Cost of Readmission: Can the 

E i xper ence I f n orm N ti l a ona P 

ay

Policy
Acknowledgement and Discl

This work is part of

a broader project fun

HSR&D IIR 08

‐067

‐

2

(Kaboli and Restucci

readmission work in particular is in collawith Jim Burgess Burgess

, Justin Glasgow Glasgow, Mary V

V

Sarrazin, and Peter Kaboli.

The work presented presented here in no way refle

refle

position or opinion of the Veterans Healt

Administration Administration. All errors and opinions opinions e

e

are that of the authors alone.
Background

Hospitalization is costlyCurrently, readmission to the hospital is There is concern th t a rates of readii m ss o

high’ and could and should be reduced
Background

National policy is moving toward penalizhospitals for higher than expected readmrates for AMI, CAP and CHFB dl d un e payment approaches for epi d so 

as an option to reduce costs and incent b

coordinated care are being explored
Motivation for our wor

T ti arge ng readii m ss on to reduce h lth ea care 

questions:

Is readmission a consistent measure of quaDo facility rates of readmission predict indivreadii m ss on or pati t en epi d so e cost ?sWhat are the actual costs of patients who a

readmitted?
Our contribution

We examine examine AMI, CAP and CHF admissions admissions t1. Whether historic facility readmission rates pati t en readii m ss on2. Whether historic facility readmission rates individual individual patient patient costs in the contemporane

contemporane

3. The difference in hospital episode cost of c

patients readmitted within 30 days of discha
Setting

Veterans Health Administration (VHA) achospitalsWe focus on those h il osp tals averaging > 

admissions per quarter for a given condit
Comparing readmissions 

Comparing readmissions 

30 day readmission rates

Medicare FFS* VHA

AMI 19 8%. 18 8%.

CAP 18.4% 14.0%CHF 24.8% 20.1%

*R t t k f M di H it l C R

a

tes 

t

a

ken 

from 

M

edicare 

Hospit

al Compare
Data

Patient level health data on hospital admPatient level socioeconomic and demogr

ch ii aracteristics

Cost data from VHA Decision Support Sys
Summary characteristic

Index hospitalization admitting condition

AMI CAP 

No of patients No 15,603 63,679 

. of patients 15,603 63,679 

No. of facilities# 35 101 

mean + sd mean + sd 

Episode CharacteristicsTotal episode cost ($)^ 21114 + 7277 14307 + 555Total episode cost ($)Index hospitalization cost ($) 18572 + 6117 12293 + 491Readmission hospitalization cost ($) 2542 + 3184 2014 + 207Readmission hospitalization cost ($)Readmission rate (%) 18.84 + 10.21 13.97 + 7.1

Index visit LOS (days) 5.40 + 1.51 5.86 + 1.3
Summary characteristics characteristics (co

Index hospitalization admitting AMI CAP conditionDemographic Characteristics mean + sd mean + sd Female (%) 1 96 . + 3 51 . 3 15 . + 3 60 . Age (years) 67.6 + 3.8 69.4 + 3.7 Divorced (%) 32.61 + 12.44 29.16 + 10.0

Never married (%) 8.07 + 7.84 9.59 + 8.09 

Widowed (%) 11.02 + 7.83 14.43 + 7.31 Hispanic (%) 1.40 + 4.53 0.94 + 3.53 Black (%) 8.93 + 12.23 12.94 + 15.98 

Asian (%) 1.00 + 3.33 0.75 + 2.11 

Native American (%) 0.47 + 2.10 0.45 + 1.51 

Missing (%) 44.0 + 34.00 35.45 + 35.1
Model predicting readmiss

Ri,j,t= β0 + β1 rj,t‐1 + β2 iLOSi + β3 Xi + φ

WhereR is readii m ss on i di n cator

r is the condition‐specific readmission rate in th

quarter

iLOS is the patient’s index admission length of s

X is the patient’s health, demographic and socio

characteristics

φ are hospital fixed effects
Results

I dn ex h it li ti ospitalization ad itti m ng AMI CAP 

condition

AMI CAP 

Hospital Hospital readmission readmission rate (t‐1)  ‐0 0005 . ‐0 0002 . 

(.0004) (0.0002) 

Index hospitalization LOS (in days) 0.0028** 0.0027*** 

(0 0009) (0.0009) (0 0003) (0.0003) 

N 15603 63679 

These mod l e s are adj t d us e for comorbid conditions, race, gender, marit 

Standard errors in parentheses are robust clustered at the facility level

*

p < 0.05, ** p < 0.01, *** p < 0.001
Episode cost modeling

We first addressed whether facility readmiss

impacts risk adjusted bundled hospital episo

estimating:ln(Ep. Costi,j,t)= β0 + β1 rj,t‐1 + β2 eLOSi + β3 Xi +Where all covariates are defined the same as iprevious except eLOS which is the total episod

stay
Results

(1) (2) 

Index hospitalization admittingcondition

AMI CAP 

Hospital readmission rate (t‐1) 31.65 ‐12.36 

(20 65) (20.65) (16 50) (16.50) 

Index hospitalization LOS (in days) 2789*** 2701*** Index hospitalization LOS (in days) 2789 2701 

(148) (294) 

N 15603 63679 

These mod l e s are adj t d us e for comorbid conditions, race, gender, marit 

Standard errors in parentheses are robust clustered at the facility level

*

p < 0.05, ** p < 0.01, *** p < 0.001
Episode cost modeling

We then address the difference in the averag

adjusted bundled hospital episode cost for re

patients :

ln(Ep. Costi,j,t)= β0 + β1 rj,t‐1+ β2 eLOSi+ β3 Xi+ β4 R

Where all covariates are defined the same as iprevious and the readmission indicator R is inc
Results

Index hospitalization admitting AMI CAP conditionHospital readmission rate (t‐1) 32.15 ‐10.55 

(19.07) (16.52) 

Readmission indicator 12,681*** 13,326*** 

(679) (425) 

Ep. hospitalization LOS (in days) 2770*** 2694*** 

(147) (295) 

N 15603 63679 

Models are adjusted for total episode LOS, comorbid conditions, race, gender, mari

Standard errors in parentheses are robust clustered at the hospital level

*

p < 0.05, ** p < 0.01, *** p < 0.001
Episode cost modeling

Because linear models can obscure tail effects 

estimate episode costs via quantile regressi

Q (Ep costi j )= β 0 + β 1 rj 1 + β 2 eLOS Qq i + β 3 

(Ep. costi,j,t) βq,0 + βq,1 rj,t‐1 + βq,2 eLOSi + βq,3 

A dn evaluate the mod l e at q=.10, .25, .50, .75 a
Results

percentile: 10th 25th 50th 

AMI

Patient was readmitted 3369*** 3369*** 5368*** 8 0 *** 8505*** 136

36

(154) (168) (303) (

Hospital readmission rate ‐15.16** ‐22.13*** ‐30.34*** ‐2in (t‐1) (5 20) (6 79) (6 66) (1in (t‐1) (5.20) (6.79) (6.66) (1N=15,603

Models are adjusted for total episode LOS, comorbid conditions, race, gender, maritaBootstrapped standard errors (100 reps) in parentheses*

p < 0.05, ** p < 0.01, *** p < 0.001
Results

percentile: 10th 25th 50th 

CAP

Patient was readmitted 3 88*** 3588*** 5415*** 8 98*** 8498*** 141

(113) (113) (142) (

Hospital readmission rate ‐4.45*** ‐12.28*** ‐20.91*** ‐19in (t‐1) (1 63) (1 98) (2 42) (in (t‐1) (1.63) (1.98) (2.42) (N=63,679

Models are adjusted for total episode LOS, comorbid conditions, race, gender, maritaBootstrapped standard errors (100 reps) in parentheses*

p < 0.05, ** p < 0.01, *** p < 0.001
Results

percentile: 10th 25th 50th 

CHF

Patient was readmitted 30 9*** 3079*** 4589*** 7572*** 13 

4

(52) (55) (86) (

Hospital readmission rate ‐0.094 ‐7.29*** ‐6.97*** ‐8in (t‐1) (2 71) (1 88) (2 16) (in (t‐1) (2.71) (1.88) (2.16) (N=64,391

Models are adjusted for total episode LOS, comorbid conditions, race, g , ender maritaBootstrapped standard errors (100 reps) in parentheses*

p < 0.05, ** p < 0.01, *** p < 0.001
Patients treated in facilities with > 3 diseas

discharges per quarter discharges per quarter

30000

s

25000

.S. Dollars 15000 20000 U.

1000015000

5000

0

Q10 Q25 Q50 Q75 

Quantiles
Findings

R t ecen h it l ospital readii m ss on rate does no

to predict individual patient readmission

Risk adjusted episode costs are not impah l’ ospitals’ recent readmission ratesHospital episode costs are higher for rea

patients, and this increased cost can be s
Conclusion

Penalizing hospitals based on rates may 

misguided

Recent i i nterventions suggest the cost pe

to reduce readmission is greater than the 

readmission itselfWhile reducing readmission may have vatheory that it will reduce costs is in ques

ques