The risk assessment that reduced length of stay by 20%

It's often a struggle for hospitals to ensure every patient who requires post-acute care is discharged to the appropriate setting in a timely way. The challenge is exacerbated by the costly and time-consuming discharge process.

Identifying at-risk patients for resource-intensive discharge planning

Mayo Clinic, a two-hospital health system in Rochester, Minnesota, found when individual clinicians were responsible for referring patients for complex discharge planning, the results varied widely.

Risk-averse clinicians over-referred patients, while others referred fewer patients. This resulted in some complex patients failing to receive the comprehensive planning they needed, while other patients were evaluated unnecessarily.

To address this inefficiency, Mayo’s discharge planners developed a principled method for determining patients’ relative need of post-discharge assessment.

Using evidence to develop a simple screen

To develop the screening tool, the discharge planners conducted a comprehensive literature review and identified 24 different variables that could be associated with a need for further discharge planning.

Analysis showed that only four easy-to-evaluate variables were statistically significant in predicting a patient’s need for specialised discharge planning:

  • Level of disability
  • Whether or not the patient lives alone
  • Self-reported walking limitation
  • Age

Using a regression analysis for the four variables, Mayo developed an Early Screen for Discharge Planning algorithm.

The algorithm calculates a score for patients ranging from zero to 23. A score above 10 indicates the need for intensive discharge planning—this maximised the specificity and sensitivity of the tool.

Patient receives risk score upon admission

The Early Screen for Discharge Planning assessment can be conducted on admission for every patient, and if indicated, referrals for complex discharge planning are made immediately. The simple tool minimises the discharge planning burden placed on busy clinicians.

Calculating scores using hospital electronic medical records

The Early Screen for Discharge Planning was first developed in 2006 as a paper-based tool. In 2009 the Mayo Clinic embedded it into their electronic medical record (EMR), with additional information for nurses’ reference, such as how to determine the Rankin disability level and a reminder that patients with a score of 10 or above should be referred for specialised discharge planning.

The results speak for themselves. Since implementation, length of stay (LOS) for complex patients has decreased more than 20%. Mayo staff attribute the effect to more consistent, earlier referrals for the most complex patients.

The screening tool enables clinicians to be selective about which patients receive intensive discharge planning, while giving them confidence that no patient will slip through the cracks.

The following graphic illustrates the algorithm developed at the Mayo Clinic, including the values assigned to individual patient indicators.

Embedding the assessment tool into the hospital’s EMR further increased effectiveness and ease of use for nursing staff.

The Rankin Disability Score, one of four key indicators used in the Mayo Clinic’s Risk Scoring Algorithm, is also straightforward and relatively easy to assess.

Uncover your hidden potential

Most opportunities to improve discharge efficiency are within the control of the hospital. Here, we’ve outlined your strategic plan for creating capacity by eliminating end-of-stay delays.