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
Recent years have seen a proliferation of programs designed to prevent readmissions, including patient education initiatives, financial assistance programs, postdischarge services, and clinical personnel assigned to help patients navigate their posthospitalization clinical care. Although some strategies do not require direct patient participation (such as timely and effective handoffs between inpatient and outpatient care teams), many rely upon a commitment by the patient to participate in the postdischarge care plan. At our hospital, we have found that only about 2/3 of patients who are offered transitional interventions (such as postdischarge phone calls by nurses or home nursing through a “transition guide” program) receive the intended interventions, and those who do not receive them are more likely to be readmitted.1 While limited patient uptake may relate, in part, to factors that are difficult to overcome, such as inadequate housing or phone service, we have also encountered patients whose values, beliefs, or preferences about their care do not align with those of the care team. The purposes of this exploratory study were to (1) assess patient attitudes surrounding readmission, (2) ascertain whether these attitudes are associated with actual readmission, and (3) determine whether patients can estimate their own risk of readmission.
METHODS
From January 2014 to September 2016, we circulated surveys to patients on internal medicine nursing units who were being discharged home within 24 hours. Blank surveys were distributed to nursing units by the researchers. Unit clerks and support staff were educated on the purpose of the project and asked to distribute surveys to patients who were identified by unit case managers or nurses as slated for discharge. Staff members were not asked to help with or supervise survey completion. Surveys were generally filled out by patients, but we allowed family members to assist patients if needed, and to indicate so with a checkbox. There were no exclusion criteria. Because surveys were distributed by clinical staff, the received surveys can be considered a convenience sample. Patients were asked 5 questions with 4- or 5-point Likert scale responses:
(1) “How likely is it that you will be admitted to the hospital (have to stay in the hospital overnight) again within the next 30 days after you leave the hospital this time?” [answers ranging from “Very Unlikely (<5% chance)” to “Very Likely (>50% chance)”];
(2) “How would you feel about being rehospitalized in the next month?” [answers ranging from “Very sad, frustrated, or disappointed” to “Very happy or relieved”];
(3) “How much do you think that you personally can control whether or not you will be rehospitalized (based on what you do to take care of your body, take your medicines, and follow-up with your healthcare team)?” [answers ranging from “I have no control over whether I will be rehospitalized” to “I have complete control over whether I will be rehospitalized”];
(4) “Which of the options below best describes how you plan to follow the medical instructions after you leave the hospital?” [answers ranging from “I do NOT plan to do very much of what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I plan to do EVERYTHING I am being asked to do by the doctors, nurses, therapists and other members of the care team”]; and
(5) “Pick the item below that best describes YOUR OWN VIEW of the care team’s recommendations:” [answers ranging from “I DO NOT AGREE AT ALL that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team” to “I FULLY AGREE that the best way to be healthy is to do exactly what I am being asked to do by the doctors, nurses, therapists, and other members of the care team”].
Responses were linked, based on discharge date and medical record number, to administrative data, including age, sex, race, payer, and clinical data. Subsequent hospitalizations to our hospital were ascertained from administrative data. We estimated expected risk of readmission using the all payer refined diagnosis related group coupled with the associated severity-of-illness (SOI) score, as we have reported previously.2-5 We restricted our analysis to patients who answered the question related to the likelihood of readmission. Logistic regression models were constructed using actual 30-day readmission as the dependent variable to determine whether patients could predict their own readmissions and whether patient attitudes and beliefs about their care were predictive of subsequent readmission. Patient survey responses were entered as continuous independent variables (ranging from 1-4 or 1-5, as appropriate). Multivariable logistic regression was used to determine whether patients could predict their readmissions independent of demographic variables and expected readmission rate (modeled continuously); we repeated this model after dichotomizing the patient’s estimate of the likelihood of readmission as either “unlikely” or “likely.” Patients with missing survey responses were excluded from individual models without imputation. The study was approved by the Johns Hopkins institutional review board.
