Developing a Predictive Risk Score for 30-Day Readmission … · 2017. 9. 6. · Mathu A....
Transcript of Developing a Predictive Risk Score for 30-Day Readmission … · 2017. 9. 6. · Mathu A....
Mathu A. Kumarasamy1, Gregory J. Esper1,2
1. Emory Healthcare, Atlanta, GA
2. Emory University School of Medicine, Atlanta, GA
Abstract
Background
Methods Conclusions
Future Directions Results
As healthcare continues to transition towards a value-based model, 30-day hospital readmission rates are increasingly being
utilized as an indicator of quality. As healthcare reform progresses, hospitals are also increasingly being held financially
accountable for readmission rates as penalties and reimbursement cuts threaten hospitals with above average 30-day readmission
rates. In an effort to learn more about this high-risk population, and develop strategies to increase value by improving quality and
reducing costs, Emory Healthcare developed a predictive risk model to identify risk factors of “frequent flyers”—patients who have
had two or more 30-day readmissions—across our multihospital academic health system.
The project was developed to research and develop a predictive risk score to identify patients with a high likelihood of being
serially readmitted within 30 days. Specifically, we wanted to identify risk factors of patients who had two or more 30-day
readmissions within the span of one year. A preliminary analysis of readmissions across our healthcare system revealed that of all
patients who were readmitted within 30 days, 25% had two or more 30-day readmissions—these 25% of patients were known as our
"frequent flyers". Additionally, these 25% of patients, or frequent flyers, disproportionally accounted for nearly 50% of all 30-day
readmissions across the healthcare system. In an effort to learn more and operationalize workflows towards this disproportionate
high-risk population, we developed a predictive risk model to identify risk factors associated with serially readmitted patients across
our multihospital academic health system.
A total of 6,070 30-day readmission encounters made up of 4,310 unique patients were analyzed from September 2012 through
August 2013 from Emory University Hospital, Emory University Hospital Midtown, Emory Saint Joseph's Hospital, and Emory Johns
Creek Hospital. 1,064 (or 25%) of patients were readmitted two ore more times within 30 days and accounted for 2,824 (or 47%) of
all 30-day readmissions. The risk model was developed by analyzing a collection of demographic, clinical, and financial data with
a total of over 50 variables incorporated into the model from both the index and readmission encounters of the first readmission
pair. The University HealthSystem Consortium (UHC) related and unrelated 30-day readmission definitions were used. The Centers for
Medicare & Medicaid Services’ (CMS) QualityNet Specifications Manual and Iezzoni’s Chronic Conditions from The Dartmouth Atlas
of Health Care were used to sort ICD-9 diagnoses when analyzing clinical comorbidities. Patients with a discharge disposition of
expired during their readmission encounter were excluded from the analysis. The analysis was stratified by hospital type within our
healthcare system. Specifically, the analysis was stratified to identify the risk factors across the two academic hospitals within our
system (Emory University Hospital and Emory University Hospital Midtown) and the two community hospitals within our system (Emory
Saint Joseph's Hospital and Emory Johns Creek Hospital). Accordingly, our analysis also aimed to reveal the within system
differences of serial readmission risk factors between academic hospitals and community hospitals.
Nominal logistic regression via backward elimination was performed to reveal the strongest predictors of serial 30-day readmissions.
Risk factors were stratified based on whether a patient was seen at the academic hospitals versus the community hospitals. Risk
factors most predictive of a serial 30-day readmission at the academic hospitals included having a septicemia diagnosis (odds
ratio = 1.72, P < .0001), palliative care encounter during the readmission encounter (odds ratio = 0.42, P < .0001), pneumonia
diagnosis (odds ratio = 1.67, P < .0001), mental disorder diagnosis (odds ratio = 1.51, P < .0001), admission type of urgent or
emergent during the index encounter (odds ratio = 1.60, P < .0001), home self care discharge disposition during the readmission
encounter (odds ratio = 1.42, P < .0001), chronic renal failure diagnosis (odds ratio = 1.41, P < .0001), severe chronic liver disease
diagnosis (odds ratio = 1.69, P < .0003), and a prior inpatient hospital visit (odds ratio = 1.33, P < .0013). Risk factors most predictive of
a serial 30-day readmission at the community hospitals included having a drug dependence diagnosis (odds ratio = 2.90, P < .0014),
pneumonia diagnosis (odds ratio = 2.05, P < .0041), surgery attending specialty during the index encounter (odds ratio = 0.38, P <
.0020), and chronic renal failure diagnosis (odds ratio = 1.75, P < .0109).
The results from our research are currently being leveraged to create a report which will be hardwired into our clinical data
warehouse to deliver real-time risk scores of patients currently in-house who have been recently readmitted within 30 days. The risk
tool will then be able to assess the risk of a serial 30-day readmission per patient based on risk factors identified from our model.
These risk scores will then be stratified to trigger appropriate workflows and/or consultations (i.e. home health service orders, social
work consultations, palliative care consultations) across the different hospitals within our system. Our analysis has shown that despite
being a comprehensive healthcare system, there are different risk factors which require certain tailoring and targeting of initiatives
to drive better patient outcomes across our four hospitals. Other comprehensive academic health systems should be aware that
certain initiatives and quality improvement work across the system may require customization and adjustments based on different
patient populations. Additionally, our work has shown how focused and targeted research could be leveraged to reveal valuable
insights and drive quality improvement to ultimately provide patients with better value.
Developing a Predictive Risk Score for 30-Day Readmission Frequent Flyers Across a Multihospital Academic Health System
Despite being a comprehensive multihospital
healthcare system, there exists unique risk
factors of serial readmissions based on the
hospital setting and type.
Certain initiatives and quality improvement
work across a multihospital healthcare
system may require customization and
adjustments based on different patient
populations and hospital settings. In fact,
certain databases and consortiums (i.e.
UHC) have created new predictive risk
model stratifications to take into account
whether a hospital is an academic hospital
or a community hospital.
Hospital specific research within a system
could be leveraged to reveal valuable
insights to drive quality improvement and
ultimately provide patients with better value
and more personalized care.
Create and validate the risk scoring tool
among patients in both the academic and
community hospital settings.
Incorporate additional comorbidity indexes or
groupings (i.e. AHRQ HCUP CCS diagnosis
clusters).
Embed risk scoring tool into the clinical data
warehouse and/or electronic medical record
to identify patients in real-time.
Stratify and operationalize risk scores into
provider workflow to trigger consults (i.e.
social work, palliative care, home health)
when appropriate.
Broaden analysis to investigate readmissions
where the index hospital is different from the
readmission hospital.
Explore outpatient and primary care provider
activity as a potential risk factor for serial
readmissions and an opportunity for care
coordination and discharge planning.
Mathu A. Kumarasamy | Emory Healthcare | Office of Quality & Risk | [email protected]
Emory Healthcare (EHC) is the largest healthcare system in Georgia, comprised of 4 hospitals,
two of which are academic AAMC teaching hospitals, and two of which are non-AAMC
community hospitals.
AAMC Teaching Hospitals: Emory University Hospital (EUH), Emory University Hospital Midtown
(EUHM)
Non-AAMC Community Hospitals: Emory Johns Creek Hospital (EJCH), Emory Saint Josephs
Hospital (ESJH)
30-day hospital readmission rates are increasingly being utilized as an indicator of quality across
the United States.
Of all patients who were readmitted within 30 days, 25% had two or more 30-day readmissions—
these 25% of patients were known as our "frequent flyers". Additionally, these 25% of patients, or
frequent flyers, disproportionally accounted for nearly 50% of all 30-day readmissions across the
healthcare system in the span of one year.
Emory Healthcare developed a predictive risk model to identify risk factors of “frequent flyers”—
patients who have had two or more 30-day readmissions—across our multihospital academic
health system.
Data Filters and Assumptions:
UHC Encounter Pairs Subreport: Selects index and immediate subsequent encounter pairs for
inpatients and newborns admitted and readmitted to the same hospital.
Readmission Encounter Exclusions: Excludes encounters with birth/delivery diagnoses, primary
diagnosis chemotherapy, dialysis, radiation or rehabilitation and those with primary procedures
for radiation or chemotherapy.
UHC Readmissions Denominator Filter: Selects inpatients and newborns who were not
discharged as expired for a selected index encounter discharge month.
Select Emory Healthcare Entity: Encounter pairs with an index admission and readmission at a
particular EHC hospital.
Days Between Encounters: 30 days between the latest index discharge date and readmission
encounter admit date.
Index Discharge Month: September 2012 – August 2013 (Fiscal Year 2013)
Unrelated and Related Encounters (All Cause): UHC definition of related encounters: Index and
subsequent encounter must match on primary diagnoses, primary procedure, MS-DRG, CCS
Procedure Category, CCS Diagnosis Category or the readmission primary diagnosis is a
complication code. This analysis looks at both related and unrelated encounters.
Given the multiple number of encounters for each patient, data were analyzed based on a
patient’s first readmission pair. For example: If a patient were readmitted 4 times, the analysis
would only look at encounter level data from the patient’s 1st readmission pair.
Patients with a discharge disposition of expired during their readmission encounter were
excluded from the analysis.
PREDICTOR ODDS RATIO P-VALUE
Septicemia Diagnosis 1.72 p < 0.0001
Palliative Care Encounter During Readmission 0.42 p < 0.0001
Pneumonia Diagnosis 1.67 p < 0.0001
Mental Disorder Diagnosis 1.51 p < 0.0001
Admission Type of Urgent or Emergent During Index 1.60 p < 0.0001
Home Self Care Discharge Disposition During Readmission 1.42 p < 0.0001
Chronic Renal Failure Diagnosis 1.41 p < 0.0001
Severe Chronic Liver Disease Diagnosis 1.69 p < 0.0003
Nervous System Disorder Diagnosis 1.38 p < 0.0002
Prior Inpatient Hospital Visit 1.33 p < 0.0013
PREDICTOR ODDS RATIO P-VALUE
Drug Dependence Diagnosis 2.90 p < 0.0014
Pneumonia Diagnosis 2.05 p < 0.0041
Surgery Attending Specialty During Index 0.38 p < 0.0020
Chronic Renal Failure Diagnosis 1.75 p < 0.0109
Read as “Patients who have already been readmitted within 30-days once, are [ODDS RATIO]
times as likely of being serially readmitted within 30 days if they have had a [PREDICTOR].”
AAMC Teaching Hospitals Non-AAMC Community Hospitals