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Management: Electronic Medical Records (EMR)

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Total 24 results found since Jan 2013.

Assessment Model to Identify Patients With Stroke With a High Possibility of Discharge to Home Clinical Sciences
Conclusions—We have developed an assessment model for identifying patients with a high possibility of being discharged to their homes after an acute stroke. This model would be useful for health professionals to adequately plan patients’ discharge soon after their admission.
Source: Stroke - September 25, 2017 Category: Neurology Authors: Takahiro Itaya, Yusuke Murakami, Akiko Ota, Eiichi Nomura, Tomoko Fukushima, Masakazu Nishigaki Tags: Epidemiology, Quality and Outcomes Original Contributions Source Type: research

Preliminary development of a prediction model for daily stroke occurrences based on meteorological and calendar information using deep learning framework (Prediction One; Sony Network Communications Inc., Japan).
Conclusion: Our preliminary results suggested a probability of the DL-based prediction models for stroke occurrence only by meteorological and calendar factors. In the future, by synchronizing a variety of medical information among the electronic medical records and personal smartphones as well as integrating the physical activities or meteorological conditions in real time, the prediction of stroke occurrence could be performed with high accuracy, to save medical resources, to have patients care for themselves, and to perform efficient medicine. PMID: 33598347 [PubMed]
Source: Surgical Neurology International - February 21, 2021 Category: Neurosurgery Tags: Surg Neurol Int Source Type: research

E-076 improvement of patient care, quality and research through an automated cerebrovascular data collection (acdc)
ConclusionsAccess to real-time patient data will allow for improved clinical quality at the point of care. It will provide healthcare providers the ability to impact care prior to discharge which benefits patients, the clinical team, and Vanderbilt as a whole. Uniformity in data collection will reduce system redundancies and streamline clinical workflow. Currently VUMC employs two individuals for the sole purpose of manually abstracting stroke patient data. By reducing the burden of manual data abstraction, we will implement a cost-saving application which improves patient care at VUMC through increased efforts towards imp...
Source: Journal of NeuroInterventional Surgery - July 26, 2015 Category: Neurosurgery Authors: Gilchrist, E., Burks, B., Espaillat, K., Fortenberry, T., Baggett, S., Kearney, M., Goggins, B., He, L., Mocco, J., Froehler, M. Tags: SNIS 12th Annual Meeting Electronic Poster Abstracts Source Type: research

Has Brazil found the way to better health care?
Under Brazil’s family health program, when a woman learns that she is pregnant, she contacts her local community health agent, who often is a neighbor. Typically, the agent visits her home to arrange an appointment with the neighborhood’s family health team, and the woman visits the health center for an assessment by a nurse assistant and a physician. During the pregnancy, if she misses a prenatal care appointment, the agent checks in on her at home and helps her reschedule her visit. Any prenatal medications she needs are provided free of charge. Brazil — home to the world’s fifth-largest population and seventh-l...
Source: UCLA Newsroom: Health Sciences - June 5, 2015 Category: Universities & Medical Training Source Type: news

Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using mount sinai heart failure cohort.
PREDICTIVE MODELING OF HOSPITAL READMISSION RATES USING ELECTRONIC MEDICAL RECORD-WIDE MACHINE LEARNING: A CASE-STUDY USING MOUNT SINAI HEART FAILURE COHORT. Pac Symp Biocomput. 2016;22:276-287 Authors: Shameer K, Johnson KW, Yahi A, Miotto R, Li LI, Ricks D, Jebakaran J, Kovatch P, Sengupta PP, Gelijns S, Moskovitz A, Darrow B, David DL, Kasarskis A, Tatonetti NP, Pinney S, Dudley JT Abstract Reduction of preventable hospital readmissions that result from chronic or acute conditions like stroke, heart failure, myocardial infarction and pneumonia remains a significant challenge for improving the outcom...
Source: Pacific Symposium on Biocomputing - November 30, 2016 Category: Bioinformatics Tags: Pac Symp Biocomput Source Type: research

Abstract 150: Machine Learning Methodology Predicts Comorbidities are Associated With Increased Total Healthcare Costs Among Patients With Severe Peripheral Artery Disease Session Title: Poster Session II
Conclusion: In this study, the presence of chronic ulcers in the lower extremities and CKD were two factors most predictive of increased all-cause total HC in a geographically diverse population of severe PAD patients.
Source: Circulation: Cardiovascular Quality and Outcomes - March 31, 2017 Category: Cardiology Authors: Berger, J. S., Haskell, L., Ting, W., Lurie, F., Eapen, Z., Valko, M., Alas, V., Rich, K., Crivera, C., Schein, J. Tags: Session Title: Poster Session II Source Type: research

CBN: Constructing a Clinical Bayesian Network based on Data from the Electronic Medical Record
Publication date: Available online 3 November 2018Source: Journal of Biomedical InformaticsAuthor(s): Ying Shen, Lizhu Zhang, Jin Zhang, Min Yang, Buzhou Tang, Yaliang Li, Kai LeiAbstractThe process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise stat...
Source: Journal of Biomedical Informatics - November 4, 2018 Category: Information Technology Source Type: research

Psychosis Polyrisk Score (PPS) for the Detection of Individuals At-Risk and the Prediction of Their Outcomes
Conclusions The combination of risk/protective factors encompassing genetic (PRS) and non-genetic information (PPS) holds promise for overcoming the epidemiological weakness of the CHR-P paradigm. The PPS conceptually and empirically developed here will facilitate future research in this field and hopefully advance our ability to detect individuals at-risk for psychosis and forecast their clinical outcomes. Ethics Statement This study was supported by the King's College London Confidence in Concept award from the Medical Research Council (MRC) (MC_PC_16048) to PF-P. This study also represents independent researc...
Source: Frontiers in Psychiatry - April 16, 2019 Category: Psychiatry Source Type: research

The Predictive Capacity of the Buffalo Concussion Treadmill Test After Sport-Related Concussion in Adolescents
Conclusion This study found that the ΔHR (HRt minus resting HR) correlated with duration of clinical recovery in participants who were prescribed relative rest or a placebo-stretching program but not for participants prescribed sub-threshold aerobic exercise. A ΔHR of ≤50 bpm on the BCTT was 73% sensitive and 78% specific for predicting delayed recovery in concussed adolescents prescribed the current standard of care (i.e., cognitive and physical rest). This has implications for planning team and school activities in adolescents who sustain SRC. Ethics Statement This study was carried out in acco...
Source: Frontiers in Neurology - April 23, 2019 Category: Neurology Source Type: research

Role of Artificial Intelligence in TeleStroke: An Overview
Teleneurology has provided access to neurological expertise and state-of-the-art stroke care where previously they have been inaccessible. The use of Artificial Intelligence with machine learning to assist telestroke care can be revolutionary. This includes more rapid and more reliable diagnosis through imaging analysis as well as prediction of hospital course and 3-month prognosis. Intelligent Electronic Medical Records can search free text and provide decision assistance by analyzing patient charts. Speech recognition has advanced enough to be reliable and highly convenient. Smart contextually aware communication and ale...
Source: Frontiers in Neurology - October 6, 2020 Category: Neurology Source Type: research

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
CONCLUSION: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.PMID:33965930 | DOI:10.3233/SHTI210100
Source: Studies in Health Technology and Informatics - May 9, 2021 Category: Information Technology Authors: Michael Schrempf Diether Kramer Stefanie Jauk Sai P K Veeranki Werner Leodolter Peter P Rainer Source Type: research