Using data to help manage solutions
Almost patient involved in the opioid crisis starts their use because of a genuine medical need. Chronic pain due to a long term condition, or severe acute pain frequently due to a surgical procedure are the most frequent reasons, but the start is nearly always clinical.
What happens next is the critical issue. What are the triggers that lead patients to seek prescriptions from multiple physicians? We know that this leads to a buildup of their drug supplies which can lead to overdose, but any solutions to this crisis will need to include a good understanding of why this is occurring. Data analytics play an increasingly important role in helping to solve the opioid crisis.
One thing we can be sure of is that patients involved in the opioid crisis are not simple. They have co-morbidities that require treatment. This means they are taking other drugs, getting other procedures. And have complex treatment patterns. Many have conditions that were being treated before they had their first opioid, and many develop new conditions whilst they are receiving opioid therapy. How do these factors combine? Can they be assessed to identify patients at risk of abusing opioids before they do?
The answer is yes, these factors combine and yes they can be assessed – and most of the data required are readily available, but the analytics required are not straightforward.
Galileo analytics experience and expertise
At Galileo Analytics we have worked this kind of problem a number of times. Building the complex cohorts that are based on combinations of demographics, comorbidities, treatment patterns also requires the addition of sequencing. The way in which opioid crisis patients are treated changes due to both existing and new conditions. Identifying cohorts based on diagnosis sequence, treatment flows, the presence or absence of certain procedures are key factors that come into play when trying to determine how they affect an outcome.
Whether trying to identify risk factors for opioid abuse or hypoglycemia, or any other outcome, the analysis requires the ability to combine all facets of the patient’s condition and treatment – and this means using longitudinal data like claims or EHR (or both).
We have extensive expertise and experience in dealing with these datasets to solve problems like this. If we can identify patients at risk of opioid abuse we can help identify treatment pathways that can head off the serious problem, and the same applies to all the different serious outcomes that we want to help prevent (as well as the good outcomes we want to encourage).