AI is your power source for the future — as an organization dedicated to healthcare, we want you to make it to your best destination. In this series, Lumiata walks you through the AI journey so your organization can:
- Build an AI-enabled strategy (not just acquire new technology)
- Focus your AI investments
- Streamline decision-making processes
- Plot a path forward into the future
- Generate material impact on your business
This series will prepare you to mature through the AI Journey curve, understanding what’s happened with your data and why it happened, predicting what will happen in the future, and then optimizing your actions and decisions. Check back for more in the series.
The world’s been going through a lot of change lately and healthcare technology hasn’t been immune. Just a few years back, AI was something futurists speculated on and only the most innovative organizations invested in.
Well, that’s all changed.
AI is now a key player in your tech, data, and IT strategies, especially for organizations that want to act confidently today to solve tomorrow’s problems.
At Lumiata, we see AI as the “tip of the spear” — a military concept that refers to the forces that are used to break an enemy’s front defenses. When discussing healthcare challenges, it applies to the AI-forward approach that our customers use to solve problems. As a part of your AI Journey, taking this tip of the spear approach is the foundation to understanding the extensive potential that AI holds for your organization.
What are our goals?
While AI can open up a new world of possibility for healthcare organizations, it’s best to start by focusing on areas of potential that best align with your current goals. This is where you’ll find the most immediate opportunity to shape your strategy in a way that’s informed by the potential of AI. Remember — if your current goals aren’t AI-enabled, you have ample opportunity for straightforward wins with automation.
Start by detailing your current objectives. For each, ask whether it is currently enabled by machine learning or AI. For example, COVID-19-related challenges around supply and demand could be addressed with natural language processing (NLP) and rapid response virtual agents. You could also consider the implications of AI on taking appropriate action around cost prediction, determining risk of future medical events, or analyzing disease onset — all of which relate to the quadruple aim.
Next, you’ll move on to identifying new potential to reshape those goals according to the promise of AI. Consider focusing on strategic weaknesses, areas where competitors have been gaining traction in healthcare, and initiatives that might have previously seemed out of reach.
What are we working with now?
Now it’s time to map out your current software and data ecosystem. This step should solidly incorporate the lessons learned from the first leg of your AI Journey, including how you use data and the results you’ve seen.
Software asset management tools (examples include ServiceNow, Snow, Freshservice, and Spiceworks) will come in handy as you’re evaluating and rating platforms, cloud-based tools, and vendor relationships. Consider focusing on the following categories for each asset:
- How and whether they’ve enabled automation initiatives
- Total cost of ownership
- Impact on existing network and organization
- Gap analysis against your new, AI-enabled strategy
Even if you’ve kept meticulous records of your software ecosystem, this step will require conversations across the organization. This is because now you’re evaluating software within the context of the AI Journey.
Where are our opportunities?
The results of these conversations are going to illuminate myriad options to leverage AI tools in your organization. They’ll be clues to filling tech and strategy gaps, improving strategic positions, and enhancing your overall approach to automation.
You’ll likely find yourself with a mix of opportunities to update your AI toolkit. When you reach this point, consider categories and functionality, including:
- Data Management: Matillion, Panoply, Profisee, Aparavi, Databricks, Lumiata AI Studio
- Analytics: Tableau, Looker, Lumiata AI Studio, QlikView
- Machine Learning: RStudio, H20, Jupyter Notebook, Pandas, Spark, PySpark, TensorFlow, Lumiata AI Studio, Databricks, SageMaker
- MLOps: Lumiata AI Studio, Iguazio, DataRobot, Azure, SageMaker
You’ll notice one player, Lumiata AI Studio, mentioned under all four categories. We hate to brag, but we love spreading the word, since it’s the only toolset robust enough to cover the entire field.
How are we defining our tool-specific requirements?
Before we move on to the next stage of your AI Journey, let’s take a step back and decide on requirements for each individual tool category.
Look at the results from your conversations from Part 1 in the series (How have we evaluated success?), and define specific results and criteria. For example, for each category on your shortlist, you should be able to outline its strategic role and list specific desired outcomes.
This stage in the process is going to bring up questions that your organization possibly hasn’t had to answer before. We want to invite you to put together your hardest questions and bring them to your free Lumiata AI Studio trial experience today.