Success Planning Analytics Archives - Management Information Analysis

Success Stories: Hospital

March 19, 2015 by miawebadmin

The Challenge:

In healthcare delivery, there is a huge risk in making capital decisions to expand or build facilities. Mistakes are obviously costly. And the inability to meet patient care needs means a competitor can come in and take market share or the organization forgoes a robust revenue stream.

The Methods:

MIA analyzed data looking at the utilization of an entire market and compared it to other cities in Texas and as well as nationally. We projected our base numbers (2002-2004) out to 2020 so the facility would know what utilization would be. And then . . . we really went to work. We:

Estimated what the market share would be as well as dollar growth
Analyzed peak utilization to assure bed need would be met – we provided data that showed what expansion was needed, taking into account suppressed demand (when there is no facility, it is not used)
Provided information that allowed for new sub-specialists to be added because the capacity was available – (all using Planning Analytics)

The Results:

MIA was able to fight for and justify the number of required beds to an independent auditing firm. We were able to back up the data and justify expansion of the facility.

Bonus:

We made sure that the customizations to the projections were appropriate to the facility and developed a way to calculate “turn time” (cleaning and setup for next procedure), helping resolve capacity issues for hospital.

Success Stories: Medical Client

March 19, 2015 by miawebadmin

The Challenge:

This joint venture client had approximately 14 different providers and a $2 million per year operating budget. They rarely shared information. Drill down a level and you discover that the client provided sliding fee scale care for approximately 85,000 unique individuals who accessed one or more of the providers. Yikes!

So how do you get data from all different providers, standardize it, crosswalk all individuals across all systems – dealing with cumulative data over 3 years and 150,000 unique individuals.

The Methods:

In order to obtain a single unified data set, MIA assembled and de-identified all of the data (ETL Plus*).

The dataset was then sent to an actuarial firm who ran the unified data through their system and came up with what the cost would have been had Medicare reimbursed the cost of care.
MIA was then able to build a budget using that data for the client as a whole (Planning Analytics).

The Results:

Client was able to:

Send MIA back the cost weekly so they can see what the specific costs are for each diagnostic grouping
Identify a subset of patients with the largest bills to create health protocols which lessen the cost of service.
Note; this last outcome is extremely powerful. Once you can identify the “heavy users” you can create an action plan that works to lower utilization of the most valuable categories of assets – like trauma centers.