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Original research
The association between patient experience factors and likelihood of 30-day readmission: a prospective cohort study
  1. Jocelyn Carter1,
  2. Charlotte Ward2,3,
  3. Deborah Wexler4,
  4. Karen Donelan1,5
  1. 1 Department of Medicine, Massachussetts General Hospital, Boston, Massachusetts, USA
  2. 2 Center for Healthcare Studies, Northwestern University, Bridgeview, Illinois, USA
  3. 3 Center for Health Statistics, University of Chicago, Chicago, Illinois, USA
  4. 4 Diabetes Center, Massachusetts General Hospital, Boston, Massachusetts, USA
  5. 5 Mongan Institute for Health Policy Centre, Massachusetts General Hospital, Boston, Massachusetts, USA
  1. Correspondence to Dr. Jocelyn Carter, Department of Medicine, Massachussetts General Hospital, 50 Staniford Street, Suite 503A, Boston 02114, MA, USA; jcarter0{at}partners.org

Abstract

Objective Hospital care comprises nearly a third of US healthcare expenditures. Fifteen to 20 per cent of this spending is considered to be potentially preventable. Risk prediction models have suboptimal accuracy and typically exclude patient experience data. No studies have explored patient perceptions of the likelihood of readmission during index admission. Our objective was to examine associations between patient perceptions of care during index hospital admission and 30-day readmission.

Design Prospective cohort study.

Setting Two inpatient adult medicine units at Massachusetts General Hospital, Boston, Massachusetts.

Participants Eight hundred and forty-six patients admitted to study units between January 2012 and January 2016 who met eligibility criteria and consented to enrolment.

Main outcome Odds of 30-day readmission.

Results Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) were readmitted within 30 days. Readmitted participants were less likely to have a high school diploma/GED (44.3% not readmitted vs 53.5% readmitted, P=0.02). In multivariable models adjusting for baseline differences, respondents who reported being ‘very satisfied’ with the care received during the index hospitalisation were less likely to be readmitted (adjusted OR 0.61, 95% CI 0.43 to 0.88, P=0.007). Participants reporting doctors ‘always listened to them carefully’ were less likely to be readmitted (adjusted OR 0.68, 95% CI 0.48 to 0.97, P=0.03). Participants reporting they were ‘very likely’ to be readmitted were not more likely to be readmitted (adjusted OR 1.35, 95% CI 0.83 to 2.19, P=0.22).

Conclusion Participants reporting high satisfaction and good provider communication were less likely to be readmitted. Rates of readmission were increased among participants stating they were very likely to be readmitted though this association was not statistically significant. Incorporating patient-reported measures during index hospitalisations may improve readmission prediction.

  • patient satisfaction
  • patient-centred care
  • hospital medicine

https://doi.org/10.1136/bmjqs-2017-007184

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    Introduction

    In 2011, there were approximately 3.3 million adult 30-day all-cause hospital readmissions in the USA, generating $41.3 billion in hospital costs.1 These readmissions accounted for 32.1% of the total healthcare expenditures in the USA.2 Conservatively, up to $8.26 billion (15–20%) of readmissions are considered to be potentially preventable.3 Reasons for readmissions are multifactorial. Numerous studies have demonstrated correlations between hospital readmissions and specific disease comorbidities/conditions,4–7 older age,3 8 general poor health9 or high previous healthcare utilisation.6 10 11 Racial/ethnic disparities,9 12–13 low educational attainment,14–15 socioeconomic status16–18 and lack of social support19 have all been cited as core contributors to readmission rates and/or poor care transitions.

    Despite a plethora of readmission risk prediction tools,20–21 countless care coordination interventions and even the threat of readmission penalties, the rate of hospital readmission remains elevated. Few studies have included patient-reported measures associated with readmission in the available readmission risk prediction tools. In prior reports examining patient-reported measures in association with readmission rates, the focus has centred on patient-perceived healthcare quality after discharge,22 23 impact of postdischarge interventions,24 activities of daily living6 and the use of readmission risk factors to develop viable readmission risk prediction tools.25 26 Rarely have patient perspectives been examined prior to index discharge. While patient perspectives on discharge readiness have recently been examined after discharge in a single study set in European hospital settings,27 patient-reported prediction of 30-day readmissions, a potentially sensitive, specific and easily obtained measure at the time of index admission, has been absent from any patient experience surveys. Engaging patients in an assessment of communication quality, unmet needs, concerns and overall experience during an admission may help to identify issues that might not be captured in standard postdischarge surveys, when the appropriate time for quality improvement interventions has passed.

    In order to better understand the relationship between inpatient patient-reported measures and 30-day readmissions, we interviewed patients during index hospital admissions and subsequent readmission over a 4-year period. We hypothesised that patient-reported measures would inform our understanding of the factors driving hospital readmission, generate novel predictors of readmission and identify appropriate targets for interventions during the hospital stay.

    Methods

    Study population and setting

    The population consisted of inpatients admitted to one of two inpatient medical units at the Massachusetts General Hospital between January 2012 and January 2016. Patients eligible for the study were greater than 18 years of age, had the capacity to complete the questionnaire and spoke English (figure 1). Patients were excluded if their readmission was planned or scheduled, or if admission and discharge occurred in a time interval that made patient consent impossible. The two units included medical patients with similar distributions of insurance coverage and medical conditions. Eligible study participants were identified by daily surveillance of the census on both units. Efforts were made to enroll all eligible patients admitted to the study units, but for practical reasons (staffing, shift changes, etc) not all potentially eligible patients were able to be approached and consented prior to discharge. There were no significant differences in the demographic characteristics, proportion of disease categories or baseline readmission rates of study participants and those that were eligible for the study but not enrolled.

    Figure 1
    Figure 1

    Flow diagram for study participation and survey January 2012–January 2016.

    Sources of data

    Patient surveys

    The patient experience data reported here are drawn from interviewer-administered surveys conducted with eligible and consenting patients during index admissions. The patient survey was developed by study investigators for interviewer-assisted administration with hospitalised patients. We used a qualitative process to identify core domains through key informant interviews with patients, community-based primary care physicians and facility providers. This was coupled with review of the literature on hospital patient experience of care surveys and consultations with experts in survey and health services research. For benchmarking, we used some standard established measures of the patient experience from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS).28 Draft surveys were pretested with 30 patients and revised prior to study administration. The final index admission questionnaire included 20 items: patient perceptions of physical and mental health, confidence in ability to perform self-care after discharge, satisfaction with inpatient care received, patient-predicted likelihood of readmission within 30 days, understanding of the care plan, basic demographic information (language preference, highest level of education, racial/ethnic identity) and presence of a caregiver at home. Open-ended questions queried patients regarding anything that would help them manage their health outside the hospital. Text and response scales are detailed in the tables and in the text of the Results section; the full questionnaire is available in an online appendix. 

    Supplementary file 1

    Trained research coordinators interviewed patients and verbally administered questionnaires on the day of or day prior to discharge after obtaining verbal consent. Patient index admission questionnaires took between 10 min to 12 min to complete. Patient responses were recorded and stored in a secure online database (REDCap V.7.0.14, Vanderbilt University).29

    Clinical data repository

    In addition to patient-reported data, research coordinators completed a structured medical record review to obtain clinical history using the electronic medical record with data captured in the same REDCap database. Abstracted data included demographic information, insurance status, education, primary language spoken, homelessness, primary diagnosis associated with admission, and major medical and psychiatric comorbidities including substance use disorder. Both chart review and patient interview responses were used to construct clinical or system-based underlying causes for admission.

    Statistical analysis

    Thirty-day readmission was the primary outcome. The primary covariates of interest were participant responses to items on the admissions questionnaire. Items which had more than two response categories were dichotomised based on the most positive response category. For example, for self-rated overall health on a 5-point scale from ‘poor’ to ‘excellent,’ ‘excellent’ is the most positive response category and would be coded as ‘1’, while all other responses would be recoded as ‘0’. Due to the small amount of missing data from survey items (ranging from 0% to 4.7% with an average of 1.1%), missing data were excluded from the analysis. Demographic covariates included gender, age category, race, ethnicity, marital status, education, insurance status, primary language spoken, history of homelessness or a substance use disorder.

    Bivariate analyses, using Pearson’s χ2 tests, were performed to assess differences between readmission status groups on all demographic and survey item variables. Associations between readmission status and survey item responses were assessed using separate multivariable logistic regression models for each survey item controlling for demographic covariates which had a bivariate P value <0.25. A P value of <0.05 was considered statistically significant. The proportion of missing data (as noted in the unadjusted results tables) was assessed for random missingness at values <5%; the rate of missing data for all variables had negligible missingness and was 1.1% on average. All analyses were performed using SAS software V.9.4 (SAS Institute, Cary, North Carolina, USA).

    Results

    Eight hundred and forty-six patients were enrolled and completed admission interviews (figure 1) during their index admission. These patients represent 55.4% of unique eligible patients admitted for index admissions during the study period who were screened and invited to participate in the interview. Of the 846 enrolled, 201 of these patients were readmitted to the index hospital within 30 days of their hospital discharge. Participant baseline characteristics are listed in table 1. Participants who were readmitted were less likely to have been educated beyond a high school degree or equivalent compared with those who were not readmitted (44.3% vs 53.5%, P=0.02).

    View this table:
    Table 1

    Patient characteristics

    The most common primary reasons for admission were infectious disease, respiratory, gastrointestinal bleeding, cardiac or psychiatric diagnoses. Those who were readmitted were less likely than those not readmitted to have been diagnosed with a respiratory condition on index admission (18.9% vs 28.8%, P=0.02).

    Unadjusted results of the index admission survey are listed in (table 2). Readmitted patients were less likely to report being ‘very satisfied’ with their overall care since being admitted to the hospital during the index admission compared with those who were not readmitted (67.7% vs 76.4%, P=0.045). Similarly, readmitted patients were less likely to report that physicians ‘always listened’ to them during the index hospital stay (65.7% vs 73.2%, P=0.048). In multivariable adjusted analyses (table 3), we found that those who reported being ‘very satisfied’ with their overall care (OR 0.61, 95% CI 0.43 to 0.88, P=0.007) and those who reported that physicians ‘always listened’ to them during the hospital stay (OR 0.68, 95% CI 0.48 to 0.97, P=0.03) were less likely to be readmitted within 30 days. In contrast, those who reported that physicians, nurses or other hospital staff talked to them about whether they would have help after hospital stay were more likely to be readmitted (OR 1.56, 95% CI 1.02 to 2.39, P=0.04). Participants reporting they were ‘very likely’ to be readmitted were not more likely to be readmitted (adjusted OR 1.35, 95% CI 0.83 to 2.19, P=0.22). No statistically significant differences were observed in patient responses to questions regarding physical health, mental health or confidence in managing health outside the hospital between patients who were and were not readmitted.

    View this table:
    Table 2

    Admissions questionnaire items by readmission status

    View this table:
    Table 3

    Multivariable adjusted ORs (95% CIs) for survey items in predicting readmission

    Discussion

    In this survey of 846 hospitalised general medical patients, we found that certain patient-reported measures predicted readmissions after adjusting for demographic characteristics. In particular, respondents who were very satisfied with overall care received were 39% less likely to be readmitted. We also found that respondents who indicated that their doctors always listened to them carefully also had lower rates of readmission. This group of respondents was 32% less likely to be readmitted than those who indicated that doctors did not always listen to them carefully.

    A handful of studies have attempted to predict readmission using patient-reported measures,27 30–31 but most have used postdischarge data. None of these studies have considered patient-perceived likelihood of readmission at the time of index admission. In this cohort, patient prediction of their own likelihood of readmission was not statistically significant. However, responses among larger or different study populations may have produced different results.

    With regard to patient experience factors, there is some discussion as to the validity or legitimacy of the relationship between patient experience variables and healthcare quality. However, a number of studies have demonstrated clear relationships between higher levels of patient satisfaction and adherence to guidelines as well as lower rates of hospital readmission.32–34 While questions about healthcare providers listening while hospitalised have not been asked of hospitalised patients in prior readmission studies, they have been asked of patients after discharge as a part of HCAHPS questionnaires. Similar findings were noted in a single study by Hachem et al (2014),35 which demonstrated that respondents discharged 2–6 weeks prior were 18% less likely to be readmitted within 30 days of discharge if they indicated that doctors/nurses always listened to them while they were hospitalised. Asking inpatients about their satisfaction and whether doctors listen to them prior to discharge may help identify patients who are at increased risk for readmission in real time. In doing so, this may provide an opportunity to reduce risk of future readmissions by partnering with patients to address gaps in care or care plan comprehension and tailor discharge planning accordingly.

    Though not statistically significant, we noted that respondents who indicated that doctors, nurses or other hospital staff always talked to them about whether they will have help after the hospital stay were more likely to be readmitted. While seemingly counterintuitive, this association likely stems from allocation bias driven by systematic and routine hospital-based initiatives to improve patient care for those who clearly have increased needs for support.35

    Some of the correlates of readmission that we identified are similar to prior reports, while others are unique. Increased likelihood of readmission among patients with low educational attainment underlines ongoing disparities of populations with socioeconomic challenges. This is consistent with what has been seen in other cohorts, as well as with the well known relationship between socioeconomic status and health outcomes.24 While we did not explore the domain of preventability here since we think that this is best presented in the context of identified reasons for readmission, we believe that both clinical and socioeconomic factors are central in this sphere. Although respondents over the age of 65 years were more likely to have less than a high school education, this finding persisted after controlling for age. We identified many factors that have not been reported previously. There was no increased likelihood of readmission associated with specific levels of insurance, although many prior studies have demonstrated that readmission rates are higher among Medicare or Medicaid/self pay populations.6 This lack of association may be related to the fact that universal health insurance coverage in the Commonwealth of Massachusetts was in place during the study period. We also found in our sample that respondents over the age of 45 years were more likely to be readmitted relative to younger patients. This is different from other studies that have demonstrated vulnerability with regard to readmission in terms of populations older than age 65 years.3 10 14 16 We also found that marital status was not particularly discriminating with regard to determining risk for readmission, in contrast to some prior reports.14 23 It seems possible that the study participant sample, which was drawn from a medicine service designed to care for complex patients at a tertiary care centre, drove clinical complexity across participants from each age category, and that the risk factors for readmission may differ in the setting of greater burden of illness.

    There were a number of limitations of this study. It was not possible to track readmissions to other health systems which may have resulted in an underestimation of readmissions. Also, ‘healthier’ user bias, with patients that were sickest being unable to complete the survey, may have resulted in under-representation of patients with even higher rates of medical complexity. The fact that this study was conducted on a single medical service among English-speaking patients also limits the generalisability of our findings.

    These limitations are balanced by strengths that include a broad representation of general medicine patients with common admitting diagnoses as well as inperson detailed interviews with rich, open-ended response data. Predicting hospital readmission is a complicated area. We sought to add to the current literature by gathering qualitative data regarding the patient experience and by exploring patient-prediction of readmission during index admission. During index admission, there may be yet undiscovered opportunities to change the trajectory of patient care. Prior reporting of postdischarge physician reviews of 30-day readmissions supports index admission as the phase of care where intervention to reduce readmission would have been most effective.25 Moreover, we are unaware of any previous studies that include patient perspectives on likelihood of readmission and other communication and patient-reported domains prior to discharge from index admission. While it is becoming increasingly apparent that there is no single solution in terms of interventions that reliably reduce risk of preventable readmissions for entire populations of patients,35–37 clinical, social, demographic and patient perspective-based identifiers must be explored in order to target interventions more effectively. While we were not able to demonstrate a relationship between patient ability to predict 30-day readmissions by rating their likelihood of readmission during index admission, we found decreased risk of readmission associated with higher levels of patient satisfaction with care received during hospitalisation and patient perception of providers always listening during hospitalisation. These findings give credence to patient perspective or patient experience factors as real time indicators of risk for readmission. Ideally, the integration of a relatively small number of patient experience questions into readmission prediction models designed for use during index admission could help identify patients that may benefit from additional interventions prior to discharge. Doing so would generate models with higher predictive ability, and clinically actionable awareness that could be tailored to patient-specific circumstances and to help reduce preventable readmission.

    Conclusions

    The impact of patient-reported measures with respect to hospital discharge and care transitions has yet to be fully realised in terms of predicting and preventing readmission. This is the first study to evaluate the relationship between the patient-reported outcomes and patient experience data during index admission and 30-day hospital readmission rates. We found that lower rates of readmission were associated with higher rates of patient satisfaction and higher level of patient perception of providers listening to them. We were not able to find a statistically significant association between those who stated that they were more likely to be readmitted within 30 days after discharge and those that were actually readmitted. However, the observation that the estimate suggested an increased risk warrants further exploration in different populations and should be explored in larger studies. Additional research is needed to study the integration of patient experience data into models to identify patients at risk for readmission prospectively.

    Acknowledgments

    The authors thank all of the research coordinators that contributed to this work including Lindsey Lebel, Courtney Kaiser,Thomas Cunningham, Catherine Caughlin, Rebecca Kogan, Aradhna Agarwal, Noemia Nau, Layne Keating, Rachel Gallimore, Chelsea Hicks, Keneolisa Ogamba, Yolaine N Kamga, Rachel Weinstock, Montane Silverman, Muhammad Farhan Asghar. The authors also thank Yuchiao Chang, PhD of the Division of General Internal Medicine at Massachusetts General Hospital for senior statistical support.

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    Footnotes

    • Handling editor Kaveh G Shojania

    • Contributors JAC, DW and KD designed the study and outcomes of interest. JAC wrote the original manuscript. DW, KD and CW edited the manuscript. CW performed all statistical analysis.

    • Funding 2012 Clinician Teacher Development Award, MGPO, Massachusetts General Hospital

    • Competing interests DW reports serving as a consultant to Novartis on the management of hyperglycaemia associated with a novel oncology drug in May 2016.

    • Ethics approval The institutional review board at the Partners Human Research Committee.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement Anonymised full data set and statistical codes are available upon request.

    • Correction notice A change has been made to the original version of this article. The following sentence in the abstract was updated “Hospital readmissions comprise nearly a third of US healthcare expenditures” to be changed to “Hospital care comprises nearly a third of US healthcare expenditures.”