According to Rock Health’s funding report, one of the most funded categories in 2016 was Population Health Management (PHM) software. For some health organizations and even Health IT professionals drawing the connection between using software that can bring value-based care to populations is fuzzy. This week I decided to dissect some medicare spending expenditures to discover how PHM could potentially be used to measure a population’s health and help three counties in South Florida save money.
What is Population Health Management?
It is a full delivery system with tools to manage the health of a population under the Accountable Care Organizations models. Models like Center for Medicare and Medicaid’s new patient engagement models will shift the way organizations measure value-based care. It will also create opportunities that emerging startups will be sure to capitalize on.
If we observe the data above we will notice a majority of the spending is going towards end-stage renal disease (ESRD). The average of the three South Florida counties is $85,240. If compare the data for those who are disabled beneficiaries the expenditures are roughly the same with Palm Beach spending the less out of the three counties with $12,908. The other 2 are in the range of $14-15k in expenditure costs.
What about the aged audience? If we look at the data it says dual and nondual. Dual means those who are eligible for both Medicare and Medicaid. For those who have both, they cost the system more money with Palm Beach leading with $21,491 compared to $12,018 for non-dual. The average difference between aged/non-dual and aged/dual is $8,756. This data highlights that second to Renal disease beneficiaries who have both medicare and medicaid are the most costly for these counties.
If we look at the graph above we can see that compared to the other two counties Palm Beach is leading in beneficiaries. According to Census, Palm Beach County has approx 1.4 million people with 23% being over the age of 65. That may be a reason why the beneficiary number is so high.
The data is showing that Miami-Dade had 3,842 assigned to them while Broward had 1,542. Palm Beach had about 43,900. When ranked by type of issue Miami-Dade had 448 disabled and estimate of 92 that had an end-stage renewable disease. The bulk of assigned beneficiaries in Palm Beach were actually disabled with a total estimate of 2,031 and those who were AGED/non-dual had 39,575.
The blue outlier you see on the right is representing that $3.8 billion in expenditures are happening here in Palm Beach for beneficiaries with end-stage renal disease (ESRD). Across these 3 counties, the disabled are costing over a billion in each county. With this data we have identified the potential size of a problem for multiple population. Where do we go from there and how does population health management software fit?
Source: Evolent Health
In the above formula given by a public population health management company, we can see that success is measured for them with 3 variables. Perhaps a way population health can help address this is issue is by using formulas and designing software that can automate the process of measuring success in delivering value-based care to a community. The growth of tools like Apple’s HealthKit and Mobile are making the monitoring and distribution of health services more measurable.
Capturing value can be through using electronic medical records and demographic data to suggest specific treatments that can identify options that lead to better outcomes. Mobile Health is also looking to be a good way to distribute and administer services to a population. Moving forward, I see the market producing more software providers aimed at target populations. The biggest amount of spending will continue to be in disease-related areas and problems for the aging population. The data shows that in South Florida software aimed at these segments have an opportunity to help Medicare and Medicaid save millions of dollars.
I am not a clinician. These observations were taken from Open Data.
*Monroe County is also a part of South Florida but was absent due to lack of data reported by CMS. All data used here is public and available on CMS.