To locate health records, statistical matching attempts to string together enough identifying information about an individual to substitute for a unique personal identifier. It involves matching attributes, such as last name, first name, birth date, address or zip code, and gender, and it may use medical-record numbers and all or part of the Social Security number.
The problem with personal attribute keys such as name and address is that they are usually not unique to the individual, change over time, and are often entered into different systems in different formats. And data-entry errors, such as misspellings, add to the difficulties with this type of key. Repeated collection, distribution, storage, and use of these data also represent an important identity-theft risk.
Statistical matching can attempt to correct for some of these changes and errors: The most straightforward process is to tag all of the near matches for human resolution, or disambiguation. Such disambiguation imposes significant costs and operational inefficiencies, particularly if the physician must resolve the ambiguities. Advanced approaches “score” matches on “closeness” to the input set. Those with a high score may be accepted as a match. However, all such efforts are subject to the probabilistic errors inherent in statistical matching systems.
As discussed in earlier posts, there are two types of errors - false positives, in which two different persons’ records are declared to be a match, which can lead to such errors as the wrong patient’s health data being obtained; and false negatives, in which two records for the same person are thought to relate to different people, leading to such consequences as some of the patient’s data being excluded. Both of these errors can lead to serious medical errors, waste (e.g., repeats of tests or the wrong tests), and considerable deviation from the promises of continuity and quality of care postulated for a connected digital health care system.
Disambiguation is a process through which multiple potential identification matches are further parsed until the patient can be matched with his or her data with sufficient certainty to allow for the delivery of a health service with reasonable confidence. The complexity of disambiguation varies according to factors such as the number of potential matches and the type of information available for further analyses. When sufficient digital data are not available to further differentiate potential matches, automated disambiguation may not be possible and may require human involvement. This last case will be the focus of the rest of this post and my next post.
Disambiguation entails implementing significant new workflows and may require substantial time and resources. When human involvement is required, many of the potential benefits of automation are lost. For example, at the point of care, disambiguation is often done by asking the patient further questions regarding personal characteristics and/or health care history. In some situation, disambiguation may not be possible, as when the patient is not present and information needed to further facilitate matching may not be accessible.
I will show how one vendor, Oracle, implements human tasks that can provide workflows such as those identified above. However, these Oracle services can be accessed by applications created with development tools from other vendors (my discussion will use Microsoft Visual Studio).
Introduction to BPEL and Human Workflow
Business Process Execution Language (BPEL), one of the key technologies for Service Oriented Architecture (SOA), has become the accepted mechanism for defining and executing business processes in a common vendor-neutral way. Apropos of this discussion, business processes often require human interactions as well.
User Interaction in Business Processes
BPEL business processes are defined as collections of activities that invoke services. BPEL doesn't make a distinction between services provided by applications and other interactions, such as human interactions. And that's important since real-world business processes often integrate not only systems and services but also users. User interactions in business processes can be simple, such as approving certain tasks or decisions, or complex, such as delegation, renewal, escalation, nomination, or chained execution . . . and matching an ID with an individual.
Task approval is the simplest and probably the most common user interaction. In a business process for opening a new account, a user interaction might be required to decide whether the user is allowed to open the account. If the situation is more complex, a business process might require several users to make approvals, either in sequence or in parallel. In sequential scenarios, the next user often wants to see the decision made by the previous user. Sometimes, particularly in parallel user interactions, users aren't allowed to see the other users' decisions. This improves the decision potential. Sometimes one user doesn't even know which other users are involved - or whether any other users are involved at all.
A common scenario for involving more than one user is workflow with escalation. Escalation is typically used in situations where an activity doesn't fulfill a time constraint. In such a case, a notification is sent to one or more users. Escalations can be chained, going first to the first-line employees and advancing to senior staff if the activity isn't fulfilled.
Sometimes it's difficult or impossible to define in advance which user should perform an interaction. In this case, a supervisor might manually nominate the task to other employees; the nomination can also be made by a group of users or by a decision-support system.
In other scenarios, a business process may require a single user to perform several steps that can be defined in advance or during the execution of the process instance. Even more complex processes might require that one workflow is continued with another workflow.
User interactions aren't limited to approvals; they can also include data entries or process management issues, such as process initiation, suspension, and exception management. This is particularly true in long-running business processes, where, for example, user exception handling can prevent costly process termination and related compensation for those activities that have already been successfully completed.
As a best practice for human workflows, it's usually not wise to associate human interactions directly with specific users; it's better to connect tasks to roles and then associate those roles with individual users. This gives business processes greater flexibility, letting any user with a certain role interact with the process and enabling changes to users and roles to be made dynamically.
BPEL and User Interaction
So far we've seen that user interaction in business processes can get quite complex. Several vendors today have created workflow services that leverage the rich BPEL support for asynchronous services. In this fashion, people and manual tasks become just another asynchronous service from the perspective of the orchestrating process and the BPEL processes stay 100% standard.
In my next post, I’ll talk about some of the specifics of how you might implement a BPEL process that includes human workflow/tasks for disambiguation using tools such as Oracles JDeveloper and Microsoft Visual Studio. The next two figures are meant to give you a preview of that discussion.
