Reduce uncertainty in your data stewardship program
Did you know Verato can automate data stewardship decisions that would otherwise require expensive and time-consuming manual review by data stewards? We can, and it’s pretty amazing – read more about Verato AUTO-STEWARD. Automating data stewardship can have a big impact on an organization’s bottom line compared to the alternative of manually stewarding a huge backlog of potentially matching patient records. But that’s not what I want to talk about today – what I want to talk about today is how Verato AUTO-STEWARD can also reduce the uncertainty associated with data stewardship.
Organizations who already devote time and money to stewarding the potentially-matching records from their MPI/MDM system still face uncertainty with their data stewardship program. How can I be certain my data steward made the right decision? If I’m not convinced that two potentially-matching records are the same person, how can I be certain they are two different people? Am I using my limited data stewardship resources on the most valuable data stewardship tasks?
Verato AUTO-STEWARD was designed to improve the efficiency of data stewardship, but it also addresses these uncertainties.
How can I be certain my data stewards made the right decision?
When data stewards look at a set of potentially-matching patient records and decide whether they are the same person, often they just have to make the decision based on the limited data available – perhaps there is some address history for one of the patient records that is the same zip code as the other, and that is the deciding factor for the data steward to be confident they are the same person. But you can only be as certain about the decision as the quality of the data available, which is often limited within your MPI/MDM database. To eliminate the uncertainty, you would want to have an objective 3rd source of information to help prove that the potentially-matching records really are the same person. This is exactly what Verato does – we use our own curated reference data to make the same-person/not-same-person decision, which takes away the uncertainty.
What if I think the patient records in my data stewardship task are not the same person?
Often patient records end up as potential matches in a data stewardship task because they have just partial information in common. For example, you might have two patient records both named Mary, both born on the same birth date, but they have different last names and different addresses. Are they different people? Are they the same person, and Mary simply got married resulting in a name change and an address change? The best a data steward can hope for is to have enough information to cast doubt on the likelihood that the two potentially-matching records are the same person. In that case, you can either leave the task as unresolved, or you can err on the side of closing the task and marking it as ‘not-same-person.’ But you don’t really know if they are different people, you’re just certain you don’t want to declare them the same person. But by comparing both to identities in Verato’s comprehensive reference database, we can see that the two Mary’s are indeed two different people who just happen to share the same birth date, and we can confidently tell you they are not the same person. This eliminates the uncertainty you face if you choose to have a human data steward mark the task as ‘not same person’ even though they don’t know for sure based on just your own data.
Am I using my limited data stewardship resources on the most useful tasks?
This is a common question that data stewardship teams grapple with. Most organizations have more data stewardship tasks in their work queue than they can keep up with. So how do you focus your data stewards on the most useful work? The most comprehensive approach is to use Verato AUTO-STEWARD as a first line of defense but still keep your data stewards to focus on more difficult tasks. Using Verato AUTO-STEWARD will automatically clear up the majority of your data stewardship tasks, the run-of-the-mill potential duplicate stewardship tasks. But there will still be some potential duplicates that even Verato can’t definitively resolve. And there will still be other types of data stewardship tasks that might revolve around data elements unique to your organization (examples such as suspicious-looking insurance ID numbers that you want to investigate as potential fraud, or cases where the same MRN is used for two very different-looking sets of patient data). Verato AUTO-STEWARD still helps reduce the uncertainty about how to tackle the still-unresolved data stewardship tasks.
This might happen in cases where the identity is a potentially fake/fraudulent identity that does not exist in our reference database – if we didn’t come close to finding a match in our reference database, we tell you that in our AUTO-STEWARD response, and you can use that information to help either de-prioritize the task (don’t bother trying to find more information if we didn’t even have it) or move it to a different team (perhaps it should be investigated by a fraud team). Or this might happen in cases where one of the potentially-matching identities is potentially a match to more than one person in our reference database – maybe you sent us two John Smith identities at the same address, but one was missing a birth date. If we know there’s a John Smith Sr. and John Smith Jr. both living at that address, we can’t definitively say which is the right person, and we tell you that there are multiple potential matches in our AUTO-STEWARD response – you can use this information to change the priority of manual review. Perhaps you want to make this a higher priority for your data stewards, because you know they can look for pediatric records associated with the MRNs to definitively know which one is the ‘junior.’ And in many cases one of the potentially-matching records you send to Verato AUTO-STEWARD just has enough missing or incorrect data that we can’t confidently declare it a match to our reference database. Guess what? We tell you that as well in our AUTO-STEWARD response – we’ll tell you that one of the input records was definitely a match to a known person, but the other input record was not a confident-enough match to that same reference identity. Even though we have to fall back on an ‘uncertain’ answer, the extra detail we send back can still be used to help you decide whether you want to prioritize the data stewardship task for human review.
By running your MPI/MDM data stewardship tasks through Verato AUTO-STEWARD first, you get extra insight that will help you choose which remaining data stewardship tasks are worth human review and which are not, further reducing the uncertainty about whether you’re focusing your stewardship team on the most relevant tasks.
Automating data stewardship decisions with Verato does wonders for the efficiency of your stewardship team and your bottom line. But it also comes with an extra icing on the top – giving you a much higher degree of certainty about the stewardship decisions and letting you focus your stewardship team on the highest-value stewardship tasks that remain.