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Identifying patients at highest-risk: the best timing to apply a readmission predictive model.

Natalie Flaks-ManovMaxim TopazMoshe HoshenRan D BalicerEfrat Shadmi
Published in: BMC medical informatics and decision making (2019)
The timing of readmission risk prediction makes a difference in terms of the population identified at each prediction time point - at-admission or at-discharge. Our findings suggest that readmission risk identification should incorporate a two time-point approach in which preadmission data is used to identify high-risk patients as early as possible during the index admission and an "all-hospital" model is applied at discharge to identify those that incur risk during the hospital stay.
Keyphrases
  • emergency department
  • healthcare
  • end stage renal disease
  • newly diagnosed
  • chronic kidney disease
  • ejection fraction
  • electronic health record
  • prognostic factors
  • adverse drug
  • big data