In this systematic review, we found 26 readmission risk prediction models of medical patients tested in a variety of settings and populations. Several are being applied currently in clinical, research or policy arenas. Half the models were largely designed to facilitate calculation of risk-standardized readmission rates hospital comparison purposes. The other half were clinical models that could be used to identify high-risk patients for whom a transitional care intervention might be appropriate. Most models in both categories have poor predictive ability.
Readmission risk prediction remains a poorly understood and complex endeavor. Indeed, models of patient level factors such as medical comorbidities, basic demographic data, and clinical variables are much better able to predict mortality than readmission risk.18, 24, 29
Broader social, environmental, and medical factors such as access to care, social support, substance abuse, and functional status contribute to readmission risk in some models, but the utility of such factors has not been widely studied.
It is likely that hospital and health system-level factors, which are not present in current readmission risk models, contribute to risk.36
For instance, the timeliness of post-discharge follow-up, coordination of care with the primary care physician, and quality of medication reconciliation may be associated with readmission risk.37, 38
The supply of hospital beds may independently contribute to higher readmission rates.39
Finally, the quality of inpatient care could also contribute to risk,40
though the evidence is mixed.41
Though the inclusion of such hospital-level factors would conceivably improve the predictive ability of models, it would be inappropriate to include them in models that are used for risk-standardization purposes. Doing so would adjust hospital readmission rates for the very deficits in quality and efficiency that hospital comparison efforts seek to reveal, and which could be targets for quality improvement interventions.
Public reporting and financial penalties for hospitals with high 30-day readmission rates are spurring organizations to innovate and implement quality improvement programs.42, 43
Nevertheless, the poor discriminative ability of most of the administrative models we examined raises concerns about the ability to standardize risk across hospitals in order to fairly compare hospital performance. Until risk prediction and risk adjustment become more accurate, it seems inappropriate to compare hospitals in this way and reimburse (or penalize) them on the basis of risk-standardized readmission rates. Others have reached similar conclusions,44
and have also expressed concern that such financial penalties could exacerbate health disparities by penalizing hospitals with fewer resources.45
Still others have argued that readmission rate is an incomplete accountability measure that fails to consider “the real outcomes of interest – health, quality of life, and value.”46
Use of readmission rates as a quality metric assumes that readmissions are related to poor quality care and are potentially preventable. However, the preventability of readmissions remains unclear and understudied. We found only one validated prediction model that explicitly examined potentially preventable readmissions as an outcome, and it found only about one-quarter of readmissions were clearly preventable.13
A recent systematic review of 34 studies found wide variation in the percentage of readmissions considered preventable; estimates ranged from 5% to 79%, with a median of 27%.47
More work is needed to develop readmission risk prediction models with an outcome of preventable readmissions. This could not only improve risk-standardization efforts, but also allow hospitals to better focus limited clinical resources in readmission avoidance programs.
As with models that are used for risk-standardization, readmission risk models that are intended for clinical use also have certain requirements and limitations. Clinical models would ideally provide data prior to discharge, discriminate high- from low-risk patients, and would be adapted to the settings and populations in which they are to be used. Very few models met all these criteria, and only one of these – a single-center study – had acceptable discriminative ability.24
As with the risk-adjustment models, most of the models developed for clinical purposes had poor predictive ability, though notable exceptions suggest the addition of social or functional variables may improve overall performance.24, 27
The best choice of model may depend on setting and the population being studied. The success of some models in certain populations and the lack of success of others suggest the patient-level factors associated with readmission risk may differ according to the population studied. For example, while medical comorbidities may account for a large proportion of risk in some populations, social determinants may disproportionately influence risk in socioeconomically disadvantaged populations. Our review finds, though, that very few models have incorporated such variables.
Even though the overall predictive ability of the clinical models was poor, we did find that high- and low-risk scores were associated with a clinically meaningful gradient of readmission rates. This is important given resource constraints and the need to selectively apply potentially costly care transition interventions. Even limited ability to identify a proportion of patients at risk for future high-cost utilization can increase the cost-effectiveness of such programs.26, 48
Of note, very few models incorporated clinically actionable data that could be used to triage patients to different types of interventions. For example, marginally housed patients, or those struggling with substance abuse, might require unique discharge services. Relatively simple, practical models that use real-time clinically actionable data, such as the Project BOOST model, have been created, but their performance has not yet been rigorously validated.49
Our review concurs with and adds to the findings of several other reviews that found deficiencies in the predictive abilities of risk prediction models. One recent review limited to US studies examined general risk factors for preventable readmissions, but did not search explicitly for validated models, and many of the included studies suffered from poor study design.50
The authors suggest that, in general, measures of poor health such as comorbidity burden, prior utilization, and increasing age were associated with readmissions. Two other reviews focused on specific diagnoses and found very few readmission risk models for heart failure,44
or myocardial infarction.52
Our review has certain limitations. We included studies outside the United States, given that portions of US health care may resemble other countries' health systems, but applicability of models from other countries to the US may still be limited. Our classifications of data types, data collection timing, and the intended use of each model, are subject to interpretation, but we attempted to mitigate subjectivity by using a dual-review and consensus process. Finally, few studies directly compared models within the same population, and summary statistics such as the c-statistic should not be used to directly compare models across different populations.
Additional research is needed to assess the true preventability of readmissions in US health systems. Given the broad variety of factors that may contribute to preventable readmission risk, models that include factors obtained through medical record review or patient report, may be valuable. Innovations to collect broader variable types for inclusion in administrative data sets should be considered. Future studies should assess the relative contributions of different types of patient data (e.g., psychosocial factors) to readmission risk prediction by comparing the performance of models with and without these variables in a given population. These models should ideally be based on population specific conceptual frameworks of risk. Implementation of risk stratification models and their effect on work flow and resource prioritization should be assessed in a broad variety of hospital settings. Also, given that many models have limited predictive ability and may require some investment of time and cost to implement, future studies should further evaluate the relative value of clinician gestalt compared to predictive models in assessing readmission risk.
In summary, readmission risk prediction is a complex endeavor with many inherent limitations. Most models created to date, whether for hospital comparison or clinical purposes, have poor predictive ability. Though in certain settings such models may prove useful, better approaches are needed to assess hospital performance in discharging patients, as well as to identify patients at greater risk of avoidable readmission.