By Greg Jones, chief technology officer, MobileSmith Health.
The COVID-19 pandemic has rapidly changed the healthcare landscape, and with that, the amount of disruptive technologies flooding the industry at the same time has drastically increased. IT leaders are now not only facing new opportunities brought about by emerging technologies, but many previously unforeseen challenges as well – and this will only continue in the year, and even years ahead.
From new technologies to the people needed to implement them, here are four of the top challenges keeping many IT leaders awake at night.
- Maintaining a healthy continuous integration (CI) and continuous delivery (CD)
This practice ensures a faster delivery of a developed service and can provide a competitive edge. The challenge today is maintaining a CI/CD pipeline with changing cloud architectures, while also maintaining proper security compliances and legacy services without greatly increasing technical debt. Cloud service providers change supported versions that can impact new development and force tech debt to take priority.
Additionally, having a solid CI/CD pipeline with testing, compiling and automatic deployment is key. The solution to this challenge is ensuring that all services are initially built with CI/CD in mind. Development leads must ensure their teams are approaching the development of every solutions based on this mindset and must be given the time to keep up with technology, service provider changes and too gather current and future service changes.
- Getting Artificial Intelligence (AI) and Machine Learning (ML) buy in
Today, machine learning is commonly talked about – similarly to how data was talked about in the ‘90s and “analytics” in the ‘00s. It is essential that business leaders understand the change a company will introduce when implementing AI/ML. This may include new product features, knowledge required by staff, new staff positions, etc.
To successfully combat this challenge, business leaders must start with a feasibility study (data availability, model reliability, costs, product value, supportability, governance issues). This may be performed by knowledgeable staff or consultants and will help communicate at a business level what is means to introduce machine learning into the company or into a product. This can also help start the governance process to ensure expected outcomes match real outcomes.