Approximately 12 million adults seeking outpatient medical care in the US are misdiagnosed- according to a new study published in BMJ Quality & Safety. This figure amounts to 1 out of 20 adult patients. Researchers say in half of those cases, the misdiagnosis has the potential to result in severe harm.
In an earlier analysis from the Johns Hopkins University School of Medicine, misdiagnosis and medical error was found to account for 9.5% of all US deaths. This makes medical error the third-most deadly killer of Americans, accounting for more that 250,000 deaths each year!
Medical errors are mistakes made by providers across the entire continuity of care - diagnosis, treatment and follow-up. Misdiagnosis can lead to the wrong treatment, or missed treatment. Mistakes in treatment can be caused by prescribing the wrong medication or dosage and by mistakes in performing medical procedures. Mistakes in follow-up can lead to patient relapse.
The potential impact of AI services that assist health professionals to avoid such medical errors can therefore be measured in human terms: averted suffering and lives saved.
Challenges create Opportunities
Unfortunately, the digital transformation of health-care has historically lagged other sectors. This is largely due to the fragmented nature of the sector and the resulting fragmentation of health data across legacy systems, which are often still off-line.
The necessities of the COVID crisis has created tremendous pressure to overcome these deficiencies. One example of this is the coordination of testing and the reporting of test results which requires real-time access to data across multiple sources. The current crisis is also driving the adoption of telehealth services which, in turn, are driving adoption of other cloud-based services.
According to Gartner, hospitals alone plan to spend more than $5 billion on cloud computing by 2025. Some legacy systems are being cloud-enabled by being ported entirely to the cloud or deployed in a hybrid manner.
This migration of health data to the cloud will provide fertile ground for AI-based services that are able to turn this data into improved health outcomes. Some examples of near-term opportunities for AI-assisted services in the healthcare sector are:
To improve quality and efficiency of diagnosis and treatment
medical image interpretation
tele-health and virtual nurse care assistants
precision medicine using genomic and diagnostic test data
To accelerate and reduce the cost of drug development and improve drug treatment outcomes
predictive modeling in drug development
clinical trial design and automation
medication monitoring and compliance
To streamline admin workflow to reduce cost and improve patient experience
hospital logistics, automation and business process solutions
predictive data security and fraud prevention
machine-aided patient engagement and follow-up
According to Accenture, the total investment in healthcare AI is expected to reach $6.6 billion by 2021 and key clinical health AI applications could potentially create $150 billion in annual savings for the U.S. healthcare economy by 2026. The economic impact of AI in Healthcare over the next decade therefore has the potential to exceed that of any other sector.
Most importantly, in human terms, the promise of AI is that this investment will improve access to healthcare by simultaneously improving quality, efficiency and equity.
We are on a mission to support a new generation of startup founders building ethical cloud-based AI tools that empower caregivers.
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