AI to save healthcare sector $52 billion in 2021: report

New artificial intelligence (AI) applications in healthcare will lead to billions in savings within the sector over the next four years, according to analyst firm ABI Research.

In its recent report, ABI Research found that while only a few AI applications have resulted in commercialized and scaled solutions so far, most do show promise in Proof of Concepts (PoCs). If successful, these applications could produce more effective drugs, save doctors’ time, and save lives.

Hospitals in Israel and the United States have already started to adopt AI-based predictive analytics. The number of patient monitoring devices using the data to train AI models for predictive analytics will rise from 53,000 at the end of 2017 to 3.1 million in 2021 with a CAGR of 176%. This includes the use of AI for home-based preventive healthcare solutions.

With more devices connected to AI-based predictive analytics models, ABI said hospitals will save $52 billion in 2021, led by North America with $21 billion in savings.

“If AI vendors hope to fulfill the potential of their applications in hospitals and medical institutions, they must help implement the communications, network, and IT infrastructure necessary to deliver actionable analytics,” said Pierce Owen, principal analyst at ABI Research.

“Unfortunately, clinicians in most hospitals often must work with pen and paper or pagers from twenty years ago and have limited access to secure, networked devices. These institutions need help to collect data in a secure manner and deliver actionable analytics while staying compliant with all regulations.”

Applications that target hospitals and medical institutions include predictive analytics for patient monitoring, finding patients for clinical trials and transcribing notes for electronic health records (EHRs).

EarlySense builds AI-based predictive analytics that use data from a contact-free sensor that goes underneath a mattress. The company has launched products for both hospitals and homes.

Deep 6 AI finds and matches patients to clinical trials. It claims it shortens the process from ten months to seven minutes. It scans complete EMRs including the free text and reports using natural language processing (NLP) to find the best possible matches. Cedars-Sinai Medical Center in Los Angeles has used Deep 6 and already sees an ROI of $10,500 per trial.

LexiconAI has expanded its NLP voice-controlled transcription software to healthcare to save clinicians time in filling out EHRs. Its PoCs have resulted in $5,000 per user per year in efficiency and savings, and it only takes early adopters one month to reach ROI.

Of course, not all the applications examined within the report target hospitals. Pharmaceutical companies continue to invest in AI for drug discovery, and some AI vendors target patients for pre-primary care.

AI has yet to generate a molecule design for a drug that has gone to market but some from BERG Health have made it into Phase I and Phase II trials with successful results so far. AI-based pre-primary care apps with NLP, such as Your.MD, have already gone to market.

With human lives potentially at stake, AI vendors also need to establish a reputation of trust through PoCs and proper regulatory approval before attempting to scale. Once scaled, they may need to assist with data curation through managed services for clients that lack data scientists.

“We as a society need AI to transform the healthcare sector across the board. Already, people without insurance cannot afford care, and the massive increases in costs and healthcare spending will become a drag and burden on the economy if they continue. Luckily, a couple of these AI applications have already proven they can save money and lives,” said Owen.

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