Patient Perceptions of Readmission Risk: An Exploratory Survey
Preventive Medicine Residency Program.; 5Department of Care Coordination, Johns Hopkins Hospital, Baltimore, MD Interventions to prevent readmissions often rely upon patient participation to be successful. We surveyed 895 general medicine patients slated for hospital discharge to (1) assess patient attitudes surrounding readmission, (2) ascertain whether these attitudes were associated with actual readmission, and (3) determine whether patients can estimate their own readmission risk. Actual readmissions and other clinical variables were captured from administrative data and linked to individual survey responses. We found that actual readmissions were not correlated with patients’ interest in preventing readmission, sense of control over readmission, or intent to follow discharge instructions. However, patients were able to predict their own readmissions (P = .005) even after adjusting for predicted readmission rate, race, sex, age, and payer. Reassuringly, over 80% of respondents reported that they would be frustrated or disappointed to be readmitted and almost 90% indicated that they planned to follow all of their discharge instructions. Whether assessing patient-perceived readmission risk might help to target preventive interventions warrants further study.
© 2018 Society of Hospital Medicine
RESULTS
Responses were obtained from 895 patients. Their median age was 56 years [interquartile range, 43-67], 51.4% were female, and 41.7% were white. Mean SOI was 2.53 (on a 1-4 scale), and median length-of-stay was representative for our medical service at 5.2 days (range, 1-66 days). Family members reported filling out the survey in 57 cases. The primary payer was Medicare in 40.7%, Medicaid in 24.9%, and other in 34.4%. A total of 138 patients (15.4%) were readmitted within 30 days. The Table shows survey responses and associated readmission rates. None of the attitudes related to readmission were predictive of actual readmission. However, patients were able to predict their own readmissions (P = .002 for linear trend). After adjustment for expected readmission rate, race, sex, age, and payer, the trend remained significant (P = .005). Other significant predictors of readmissions in this model included expected readmission rate (P = .002), age (P = .02), and payer (P = .002). After dichotomizing the patient estimate of readmission rate as “unlikely” (N = 581) or “likely” (N = 314), the unadjusted odds ratio associating a patient-estimated risk of readmission as “likely” with actual readmission was 1.8 (95% confidence interval, 1.2-2.5). The adjusted odds ratio (including the variables above) was 1.6 (1.1-2.4).
DISCUSSION
Our findings demonstrate that patients are able to quantify their own readmission risk. This was true even after adjustment for expected readmission rate, age, sex, race, and payer. However, we did not identify any patient attitudes, beliefs, or preferences related to readmission or discharge instructions that were associated with subsequent rehospitalization. Reassuringly, more than 80% of patients who responded to the survey indicated that they would be sad, frustrated, or disappointed should readmission occur. This suggests that most patients are invested in preventing rehospitalization. Also reassuring was that patients indicated that they agreed with the discharge care plan and intended to follow their discharge instructions.
The major limitation of this study is that it was a convenience sample. Surveys were distributed inconsistently by nursing unit staff, preventing us from calculating a response rate. Further, it is possible, if not likely, that those patients with higher levels of engagement were more likely to take the time to respond, enriching our sample with activated patients. Although we allowed family members to fill out surveys on behalf of patients, this was done in fewer than 10% of instances; as such, our data may have limited applicability to patients who are physically or cognitively unable to participate in the discharge process. Finally, in this study, we did not capture readmissions to other facilities.
We conclude that patients are able to predict their own readmissions, even after accounting for other potential predictors of readmission. However, we found no evidence to support the possibility that low levels of engagement, limited trust in the healthcare team, or nonchalance about being readmitted are associated with subsequent rehospitalization. Whether asking patients about their perceived risk of readmission might help target readmission prevention programs deserves further study.
Acknowledgments
Dr. Daniel J. Brotman had full access to the data in the study and takes responsibility for the integrity of the study data and the accuracy of the data analysis. The authors also thank the following individuals for their contributions: Drafting the manuscript (Brotman); revising the manuscript for important intellectual content (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); acquiring the data (Brotman, Shihab, Tieu, Cheng, Bertram, Deutschendorf); interpreting the data (Brotman, Shihab, Tieu, Cheng, Bertram, Hoyer, Deutschendorf); and analyzing the data (Brotman). The authors thank nursing leadership and nursing unit staff for their assistance in distributing surveys.
Funding support: Johns Hopkins Hospitalist Scholars Program
Disclosures: The authors have declared no conflicts of interest.
