Which analysis activity uses knowledge discovery methods to build a predictive model of patient falls from warehoused data?

Which analysis activity uses knowledge discovery methods to build a predictive model of patient falls from warehoused data?

• A. Tracks health consumer data to establish a weighted case mix to determine nursing personnel allocations
• B. Aggregates multisite research data related to diagnosis and nursing procedures or risk mapping
• C. Retools current work processes after examining existing system data in customized QA reports
• D. Applies knowledge discovery methods to warehoused electronic data to build a predictive model of patient falls

Answer: (D)

Knowledge discovery in databases and data mining involve using methods to find patterns and build predictive models from large, stored data. When this approach is applied to warehoused electronic data, it enables creation of a model that can predict events like patient falls, so practitioners can intervene proactively. The option that describes applying knowledge discovery methods to warehoused electronic data to build a predictive model of patient falls best fits because it explicitly links data warehouse data, knowledge discovery techniques, and the goal of predicting falls. The other options focus on data aggregation for staffing decisions, multisite data synthesis and risk mapping, or process improvements through QA reports—activities that don’t center on deriving a predictive model from warehouse data using knowledge discovery techniques.

Knowledge discovery in databases and data mining involve using methods to find patterns and build predictive models from large, stored data. When this approach is applied to warehoused electronic data, it enables creation of a model that can predict events like patient falls, so practitioners can intervene proactively.

The option that describes applying knowledge discovery methods to warehoused electronic data to build a predictive model of patient falls best fits because it explicitly links data warehouse data, knowledge discovery techniques, and the goal of predicting falls. The other options focus on data aggregation for staffing decisions, multisite data synthesis and risk mapping, or process improvements through QA reports—activities that don’t center on deriving a predictive model from warehouse data using knowledge discovery techniques.