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August 19, 2021

Digital pharma trends: Artificial intelligence leads Twitter mentions in Q2 2021

Pharmaceutical Technology lists the top five terms tweeted on digital pharma in Q2 2021, based on data from GlobalData’s Influencer Platform.

By GlobalData Healthcare

Artificial intelligence leads the top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform.

1. Artificial intelligence – 802 mentions

The steps being taken to integrate artificial intelligence (AI) into healthcare and the use of AI techniques in the detection and management of various diseases were popularly discussed in Q2.

Rafael Grossmann, a surgeon and clinical innovator, shared an article on two new companies namely Anumana and Lucem Health being launched by healthcare company Mayo Clinic that will collect and analyse patient data gathered from remote monitoring devices and tools to enable early detection and diagnosis of diseases.

Mayo Clinic will launch a remote monitoring platform that will enable clinicians and physicians to make quicker and better decisions with the help of the collected and analysed patient data thereby speeding up the diagnosis before symptoms appear. It will also allow patients to take more control of their health and related decisions.

AI was also discussed with reference to how the technology is helping in the treatment and management of diseases such as Parkinson’s. Engineers from Imperial College and a start-up have enrolled a patient with Parkinson’s into a trial that uses a wearable device developed using AI techniques to monitor and manage the disease, according to an article shared by Berci Meskó, director of The Medical Futurist Institute. The trial aims to collect data on all the symptoms of the disease as well as data on how a patient responds to medication.

The term was also mentioned in an article shared by Gary Monk, a healthcare technologist, on biotechnology firm Viome receiving breakthrough device designation from the US Food and Drug Administration (FDA) for its mRNA analysis technology tool and AI platform. The technology analyses the interactions between microbial activities and human gene expression in saliva samples to detect and predict the progression of oral and throat cancers.

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2. Telehealth and telemedicine – 885 mentions

Physicians being in favour of permanent telehealth expansion, telehealth services reporting high patient satisfaction during the pandemic, and extending telemedicine to the homeless community were popularly discussed in the quarter.

A recent study conducted by a team of researchers from Syracuse University has found that physicians are in favour of telehealth services being made permanent, according to an article shared by Vince Kuraitis, founder of Better Health Technologies, an independent healthcare consulting company. Many medical health professionals, however, opine that the challenges related to virtual care technologies should not be ignored.

Telehealth services, in some cases, limited physicians’ ability to provide healthcare, while organisational support for these services was also lacking. Health economists argue that although telehealth services increase healthcare access to many people, especially in remote settings, a higher market concentration can adversely reduce access.

Discussions around telehealth also included an article shared by Harry Greenspun, a physician and partner at management consulting firm Guidehouse, on telehealth services registering high patient satisfaction rates during the pandemic, according to a survey conducted by Covid-19 Healthcare Coalition.

The survey found that more than 70% of the patients preferred to access virtual care even after Covid-19. It also found that 83% of the respondents considered telehealth services to be effective, while 78% believed their health concerns were proactively addressed. Experts, however, believe that more training is required to ensure telehealth access to patients aged 65 years and older who face difficulties in using telehealth services.

Telemedicine was also discussed with reference to how the technology can be expanded to the vulnerable sections of the society such as the poor and the homeless, according to an article shared by Jamey Edwards, the CEO of Cloudbreak Health, a unified telemedicine company. A number of service providers and local officials implemented telemedicine services to prevent a surge in infections among the poorer communities. A collaboration in 2020, for example, allowed homeless veterans to consult through telehealth kiosks.

3. Covid-19 – 484 mentions

Various Covid-19 detector tools being developed by companies were popularly discussed in the second quarter of 2021.

Multi-national conglomerate GE is working on a Covid-19 detector that is being integrated into an iPhone, according to an article shared by John Nosta, founder of NostaLab, a healthcare think tank. The sensors can be fitted into devices such as smartwatches and phones to identify airborne viruses and bacteria on surfaces or in a person’s breath, the article noted.

The GE microchips are expected to be upgraded in time to detect a seasonal flu and other pathogens. The company along with the National Institute of Health (NIH) has been awarded a grant to develop a prototype sensor chip that can initially detect one virus and later be customised to detect multiple viruses.

Covid-19 was also discussed in the context of technology company Apple launching a new smartwatch study to identify the disease especially in at-risk groups, according to an article shared by Daniel Kraft, a Stanford and Harvard trained physician-scientist. The Apple respiratory study will analyse optical heart sensor of wearable devices and conduct weekly surveys to determine patterns among iPhone participants who are infected with the SARS-CoV-2 virus, flu, or other infections.

The study is being conducted by researchers from the University of Washington School of Medicine, Apple, the Seattle Flu Study, and the Brotman Baty Institute for Precision Medicine. It will assess disease patterns in individuals aged 22 years and older who are at a higher risk of contracting infections due to their frontline jobs and other close group activities. Researchers will also focus their study on indigenous, Black, and Latin communities who have been disproportionately affected by the pandemic in the US.

4. Innovation – 475 mentions

Collaborations and partnerships formed in healthcare research and innovation, and exponential developments in healthcare technology were popularly discussed in Q2.

Five industry associations specialising in areas such as biotech, pharmaceutical, and medical technologies in Europe have collaborated with the European Commission (EC) on a strategic agenda for research and innovation in healthcare over the next ten years, according to an article shared by Sherry Reynolds, an eHealth strategy expert.

The collaboration, named Innovative Health Initiative (IHI) framework, is part of the $132.35bn Horizon Europe funding programme that seeks to strengthen healthcare systems. It will create a robust research, development and innovation ecosystem that promotes cost-effective public-private innovations as well as cross-sectoral innovations in the region, the article highlighted.

Innovation was also discussed in an article shared by Donna K Lencki, a health tech entrepreneur, on Cleveland Clinic’s plans to expand its commercialisation arm by adding a new digital health partner who will oversee innovations as well as develop a pipeline of health information technology (IT) start-ups. The new partner will help start-ups raise funds for commercialisation of technology and provide assistance in project development and management.

5. Machine learning – 264 mentions

A machine learning (ML) model that helps in predicting whether Covid-19 patients need dialysis, the US Food and Drug Administration (FDA) authorising an ML software to help in diagnosing autism, and the use of ML use in detecting natural product drugs, were popularly discussed in the second quarter.

A team of researchers used data from hospitalised Covid-19 patients from the Mount Sinai Health System and deployed an ML model to help predict whether a patient requires dialysis or is at a high risk of death, according to an article shared by Ken Congdon, director of content marketing at Prognos Health, a clinical data and analytics platform. Five ML models namely LASSO, XGBoost with imputation, random forest, XGBoost without imputation, and logistic regression were evaluated as part of the study.

The team found that the XGBoost with imputation ML model was the most effective in predicting dialysis or death in patients. Researchers claim that ML models can help in identifying complex patterns in large amounts of data. They can also help in Covid-19 disease prognosis by identifying the associated complications and underlying factors that can worsen or improve the condition of a patient.

Other discussions around ML involved Gary Monk’s tweet on the US FDA granting marketing authorisation to an ML software that helps diagnose autism. The ML system was developed by digital health start-up Cognoa and utilises information from home videos and questions from parents and clinicians to help paediatricians in diagnosing autism.

The goal of the model is to detect autism spectrum disorder (ASD) in the early stages as data reveals that kids in the US develop autism as early as four years but many are not able to meet a specialist in time. The FDA authorisation comes after a double-blind study that was conducted on 425 kids ranging in age from 18 months to five years in 14 different locations. Cognoa also ensured that the participants were from different ethnic groups and gender.

Berci Meskó shared on article on a new process using ML algorithms developed by Carnegie Mellon University researchers that can detect natural product drugs, which can be used to treat various ailments including cancers and viral infections.

Penicillin is one of the popular drugs derived from natural products and was discovered by chance. Replicating the drug for potential human use and efficacy, however, is a difficult task for laboratories. ML algorithms can assist researchers in detecting and isolating natural products that can be used to develop natural product drugs for humans, the article highlighted.

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