Using a rule/automation engine and placing rules at system boundaries - this entire pattern can be formalized into technology. If a later result or exception can be traced to the events that initiated them - rules can be defined and written to correct the input data.
Here is an example: If transactions going out to insurance companies are run across a processing or rule engine, non paid claims or ones coming back for clarification can be tied to the sent data. This provides a place to automate to ensure that the items going out in the future do not have the conditions that caused the failure to take place. This pattern is often called continual process improvement
Intercepting Data and Adding Rules Decisions for HL7 has an optional feature called Interceptor Rules that allow you to stop data at the system boundary and create rules from that data. You don’t have to understand or define all of the rules and combinations of rules before implementing the system, because the rules are added to the system over time using the actual data that’s being moved through the system, which allows the rule engine to “learn” your business. Rules are written to determine which data needs to be stopped for manual review (Intercepted). When intercepted, data can be fixed in a custom user interface OR a rule or can be created to automatically fix this type of data and run on the stream of data going forward to fix future occurrences of this condition. For example: if a system is sending in custom procedure codes or incorrect CPT codes, rules can be written to intercept these. When patterns are established (look for this in description field) rules can be created to auto correct these.