Most healthcare applications present clinical data by date or encounter. But, studies have shown a real advantage to a problem-centered workflow for healthcare data retrieval.
No matter what you call it: problem-centric view, problem-oriented view, problem concept maps, relevant data display, it doesn’t matter. Healthcare data displays are generally date-centric, or encounter-centric, and not problem-centric.
What do I mean by that? Quite simply it means that it doesn’t matter what application you are using, whether it’s an electronic health record (EHR), an analytics or care management platform, or a multitude of other healthcare applications, the data per patient is generally presented by; date or encounter. Each entry is likely identified as a particular type of report (i.e. lab, radiology, pathology, etc.) and if you are lucky, the data is tagged by specialty. But if not, the clinical notes will usually identify the specialty of the provider that created them.
The Journal of the American Medical Informatics Association (JAMIA) recently published an article on the advantages of a problem-centered workflow for data retrieval using problem concept maps created by the University of Washington. In this article, the value of aggregating data by condition for review by a clinician for medical decision making is clearly articulated. This article also points out that the need for a problem-oriented medical record has been discussed starting in 1968! While we have advanced our collection, retrieval, and use of data in the healthcare world today, a large gap still remains.
Why take a problem-centric approach to healthcare data?
Increasingly, payers, especially those involved in value-based care arrangements are asking for the ability to aggregate data in a problem-centric way. We often hear from our payer clients that they want to be able to identify all their members that are taking a medication related to a certain condition (e.g. bronchodilators for the treatment of COPD). Or, in the reverse, they want to be able to identify all of the medications commonly used to treat a condition (e.g. Diabetes Mellitus Type II with Peripheral Neuropathy). Similarly, many of our clients want to be able to quickly identify lab results, radiology tests, and other procedures that should be performed when diagnosing or treating a specific condition.
The desire to aggregate data in this way is driven by the need to accurately identify risk, highlight potential gaps in care, support quality related activities, and improve health outcomes of the high-risk, costly patient populations by proactively intervening at the right time.
For instance, a member newly diagnosed with COPD should be evaluated for their lung function through standard Pulmonary function tests such as, spirometry, chest x-rays, and 6-minute walk tests. Members with acute exacerbations of their COPD will likely be given a prescription for a Corticosteroid to control the exacerbation, as well as having a spirometry test, a chest x-ray and even possible supplemental oxygen. Traditionally, payers could comb through their claims databases to understand if these tests, procedures, or medication have been billed out in conjunction with a diagnosis of COPD but that method is retrospective in nature. Claims data is used in analytics only after adjudication which can take weeks or even months if appeals and secondary claims are required.