Mediaboss Marketing

It has been a landmark year for Artificial Intelligence. What was once the reserve of science fiction is now becoming an intrinsic part of our everyday lives. From voice-controlled digital assistants in our homes to customer service chat bots, AI is now entrenched in the mass market. Most significantly, it has also been a year in which AI in healthcare has put down roots for a more radical transformation.

AI and machine learning have been quietly revolutionising the health sector for years by delivering everything from robotic surgery and 3D image analysis to intelligence biosensors that allow diagnoses and treatments to be managed remotely. But while the COVID-19 pandemic has been devastating, it has also catalysed technological developments in and awareness of healthcare AI. In the first quarter of 2020 alone, almost $1bn was invested in AI-focused healthcare start-ups and a recent projection shows the global industry growing at a rate of 44% until 2026.

The potential uses of Artificial Intelligence in the healthcare sector are vast, and the technology is rapidly gaining momentum with investors as a result. With its applications ranging from disease prevention and diagnostics to acute care and long-term disease management, the industry is reaching a tipping point in 2020 and AI is finally becoming mainstream.

Yet it still seems we have only scratched the surface; and like any revolution witnessed in real time, the possibilities are seemingly limitless. For healthcare providers and associated organisations, it remains a real challenge to turn vision into reality. To move from testing to regular use, and to change the patient experience more fundamentally, organisations wanting to engage with AI must approach the issue strategically.

The technology behind Artificial Intelligence is evolving at breakneck speed, but the real test of an organisation is how it can harness and implement that technology for its own ends. The pressure of the pandemic has no doubt accelerated innovations, but before we look at how they can be put into practice, it is useful to consider what AI actually is and what it looks like in a healthcare setting.

At its core AI is machine learning, which is comprised of three cognitive nodes: computer vision, natural language processing and data inference. Computer vision is the eyes of AI, as it is capable of recognising visual patterns, objects, scenes and activities in digital imagery far quicker humans. Natural language processing refers to the technology that recognises and understands spoken language. Structured data inference is the technology that uses data, most often numerical, to solve problems. We have seen exciting developments for healthcare in all three in 2020.

Take natural language processing, which has come under the spotlight during the pandemic as healthcare providers have been forced to move operations online. The telehealth industry has grown exponentially because it has enabled providers to automate and streamline basic services in order to free up resources to deal with the crisis. In France, for instance, telemedicine appointments increased from 10,000 to a staggering 500,000 per week during the initial peak of the pandemic.

Recent developments in AI show that telehealth can be more than a platform for consultation. One startup, Vocalis Health, is exploring the use of voice data as a biomarker for disease progression. Using AI, the technology can detect signs of pulmonary hypertension in specific segments of speech, which can be recorded into a smartphone. Similar efforts are being focused on voice-based COVID-19 screening apps and also on using data to track neurological conditions like Parkinsons disease. The potential for this is significant and it promises to elevate telehealth to whole new level.

Huge strides in healthcare AI have been made by larger operations too, such as Alphabets AI subsidiary DeepMind. In November, DeepMinds AlphaFold project revealed it had in large part resolved a half-century-old challenge for scientists by understanding how a protein folds into a unique three-dimensional shape. This paves the way for a much greater understanding of diseases and the creation of designer medicines. On a wider scale, it even can help break down plastic pollution. Once more, the implications are enormous and not only for research scientists but for the role of Artificial Intelligence in the healthcare sector as a whole.

AIs ability to solve incredibly complex problems using huge sets of data far surpasses our own; and for the decades ahead, the sky really is the limit for the businesses pioneering change so how can a healthcare provider think about effectively building-in such developments into strategy?

Artificial Intelligence is a vast field with many potential applications. There is no single, fool proof blueprint for its implementation, so healthcare organisations looking to harness its potential must make choices that fit their financial and technical capabilities.

The first key question that providers should ask themselves before embarking on their AI journey is: do we have the capacity to build out these capabilities in-house? Having the internal resources, proprietary data and capital to develop AI solutions in-house comes with obvious benefits in terms of control, but businesses will need to decide for themselves whether its realistic given their goals and timeline.

Next, should we consider partnerships or acquisitions? Even with the best resources and in-house capabilities, partnerships can rapidly increase the development and deployment of AI systems and tools. Investments in AI start-ups or acquisitions of smaller companies can also give an organisation fast access to development phases and provide greater expertise and capabilities.

Finally, businesses will need to think about which key enablers will accelerate their AI strategy. This means thinking about everything from building or acquiring new technologies, to leadership alignment and team allocation.

We know that AI can transform many aspects of healthcare; and as we have seen this year, it is evolving rapidly on a global scale. However, healthcare providers engaging with AI face specific challenges, especially when implementing it.

Data is AIs raison dtre: without a continuous supply of data, AI technology simply could not have achieved what it has to date. However, it can also be a nuisance for organisations which are grappling with the challenge of dirty data, which is not yet standardised and remains disparate. Privacy protocols and security requirements present additional barriers to progress, but as they concern protections for patient rights, these are hills that must be climbed. Consent for the use of patients data and the need to address perceived bias in algorithms are additional ethical issues of which all organisations must be wary.

Necessity is the mother of invention, which explains in part why so much ground has been made this year. However, the healthcare business model could do more to incentivise innovation. While there is a broad range of industry players in this sector, larger technology companies are known to lure talent away from start-ups, who also face difficulties scaling up their products without partnerships.

These challenges are certainly real, but they are by no means insurmountable. While the success of engaging with AI relies on careful preparation, it is an innovation that is not just worth pursuing, but one that will be integral to healthcares story in the years to come. As such, organisations need to prioritise AI initiatives and plan for implementation. On a basic level, this means ensuring leadership is on board and the right talent is being supported.

Many organisations throughout the healthcare chain are already deep into their digital transformation journey. While some of these will have well-developed AI strategies in play, others will not. It is worth bearing in mind that the road to AI-enabled healthcare is long, which makes having a strategy to turn vision into reality key to a successful journey.

Overall, approaches may vary and will be dependent on specialism and sub-sector. But what sets healthcare ahead of other industries is the universal recognition of the power of AI and machine learning, and the sheer scale from start-ups to multinational companies involved.

The medical landscape of tomorrow is likely to look very different, but it is down to healthcare organisations across the board to steer their own path in a future defined by Artificial Intelligence.

This article is from issue 16 ofHealth Europa.Clickhere to get your free subscription today

Recommended Related Articles

Go here to read the rest:
From vision to reality: the rise of Artificial Intelligence in the healthcare sector - Health Europa

Pune, India, Feb. 17, 2021 (GLOBE NEWSWIRE) -- The global process mining software market size is estimated to showcase robust growth owing to the increasing implementation of artificial intelligence (AI) in the software by leading industry players, observes Fortune Business Insights in its report, titled, Process Mining Software Market Size, Share & COVID-19 Impact Analysis, By Type (Cloud-base, and On-premises), By Enterprise Size (Large Enterprises and Small & Medium Enterprises), By End User (BFSI, Healthcare, Retail, Manufacturing, IT and Telecommunication, Logistics and Transportation, and Others), and Regional Forecast, 2021-2028. As per our findings, the market value stood at USD 421.9 million in 2020 and is anticipated to reach USD 10,383.0 million by 2028, exhibiting a CAGR of 49.3% during the forecast period.

Information Technology to Boom amid COVID-19 due to Increasing Demand

The COVID-19 pandemic has compelled various industries to establish their presence online owing to prolonged nationwide lockdowns across countries. This has positively impacted the information technology sector. In addition, the sudden increase in demand for digital infrastructure after the rapid adoption of work-from-home settings due to social distancing norms has strengthened the growth of the global IT sector. However, the demand-supply gap amid the crisis has brought fresh challenges to prominent players. At Fortune Business Insights, we are focusing on finding innovative solutions to the current challenges.

To get to know more about the short-term and long-term impact of COVID-19 on this market,

Please Visit: https://www.fortunebusinessinsights.com/process-mining-software-market-104792

Highlights of the Report:

While making the report, we segmented the market on the basis of product, type, consumption, distribution channel, and region. Based on the segmentation, we made a list of companies and conducted a detailed analysis of their financial positions, product portfolios, and growth strategies. Our next step included the study of core competencies of key players and their market share to anticipate the degree of competition. The bottom-up procedure was conducted to arrive at the overall size of the market.

Drivers & Restraints-

Integration of Software with Latest Technologies to Drive Growth

Advantages, such as regular insights from real-time analysis and efficient operational business tasks, are the key factors driving developers and leading corporations to integrate artificial intelligence with process mining software. An increasing demand for such software is augmenting the growth. For instance, in April 2020, Automation Hero introduced Hero_Sonar, an AI-enabled intelligent process mining software. The software offers valuable insights from low-quality data, which helps in developing AI decision models. However, the high risk associated with customers privacy is predicted to hinder process mining software market growth.

Segment-

BFSI Segment to Lead the Market Owing to High Adoption

Based on the end-user, the BFSI segment dominated the market with a leading share of 26.8% in 2020. The segment growth is attributed to the seamless management offered by the software to banks for their operations. Based on type, the cloud-based segment is estimated to hold the leading share. The increasing growth of this segment is attributed to the cloud-based process mining softwares capability of providing valuable insights on a real-time basis.

Get Sample PDF Brochure: https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/process-mining-software-market-104792

Regional Insights-

Presence of Key Players to Help Europe Dominate

Europe is anticipated to dominate the global market with a value of USD 165.3 million in 2020. Increasing demand for process mining software in the energy space amid the surging digital transformation across industries is projected to drive its growth in the region. In addition, the presence of key players in major countries of the region is estimated to further propel growth.

North America is predicted to register a considerable process mining software market share during the projected timeline. One of the major factors set to propel the demand for this software in the region is the increasing adoption of automation in the U.S.

Competitive Landscape-

Key Players Focus on Offering Innovative Products to Expand their Product Lines

Prominent enterprises operating in the global market are focusing on innovating cutting-edge products in order to help their customers run smooth business operations. This will also help them expand their product portfolios. For instance, in April 2020, Celonis GmbH launched the next-generation platform of its AI-assisted process mining software. This new platform will help businesses and clients develop purpose-built operational applications.

Industry Developments-

A List of Key Manufacturers Operating in the Global Process Mining Software Market:

Quick Buy Process Mining Software Market Research Report: https://www.fortunebusinessinsights.com/checkout-page/104792

Detailed Table of Content:

TOC Continued..

Get your Customized Research Report:https://www.fortunebusinessinsights.com/enquiry/customization/process-mining-software-market-104792

Have a Look at Related Research Insights:

Task Management Software Market Size, Share & Industry Analysis, By Component (Software, Services), By Organization Size (SMEs and Large Enterprises), By Deployment Type (Cloud and On-Premises), By End-User (Banking, Financial Services, and Insurance (BFSI), IT and Telecommunications, Retail, Healthcare, Manufacturing, Travel and Hospitality, Media and Entertainment, and Others) and Regional Forecast, 2020-2026

Data Center Cooling Market Size, Share & Industry Analysis, By Product (Air Conditioners, Precision Air Conditioners, Chillers, Air Handling Unit, and Others), By Data Center Type (Large Scale DC, Medium Scale DC, and Small Scale DC), By Cooling Technique (Room-based Cooling, Rack-based Cooling, and Row-based Cooling), By Industry (BFSI, IT and Telecommunication, Manufacturing, Retail, Healthcare, and Others) and Regional Forecast, 2020-2026

Learning Management System (LMS) Market Size, Share and Global Trend By Component (Solution, Services), By Deployment (On-Premises, Cloud), By Organization Size (SMEs, Large Enterprises), By End User (Academic, Corporate) and Geography forecast till 2026

Retail Analytics Market Size, Share and Global Trend By Type (Software, Services), By Deployment (On-Premises, Cloud), By Organization Size (SMEs, Large Enterprises), By Function (Customer Management, Supply Chain, Merchandising, In-Store Operations, and Strategy & Planning) and Geography Forecast till 2026

Cyber Security Market Size, Share & Industry Analysis, By Component (Solutions, Services), By Deployment (On-Premises, Cloud), By Organization Size (SMEs, Large Enterprises), By End-User (BFSI, IT and Telecommunications, Retail, Healthcare, Government, Manufacturing, Travel and Transportation, Energy and Utilities and Others) and Regional Forecast, 2020 2026

About Us

Fortune Business Insights offers expert corporate analysis and accurate data, helping organizations of all sizes make timely decisions. We tailor innovative solutions for our clients, assisting them to address challenges distinct to their businesses. Our goal is to empower our clients with holistic market intelligence, giving a granular overview of the market they are operating in.

Our reports contain a unique mix of tangible insights and qualitative analysis to help companies achieve sustainable growth. Our team of experienced analysts and consultants use industry-leading research tools and techniques to compile comprehensive market studies, interspersed with relevant data.

At Fortune Business Insights, we aim at highlighting the most lucrative growth opportunities for our clients. We, therefore, offer recommendations, making it easier for them to navigate through technological and market-related changes. Our consulting services are designed to help organizations identify hidden opportunities and understand prevailing competitive challenges.

Contact Us:

Fortune Business Insights Pvt. Ltd.308, Supreme Headquarters,Survey No. 36, Baner,Pune-Bangalore Highway,Pune- 411045, Maharashtra,India.Phone:US: +1-424-253-0390UK: +44-2071-939123APAC: +91-744-740-1245Email:sales@fortunebusinessinsights.comFortune Business InsightsLinkedIn|Twitter|Blogs

Press Release: https://www.fortunebusinessinsights.com/press-release/global-process-mining-software-market-10526

More:
Process Mining Software Market to Reach USD 10,383.0 Million by 2028; Increasing Utilization of Artificial Intelligence to Aid Growth, states Fortune...

This is an automated machine translation of an article published by Business Insider in a different language. Machine translations can generate errors or inaccuracies; we will continue the work to improve these translations. You can find the original version here.

The asset management industry is moving at the same pace as the planet as a whole.

Increased digitization and the use of digital tools is taking hold. Artificial intelligence is making its way into the financial industry and one of the debates is whether it can end up doing away with the figure of the manager and whether, in addition, it is the key factor on which indexed management - an investment strategy based on replicating indexes - is focused.

Business Insider Spain has exclusively interviewed Unai Ansejo, CEO of Indexa Capital, a fintech focused on indexed management and with a growing volume of clients, to discuss this series of questions about the future of the investment scheme, as well as delving into the expansion of its range of products with the launch of occupational pension plans.

Focusing on the advantages of artificial intelligence when it comes to managing the assets in which to invest Ansejo expounds that from his professional experience he realizes that long-term savings is not about using an algorithm that beats others, but rather about greatly reducing costs, diversifying and being invested in different areas.

"I'm incredulous of these things," he relates about nonparametrics. "I have analyzed many quantitative investment funds for more than 20 years and they always seemed very good, but then there came a time when something happened or there was any problem," he adds.

Therefore, as he explains, in the end, artificial intelligence is a very broad concept, but they would still be algorithms in which you create a series of entry points to then find an exit.

"What happens is that the process by which inputs become outputs is a black box: you don't know," he says.

At Indexa Capital, they don't use artificial intelligence to build investment models but instead focus on criteria they think are reasonable for how portfolios should be constructed over the long term: diversify a lot, reduce costs, incorporate the effect of direct taxes into portfolio construction. "In my view, AI as such is not the best way to obtain long-term performance," he notes.

Artificial intelligence with a Spanish stamp to revolutionize the financial sector: Ultramarine, the investment technology that stops trading if it detects uncertainty in the market.

Ansejo assures, however, that in the fintech they use technology a lot: "Our goal is that half of our team are technical profiles such as engineers, analysts or developers and we use technology for what needs to be done: automating processes where a person does not contribute any value".

For example, something that automates, as he relates, is that, once the client's portfolio is configured, based on their risk profile, they apply an algorithm that is public to guide how the allocation of their investors should be. "When you already have a model portfolio the daily management of your portfolio, or the request for a withdrawal to find the best fund in which there is a lower tax impact can be automated," he explains.

The Indexa Capital CEO asserts that you can't automate portfolio construction."You can't ask a computer or a machine what to invest in because there are many parameters to take into account," he says.

In this way, Ansejo reveals that to build their portfolios they carry out a quarterly review in which they try to see, among other things, if there is a new asset class in which they can invest cheaply and efficiently.

On the other hand, Indexa Capital has expanded its range of indexed products by incorporating occupational pension plans. "We do it with indexing because we think it's the best way to maximize your options to monetize a portfolio over the long term," he says. "What we have is 32,000 clients for whom this proposition works," he adds.

Along these lines, Ansejo says that they have had pension plans for 4 years and with a very clear vocation: that they should be indexed because they are cheaper. However, they saw that, apart from individual plans, in employment plans (where it is the company that creates a payment plan and contributes for the worker) the solutions available were once again very analogical. "Everything with a lot of paper and regulatory information," he describes.

On the other hand, they were usually active management, oriented towards SMEs and high costs. " So we decided to launch it to make it easier for an SME to have a plan quickly and online, and we did so by incorporating another feature, which is the life cycle," he says.

Ansejo confirms that they incorporated a large dose of innovation: that it could be done digitally, low costs and life cycle. "So, the response we are having is very good, although the amount we have is small, it is normal because in the end, when you create an employment plan you are contributing little by little to your employees," he says.

More here:
Artificial intelligence helps automation, but can't tell you where to put your money, Indexa CEO says - Business Insider

With every passing year, humans become more dependent on technology. That has several advantages but also some dangers, which Rajiv Malhotra reveals in his book, 'Artificial Intelligence and the Future of Power'.An internationally acclaimed author who has studied computer science and done extensive research on India's history, Rajiv Malhotra has interesting insights on what artificial intelligence is doing to our nation and how it will affect us in the future. He looks into how artificial intelligence will alter every aspect of our lives, from an international, to national to a personal level.Here is an excerpt of the book to give you an idea on it:Excerpts from 'Artificial Intelligence and the Future of Power' by Rajiv MalhotraThe AI-based concentration of power has taken on a terrifying new aspect. When we think of global power, countries like the US, China, and Russia readily come to mind. But today, private companies are accumulating immense power based on their ability to leverage AI and big data as tools to influence, manipulate and even control the minds of people.Some of these private companies may soon become more powerful than many nation-states, but the shift will not be obvious. They will not fly a flag or manage a currency (although some are attempting to launch their own cryptocurrency), and they will not wield military power, at least not directly. However, their unprecedented knowledge of people and things around the world, coupled with their ability to disrupt and alter the physical world and manipulate peoples choices, will lead to a new nexus of power. Such companies will decide who will, and who will not, be given access to this new form of power, and on what terms.Not one Indian company is a player in this league. Most unfortunate is that a large number of talented Indians work for American and Chinese companies in an individual capacity, including in top executive positions, but not as owners. Indians who do own companies tend to sell their stake when the right offer comes along. Whenever innovative entrepreneurs anywhere in the world develop a promising breakthrough, digital giants or venture firms that serve as their proxies are waiting to buy them out. As a result, hundreds of instant millionaires are being created at the individual level, including many living in India.I view this trend as the return of Britains East India Company, which started out in 1600 as a modest private company for the purpose of making profit from lucrative trade with India. Over its 250-year history, the East India Company became the worlds largest private business, amassing more wealth, income and military power than even its own British government. Despite being a private company, it became a colonial powercollecting taxes, operating courts, and running the military and other functions of state across many kingdoms within India. At the time, the East India Company had more ships, soldiers, money and territory under its control than any European government, though now it is remembered as a rogue machine. Since then, the lines between government and private companies have often blurred.

Follow this link:
Excerpt: 'Artificial Intelligence and the Future of Power' by Rajiv Malhotra - Times of India

Numerous chronic conditions manifest with unpredictable symptoms, which can sometimes make it difficult for clinicians to take necessary action in a timely manner when tending to patients. Researchers at U.Va. Health working in the field of predictive analytics have created a software that uses artificial intelligence to estimate a patients relative risk by combining physiological data from thousands of previous patients, with a current patient's physiological state. The software is crucial in allowing clinicians to assess a patients risk for deterioration sooner than they normally would, allowing them to take often critical proactive actions towards maintaining the patients health.

Life-threatening conditions such as lung failure, sepsis or acute respiratory distress syndrome can all manifest in a patient without displaying warning signs to clinicians until the patient is in a critically debilitating condition. This can leave providers with limited time to make imperative decisions for patients and may thus threaten chances of survival.

Dr. Randall Moorman, cardiologist and innovator in the field of predictive analytics monitoring, realized this healthcare dilemma early on in his career.

Sometimes we can look back at the data that we had about those patients, and we can see that we should have seen it coming, Moorman said.

In attempts to better monitor patient stability through early detection, many hospitals around the world have resorted to using a standardized point system, which consists of recording certain physiological parameters and outputting a standardized score that can then be used to predict the patients stability. For instance, in England the National Early Warning Score measures pulse rate, blood pressure, respiratory rate, oxygen levels, temperature and consciousness level in patients, allocating an individual score for each factor and totaling the scores. When the total reaches a threshold number designated by healthcare facilities, it alerts clinicians to take action.

However, Moorman found that such point systems were sometimes ineffective in monitoring the patient since they uniformly depended on the patient reaching a particular threshold score before clinicians were alerted. While threshold score monitoring may be helpful in some situations, these systems are not designed to indicate risk specific to each physiological factor, failing to utilize statistical tools like regression models, which use multiple variables to predict an outcome.

One of the benefits of many machine learning approaches [is] you get a continuous gradation of risk from all the possible numbers that might come in, no thresholds [are] allowed, Moorman said.

Additionally, tools like NEWS can be restraining since they do not focus on symptoms specific to a certain patient population, like cardiac patients, but instead rely on a one size fits all model.

Our own point of view has been that this is not a one-size-fits-all problem at all, that the predictors of deterioration in one part of the hospital are going to be very different from elsewhere in the hospital, Moorman said.

Generalizing symptoms can lead to clinicians who depend on a standardized score when trying to predict any patients disease progression, further leaving more room for ambiguity in executing care plans since the numbers are not always clearly indicative of a particular condition.

Approximately 20 years ago, Moorman decided to apply certain predictive concepts to proactively diagnose neonatal sepsis, which is a bacterial infection that occurs in the bloodstream of premature infants and can be deadly if not diagnosed early on. Sepsis has been particularly difficult for healthcare providers to diagnose since premature infants are unable to aptly communicate discomfort and are too fragile to have many diagnostic tests conducted on them.

Moorman analyzed data from several infants infected with sepsis and recognized distinct patterns in the heartbeat of infants that occurred before sepsis began. He then quantified the heart rate data for the heartbeat abnormality and created a software which would detect this abnormality and alert clinicians. The HeRO software, coupled with observations and skillset of clinicians, allowed for them to proactively integrate the softwares findings into their care, culminating in a 20 percent decline in premature infant mortality as shown by a randomized trial.

Consequently, Moorman expanded his work to create predictive models for adults, attempting to address a multitude of diseases using evidence from data coming from approximately 200,000 patients who have been admitted to U.Va. Health previously.

We present to the clinicians, not just the risk of sepsis, but we have developed predictive tools for early detection of other kinds of clinical deterioration like lung failure or bleeding or the need to be transferred to an ICU, Moorman said.

One of his primary goals is to use the benefits of Big Data analysis in predicting outcomes for future patients.

[We are working] toward the idea of taking all of the data that comes out from a patient and analyzing it in such a way that we can tell the clinicians that someone's risk for something bad is going up, Moorman said.

Contrary to standardizing softwares like NEWS, the Continuous Monitoring of Event Trajectories software relies on constant monitoring of the patient and previous data, working to apply algorithms which output the patients status and risk of experiencing a serious event in the next 12 hours, updating every 15 minutes. CoMET updates models by calculating the cumulative contribution of physiological information from patients including data from their electronic medical records, EKG signals, vital signs and laboratory results.

The added machine learning approach allows for patients to be assessed relative to the outcomes from thousands of other patients and is more specific to the individual patient by displaying models specific to the patients unit.

At this point we have generated truly, hundreds of predictive models, depending on where you are in the hospital, what kind of things might go wrong and what information is available, Moorman said.

The Prediction Assistant screen uses regression to display patient risk by showing comets for each patient being monitored in the unit, with more stable patients represented as small and close to the bottom of the graph, while patients at higher risk are represented by larger and brighter comets. Each of the comets are graphed as a measure of a combination of factors most relevant to the hospital unit.

University cardiologist Jamieson Bourque, in collaboration with Jessica Keim-Malpass, associate professor of nursing and pediatrics, have recently begun a two-year randomized controlled study of the CoMET software in patients in the medical-surgical floor for cardiology and cardiovascular surgery patients at the U.Va. Hospital. They intend to analyze the long term outcomes of patients and prove the softwares utility to help patients through providing clinicians with valuable predictive models from physiological data.

What CoMET does is allows you to see the small incremental changes in heart rate, respiratory rate, vital signs [and] labs that can sort of fly under the radar, but when all those values are added together, that may signify a more significant change, Bourque said.

The team is also in the process of developing a predictive model specifically for COVID-19. However, it is waiting to gain more data to better understand the unpredictable nature of the disease so is currently using pre-existing models for the respiratory distress that accompanies COVID-19. The researchers feel that a predictive model could potentially be largely beneficial to dealing with COVID-19 patients since it could help anticipate some of the unpredictable symptoms which have shown to cause mortality.

At unexpected times, a fair number of patients do deteriorate drastically, and then there are very big decisions to be made in this time of constrained resources or this time of full hospitals, Moorman said.

Main challenges researchers face with integrating CoMET involve educating clinicians on reading the patterns as well as helping them integrate the softwares usage into their daily workflow. With CoMET, clinicians are suggested to utilize the proactive warning signs and learn to construct a care plan sooner than they normally would.

Keim-Malpass, who is also trained as a nurse, is able to incorporate her first-hand perspective to CoMETs design by attempting to ensure that nurses and other clinicians in the hospital can adapt their responsibilities to the proactive nature of the software. She spoke of a time when nurses recognized a spike in the patients CoMET score trajectory that allowed them to prevent sepsis when the patient was still stable.

They went ahead and preemptively took blood cultures, and a few hours later they came back positive that they had blood infection, that they were heading towards sepsis, so that patient got antibiotics sooner [than] they would have, Keim-Malpass said.

In the future, the team plans to use more data to enhance the COVID-19 model and to implement CoMET to other hospitals around the nation.

Conflict of interest disclosure: Randall Moorman is Chief Medical Officer and owns equity in AMP3D, which licenses technology from UVALVG and markets the CoMET monitor.

Originally posted here:
Artificial intelligence used to monitor patients with chronic diseases and COVID-19 - University of Virginia The Cavalier Daily