DISMON

2011 ◽  
Vol 4 (1) ◽  
pp. 48-59 ◽  
Author(s):  
Ángel M. Lagares-Lemos ◽  
Miguel Lagares-Lemos ◽  
Ricardo Colomo-Palacios ◽  
Ángel García-Crespo ◽  
Juan Miguel Gómez-Berbís

Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON (Disease Monitor), a system based on Semantic Technologies and Social Web (SW) to improve patient care for medical diagnosis in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers.

2011 ◽  
pp. 995-1007
Author(s):  
Ángel M. Lagares-Lemos ◽  
Miguel Lagares-Lemos ◽  
Ricardo Colomo-Palacios ◽  
Ángel García-Crespo ◽  
Juan Miguel Gómez-Berbís

Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON (Disease Monitor), a system based on Semantic Technologies and Social Web (SW) to improve patient care for medical diagnosis in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers.


Author(s):  
Ángel M. Lagares-Lemos ◽  
Miguel Lagares-Lemos ◽  
Ricardo Colomo-Palacios ◽  
Ángel García-Crespo ◽  
Juan Miguel Gómez-Berbís

Information technology and, more precisely, the internet represent challenges and opportunities for medicine. Technology-driven medicine has changed how practitioners perform their roles in and medical information systems have recently gained momentum as a proof-of-concept of the efficiency of new support-oriented technologies. Emerging applications combine sharing information with a social dimension. This paper presents DISMON (Disease Monitor), a system based on Semantic Technologies and Social Web (SW) to improve patient care for medical diagnosis in limited environments, namely, organizations. DISMON combines Web 2.0 capacities and SW to provide semantic descriptions of clinical symptoms, thereby facilitating diagnosis and helping to foresee diseases, giving useful information to the company and its employees to increase efficiency by means of the prevention of injuries and illnesses, resulting in a safety environment for workers.


Circulation ◽  
2021 ◽  
Vol 144 (Suppl_2) ◽  
Author(s):  
Alexandra Weissman ◽  
Mariam Bramah Lawani ◽  
Thomas Rohan ◽  
Clifton W CALLAWAY

Introduction: Pneumonia is common after OHCA but is difficult to diagnose in the first 72 hours following ROSC, this results in early untargeted antibiotic administration based on non-specific imaging and laboratory findings. Antibiotic resistance is rising, is influenced by untargeted antibiotic administration, and can increase patient morbidity and mortality as well as healthcare costs. Precision methods of bacterial pathogen detection in OHCA patients are needed to improve patient care. This proof-of-concept pilot study aimed to assess feasibility of bacterial pathogen sequencing and comparability of sequencing results to clinical culture after OHCA. Methods: Blood and bronchoalveolar lavage (BAL) were obtained from residual clinical specimens collected within 12 hours of ROSC. Bacterial DNA was extracted using the Qiagen PowerLyzer PowerSoil DNA kit, sequenced using the MinION nanopore sequencer, and analyzed with Oxford Nanopore Technologies’ EPI2ME bioinformatics software. Sequencing results were compared to culture results using McNemar’s chi-square statistic. Study-defined pneumonia was based on presence of at least two characteristics within 72 hours of ROSC: fever (temperature ≥38°C); persistent leukocytosis >15,000 or leukopenia <3,500 for 48 hours; persistent chest radiography infiltrates for 48 hours per clinical radiology read; bacterial pathogen cultured. Results: We enrolled 38 consecutive OHCA subjects: mean age 61.8 years (18.0); 16 (42%) female; 25 (66%) White, 7 (18%) Black, 6 (16%) “Other” race; 7 subjects (18%) survived and 31 (82%) died; 16 (42%) subjects had pneumonia. Sequencing results were available in 12 hours while culture results were available in 48-72 hours after collection. There was a non-significant difference in the proportion of the same pathogens identified for each method per McNemar’s chi-square: p = 0.38, difference of 0.095 (-0.095, 0.286). Conclusions: Nanopore sequencing detects pathogenic bacteria comparable to clinical microbiologic culture and in less time. This technology can produce a paradigm shift in early bacterial pathogen detection in OHCA survivors, which can improve patient care. The technology is applicable to other patient populations and for viral and fungal pathogens.


Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra-Gayo ◽  
Juan Miguel Gómez Berbís

The advent of the information age represents both a challenge and an opportunity for medicine. New forms of diagnosis, innovation-oriented supervision and expert location paths are deeply impacting medical sciences as we know it around the word. In this new scenario, semantic technologies can be seen as new and promising tool to support knowledge-based services, and particularly for the health domain, medical diagnosis. This chapter presents MedFinder, a system based on semantic technologies and social Web to improve patient care for medical diagnosis. The main breakthroughs of MedFinder are the follow-up once the diagnosis is performed, by using a medical ontology and formal reasoning together with rules, since it makes possible to locate the most appropriate doctor for a patient using Geographical Information Systems (GIS) and taking into account user preferences given via social Web feedback.


Author(s):  
Lindsay B. Ragsdale

Understanding complex medical information can be challenging for patients and families. Especially with the large amount of information available online and in social media, maintaining clear communication and a concise plan of care can be challenging. Medical training teaches clinicians to ask questions pertaining to patients’ disease and symptoms, but this training commonly fails to prepare clinicians to assess how patients would choose to receive medical information. Medical teams should ask how patients and caregivers prefer to receive information and assess understanding of the information after tailoring the delivery to their preferences. They should allow for multiple routes of information exchange, which may vary between individuals in a family. Cultural and spiritual influences can impact understanding of medical information. Improved communication can clarify goals, improve patient care, and avoid conflicts.


1983 ◽  
Vol 22 (03) ◽  
pp. 124-130 ◽  
Author(s):  
J. H. Bemmel

At first sight, the many applications of computers in medicine—from payroll and registration systems to computerized tomography, intensive care and diagnostics—do make a rather chaotic impression. The purpose of this article is to propose a scheme or working model for putting medical information systems in order. The model comprises six »levels of complexity«, running parallel to dependence on human interaction. Several examples are treated to illustrate the scheme. The reason why certain computer applications are more frequently used than others is analyzed. It has to be strongly considered that the differences in complexity and dependence on human involvement are not accidental but fundamental. This has consequences for research and education which are also discussed.


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