“What I mean (and everybody else means) by the word ‘quality’ cannot be broken down into subjects and predicates. This is not because Quality is so mysterious but because Quality is so simple, immediate and direct.”
What do we mean when we say “data quality” or “Spend data quality” within the context of Aggregate Spend or disclosure? Why is the quality of Spend data important to the success of a company’s aggregate spend disclosure initiative? What role does it play in reporting a company’s practices to federal, state or international bodies? Where and when does quality surface in the life cycle of data usage, processing and reporting?
Quality in data is accuracy: when quality is bad, accuracy is low. Accuracy is the alignment of data to a company’s actions; actions are the embodiment of a company’s policies; and policies are the documentation of a company’s intentions. In disclosure, the data that is reported is the only representation of a company’s intentions, the more inaccurate the data, the farther it is from the intentions, the worse the representation.
“Quality” is a misnomer for our purposes, it’s accuracy that we pursue; quality is the term of the industry in conversations about data, for this reason we’ll continue to use it in this post. Knowing that we truly discuss accuracy, I’ll point out now that with respect to the quote at the beginning of this post from one of my favorite books, we will examine both subject and predicates of the accuracy of Spend and Spend related data.
Spend data quality exists in both the Spend itself and the HCP and HCO master data on which the Spend relies. The quality of a company’s data can affect the decision to engage one HCP versus another, it affects the recording of Spend, the transmittal of Spend from its source to a repository and eventually what a company reports to interested parties and all analytics thereafter. Reporting to CMS (or to states or internationally as well) is in essence the communication of the nature of a pharmaceutical or medical device company’s policies with regards to Spend and its ability and desire to adhere to its own policies. Stellar quality of one’s data cannot elevate one’s policies, but it can support the practice of the policies; conversely, low quality data will only serve to diminish the best of intentions.
To better understand the decision and communication points in which data quality plays a part of high impact, we’ll look through the lens of when data is utilized in the Spend life cycle.
Third Party Vendor Note: Take any aspect of Spend recording and communication that is difficult to do internally and add a few points. It’s like trying to juggle…while on a unicycle…with a patch over one eye to impair your depth perception; juggling is hard enough.
There are four stages that are worth noting:
The time before Spend occurs, usually the decision making process to select a given recipient for an action that will result in Spend.
The pre-activity phase concerns the facts being gathered to support a decision like selecting a Speaker or the recipient of a Grant, and how well the process is captured. Two aspects jump out during this phase that involve data accuracy, the first, is your master data, which is in support of the decision, full and accurate? Does it have sanction information? Does it hold accurate licensing and credential information? Secondly, is the entire process being captured and preserved with enough facts to support easy participation in the Aggregate Spend process?
The improvements to be had here are to the Spend capture solutions, for grants, consulting arraignments, clinical trials and the like.
The activity has occurred, now the information is being recorded. This information is recorded by T&E systems like Concur®, in accounts payable systems like SAP for contracted payments, by outside vendors like CROs, MedEd and Speaker Bureau firms or simply in spreadsheets by internal departments.
The post-activity phase is where the accuracy or data quality is the most challenged. This phase covers both more spontaneous Spend on lesser known recipients such as attendees at meetings or various non-targeted HCPs in an office, which I’ll call Ad-Hoc Spend, as well as known recipients of anticipated Spend, which I’ll call Known Spend, recorded into solutions incapable of recording enough facts, such as AP systems that can be costly to enhance. The challenge with both Ad-Hoc and Known Spend is in recording the who, what, when, where and why of Spend in a way that allows it to be communicated and understood by a downstream system or person during the Aggregation phase; and getting all of that information recorded as accurately as possible.
Ad-Hoc Spend posses more of a challenge than Known Spend, simply in that the recipients are less well known to those that are recording the facts. This process, beyond the human parts of policy and knowledge gathering about the actual recipients, hinges on recording the information into a system or document as accurately as possible. Can the solution for activity X capture all of the salient facts? Does the user have access the life science company’s master data or at least solid industry information?
Known Spend is notorious for eventually landing in AP systems which were never designed to capture facts that are not purely financial in nature, and can be very expensive to augment. One of the leading misconceptions in early stages of Aggregate Spend initiatives is to think that all Spend will be in the AP system and that it will be usable. This data is more controllable since it usually starts with contracts for either Consultants, Researchers, KOLs and more. The issue resides not as much in identifying the Recipient as it is in the limitations of the related systems to robustly capture and communicate all of the other salient facts about Spend.
The improvements to be had here are in ensuring master data involvement in the recording of Recipients to Spend, for both internal departments and external vendors. The key is to have either internal identifiers or solid industry identifiers attached to each record of Spend. Another improvement is to ensure the full capturing of relevant facts and enforcing that said facts are inline with corporate expectations. This juncture is where data quality solutions, such as our Consummate Provider™, will begin to come into play to help with enforcement of standards, augmentation and the communication of unresolved issues.
Send the post activity information collected in the various points systems, into a centralized solution for intelligence and reporting. This stage comes in many flavors and represents all steps needed to make coherent sense of all of the collected Spend, in order to report.
After Post-Activity and before Aggregation is the primary juncture at which quality should be assessed, reviewed, addressed and assured, particularly for Ad-Hoc Spend. Assuming that effort has been taken during Pre and Post Activity phases, this is the big quality blanket phase that no Spend should pass through without being assessed. Most companies have historically built a collection of macro type processes that together with a good amount to manual effort, try to address the need for enforced data quality. Another approach is to try to clean up the issues inside of the same solution that handles aggregation and reporting, which leaves a very messy audit trail.
The improvement in this phase is beginning to be addressed by formal products such as our Consummate Provider™ solution; such solutions need to excel at managing qualitative rules, revealing problems, allowing for resolution by the responsible parties and a good bit more. It’s also key that these solutions sit apart from the key reporting solutions to leave a clean firewall between the job of assembling clean and accurate data and the job of aggregating, interpreting and reporting.
The Spend has been collected and aggregated and is in a coherent and ready state, this is the last period in the company’s control, where the accuracy aspect of our discussion truly comes to the fore.
Although we have a solution for pre-disclosure called Consummate Review, it took a conversation with a friend to make me see that this phase again, although about relationships and prevention over cure, it’s also very much again about data accuracy and quality. The pre-disclosure phase is the last check around the house to see if the oven or lights are still on, before you leave for vacation.
Master Data Note: Although the topic of this post is Spend data related, we can’t ignore the prime role that master data of health care providers, including NPPES data and health care organizations with a focus on teaching hospitals, plays in the overall story of Spend data quality, capture and disclosure. This is too large of a subtopic to focus on here, the accuracy of a company’s master data will affect multiple aspects of the Spend life cycle, from selecting the right, perhaps non-sanctioned HCP for an activity, to recording the accurate HCP as a recipient of Spend, to the designation of an HCP or HCO as reportable or not for a given governing body. All of these aspects rely on the accuracy and completeness of the master data, the quality of which raises or lowers the results of all other efforts.
Enforce quality/accuracy early and often and you may as well through in a sanity check at the end as well.