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 | mathu.kumarasamy@emoryhealthcare.org

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