of LPF load/reserve. These algorithms are scientifically new and make it possible to monitor the current state of the mass service system and predict the functional completeness/incompleteness of the system with justification of necessary correction of its parameters. In the article the analysis of occurrence of typical problems of identification of typical situations and recommended actions for bringing LRC to a new state, in the best way, corresponding to the task of the guaranteed granting of medical services to the population before the moment of the new primary information is resulted. The author has proposed the use of simulation results in the formation of a functional management of the state of health care facilities. The proposed intelligent control mechanisms ergonomically well match the capabilities of the personnel of usual qualification.
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Cole, William G., and James G. Stewart. "Metaphor graphics to support integrated decision making with respiratory data." International Journal of Clinical Monitoring and Computing 10, no. 2 (May 1993): 91–100. http://dx.doi.org/10.1007/bf01142279.
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Berg, Sheri M., and Edward A. Bittner. "Disrupting Deficiencies in Data Delivery and Decision-Making During Daily ICU Rounds*." Critical Care Medicine 47, no. 3 (March 2019): 478–79. http://dx.doi.org/10.1097/ccm.0000000000003605.
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Parra-Rodriguez, Luis, and M. Cristina Vazquez Guillamet. "Antibiotic Decision-Making in the ICU." Seminars in Respiratory and Critical Care Medicine 43, no. 01 (February 2022): 141–49. http://dx.doi.org/10.1055/s-0041-1741014.
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AbstractIt is well established that Intensive Care Units (ICUs) are a focal point in antimicrobial consumption with a major influence on the ecological consequences of antibiotic use. With the high prevalence and mortality of infections in critically ill patients, and the clinical challenges of treating patients with septic shock, the impact of real life clinical decisions made by intensivists becomes more significant. Both under- and over-treatment with unnecessarily broad spectrum antibiotics can lead to detrimental outcomes. Even though substantial progress has been made in developing rapid diagnostic tests that can help guide antibiotic use, there is still a time window when clinicians must decide the empiric antibiotic treatment with insufficient clinical data. The continuous streams of data available in the ICU environment make antimicrobial optimization an ongoing challenge for clinicians but at the same time can serve as the input for sophisticated models. In this review, we summarize the evidence to help guide antibiotic decision-making in the ICU. We focus on 1) deciding if to start antibiotics, 2) choosing the spectrum of the empiric agents to use, and 3) de-escalating the chosen empiric antibiotics. We provide a perspective on the role of machine learning and artificial intelligence models for clinical decision support systems that can be incorporated seamlessly into clinical practice in order to improve the antibiotic selection process and, more importantly, current and future patients' outcomes.
Dissertations / Theses on the topic "Critical care medicine Decision making Data processing"
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Farooq, Kamran. "A novel ontology and machine learning driven hybrid clinical decision support framework for cardiovascular preventative care." Thesis, University of Stirling, 2015. http://hdl.handle.net/1893/22328.
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Clinical risk assessment of chronic illnesses is a challenging and complex task which requires the utilisation of standardised clinical practice guidelines and documentation procedures in order to ensure consistent and efficient patient care. Conventional cardiovascular decision support systems have significant limitations, which include the inflexibility to deal with complex clinical processes, hard-wired rigid architectures based on branching logic and the inability to deal with legacy patient data without significant software engineering work. In light of these challenges, we are proposing a novel ontology and machine learning-driven hybrid clinical decision support framework for cardiovascular preventative care. An ontology-inspired approach provides a foundation for information collection, knowledge acquisition and decision support capabilities and aims to develop context sensitive decision support solutions based on ontology engineering principles. The proposed framework incorporates an ontology-driven clinical risk assessment and recommendation system (ODCRARS) and a Machine Learning Driven Prognostic System (MLDPS), integrated as a complete system to provide a cardiovascular preventative care solution. The proposed clinical decision support framework has been developed under the close supervision of clinical domain experts from both UK and US hospitals and is capable of handling multiple cardiovascular diseases. The proposed framework comprises of two novel key components: (1) ODCRARS (2) MLDPS. The ODCRARS is developed under the close supervision of consultant cardiologists Professor Calum MacRae from Harvard Medical School and Professor Stephen Leslie from Raigmore Hospital in Inverness, UK. The ODCRARS comprises of various components, which include: (a) Ontology-driven intelligent context-aware information collection for conducting patient interviews which are driven through a novel clinical questionnaire ontology. (b) A patient semantic profile, is generated using patient medical records which are collated during patient interviews (conducted through an ontology-driven context aware adaptive information collection component). The semantic transformation of patients’ medical data is carried out through a novel patient semantic profile ontology in order to give patient data an intrinsic meaning and alleviate interoperability issues with third party healthcare systems. (c) Ontology driven clinical decision support comprises of a recommendation ontology and a NICE/Expert driven clinical rules engine. The recommendation ontology is developed using clinical rules provided by the consultant cardiologist from the US hospital. The recommendation ontology utilises the patient semantic profile for lab tests and medication recommendation. A clinical rules engine is developed to implement a cardiac risk assessment mechanism for various cardiovascular conditions. The clinical rules engine is also utilised to control the patient flow within the integrated cardiovascular preventative care solution. The machine learning-driven prognostic system is developed in an iterative manner using state of the art feature selection and machine learning techniques. A prognostic model development process is exploited for the development of MLDPS based on clinical case studies in the cardiovascular domain. An additional clinical case study in the breast cancer domain is also carried out for the development and validation purposes. The prognostic model development process is general enough to handle a variety of healthcare datasets which will enable researchers to develop cost effective and evidence based clinical decision support systems. The proposed clinical decision support framework also provides a learning mechanism based on machine learning techniques. Learning mechanism is provided through exchange of patient data amongst the MLDPS and the ODCRARS. The machine learning-driven prognostic system is validated using Raigmore Hospital's RACPC, heart disease and breast cancer clinical case studies.
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Beauchemin, Melissa Parsons. "Supporting Clinical Decision Making in Cancer Care Delivery." Thesis, 2019. https://doi.org/10.7916/d8-70wy-w603.
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Background: Cancer treatment and management require complicated clinical decision making to provide the highest quality of care for an individual patient. This is facilitated in part with ever-increasing availability of medications and treatments but hindered due to barriers such as access to care, cost of medications, clinician knowledge, and patient preferences or clinical factors. Although guidelines for cancer treatment and many symptoms have been developed to inform clinical practice, implementation of these guidelines into practice is often delayed or does not occur. Informatics-based approaches, such as clinical decision support, may be an effective tool to improve guideline implementation by delivering patient-specific and evidence-based knowledge to the clinician at the point of care to allow shared decision making with a patient and their family. The large amount of data in the electronic health record can be utilized to develop, evaluate, and implement automated approaches; however, the quality of the data must first be examined and evaluated.
Methods: This dissertation addresses gaps the literature about clinical decision making for cancer care delivery. Specifically, following an introduction and review of the literature for relevant topics to this dissertation, the researcher presents three studies. In Study One, the researcher explores the use of clinical decision support in cancer therapeutic decision making by conducting a systematic review of the literature. In Study Two, the researcher conducts a quantitative study to describe the rate of guideline concordant care provided for prevention of acute chemotherapy-induced nausea and vomiting (CINV) and to identify predictors of receiving guideline concordant care. In Study Three, the researcher conducts a mixed-methods study to evaluate the completeness, concordance, and heterogeneity of clinician documentation of CINV. The final chapter of this dissertation is comprised of key findings of each study, the strengths and limitations, clinical and research implications, and future research.
Results: In Study One, the systematic review, the researcher identified ten studies that prospectively studied clinical decision support systems or tools in a cancer setting to guide therapeutic decision making. There was variability in these studies, including study design, outcomes measured, and results. There was a trend toward benefit, both in process and patient-specific outcomes. Importantly, few studies were integrated into the electronic health record.
In Study Two, of 180 patients age 26 years or less, 36% received guideline concordant care as defined by pediatric or adult guidelines, as appropriate. Factors associated with receiving guideline concordant care included receiving a cisplatin-based regimen, being treated in adult oncology compared to pediatric oncology, and solid tumor diagnosis.
In Study Three, of the 127 patient records reviewed for the documentation of chemotherapy-induced nausea and vomiting, 75% had prescriber assessment documented and 58% had nursing assessment documented. Of those who had documented assessments by both prescriber and nurse, 72% were in agreement of the presence/absence of chemotherapy-induced nausea and vomiting. After mapping the concept through the United Medical Language System and developing a post-coordinated expression to identify chemotherapy-induced nausea and vomiting in the text, 85% of prescriber documentation and 100% of nurse documentation could be correctly categorized as present/absent. Further descriptors of the symptoms, such as severity or temporality, however, were infrequently reported.
Conclusion: In summary, this dissertation provides new knowledge about decision making in cancer care delivery. Specifically, in Study One the researcher describes that clinical decision support, one potential implementation strategy to improve guideline concordant care, is understudied or under published but a promising potential intervention. In Study Two, I identified factors that were associated with receipt of guideline concordant care for CINV, and these should be further explored to develop interventions. Finally, in Study Three, I report on the limitations of the data quality of CINV documentation in the electronic health record. Future work should focus on validating these results on a multi-institutional level.
Books on the topic "Critical care medicine Decision making Data processing"
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A, Sonnenberg Frank, ed. Decision making in health care: Theory, psychology, and applications. New York: Cambridge University Press, 2000.
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David, Ellis. Medical computing and applications. Chichester: E. Horwood, 1987.
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Medical computing and applications. Chichester: Ellis Horwood, 1987.
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Langkafel, Peter, and Timo Tobias Baumann. Big Data in Medizin und Gesundheitswirtschaft: Diagnose, Therapie, Nebenwirkungen. Heidelberg: Medhochzwei, 2014.
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The measures of medicine: Benefits, harms, and costs. Cambridge, Mass., Mass: Blackwell Science, 1995.
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Expert critiquing systems: Practice-based medical consultation by computer. New York: Springer-Verlag, 1986.
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Lawrence, Kuhn Robert, ed. Frontiers of medical information sciences. New York: Praeger, 1988.
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A, Kreider Nancy, Haselton Becky J, and Midwest Alliance for Nursing Informatics., eds. The systems challenge: Getting the clinical information support you need to improve patient care. Chicago: American Hospital Pub., 1997.
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Decision Support Systems in Critical Care. Springer, 2011.
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Michael, Shabot M., and Gardner Reed M, eds. Decision support systems in critical care. New York: Springer-Verlag, 1994.
Find full textBook chapters on the topic "Critical care medicine Decision making Data processing"
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Gardner, Reed M. "Computerized Data Management and Decision Making in Critical Care." In Computers and Medicine, 212–23. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2698-7_14.
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Rao, Fabrizio. "Using Domiciliary Noninvasive Ventilator Data Downloads to Inform Clinical Decision-Making to Optimize Ventilation Delivery and Patient Compliance." In Noninvasive Ventilation in Sleep Medicine and Pulmonary Critical Care, 103–10. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42998-0_12.
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Braga, André, Filipe Portela, Manuel Filipe Santos, António da Silva Abelha, José Machado, Álvaro Silva, and Fernando Rua. "Applied Pervasive Patient Timeline in Intensive Care Units." In Hospital Management and Emergency Medicine, 567–79. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2451-0.ch028.
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This study has the objective of introducing an innovative way of presenting and representing information concerning patients in Intensive Care Units. Therefore, the Pervasive Patient Timeline, which has the purpose of offering support to intensivists' decision-making process, by providing access to a real-time environment, was developed. The solution is patient-centred as it can be accessed from anywhere, at any time and it contains patients' clinical data since they are admitted to the ICU until their discharge. The environment holds data concerning vital signs, laboratory results, therapeutics, and data mining predictions, which can be analysed to have a better understanding of patients' present and future condition. Due to the nature of the critical care environment, the pervasive aspect is crucial because it allows intensivists make decisions when they have to be made. The Pervasive Patient Timeline is focused on improving the quality of care by helping the intensivists perform better in their daily activity.
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Marimuthu, Ramalatha, Shivappriya S. N., and Saroja M. N. "Generation and Management of Data for Healthcare and Health Diagnostics." In Theory and Practice of Business Intelligence in Healthcare, 106–32. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2310-0.ch005.
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Healthcare Analytics deals with patient records, effective management of hospitals, and clinical care. But the big data available is still not enough for focused research as it is complicated to find insights from complex, noisy, heterogeneous, and voluminous data, which takes time and effort, while a small clinical data will be more effective for decision making. The health care data also varies in data collection methods and their processing methods. Data generated through patient records is structured, wearable technologies generate semi structured data, and X rays and images provide unstructured data. Storing and extracting information from the structured, semi-structured, and unstructured data is a challenging task. Different machine learning techniques can simplify the process. The chapter discusses the data characteristics, identifying critical attributes, various classification and optimization algorithms for decision making purposes. The purpose of the discussion is to create a basis for selection of algorithms based on size, temporal validity, and outcomes expected.
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Lucius-Hoene, Gabriele, Christine Holmberg, and Thorsten Meyer. "Introduction." In Illness Narratives in Practice: Potentials and Challenges of Using Narratives in Health-related Contexts, edited by Gabriele Lucius-Hoene, Martina Breuning, and Cornelia Helfferich, 3–10. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198806660.003.0001.
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In the last thirty years, illness narratives have not only been used as research data linking subjective suffering with medical practice, identities, social meanings, and cultural significance, but their use has also spread to practical purposes in different areas, thus widening the scope of narrative medicine. This chapter discusses why this change needs a critical reflection. It presents the richness and chances of illness narratives as well as the epistemological, methodological, and methodical problems which arise when their narratological properties are neglected. The chapter provides an overview of the book and discusses methodological and epistemological challenges, ethical and communicational aspects, and narratives in psychotherapy, rehabilitation, and vocational training, training of students and medical staff, diagnostics, decision-making, health care, and in the media.
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Rostam Niakan Kalhori, Sharareh. "Towards the Application of Machine Learning in Emergency Informatics." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220003.
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Emergency care is one of the cornerstone parts of the world health organization’s action plan. Rapid response and immediate care are considered in agile emergency care. Artificial intelligence (AI) and informatics have been applied to fulfill these requirements through automated emergency technology. Machine learning (ML) is one of the main parts of some of these proposed technologies. There are various ML algorithms and techniques which are potentially applicable for different purposes of emergency care. AI-based approaches using classification and clustering algorithms, natural language processing, and text mining are some of the possible techniques that could prove useful for investigating models of emergency prevention and management and proposing improved procedures for handling such critical situations. ML is known as a field of AI which attempts to automatically learn from data and applies that learning to make better decisions. Decision-support tools can apply the results of either supervised or various semi-supervised or unsupervised learning methods to tackle the how decisions about emergency situations are typically handled by the best professionals at the scene of an emergency, in the pre-hospital, and in healthcare facility settings. Enhanced and rapid communication at the moment of emergency, with the most effective decision making for triaging to estimate the acute nature of injuries and possible complications, how to keep a patient stable on the way to the care facility, and also avoiding adverse drug reactions, are some of the possible directions for exploring how ML can help to gather the data and to make emergency management more efficient and effective. The wide range of scenarios present in emergency situations and the complexity of different legal and ethical constraints on what responding personnel are allowed to perform on an injured subject before reaching a hospital makes for a most challenging set of problems for investigating the components of “intelligent” decision support that could help in these highly interactive and humanly tragic situations.
Conference papers on the topic "Critical care medicine Decision making Data processing"
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Kahlen, Franz-Josef, George Swingler, Anabela C. Alves, and Shannon Flumerfelt. "Decision-Making Competencies in Engineering and Medicine." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-39891.
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A number of studies conducted since the turn of the millennium have identified several deficits in engineering education; the most widely cited are deficits in critical analysis, systems thinking, and visualizing non-linear cause-effect chains. The field of engineering education has undergone a number of notable changes in response to such identified deficits but recent field studies such as Vision 2030 identified remaining shortfalls in engineering competencies as well as significant discrepancies in the perception of the severity of these deficits. While academic engineering programs feel that their programs adequately prepare engineering students for the practice of engineering, entry-level hiring managers disagree. In the practice of medicine, decision-making in practicing physicians is a critical competency which can make the difference between appropriate and incorrect diagnoses, and may affect the patient’s well-being or his life. Making a decision for an appropriate treatment plan in the face of insufficient or contradicting data points often times is compounded by the fact that time-scales can be significantly shorter than in the case of a machine design project. And while the majority of patients is discharged from hospital care in better health, medical professionals and educators are questioning their own approach to decision making in light of technological advances affecting their disciplines, and because of an improved understanding of the biochemistry and opportunities of genetic manipulations of the human body. Therefore, the field of medical decision making is also undergoing an overhaul in the education and training of medical students. This paper contrasts the current decision-making competencies that are imparted as part of the respective fields’ academic education, identifies the challenges in each discipline, and identifies opportunities for cross-pollination of better practices to develop decision-making competencies.