Sage Advice on Readmission Reporting

We recently attended the Healthcare Business Intelligence Forum and heard Ray Hess (Vice President for Information Technology at the Penn Medicine Chester County Hospital) speak on Merging Clinical and Business Intelligence to Manage Readmission Risk.

Ray is a leader in the field of healthcare information management and process improvement. He is one of the authors for Implementing Business Intelligence in You Healthcare Organization and he has won numerous awards for his work. Here are three key point Ray shared at the conference:

Clear definitions are important for report development

You will save time, energy and aggravation if you ensure clarity for the definitions you are using. Make this an early step to avoid rework later.  And, of course, consistency helps to ensure trust and buy in.

In his presentation, Ray used the definition of “readmission” as an example.  Here are some are some areas he suggested for clarification:

  • Time frame – is it 7 days? 14 days? 30 days?
  • Does the report include all causes or some causes?
  • Is data based on a rolling window or a fixed starting point?
  • Are you including observation & ED?
  • What DRG, diagnosis, primary or secondary is used?

Document definitions so readers understand them

All reports should clearly contain the inclusions you are using to generate the information. Polaris agrees and provides definitional panels for our customers. We encourage them to include the following information:  data sources, algorithms, frequency of data updates and even the data or process owners. This allows the reader to more thoroughly understand the information before them and gives them a place to direct questions.

Don’t rely on outside organizations for definitions of readmissions

There is no consistency in our industry on the way readmissions are calculated on the reports we see. This is especially troublesome when benchmarking to national estimates which are sometimes weighted by hospital characteristics (such as region, urban/rural location, bed size and ownership) as well as patient age groups. Hospitals near state borders may have difficulties because of lack of reporting across the state boundaries. Also, different payers have different definitions making them difficult to compare. These are just a few of the reasons there is so much frustration and skepticism when reconciling the numbers.

You have no control over what outside organizations do — but you do have control over your own definitions and reports. Crisp data governance is key and Polaris can help. We will work with you to find the inconsistencies, alter the parameters for needed flexibility and document the report definitions so readers have clarity. We can do this for readmission reporting, quality reporting and much more.

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