InDale Cox is a software architect at Hakkoda with 15 years’ experience solving software, architecture, and automation challenges. He created a Snowflake and LACE solution to help healthcare professionals make more informed care plans and reduce readmission risk.

What inspired you to create the LACE tool? 

I was first introduced to the LACE score and the research of Dr. Walraven, et al. during my Informatics Master Program.

During my course work, readmission was a timely topic as Meaningful Use Measures were going into effect. At that time,  I was introduced to other related researchers such as:

• Dr. Charlson and colleagues whose research for the comorbidity index informs the LACE score,
• Dr. Quan and colleagues’ research which allows for the mapping of diagnosis codes to the comorbidity index. 

I was inspired to create the LACE score generation in Snowflake because the score is so common and almost ubiquitous. Care professionals are familiar with it, and some EMRs have it built in as a service offering. I felt it was a perfect example use case to demonstrate my reference architecture for clinical index calculation. The LACE score is also a great example of the use of interdependent indexes to turn raw health data into information that can be used to inform the care process. 

• What is LACE and why does it matter? 

The LACE score helps predict patient readmission.

The higher the score, the more likely an individual is to be readmitted. This matters in two areas:

1. First, patients are readmitted due to an adverse event and may be in a worse state than when they were originally discharged, potentially leading to longer recovery times. 
2. Second, healthcare systems are penalized fiscally when patients are readmitted within a 30-day window. The penalty can vary from a percentage reduction in insurance payments or allowed costs to the care system having to fully absorb the subsequent care cost for the readmission.  

• Who is the LACE tool for? 

Care teams use the LACE score to inform individuals’ care plans post-discharge. A person with a high score might be kept an extra day, prescribed in-home visits, or more frequent follow-ups to ensure they continue to recover. 

Readmission indexing, which is done as part of the score calculation, produces a trend score that will be useful to hospital administrators and clinical leaders so they can ensure that the readmission rate remains low.

• What software does a user need to use the LACE tool? Is it for Snowflake customers?

The implementation is for Snowflake customers.

Those customers would be able to provide their data to our calculation engine, all without the data leaving Snowflake. Our engine uses data to generate the score, then exports the score in a FHIR-compliant manner, which can be imported back into any FHIR-compliant system, such as an EMR, (the primary system clinical staff use).   

• What problems does this tool help solve? What are some of the negative outcomes you’ve seen when LACE is inaccurate or ignored?
  

In healthcare, morbidity is the worst thing that can happen.

There are stories of people discharged too early, who maybe should have been kept an extra day or offered different at-home care. 

Further, fiscal impacts are a concrete example of negative outcomes that could occur in these cases. Systems with high readmission rates will see a payment reduction of up to 3% for all Medicare fee-for-service base operating diagnosis-related group payments during the financial year. 

• What are some positive outcomes of this LACE tool? 

In short, better patient outcomes.

We can infer that if a patient is not readmitted they are recovering and getting better. Another positive example, Mary E. Costantino, Ph.D., and colleagues showed in 2013 that a focused intervention for a large health plan saved them over $14M in 9months.

Get in touch with us to learn how our Snowflake and LACE solutions can transform your organization.