Using phenotypic data from the Electronic Health Record (EHR) to predict discharge

Timely discharge planning (DP) is critical to successful transitions of care from the hospital to the post-acute care (PAC) setting. PAC settings include Skilled Nursing Facilities, Long Term Acute Care facilities, and Inpatient Rehabilitation. Previous studies suggest that early DP decreases hospital length of stay and readmissions [1]. Decision-making regarding discharge destination, however, may occur late during hospitalization, leaving little time to improve the transition to PAC [2, 3].

Availability of a tool to predict discharge destination early in the hospitalization may improve transitions of care to PAC facilities by enabling social services to contact facilities, coordinate insurance authorizations, engage physical therapy to conduct a timely assessments, and aid the primary team in tailoring the patient’s discharge planning for PAC [4, 5] Currently, there are a small number of predictive models, with only one of these using data from the first day of admission [6‐8]. Other models focus on a.narrow range of clinical specialties including cardiac surgery, [9] orthopedics, [10, 11] and trauma [12]. However, there are few models which could be applied across an entire hospital population.

An ideal prediction tool for broad clinical application would automatically generate a score soon after admission, using objective and routinely available data from the electronic health record (EHR). Improvements in bioinformatics now allow for automated analysis of data routinely collected and collated in the EHR. Such EHR-derived scores have been developed for prediction of readmission [13] and physiologic deterioration [14]. The primary aim of this study was to develop and validate an EHR-derived model that produces a score predictive of discharge to PAC, using data readily available within 24 h of admission.

We sought to develop a general adult hospital prediction model that would identify, within the first 24 h of hospitalization, patients at the highest risk of requiring PAC services following discharge. We developed and internally validated a parsimonious prediction model that was well calibrated, and had high accuracy, and had an AUC of 0.875. Importantly, the prediction model exclusively utilized structured and readily available risk factors from the EHR, allowing for calculation of risk in the first 24 h. Such a model may allow hospital services to initiate earlier DP and better target case management, social work, and therapy services to those at highest risk of requiring PAC.

The current research builds upon previously published work that predicts PAC placement. Previous studies have focused on specific inpatient populations, including patients with coronary artery bypass graft surgery [9], lower limb fractures [10], acute myocardial infarction [16], older age [7, 8], or internal medicine patients [6, 7, 24]. Our study is unique in that it is a generalizable model that applies to all adult hospitalized patients and performs with equal or better predictive ability as compared to previously published models. For example, a model developed on older medical inpatients, utilizing an in person questionnaire that assessed activities of daily living (ADL) had an AUC of 0.81 [6]. Another recent model developed upon medical inpatients that utilized nurse intake ADL information has an AUC of 0.82 [7]. Our study confirms the importance of functional data to predict PAC discharge, and demonstrates the ability to apply it broadly across medical and surgical populations. We improve upon previous models by avoiding reliance on an additional functional assessment which would need to be conducted at admission. Our model not only uses age, but also uses markers of frailty such as the Braden Score to help generate predictions based on factors likely to influence need for post-acute care. The model continues to offer useful negative predictive information for older populations based on our analyses of model performance in these cohorts (Additional File 6).

By including the entire adult hospital population, this model could allow for a hospital to more holistically measure and guide resources which are often shared across services lines (e.g., case management, social work, physical therapy). In addition, it allows for the implementation of a single model into the informatics infrastructure, rather than unique models for each care area. The value of the model will be greatest in clinical areas with highest risk factor burden including increasing medication counts, fall risk, and advancing age. The one service area, as demonstrated in sensitivity analyses, for which this model would not provide additional guidance to DP is obstetrics and gynecology. These patients are, not surprisingly, at substantially reduced risk for PAC, as a large proportion of such admissions are for uncomplicated deliveries. This does not, however, diminish the validity with which it can be applied to the remaining medical and surgical populations.

Some may feel that prediction of PAC discharge is intuitive and does not require an automated score. However, the utility of an automated tool is to point busy health team members towards patients who would benefit most from early DP when the clinician may not have activated appropriate resources to arrange for timely transfer. Previously published models predict PAC discharge with the inclusion of data that can only be identified after many days in the hospital, or even after discharge. This may include risk factors such as length of stay, administrative variables (e.g., ICD-9, ICD-10 codes) that are often coded after hospital discharge [8, 10, 16]. Using data available within 24 h of admission allows for real-time calculation, and therefore, can be clinically applied in real-time. Without an automatic trigger, the timing of case management, social work, or physical therapy initiation of care may be delayed on account of referral behaviors, of admission timing, the location of the patient, or even the order of a patient in a standard database (e.g., alphabetically) [7]. Given the model’s accuracy, particularly with regard to its negative predictive value, it was embedded into our center’s EHR and offers a prediction of PAC discharge for every patient on admission.

The predictor selection is another area that our model advances prior research, particularly in using routine nursing functional assessments This is not surprising when considering many prior models have demonstrated the relative importance of functional impairment in predicting PAC discharge^10,12,28. Many functional predictors, however, require in-person research measurements or manually abstracted patient responses. Our current model extends the application of clinical measures that are markers for mobility, fall risk, and polypharmacy. The Braden Risk Score, Morse Fall Risk Score, and pre-admission medication are routinely measured for the clinical care purposes unrelated to predicting PAC risk, however, each are independently predictive of PAC discharge. We specifically chose these variables as they are commonly measured early during the hospital stay and have the potential to be generalizable to other hospitals that routinely measure these. An illness severity index was not necessary for creating a high-performing model, and may have added unnecessary complexity if these are not routinely calculated for all admissions.

Among the limitations of this analysis are the fact that it is a retrospective study that examines a diverse population but only at a single center which contains its own local discharge practices. It is possible that missing and misclassification of final discharge destination for some patients may have biased the model, however, the direction of the bias is not known and is again thought to be small. Though the AUC was high, the PPV of the model is low, in a practical sense, utilizing the model to define which patients require intensive, early discharge planning may result in misallocation of resources to some patients who will not eventually be discharged to PAC. While the random undersampling approach addressed the problem of class imbalance, the deletion of cases from the majority class may result in losing information. Hospice is a discharge destination that also requires significant early discharge planning. However, our model groups this discharge outcome as non-PAC discharge. This is because discharge planning for SNF and discharge planning for hospice overlap, but require different evaluations to organize placement. Furthermore, our model does not account for a growing emphasis on PAC which can be delivered in a home-based setting, [25, 26] nor does it incorporate data from after the COVID-19 pandemic, which had widespread impacts around transitions of care. Finally, our model is parsimonious and does not include alternative variables that could predict discharge destination (e.g., social determinants of health).

We have developed a PAC discharge prediction model for an adult hospital population. The model is parsimonious, includes EHR-derived data, and is calculated from data within 24 h of admission. Despite the limited number of variables and calculation early in the hospital stay, it is remarkably accurate with excellent calibration. Further research could externally validate as well as understand the impact of model calculations on changing and improving DP. As the model is deployed in the hospital EHR system, it may assist in targeting DP to the highest need patients and may improve the patient and provider experience of the overall discharge process.