Wound Manage Prev. 2019;65(1):36–41.
“Measurements of the 3D device appear analogous to laser devices, making it an alternative tool for clinicians interested in monitoring wound progression.”
Innovations in Wound Healing Annual Conference. Dec, 2018.
“The proposed hybrid model achieves an accuracy of 94 % in differentiating burn, pressure injury, venous ulcer, and diabetic wound. This early application of artificial intelligence on etiology identification demonstrates the potential of AI in wound care. Future work will focus on improving the robustness and generalizability of the DCN/DNN hybrid model with more images and data augmentation, including the use of infrared image, depth map, and other clinical input.”
Burns Journal. Oct, 25, 2018 (epub ahead of print).
Highlights:
•Innovative use of portable 3D wound scanners to measure scar area;.
•Questioned the gold standard of the measurement of scar area, profile method, and showed this method is not suitable for measurement of hypertrophic scar area;
•Traditional methods are adequate for flat areas, however, errors are large in radial and angled areas. Scar was divided into three categories, flat, radial (cylindrical), and angled such as joints…
Automation is everywhere – in our phones, in our cars, in our homes. The Internet of Things (IoT) is an inter-operational network of physical devices, vehicles, and other electronic items. The network enables all of these devices to connect to one another and to the internet via wireless technology. This allows developers to add technology-based features, including the ability to deliver real-time experiences.
As of this year, IoT is starting to transform healthcare. More specifically, the Internet of Medical Things (IoMT) is transforming how patients are being kept safe and healthy. The IoMT allows medical devices and applications to gather data and communicate, over a wireless network, with healthcare IT systems. A few examples of this connecting network include remote patient monitoring of chronic patients and tracking medication orders. Its main objective is to enable a greater range of services in device management, patient care, and service delivery.
Predictive analytics is a rapidly growing area of data science. It uses various statistical techniques, such as predictive modeling and machine learning, to make predictions about unknown future events. In healthcare, predictive analytics analyzes historical and real-time data to identify patient behavior, manage chronic diseases, and improve patient care. The tool allows clinicians, administrative staff, and financial experts to receive alerts about potential events before they happen. This, in turn, helps them to make more informed decisions revolving around patient care.
Today, numerous hospitals across the nation are using real-time predictive analytics. One area in particular where the integration of predictor and intervention are showing efficacy is in preventing infections before they happen.
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For decades, medical professionals and other players in the healthcare industry have been generating massive piles of data. New tech-savvy tools that allow users to connect structured and unstructured data presents a unique way to derive meaning from this large source of data. As a result, the world of healthcare is becoming a data-driven business, with patients continuously demanding better, more personalized care.
Big data refers to massive bits or sets of data – both structured and unstructured. Analyzed computationally, it intends to reveal trends, patterns, and associations – especially in human behavior. It also has one or more of the following characteristics: volume, variety, velocity, or veracity.
Big data has made an immense impact in the world of business. Now, it is doing the same in the world of medicine.
Healthcare provider decisions are becoming more and more evidence-based. This means that they are relying more on research and clinical data, as opposed to solely on their schooling and expertise. Now more than ever, there is a greater demand for big data. Perhaps the most widespread application of big data in healthcare is electronic health records (EHR). Every patient has his or her own digital record, which includes their medical history, allergies, lab results, etc.
The intersection between medicine and technology can be quite confusing. Often times, the terms telemedicine and telehealth are used interchangeably. However, there is a distinction between the two.
Telemedicine is the practice of medicine using technology to deliver care at a distance. A physician or other healthcare provider uses telecommunications technology for diagnosis, treatment, and prevention of diseases and injuries. All of the healthcare services are provided to patients remotely via video, smartphones, email, etc. That being said, this particular practice eliminates the need for an in-person visit.
Typically, telemedicine aids in the management of chronic conditions, medication management, follow-up visits, and video consultations with specialists.
Telehealth, on the other hand, includes a wide range of technologies and services to deliver care and to improve the healthcare delivery system as a whole. It facilitates patient self-management and caregiver support for patients. This particular practice encompasses 4 infrastructures:
Interoperability refers to the ability of computer systems or software to communicate, exchange, and make use of information. In order for two systems to be interoperable, they must be able to exchange data and then present that data in a manner that can be understood by the user.
Nowadays, being able to exchange data across databases, platforms and other computer-based information systems is vital to the economic sector. And the healthcare sector is no exception to this rule.
Artificial intelligence (AI) and deep learning are revolutionizing the healthcare industry on a global level. They are practical tools that are helping medical universities, hospitals, and other healthcare organizations to optimize their services, improve the standard of care, and reduce medical errors. In fact, according to research findings, 35% of healthcare organizations will implement AI solutions within the next two years. What’s more, by 2021, Al systems will generate $6.7 billion in global healthcare industry revenue.
Further advancement in care delivery
Electronic health records are just one example of deep learning in healthcare. And the availability of this data is necessary for further advancement in care delivery. Other examples of how AI and deep learning have begun to create smarter, more effective treatment methods include: Read More
