How AI and Data Analytics Can Enable Safer Care
The reality is that many patient safety incidents are never reported and, as a result, health systems miss critical insights into patient risk factors.
AI can tackle this problem from two angles. First, it can automate aspects of incident reporting, making it less time-consuming to report an event. This can set the foundation for a culture of safety where staff feel empowered to voice safety concerns, and incidents and near-misses are thoroughly reported, ultimately leading to culture and process changes that drive safer patient care.
Second, AI tools and algorithms can streamline and automate data extraction and analysis. With more thorough incident reporting and intelligent analytics, health systems can identify the risk factors that contribute to harm incidents and take proactive steps to prevent similar events.
AI can also address safety from a workforce efficiency and wellness lens. When data is situated in siloed systems, healthcare workers have less time to spend providing direct patient care. Research conducted by McKinsey found that nurses spent only seven hours out of a 12-hour shift delivering care. The remaining time was occupied by scheduling, documentation and other administrative tasks.
This data burden is contributing to the emotional exhaustion of staff and a high rate of turnover. AI has the potential to reduce administrative burden in a variety of ways. For example, generative AI can be used to transcribe and take patient notes during appointments, as well as to optimize patient communication by responding to portal messages.
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Collaboration Drives Better Safety Insights
While siloed data creates an overwhelming burden on staff, AI tools can use the wealth of healthcare data housed in hospitals and health systems to unlock safety insights without adding additional work for providers. The more data AI algorithms are trained on, the more accurate they are. Therefore, collaborating and sharing patient safety data across organizations on an industrywide level is key to supporting leaders in making better-informed decisions.
By working with a Patient Safety Organization, which aggregates and analyzes data from across participating organizations, health systems can uncover contributing risk factors to patient safety.
PSOs can harness AI to increase the speed and specificity of patient safety insights. By combining sophisticated AI algorithms and data extraction tools with event reporting data through a certified PSO, health systems can analyze thousands of incident reports in seconds. Collaborative research using AI and Big Data analytics can also enhance predictive models for identifying high-risk patients via early warning systems, arming health systems with more accurate risk and safety models to apply in the future.
Partnerships and collaborations between health systems, healthcare technology providers and AI solution providers establish a robust framework for improving patient safety by sharing data, leveraging advanced technologies and fostering a culture of learning and transparency. Prioritizing patient safety requires industry collaboration to proactively design safety into care delivery processes. Through shared responsibility, healthcare systems can more effectively address patient safety challenges and drive improvements in care delivery.
