The impact of datasets in Healthcare and How it is Reshaping the Healthcare Sector.

 

significant improvements that the healthcare sector needs. There are countless opportunities to use technology to deliver more precise, effective, and impactful interventions at precisely the appropriate time in a patient’s care, from chronic diseases and cancer to radiology and risk assessment. Artificial intelligence (AI) is poised to be the engine that drives advances across the care continuum as payment mechanisms change, patients expect more from their providers, and the volume of available medical datasets continues to expand at a startling rate. Compared to conventional analytics and clinical decision-making methods, AI has a lot of benefits. As they interact with training data, learning algorithms can become more exact and accurate, giving people access to previously unattainable insights into diagnosis, care procedures, treatment variability, and patient outcomes. 

 

Leading researchers and clinical faculty members presented the twelve healthcare technologies and sectors that are most likely to experience a significant impact from artificial intelligence within the next ten years at the 2018 World Medical Innovation Forum (WMIF) on artificial intelligence, which has been organised by Partners Healthcare. According to WMIF co-chairs Anne Kiblanksi, MD, Chief Academic Officer of Partners Healthcare, and Gregg Meyer, MD, Chief Clinical Officer, each member of this “disruptive dozen” can significantly help patients while also having the potential for widespread commercial success. 

 

• Brain-Computer Interfaces for Unification of Mind and Machine

Although computers are not new in communication, creating interfaces between technology, the human mind, and technology is cutting-edge research that could have significant applications for some patients. Some patients may lose their ability to communicate, move and interact with others and their environment due to neurological diseases or trauma to the nervous systems. Artificial intelligence and brain-computer interfaces (BCIs), which are backed up by artificial intelligence, could help restore these fundamental experiences for those who have lost them forever. Leigh Hochberg, MD, PhD is the Director of the Centre for Neurotechnology and Neuro recovery (MGH). "By using an artificial intelligence and a BCI, we can decode neural activates associated with the intended movement of one’s hand and should be able to allow that person to communicate in the same way many people have communicated with each other at least five times during the morning using ubiquitous communication technology such as a tablet computer or a phone." Patients with ALS, strokes or locked-in syndrome could see a dramatic improvement in their quality of life. This is also true for the 500,000 worldwide people who suffer from spinal cord injuries each year.

• The creation of the future radiology tools

Non-invasive radiological images are obtained using CT scanners, MRI machines, and X-rays. However, many diagnostic procedures still depend on biopsies of physical tissue. These can pose risks and even infections.

 

Experts predict that artificial intelligence will allow the next generation of radiology tools to be more precise and detailed than tissue samples, which could replace the need for them in certain cases. "We want to bring together diagnostic imaging with the surgeon, interventional radiologist, and the pathologist," stated Alexandra Golby MD, Director Image-Guided Neurosurgery (BWH). It is difficult to bring together different teams and achieve common goals. "If imaging is to provide us with information from tissue samples, we will need to be able to achieve very close registration to ensure that the ground truth of any given pixel can be known." This quest could allow clinicians to gain a better understanding of how tumours behave in general, and not just a specific segment.

 

Providers might also be better able to define the severity of cancers and provide more targeted treatments. Artificial intelligence is helping "virtual biopsy" and advance radionics. This innovative field focuses on using image-based algorithms to identify the phenotypes of and genetic characteristics of cancerous cells.

• Promoting Care access in the unserved or developing areas

Access to life-saving healthcare in developing countries can be severely limited by a shortage of qualified healthcare providers such as radiologists and ultrasound technicians. The session highlighted that more radiologists work in Boston's half-dozen hospitals along Longwood Avenue than in any other part of West Africa. Artificial intelligence could be used to reduce the impact of this severe shortage in qualified clinical staff, by taking over certain diagnostic duties normally assigned to humans.

 

AI imaging tools, for example, can screen chest radiographs for signs and symptoms of tuberculosis. They often achieve a level comparable to human accuracy. The ability could be made available through an app that is accessible to healthcare providers in low-resource locations, which would reduce the need for a trained diagnostic radiologist on site. Jayashree Kalpathy–Cramer, PhD Assistant in Neuroscience at MGH, Associate Professor of Radiology, HMS, stated, "The potential for the tech to increase accessibility to healthcare is enormous." Developers of algorithms must remember that different ethnicities or residents from different areas may have unique physiologies that can influence the presentation and spread of disease. She said that the course of a disease, and the population affected by it, may be very different in India from the US. "When developing algorithms it is very important to ensure that data includes a variety of diseases and populations. We can't base an algorithm on one population and expect it will work on all.

 

• Lowering the burden of use of electronic health records

EHRs played a key role in the industry's move towards digitalization. However, the transition has presented many problems such as cognitive overload, excessive documentation, and user burnout. EHR developers now use artificial intelligence for intuitive interfaces and to automate routine processes that can take up so much time. According to Adam Landman, MD Vice President and Chief Information Officer at Brigham Health, the majority of users spend their time on order entry and clinical documentation. While voice recognition and dictation can improve clinical documentation, natural language processing (NLP), tools may not be reaching the right level.

 

Landman stated that he thinks we might need to be bolder and look at video recording clinical encounters, much like body cameras worn by police officers. "Then you can use AI or machine learning to index these videos for future information retrieval. "And just as in the home, Siri and Alexa are used, so will the future bring virtual assistants to bedside for clinicians, to use with embedded intelligence to order entry." Artificial intelligence could also be used to process routine requests, such as medication refills or result notifications. Landman said that artificial intelligence may be able to help prioritise tasks that require attention from the clinician, which will make it easier for users and their teams to complete their to-do lists.

• Controlling antibiotic resistance risks

Overuse of antibiotics can lead to superbugs, which are a threat to the health of people all over the globe. Multidrug-resistant organisms (MDR) can cause havoc in hospitals and take thousands of lives each year. C. Difficult alone is responsible for $5 billion annually in healthcare costs and has claimed more than 30,000 lives. Identifying infection patterns can be done with electronic health records. This will highlight patients who are at the greatest risk and help them to identify the source of their symptoms. These analytics can be improved by using machine learning and AI tools to generate faster and more accurate alerts for healthcare professionals.

 

Erica Shenoy (MD, PhD), Associate Chief of MGH's Infection Control Unit, said that AI tools could live up to expectations for antibiotic resistance and infection control. "If they don’t, that’s a real failure on all our parts." Hospitals with mountains of EHR data that they are not using to their full potential and industry that is not creating faster, smarter clinical trial design and EHRs that create these data to not use them would be a failure."

 

• More accurate analysis of pathology images to be created

Jeffrey Golden, MD, Chair of the Department of Pathology at BWH, and Professor of Pathology at HMS, states that pathologists are one of the most important sources of diagnostic data for providers in all areas of care delivery. He said that seventy per cent of healthcare decisions are based upon a pathology report. "Somewhere between 70% and 75% of the data in an EHR come from a pathology report. The sooner we can get the correct diagnosis, the more accurate we will be. This is what digital pathology and AI can do. Providers can identify subtleties that might be missed by the human eye with analytics that drill down to the pixel level for large digital images.

 

Golden stated, "We are now able to do a better job of assessing if cancer will progress quickly or slowly. This could affect how patients will be treated. Based on an algorithm and not clinical staging or the histopathologic grades. This is a significant advance. He said that artificial intelligence can also increase productivity by identifying key features in slides, before a human clinician reviews and evaluates the data. AI can screen through slides and direct you to the right thing so that we can evaluate what's important. This increases efficiency and the value of each case's time spent by the pathologist.

 

• Adding intelligence to medical technologies

Smart devices are taking over consumer environments, offering everything from real-time video from a refrigerator to cars that detect when the driver is distracted. Smart devices play a critical role in monitoring patients in ICUs and other medical environments. Artificial intelligence can be used to improve the ability to detect deterioration and to reduce penalties for hospital-acquired conditions.

 

"When we talk about integrating disparate information from across the healthcare systems, integrating them, and generating an alarm that would alert an ICU physician to intervene early on – the aggregation that that data requires is something that a person cannot do very well," stated Mark Michalski MD, Executive Director at the MGH & BWH Centre for Clinical Data Science. These devices can be fitted with intelligent algorithms that reduce cognitive burdens and ensure patients get care as quickly as possible.

 

• Promoting the use of immunotherapy in the treatment of cancer

The most promising way to treat cancer is immunotherapy. Patients may be able to beat stubborn tumours by using their immune system to fight malignancies. Only a few patients can respond to the current immunotherapy options. Oncologists do not yet have a reliable way of identifying patients who will benefit from this treatment. Machine learning algorithms, which can synthesise complex data sets, may provide new opportunities for targeted therapies that target a person's genetic makeup. Long Le, MD, PhD is Director of Computational Pathology and Technology Development for the MGH Centre for Integrated Diagnostics. We still don't know everything about disease biology. This is a very complicated problem. "We need more patient data. These therapies are still relatively new and not many patients have been given them yet. Integrating data from one institution or multiple institutions will be key to augmenting patient populations to support the modelling process.

 

• Developing an accurate risk predictor from the electronic health record

EHRs contain a plethora of patient data, but it has proven to be difficult for developers and physicians to extract and analyse that data in a way that is precise, timely, and reliable. Understanding precisely how to participate in effective risk stratification, predictive analytics, and clinical decision support has proven to be exceedingly challenging due to data quality and integrity difficulties, a jumble of data formats, structured and unstructured inputs, and incomplete records. Ziad Obermeyer, MD, Assistant Professor of Emergency Medicine at BWH and Assistant Professor at HMS, said that "integrating the data into one location is part of the hard effort." Understanding what you're getting when you anticipate an illness in an EHR, though, is a different issue. When you dig further, you discover that what an algorithm is forecasting is a billing code for a stroke. You might hear that an algorithm can predict depression or stroke. The actual stroke is considerably different from that.

 

Relying on MRI findings could seem to provide a more specific dataset, he said. "However, you now need to consider who can afford an MRI and who cannot. As a result, your final prediction differs from what you first intended. In patients who can afford a diagnosis, you might be billed for a stroke rather than some kind of cerebral ischemia. EHR analytics has given rise to several effective risk scoring and stratification tools, particularly when researchers use deep learning approaches to discover unique relationships between datasets that initially appear unrelated. But Obermeyer said that to implement tools that will improve clinical treatment, it is essential to make sure that those algorithms do not confirm latent biases in the data. Before we start opening up the black box and examining how we are predicting it, the largest challenge, according to him, will be determining precisely what we are projecting.

 

• Health monitoring using personal devices and wearables

Nowadays, almost all customers have access to gadgets with sensors that can gather important information about their health. A growing amount of health-related data is produced on the go, from wearables that can monitor a heartbeat constantly to cell phones with step trackers. This data can be gathered, analysed, and supplemented with patient-provided data from apps and other home monitoring devices to provide a distinctive view into both individual and population health. For this vast and diverse treasure mine of data to yield useful insights, artificial intelligence will be crucial. However, according to Omar Arnaout, MD, co-director of the Computation Neuroscience Outcomes Center and an attending neurosurgeon at BWH, getting patients to feel comfortable sharing data from this personal, ongoing monitoring may call for a little more effort.

 

We've been quite lenient with our digital data as a culture, he claimed. However, as issues like Cambridge Analytica and Facebook become more well-known, people will start to be more cautious about sharing their personal information. The fact that people are more likely to trust their doctors than they are a giant corporation like Facebook, he continued, may assist to allay any concerns about providing data to extensive research projects.

 

• How to transform smartphone selfies into strong diagnostic tools

Continuing the theme of harnessing the power of wearable devices, experts have found that images from smartphones and other consumer sources are clinical quality imaging, especially in poorly serviced people and developing countries. I believe it will be an important aid. 

 The quality of mobile phone cameras is improving year by year, and it is possible to generate images suitable for analysis by artificial intelligence algorithms. Dermatologists and ophthalmologists benefit from this trend early on. 

 British researchers have even developed tools to identify developmental disorders by analysing images of children's faces. The algorithm can detect discrete features such as B. Child's jawline, eye and nose position, and other attributes that may indicate craniofacial abnormalities. Today, this tool can collate common images with over 90 diseases to provide clinical decision support. 

 "The majority of the population has powerful pocket-sized devices that incorporate many different sensors," said Dr Hadi Shafiee, director of BATH's Micro / Nanomedicine and Digital Health Laboratory. 

 “This is a great opportunity for us. Almost every major player in the industry is starting to embed AI software and hardware into their devices. This is no coincidence. Every day, we generate over 2.5 million terabytes of data in the digital world. For mobile phones,  manufacturers believe that this data can be used in AI to provide more personalised, faster and smarter services. "

 

1. Revolutionising clinical decision-making at the bedside with artificial intelligence.

As the healthcare industry moves away from fee-for-service,  it moves further away from responsive care. Coping with chronic illnesses, costly acute events, and sudden declines is the goal of every provider – and the ultimate reimbursement structure allows them to develop processes that  enable targeted interventions. action and prediction. 

 Artificial intelligence will provide much of the foundation for this development by powering predictive analytics and clinical decision support tools that alert providers to long-term problems. before they realise they need to act.  

 AI can provide earlier warnings for conditions like seizures or sepsis, which often require in-depth analysis of very complex data sets. 

 Machine learning can also help make decisions about whether to continue caring for critically ill patients, such as those in a coma after cardiac arrest, says Brandon Westover, MD, PhD, director of the operations centre MGH clinical data shows. Usually, providers should visually examine the EEG data from these patients, he explains. The process is lengthy and subjective, and results may vary depending on the skill and experience of each physician. 

 “In these patients, trends can change slowly,” he says. "Sometimes, when we want to see if someone is recovering, we get data from ten seconds of tracking at a time. But trying to see if that changes compared to ten seconds of data being taken 24 seconds at a time. hours ago or not, it's like trying to see if your hair grows out. 

 

 "But if you have an AI algorithm and lots  of data from many patients, it's easier to match  what you're seeing with long-term models and be able to uncover subtle improvements that can influence your care decisions." 

 

 Using AI for clinical decision support, risk scoring, and early warning is one of the most promising areas of development for this revolutionary data analysis approach. By powering a new generation of tools and systems that help clinicians be more aware of the nuances, more efficient in delivering care, and more likely to predict problems As it evolves, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.

 

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