Nazima Allaudeen|Jeffrey L. Schnipper|E. John Orav|Robert M. Wachter|Arpana R. Vidyarthi
Inability of Providers to Predict Unplanned Readmissions
VA Palo Alto Health Care System|Brigham and Women's Hospital|Harvard University|Harvard University|University of California, San Francisco|University of California, San Francisco
readmissions cause significant distress patients considerable financial costs identifying hospitalized patients high risk readmission important strategy reducing readmissions aimed evaluate well physicians case managers nurses predict whether older patients readmitted compare predictions standardized risk tool probability repeat admission pra patients aged 65 discharged general medical service university california san francisco medical center 550bed tertiary care academic medical center eligible enrollment 5week period time discharge inpatient team members caring patient estimated chance unscheduled readmission within 30 days predicted reason potential readmission also calculated pra patient identified readmissions electronic medical record emr review phone calls patientscaregivers discrimination determined creating roc curves provider group pra one hundred sixtyfour patients eligible enrollment patients five died 30day period postdischarge remaining 159 patients 52 patients 327 readmitted mean readmission predictions physician providers closest actual readmission rate case managers nurses pra overestimated readmissions ability discriminate readmissions nonreadmissions poor provider groups pra auc 050 case managers 059 interns 056 pra none provider groups predicted reason readmission accuracy study found 1 overall readmission rates higher previously reported possibly employed thorough followup methodology 2 neither providers published algorithm able accurately predict patients highest risk readmission amid increasing pressure reduce readmission rates hospitals accurate predictive tools guide efforts