Complexity Delays AI Convergence with Data Analytics: HIMSS Report
As healthcare organizations are learning how to best deploy AI solutions, many have struggled to mature, particularly around using AI scores in analytics. Why? Complexity of projects! Yet, the market is hungry for solutions.
According to the recent white paper from HIMSS, The Convergence of Data Analytics and Artificial Intelligence (AI) in Healthcare, the ability of payers and providers to utilize AI machine learning scores and convert results from basic analytics to predictive, delivers higher value.
Key Findings
This recent white paper from HIMSS, The Convergence of Data Analytics and Artificial Intelligence (AI) in Healthcare, the ability of payers and providers to utilize AI machine learning scores and convert results from basic analytics to predictive, delivering higher value.
Across the board, more healthcare organizations realize the benefits of a modernized approach to business through AI — and they’re taking action. Early success in back office applications stood out, including:
- Revenue cycle
- Operations
- Procurement
- Strategic Planning
Top areas of opportunity today are:
Source: The Convergence of Data Analytics and Artificial Intelligence (AI) in Healthcare
We believe that this is a strong indicator of even greater potential results in the healthcare revenue cycle and payment electronification processes.
Barriers to Adoption
One of the primary factors slowing adoption is “black box” anxiety. Healthcare professionals are hesitant to use what they don’t understand. Other barriers mentioned include:
- Challenges working with large amounts of unstructured data
- Problems capturing, accessing, and integrating data from disparate sources
- Difficulty in upskilling employees to accomplish increasingly complex data analytics requirements
3 Principles for Overcoming Complexity
For healthcare organizations interested in addressing their challenges with complexity, the study offers three suggestions:
- Solve well-defined problems: Look for use cases such as repetitive processes where automation and intelligence can deliver immediate value.
- Prioritize use cases that deliver meaningful outcomes: Focus on areas where AI can be deployed to deliver the most value to the most people.
- Prioritize bottom-up innovation, measurable value, confidence-building, and rapid ROI: AI doesn't require bringing on a massive team of data scientists and developers. It should empower existing teams and deliver insights to all levels of the organization.
The report endorses a platform approach to AI that allows for self-service and empowers effective solutions that address organizational challenges by driving measurable value and improving organizational confidence.