Rising admissions from emergency departments (EDs) to hospitals are a primary concern for many healthcare systems. The issue of how to differentiate urgent admissions from non-urgent or even elective admissions is crucial. We aim to develop a model for classifying inpatient admissions based on a patient’s primary diagnosis as either emergency care or elective care and predicting urgency as a numerical value. We use supervised machine learning techniques and train the model with physician-expert judgments. Our model is accurate (96%) and has a high area under the ROC curve (>.99). We provide the first comprehensive classification and urgency categorization for inpatient emergency and elective care. This model assigns urgency values to every relevant diagnosis in the ICD catalog, and these values are easily applicable to existing hospital data. Our findings may provide a basis for policy makers to create incentives for hospitals to reduce the number of inappropriate ED admissions.
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For example, the NHS introduced the “marginal rate rule” in 2010 in response to growth in the volume of patients being admitted to the hospital as emergencies. The rule sets a baseline value for income from each provider’s emergency admissions. For emergency admissions above this baseline, the provider receives only 30% of the normal price [6, 7] regardless of urgency.
If a hospital treats more cases than negotiated with the SHI, it may receive reductions in the DRG payments for additional patients . Therefore, it may be beneficial to code elective admissions as emergencies since every acute care hospital in Germany is required by law to admit all emergency cases unless it has reached capacity .
Urgency interpreted from greatest to least harm as a consequence of deferring treatment .
ICD and OPS codes were harmonized to the same version over the years using official mapping tables by DIMDI (German Institute of Medical Documentation and Information).
The operating definition of interaction is that variables m and k interact if a split on one variable, say m, in a tree makes a split on k either systematically less possible or more possible.
We have training data from all ICD chapters except for III (diseases of the blood and blood-forming organs and certain disorders involving the immune system), V (mental and behavioral disorders) and XX (external causes of morbidity and mortality), which together make up less than 5% of cases. We exclude chapter XV (pregnancy, childbirth and puerperium).
Additionally, superficial partial thickness (2nd degree a) has a lower urgency then deep partial thickness (2nd degree b).
Excluding maternity and birth.
For illustration purposes, we used logarithmic values for the hospital utilization in the lower part because of the steep age gradient.
The large increase of inpatient cases with a length of stay of <48 h in Germany is in line with this observation.
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Krämer, J., Schreyögg, J. & Busse, R. Classification of hospital admissions into emergency and elective care: a machine learning approach. Health Care Manag Sci 22, 85–105 (2019). https://doi.org/10.1007/s10729-017-9423-5
- Emergency care
- Elective care
- Machine learning
- Random forest