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lobally, healthcare organisations have accelerated adoption of artificial intelligence (AI) with the ones still implementing frameworks planning to go live within 24 months. Hardly surprising given the improved consumer engagement that result from the technology.
But more than that, the challenging economic climate is seeing healthcare organisations looking for better ways to make processes more efficient, enhance their existing products and services, and lower cost. The key to this is AI that brings with it a more innovative environment to automate manual, error-prone processes, and introduce a sophisticated layer of analytics that can deliver new insights to the wealth of data already available.
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These platforms use algorithms and machine learning to analyse and interpret data, while empowering the healthcare organisation with the means to provide more personalised customer experiences. Understanding people at an individual level means being able to enhance the relationship and identify high value leads. After all, at its core a healthcare provider must deliver effective treatment that improves the quality of life. This is where AI can be used to enhance the quality of data and therefore optimise how people are cared for.
Making sense of it all
As with so many innovations, good, clean, well-organised data is key to success, and poor quality data often represents a challenge. The respondents in an InterSystems survey identified volume and quality of data for training as a barrier and recognised that sufficient data volume and confidence in the data are critical success factors.
Considering how quickly the volume of healthcare data is increasing and the growing complexities of the data sets, it is no wonder that the global AI in healthcare market is expected to top $45 billion by 2026. As more stakeholders in the healthcare value chain start embracing AI (think hospitals, pharmaceutical companies, biotechnology providers, and so on), the sector is one that is perfectly positioned to highlight the benefits this automated way of dealing with data can bring.
A heightened focus on quality data and models that can be used by both developers and data scientists enable healthcare organisations to accelerate and leverage the true power of AI. This means that those who want to take leadership roles in the industry must now invest in their data quality and push for clean, harmonised data to implement and get the most out of their AI tools.
In practice
Some of the use cases of AI in the healthcare industry range from improving data quality, reading images to assist in diagnosis, and early identification of hospital acquired infections. And in developing markets like Africa, the advantages of being able to leverage AI take on an entirely new meaning.
In rural areas, the technology can be used to help patients book appointments by creating personal calendars for doctors and their aides. This mitigates against the risk of no-show appointments given the geographic and economic constraints of getting to a clinic or a consultation room. For their part, doctors can collaborate and exchange information concerning medical questions.
It all comes down to a willingness to adapt to the change that technology innovations enable and for healthcare organisations to embrace AI throughout the value chain. It is something that can greatly enhance data and its analysis. Ultimately, patients will be the ones that benefit the most given the inevitable improvements in the care provided to them.
