And then there are other kinds of projects: projects with more general goals, for which success can only be described in clear terms once those goals have been reached. Such projects are not like construction efforts with knowable end points. Rather, they are journeys.
Health care management in the US is in a huge state of flux, and no one knows where it will all end up. Ab Initio is helping a number of large health care providers navigate aspects of this journey. What follows is an account of two such efforts.
One big topic for health care providers is member management. Obviously, healthy members are more profitable than less healthy ones. But health care providers don’t usually get to choose their members. And if they tried to, they could understandably cause an uproar.
Ab Initio is helping one health care provider (which we shall call "HCP") take a positive approach to this problem. First, make sure healthy members stay with HCP. Second, help healthy members stay healthy. Third, identify members who could be using lower-cost generic drugs and make them aware of that option. Fourth, aggressively steer somewhat less-healthy members into programs that will identify medical issues sooner (when costs are lower) rather than later (when costs are higher). And finally, for members incurring significant out-of-pocket expenses, determine whether Medicare Part B would be a better choice; and if so, steer them there. If Medicare Part B is better for the member, chances are it will also lower costs for HCP.
While these goals are somewhat nebulous – there is no single or obvious way to achieve them – Ab Initio is helping HCP investigate and implement a variety of IT approaches. These efforts center around information capture, integration, analysis, rules definition, and finally, rules execution in systems that interact with the members.
To begin, Ab Initio has helped establish a framework for defining and executing large numbers of complex rules. These rules are applied to information for each member. The rules categorize the members (healthy, less healthy, healthy but…), and within these categories members are flagged for various actions.
A “member” can be far more complex than just a single individual – it might include entire families. The categorization of members changes as HCP better understands its customer base. And ideas about the kinds of activities it can provide its members are ever-evolving.
Some ideas work and some don’t, but the only way to find out what works is to build something and try it out. The faster you can try out ideas, the faster you can figure out which ones will succeed. The Ab Initio framework, as constructed for HCP, allows HCP to pull complex data about individuals, put it together into “members”, and then apply complex sets of rules. And it can do all of this in very small amounts of time, allowing for hugely productive what-if experiments.
The rules and data are so complex that understanding results can be a challenge. The Ab Initio Business Rules Environment and Enterprise Meta>Environment (EME) allow the business users to try out rules and immediately see the results, and to trace the computation of the results all the way back to the original data.
Additionally, the complexity of the overall problem means that being able to graphically see how the system makes decisions is critical to the business users. Once rule changes have been defined and initial testing has been done, the new rules need to be run over the entire member base for a complete analysis. The Ab Initio Co>Operating System’s raw performance is so high that not only can test runs be done quickly, but HCP is also able to run all of their rules across all their member data on a daily basis.
As a consequence of deploying the new system, HCP has recognized that it can make even better decisions by pulling in ever more data from their disparate systems. The business has recognized that their strategy now depends on truly pulling together all the information they have into a single place; and that means substantially expanding their data warehouse, which is also built and maintained with Ab Initio software.
Ab Initio is helping another health care provider with a different kind of journey, the replacement of ICD version 9 codes with ICD version 10 codes. ICD stands for "International Statistical Classification of Diseases and Related Health Problems" and these codes are used pervasively across the health care industry. The ICD10 codes represent all the same diseases and health problems as the ICD9 codes, but with far greater detail. For example, there is a single ICD9 code for “open wound of shoulder and upper arm”, but there are ICD10 codes for further describing the wound as “superficial”, “open”, “crush”, “amputation”, “dislocation/sprain/strain”, and “other”. These codes are of great interest to health care providers because they are used for many purposes, including reimbursements. How codes are assigned can make a big difference to the bottom line.
For this provider, the first step in this journey is to update all applications that use ICD9 codes to use ICD10. There are about 1,000 ICD9 codes, but close to 50,000 ICD10 codes. Ouch. This is obviously an enormous task. Ab Initio’s expertise with metadata helps the provider survey all of its datasets as well as all of its applications’ source code. The Ab Initio Data Profiler can analyze large numbers of large datasets to figure out which fields of these datasets appear to have ICD9 codes, and which other data elements appear to be related or relevant to ICD. And Ab Initio’s EME code-parsing capabilities can scan application source code for key verbs, field names, phrases, and the like.
Ab Initio helped the provider put all this information together into the EME and then built a workflow process so that humans could inspect each potential “hit” in context and determine if it is indeed valid.
At the same time that identification of all uses of the ICD9 codes was being performed, data-processing applications were built to pull together information about each claim to automatically convert ICD9 codes to ICD10. There is often sufficient other information in the datasets that when combined with ICD9 can yield the right ICD10. However, this is very specific to each dataset, and so there can be many rules for the conversion and the conversion rules can be very complex. As with the earlier HCP example, getting these rules right is an enormous challenge, so Ab Initio set up a framework for the business experts to be able to interactively specify, test and deploy their conversion rules.
As part of building conversion rules, the provider had to take into account the potential financial implications of the conversion mappings it had chosen. There is no such thing as the “correct” mapping of ICD9 to ICD10 – other data in a member’s claim can substantially change the mapping – and yet these codes are used to calculate reimbursements. So the mapping strategy can have a major impact on the bottom line.
Imagine doing a complete conversion effort and only then finding out that the choice of codes would reduce reimbursements by 10%. Unfortunately, that is mostly what is happening in the industry. For this health care provider, Ab Initio enables bulk conversions to be done very rapidly, and these are applied to large claims datasets to analyze the financial impact of the conversion rules. Rather than waiting till the end of the conversion project, as is typical, the business can steer its conversion process to make sure that it is at least revenue neutral.
While no one can claim to know where the health care system will end up a few years from now, these are but two examples of how Ab Initio – its people and its technology – is actively helping its customers navigate this long and difficult journey.