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The Future of Healthcare with IoT

2021-04-05T11:52:02.950Z

The application of the IoT in healthcare plays a fundamental role in managing chronic diseases and in disease prevention and control. Remote monitoring is made possible through the connection of powerful wireless solutions. The connectivity enables health practitioners to capture patient's data and applying complex algorithms in health data analysis.

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The IoT in Healthcare helps in delivery of healthcare facilities through improved communication, between patients and healthcare providers, via connected medical devices. IoT in Healthcare enable the stakeholders particularly, providers and patients, to enhance patient care, reduce costs related to healthcare, reduction in errors, improved patient experience, and improved patient outcomes along with better disease management. IoT in Healthcare solutions and services are replacing the tradition diagnosis and treatment procedures with advanced technology.

The IoT in Healthcare market is constantly growing with medical devices and pharmaceutical companies either competing with each other or collaborating with tech companies to offer enhanced and advanced solutions.

source: Freepik

The Future of Healthcare Is in the Cloud

2021-03-25T12:59:19.842Z

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Healthcare Cloud Computing is process of storing, managing, and processing the data, collected from various locations and delivering the required services through internet. The cloud computing technology in healthcare industry in basically used for memory, computation, networking, and storage purposes.

The healthcare cloud computing companies offers services such as Software-As-A-Service (SAAS), Platform-As-A-Service (PAAS), and Infrastructure-As-A-Service (IAAS). These services can be deployed in public cloud, private cloud, and hybrid cloud.

Growing demand of cloud for improving storage, scalability, & flexibility of data and increasing implementation of information technology for cloud adoption and reduction in healthcare costs are the major drivers which are likely to anticipate the growth of healthcare cloud computing market.

Technological advancements & proliferation of internet and favorable regulatory acts are likely to add new opportunities for this market in the coming years.

source: Freepik

Some of the key players influencing the market are Global Net Access (GNAX), Carecloud Corporation, Dell Inc., Athenahealth, Inc., Carestream Health, Inc., VMWare, Inc., Iron Mountain, Inc., IBM Corporation, Cleardata Networks, Inc., and Merge Healthcare, Inc. among others.

The New Science of Metagenomics to Bioinformatics

2021-03-23T12:18:26.284Z

The data generated by metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species. The sequencing of the cow rumen metagenome generated 279 gigabases, or 279 billion base pairs of nucleotide sequence data, while the human gut microbiome gene catalog identified 3.3 million genes assembled from 567.7 gigabases of sequence data. Collecting, curating, and extracting useful biological information from datasets of this size represent significant computational challenges for researchers.

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The first step of metagenomic data analysis requires the execution of certain pre-filtering steps, including the removal of redundant, low-quality sequences and sequences of probable eukaryotic origin (especially in metagenomes of human origin). The methods available for the removal of contaminating eukaryotic genomic DNA sequences include Eu-Detect and DeConseq.

Metagenomic analysis pipelines use two approaches in the annotation of coding regions in the assembled contigs. The first approach is to identify genes based upon homology with genes that are already publicly available in sequence databases, usually by BLAST searches. This type of approach is implemented in the program MEGAN4.

source: Freepik

The second, ab initio, uses intrinsic features of the sequence to predict coding regions based upon gene training sets from related organisms. This is the approach taken by programs such as GeneMark and GLIMMER. The main advantage of ab initio prediction is that it enables the detection of coding regions that lack homologs in the sequence databases; however, it is most accurate when there are large regions of contiguous genomic DNA available for comparison.

Internet Of Healthcare Things: Changing The Landscape: Digital Health

2021-03-22T10:46:09.730Z

Digital health has become popular in recent years with growing deployment of digital health platforms such as mobile health, telehealth, and other wireless solutions across hospitals and nursing homes so as to provide patients with real time healthcare services.

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The digital health has grown from robotics to transform surgical procedures and 3D printers printing human organs the path for digital health market expansion. Digital health industry is expected to become one of the most advanced healthcare sector in the future.

Some other technologies include Assistive technologies, rehabilitation robotics, and unobtrusive monitoring sensors that can help people with disabilities perform their daily tasks independently. Computational simulations, modeling, and machine learning approaches can model health-related outcomes. These advanced simulations are able to be repeated, replicated, and tailored to any research area.

In medical imaging, the applications for this technology helps healthcare professionals visualize genes, brain structures, and many other components of human anatomy. The flexibility in this technology also allows for more positive and accurate results. Mobile health (or mhealth) is the practice of medicine and public health supported by mobile devices.

source: Freepik

3D Printing In Healthcare: Where Are We In 2021 ?

2021-03-18T12:56:37.415Z

The rapid proliferation of Covid-19 has been putting great strain on healthcare systems across the world, with demand for critical medical equipment and supplies mounting. Major manufacturers to individuals, have responded to the Covid-19 crisis by supporting the production of vital medical equipment for hospitals. 3D Systems, Carbon, and Renishaw have begun designing and manufacturing open-source PPE for healthcare workers worldwide.

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There is a major health crisis in terms of the shortage of organs, as the population is living for longer due to medical advances. Since 2013, the number of patients requiring an organ has doubled, while the actual number of available donor organs has barely moved (HRSA, 2020).

Regenerative medicine involves many different areas, using scaffolds, biomaterials, cells, or a combination of biomaterials and cells to attempt to create organs for transplants instead of relying on the current donor model. Recently, the creation of multilayered objects (tissues/organs) from soft biomaterials such as living cells and biomimetic synthetic polymers has been the most challenging technological advancement in the field.

source: Freepik

Telehealth: IoT to Transform Healthcare World

2021-03-15T12:51:23.011Z

Delivering healthcare via telehealth solutions could open up home monitoring of aged care patients with chronic diseases, improving health outcomes and significantly saving costs.

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Telehealth is a powerful tool in reducing the rate of deaths and managing the negative health effects associated with these chronic conditions. Telehealth used information and communication technology (ICT) and mobile wireless devices which have been utilized in various clinical disease to reduce medical cost and improve outcome, especially in cardiovascular disease. With the help of advanced ICT technology, telehealth can improve the management of cardiovascular disease and also improve the access to health care and quality of health care delivery to their patients.

Cardiovascular disease is a leading cause of death in the United States where 1 to 3 deaths estimated every day. Heart failure is responsible for a high hospitalization rate in the region. To prevent cardiovascular diseases, improve care quality and reduce mortality hospitalization rate, Telehealth solutions are used.

source: freepik

AI in Pharma: the Next Big Thing in Drug Discovery

2021-03-09T13:03:48.579Z

The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs are applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. AI algorithms can also be used to analyze large amounts of data through electronic health records for disease prevention and diagnosis.

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Drug Interactions

Improvements in natural language processing led to the development of algorithms to identify drug-drug interactions in medical literature. Drug-drug interactions pose a threat to those taking multiple medications simultaneously, and the danger increases with the number of medications being taken.

source: Freepik

To address the difficulty of tracking all known or suspected drug-drug interactions, machine learning algorithms have been created to extract information on interacting drugs and their possible effects from medical literature. Efforts were consolidated in 2013 in the DDIExtraction Challenge, in which a team of researchers at Carlos III University assembled a corpus of literature on drug-drug interactions to form a standardized test for such algorithms.

Competitors were tested on their ability to accurately determine, from the text, which drugs were shown to interact and what the characteristics of their interactions were. Researchers continue to use this corpus to standardize the measurement of the effectiveness of their algorithms.

Other algorithms identify drug-drug interactions from patterns in user-generated content, especially electronic health records and/or adverse event reports. Organizations such as the FDA Adverse Event Reporting System (FAERS) and the World Health Organization's VigiBase allow doctors to submit reports of possible negative reactions to medications. Deep learning algorithms have been developed to parse these reports and detect patterns that imply drug-drug interactions.

Creation of new drugs

DSP-1181, a molecule of the drug for OCD (obsessive-compulsive disorder) treatment, was invented by artificial intelligence through joint efforts of Exscientia (British start-up) and Sumitomo Dainippon Pharma (Japanese pharmaceutical firm). The drug development took a single year, while pharmaceutical companies usually spend about five years on similar projects. DSP-1181 was accepted for a human trial.

In September 2019 Insilico Medicine reports the creation, via artificial intelligence, of six novel inhibitors of the DDR1 gene, a kinase target implicated in fibrosis and other diseases. The system, known as Generative Tensorial Reinforcement Learning (GENTRL), designed the new compounds in 21 days, with a lead candidate tested and showing positive results in mice.

The same month Canadian company Deep Genomics announces that its AI-based drug discovery platform has identified a target and drug candidate for Wilson's disease. The candidate, DG12P1, is designed to correct the exon-skipping effect of Met645Arg, a genetic mutation affecting the ATP7B copper-binding protein.

The Future of Artificial Intelligence in Healthcare

2021-02-23T13:17:35.452Z

Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.

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Primary care

Primary care has become one key development area for AI technologies. AI in primary care has been used for supporting decision making, predictive modelling, and business analytics. Despite the rapid advances in AI technologies, general practitioners' view on the role of AI in primary care is very limited–mainly focused on administrative and routine documentation tasks.

source: Freepik

Disease diagnosis

Through the use of Medical Learning Classifiers (MLC’s), Artificial Intelligence has been able to substantially aid doctors in patient diagnosis through the manipulation of mass Electronic Health Records (EHR’s). Medical conditions have grown more complex, and with a vast history of electronic medical records building, the likelihood of case duplication is high.

Although someone today with a rare illness is less likely to be the only person to have suffered from any given disease, the inability to access cases from similarly symptomatic origins is a major roadblock for physicians. The implementation of AI to not only help find similar cases and treatments, but also factor in chief symptoms and help the physicians ask the most appropriate questions helps the patient receive the most accurate diagnosis and treatment possible.

Telemedicine

The increase of telemedicine, the treatment of patients remotely, has shown the rise of possible AI applications. AI can assist in caring for patients remotely by monitoring their information through sensors. A wearable device may allow for constant monitoring of a patient and the ability to notice changes that may be less distinguishable by humans. The information can be compared to other data that has already been collected using artificial intelligence algorithms that alert physicians if there are any issues to be aware of.

Electronic health records

Electronic health records (EHR) are crucial to the digitalization and information spread of the healthcare industry. Now that around 80% of medical practices use EHR, the next step is to use artificial intelligence to interpret the records and provide new information to physicians. One application uses natural language processing (NLP) to make more succinct reports that limit the variation between medical terms by matching similar medical terms.

For example, the term heart attack and myocardial infarction mean the same things, but physicians may use one over the over based on personal preferences. NLP algorithms consolidate these differences so that larger datasets can be analyzed. Another use of NLP identifies phrases that are redundant due to repetition in a physician’s notes and keeps the relevant information to make it easier to read.

Robotics in Healthcare: The Future of Medical Care

2021-02-22T12:45:23.886Z

In our study of the medical robots market is segmented into three major segments such as product, application, and end user. The product segment is divided into surgical robots, rehabilitation robots, non-invasive radiosurgery robots, hospital & pharmacy robots and others. The application segment consists of laparoscopy, neurology, orthopedics, gynecology, urology, cardiology and others. Similarly, the end user is classified as hospitals and ambulatory surgical centers.

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The robots are designed to assist surgeons during the surgical procedures. However, the medical robots are majorly used for surgical producers, there are different types of medical robots.

The types of the medical robots include surgical robots, rehabilitation robots, hospital & pharmacy robots and more. The targeted stakeholders for the medical robots market reports include suppliers and distributors of medical robot systems medical robots and related device manufacturing companies, hospitals and diagnostic centers, ambulatory surgery centers and others.

source: freepik

Types

Surgical robots: These robots either allow surgical operations to be carried out with better precision than an unaided human surgeon or allow remote surgery where a human surgeon is not physically present with the patient.

Rehabilitation robots: facilitate and support the lives of infirm, elderly people, or those with dysfunction of body parts affecting movement. These robots are also used for rehabilitation and related procedures, such as training and therapy.

Biorobots: a group of robots designed to imitate the cognition of humans and animals.
Telepresence robots: allow off-site medical professionals to move, look around, communicate, and participate from remote locations.

Pharmacy automation: robotic systems to dispense oral solids in a retail pharmacy setting or preparing sterile IV admixtures in a hospital pharmacy setting.

Companion robot: has the capability to engage emotionally with users keeping them company and alerting if there is a problem with their health.

Disinfection robot: has the capability to disinfect a whole room in mere minutes, generally using pulsed ultraviolet light. They are being used to fight Ebola virus disease.