Letter to the Editor: Does the Risk Assessment and Prediction Tool Predict Discharge Disposition After Joint Replacement?
To the Editor,
I read the paper by Hansen and colleagues with great interest. Their study described a Risk Assessment and Prediction Tool (RAPT) that predicts discharge disposition for low- and high-risk arthroplasty patients. The advent of bundled payments provides an opportunity to use the information gathered from the RAPT in setting up payments for the entire episode of total joint replacement care based on retrospective data. This preoperative survey can guide a patient’s expectations prospectively, as well as point to where he or she could go after the hospital stay, potentially maximizing the appropriate use of resources.
The RAPT appears to be an accurate instrument for determining the appropriate disposition for patients undergoing total joint procedures. In 1999, my team at The Joint Reconstruction Center at Bridgeport Hospital in Bridgeport, CT, USA published a study  on a comparable preoperative survey that used similar data, but incorporated subscores from the preoperative SF-36 scores collected on all patients, including details like whether or not there were stairs in the patient’s home. This tool had a 73% accuracy rate based on a maximum score of 50. The advantage of this tool was that the band of lower predictability was only 25 to 26 out of 50. Hansen and colleagues describe a range from seven to 10 out of 12—a much wider band where the RAPT is less accurate.
Our own preoperative survey was never used extensively because there was no interest from any payers at the time. While we believe it would have guided us in placing people, our experienced case managers were already performing this function without the tool. The Joint Reconstruction Center also lost funding for collecting SF-36 data because no one was interested in data from a patient-assessed outcome tool. In hindsight, validating the tool with greater numbers would have decreased the number of patients going to skilled nursing facilities for the last 15 years. However, with the standard payment models of those years, no provider would have reaped any benefit from this. Only the payers would have benefited.
The SF-36 data component makes it more difficult for the Bridgeport tool to collect the necessary information, but when collected, that information may be more accurate. Also, in our region, many homes have stairs, and this can influence whether patients can go directly home from the hospital after hip or knee surgery. Therefore, including this parameter in a scoring tool seems important.
While I acknowledge that the tool described by Hansen and colleagues was not designed by them, our group’s experience suggests some modifications could be beneficial. Still, I applaud the authors for suggesting a methodology that will help guide more efficient care of total hip and knee patients going forward, especially as payment models evolve.