There isn’t much about the healthcare industry that moves extremely quickly. The best efforts to address rising healthcare costs and claims start with a well-thought-out plan that includes the necessary stakeholders and the right health intelligence tool for the job.
The thing about data analysis, especially healthcare data, is that it can get a little overwhelming and can leave people wondering where to even start. There are several different projects that employers and population health consultants can engage in when adopting healthcare software. Here are initiatives we think would be good for anyone just starting out with a new health intelligence tool.
One of the best places to start a new healthcare analytics project is identifying member population prescribed treatment. If a member of your insured population has been diagnosed by a doctor with hypertension, they likely will have received a prescribed treatment as well, often in the form of medication to treat the condition. This level of member data is fairly standard and easily collectible across all insurance carriers, but when integrated into the right health intelligence software, it can be extremely useful to employers.
The importance of measuring prescribed treatment for illnesses is searching for what we at Springbuk call “Gaps in Care.” When someone lapses in the treatment for their condition, there’s a statistically significant health risk because of a worsening condition. These gaps in care are quantifiable, and health intelligence tools like Springbuk can predict the future costs of a person who isn’t compliant to care. By identifying the costs in these gaps in care, employers can strategize ways to reduce and contain medical costs that are associated with recurring claims.
This is a great project to start with because of the availability of the member data associated with building a gap in care model and the potential impact on bottom line healthcare costs if those gaps are properly addressed.
Another great use for a health analytics solution is the grouping of members (i.e., a cohort or focus population) of your insured population based on different factors. Building groups can be as simple as separating people based on employer/dependent, age, or risk level. However, more insights become available when more intricate groups are created. Creating a group of members starts to reveal a lot more information about health and claims coverage for a specific section of the member population. For example, let’s build a focus population based around members that:
With a more focused group, the answers to questions are much more illuminating. How much are these members spending on prescription medications? How can we help improve their health without incurring more claims? Are there any similarities between these members that we can address from a company level?
Specificity is key for a focus population project like this one. Health intelligence companies make this type of work easy, because data points like these would be more than a little difficult to wrangle together through individual records whether they’re digital or analog. But, if handed the right health analytics tool, wellness professionals and consultants can make sense of all the data in a much more efficient manner.
One of the largest healthcare costs that goes unaddressed by employers and their benefits consultants is associated with prescription drugs. Making sure that the member population receives their prescribed medication is the number one priority, but all too often, there is little to no insight into the Rx situation beyond that.
The first step to cutting back on unnecessary drug costs is identifying which prescriptions the members currently have. Ideally, employers will pair a health intelligence tool like Springbuk with a self-funded insurance plan. That way, prescription data is updated on a much more frequent basis. Armed with this relative employee data, employers and their benefits consultants can identify which members are taking specialty drugs, which members are taking name-brand drugs when there is a generic available, and which members have unnecessary prescriptions. These insights don’t require a lot of data analyzation beyond identifying which members can make the switch toward a most cost-focused prescription, so it’s a great project to undertake early after enabling a health intelligence platform.
These three projects are great examples of business intelligence in healthcare and should provide any employer with the information they need to begin to tackle unnecessary healthcare costs. But, this is just the beginning. There are many more ways that healthcare data analyzation can be used.