The Rothman Index Case Study

There is limited data on predictors of discharge and readmission for hospital inpatients. According to Rothman, Rothman, & (), “Unplanned hospital admissions are a major quality and cost issue in the US healthcare system”. About 20% of Medicare patients are readmitted to the hospital within 30 days, at an estimated cost of $17 billion per year (). Now that Medicare has begun to reduce payment to hospitals with high readmission rates, hospitals are looking for more effective ways of reducing readmissions. In order to develop new systems to address these concerns, there must be evidence in place to support to their use.

Evidence can come in a variety of forms. It can come from data, information, our own observations and vital signs, as well as patient/family member concerns. In order to be considered evidence however, the information must meet several criteria. It must be objective in that it does not house our personal opinions, it must be relevant towards the situation at hand, and timely in that the information is not out of date. It must also be transparent in that everyone has access to view the information as well

The Rothman Index is a patient acuity score founded by the summation of excess risk functions that utilize additional data from the electronic medical record (Finlay, Rothman, & Smith, 2014). RI data is presented on a graph laid out over three horizontal bars of color which represent the patient 's risk for poor outcome: blue represents least risk for poor outcome; yellow indicates moderate risk for poor outcome; and red represents high risk for poor outcome (PeraHealth, 2015). Data is represented by a dot on a vertical axis, posted hourly as new results are entered. Healthcare providers can then select multiple dots or points of data on the graph to compare the scores as well as see what contributed to that calculated