VA Health Services Research and Development Service CyberSeminar Series

February 20, 2013

The Center for Comprehensive Access and Delivery 

Research and Evaluation

Hospital Readmission: 

A Measure of Hospital Quality? 

Peter J. Kaboli, MD, MS

CADRE Investigator and Hospitalist, 

Iowa City VA Healthcare System

Professor, Department of Internal Medicine, 

University of Iowa Carver College of Medicine

Iowa City, IA

1
Objectives

1. Describe rates of hospital readmissions, association 

with LOS, and factors associated with higher rates. 

2. Discuss role of readmissions as a “quality” metric.

3. Outline proven strategies for improvement.

4. Implications of “bundled” payments and future of 

readmissions in VA.

5. Research opportunities.

2
Audience Response Question

 In the past 14 years, hospital LOS has 

steadily declined in VA. Over the same 

time, what happened to 30-day 

hospital readmission rates?

1. Increased 

2. Decreased

3. Remained unchanged

3
Annals of Int Med, 

Dec 18, 2012

4
Reduction of LOS Significant for All Conditions 

5
Adjusted LOS (Mean, days)

Fiscal 

Year 

All Med 

Dx 

CHF COPD AMI CAP GIH

97-98 5.44 6.33 5.68 6.63 7.18 5.08

03–04 4.54 5.31 4.53 4.90 5.74 4.17

09–10 3.98 4.40 3.89 3.78 4.96 3.68

Change -1.46 -1.93 -1.79 -2.85 -2.22 -1.40

Reduction of LOS Significant for All Conditions 

6
Reduction in 30-Day Readmission Rates

7
Adjusted Readmission Rates

Significant Reductions in 30-Day Readmissions

Fiscal 

Year 

All Med 

Dx 

CHF COPD AMI CAP GIH

97-98 16.5% 20.4% 17.9% 22.6% 14.7% 14.1%

03–04 15.0% 19.3% 15.5% 20.2% 13.7% 13.1%

09–10 13.8% 19.0% 14.6% 19.8% 13.8% 12.2%

Change -2.7% -1.4% -3.3% -2.8% -0.9% -1.9%

8
Advanced statistics 

9
Association between LOS reduction and 

Readmissions

0.7

0.75

0.8

0.85

0.9

0.95

1

97-98 99-00 01-02 03-04 05-06 07-08 09-10

40% LOS Reduction

25% LOS Reduction

10% LOS Reduction

No Change in LOS

Risk adjusted decrease in readmission rates for hospital with 

0%, 10%, 25% and 40% reduction in LOS

10
Expected LOS and Readmission Rate

 A patient discharged from a hospital with LOS that 

was 1 full day lower than expected:

 given the year and patient characteristics

 was 6% more likely to be readmitted (OR=0.94; 95% CI, 

0.93-0.95; p<.001), compared to a patient discharged 

from a hospital with mean LOS equal to the expected LOS 

for the year

 Within hospitals, each additional day of stay was 

associated with a 3% relative increase in the 

likelihood of readmission. 

11
Stated another way...

 Reducing LOS to much below expected will result in 

a small trade off for higher readmission rate. 

 Patients with longer LOS have higher readmission 

rates.

 Duh, these people are sicker and need to return to 

hospital more frequently

12
Audience Response Question

 Okay, so VA shortened LOS (has 

become more efficient) AND at the 

same time reduced hospital 

readmissions. However, we have 

improved palliative care services (i.e., 

the dead don’t get readmitted), thus 30 

day mortality has:

1. Increased 

2. Decreased

3. Remained unchanged

13
Mortality Trends over 14 years

 30-day mortality decreased by 25% 

 (6.4% to 4.8%)

 90-day mortality decreased by 18% 

 (11.46% to 9.4%) 

 Logistic regression analyses, adjusting for patient 

demographics and comorbidity and hospital random 

effects, found similar reductions (P<0.0001). 

14
Variation in LOS and Readmission Rates: 129 VAs

0

2

4

6

8

10

12

Length of Stay

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Readmission Rate

length of stay readmission rate

Readmission 15% 

LOS 6.0 days LOS 3.7 days

15
SUMMARY 

 Unadjusted LOS decreased from 1997 to 2010 with 

concomitant decrease in unadjusted readmissions 

 In multivariable analyses: similar results

 Greater reductions in LOS resulted in less reduction 

in 30-day readmissions

 Considerable variation suggests fixable solutions

 Reassuring to show increased inpatient efficiency has 

not resulted in increased hospital readmission

16
 Eugene Oddone and Morris Weinberger, Durham VAMC

 “The authors acknowledge that a limitation of the 

study is that it was conducted in a single health care 

system (VA). That statement is included in all articles 

from VA investigators. Perhaps it is time to embrace 

the VA as the largest US ACO. Let the VA serve as 

an example of how to enhance both efficiency 

(reduced LOS) and quality (reduced readmissions 

and mortality).” 

17
Your host, Dr. Todd Wagner

Palo Alto VAMC

18 Break #1 for Questions
Audience Response Question

 Do you believe 30-day hospital 

readmission rate is a measure of 

healthcare quality?

1. Yes 

2. No

3. I’m not sure, but I will have a more 

informed opinion at the end of the 

hour.

19
Is 30-day Readmission a Quality Metric?

 Quality: a degree of excellence; superiority in kind

 Metric: a standard of measurement to capture 

performance

 Readmits: Easy to measure, but not necessarily quality

 Donabedian Model of Quality

 Structure: presence of hospitalists/intensivists

 Process: ASA for AMI

 Outcomes: a health state like death

 No external validity for readmission rates

Thehealthcareblog.com/blog/2013/02/14 (Ashish Jha, MD)

20
Is Readmission an Error or Adverse Event? 

 Error: includes terms such as mistakes, close calls, 

near misses, active errors, and latent errors

 Adverse event: includes terms that usually imply 

patient harm (i.e., medical and iatrogenic injury)

 Readmission may result from an error or adverse event

 e.g., wrong dose of diuretic for HF patient

 e.g., fall resulting from re-conditioning

 More frequently from the disease process or 

uncontrollable factors. 

 e.g., homeless man with O2 dependent COPD

Thomas, EJ, Peterson, LA, Measuring Errors and Adverse Events in 

Health Care. JGIM. 2003 Jan; 18(1) [Houston VA]

21
Audience Response Question

 So, if readmissions are not an Adverse 

Event or great Quality Metric, are they 

preventable? Assuming yes, what % are 

preventable?

1. 10%

2. 25%

3. 50%

4. 60%

5. 90%

22
Preventability of Readmissions

 AJM, Frankl, Breeling, Goldman, 1991

 N=2,626 medicine admits over 4 months; 12% 30-day readmissions

 9% (or 1% of total) were “preventable”; 89% occur within 10 days

 1/3 system failure, 1/3 hope patient would improve, 1/3 suboptimal judgment in evaluation or treatment 

 Health Care Fin Rev, Goldfield, et al, 2008

 N=4,311,652 medicine and surgery admissions; 234 Florida hospitals

 Medical readmission with: 1) same diagnosis, 2) de-compensation of a 

chronic condition, or 3) medical complication (3M PPR Software)

 ~13% overall readmission rate

 ~60% (or 7.9% of total) met criteria for “potentially preventable”

23
Proportion of Hospital Readmissions 

Deemed Avoidable: A Systematic Review

 van Walraven, et al, CMAJ, 2011

 Systematic review of 34 studies 

 Median proportion of readmissions deemed 

avoidable: 27.1% [Range 5-79%]

 Answer: ~25% are potentially preventable based 

on the literature

 However, likely you can’t prevent all of them

24
Audience Response Question

 So, readmissions are a poor quality 

metric, rarely represent an error, and 

only ~25% are preventable. Can we at 

least predict who will be readmitted?

1. No, models are no better than coin flip 

(auc=.500)

2. Yes, but prediction models aren’t great 

(auc=.625)

3. Yes, and prediction models are great 

(auc=.950)

25
Readmission Prediction Models Stink

1 point each

 Age>70

 Dx: anemia, DM, RF, HF, COPD

 Lab: Cr>2, Alb>3.3, Hct >55 or 

<25, glu >180 or <60

 LOS>7 days

 Cancer (2pts)

 Prior admit <6 mo

 1-5 medications (3pts)

 >6 medications (4pts)

 Medicare (5pts)

 Medicaid (4pts)

 Self-pay (4pts)

 Charlson Index

 LOS>2days (3pts)

 Admits in last year

 1-3 (4pts)

 4 (9pts)

 >5 (11pts)

 Have PCP (3pts) 

 Married (2pts) and SF-12

Render IPEC Model: auc = 0.625 Hasan Univ Model auc = 0.650

Hasan, et al. Hospital readmission in gen med patients: a prediction model. JGIM. 

Dec 15, 2009

26
Further Proof of Model Stink Factor

 Kansagara, et al, Risk Prediction Models for Hospital 

Readmission: Systematic Review. JAMA, 2011 (306)15.

 30 studies (26 unique models)

 14 used retrospective admin data for hospital comparisons

 Auc=.55-.65

 7 could identify high-risk patients during hospital stay

 Auc=.56-.83

 5 used at hospital discharge

 .68-.83

 2 models found functional and social variables improved 

discrimination, but rarely available

[Portland VA]

27
Audience Response Question

 Now do you believe 30-day hospital 

readmission rate is a measure of 

healthcare quality?

1. Yes 

2. No

3. I’m still not sure, will have to listen until 

the end.

28
Your host, Dr. Todd Wagner

Palo Alto VAMC

29 Break #2 for Questions
Audience Response Question

 This presentation is a buzz-kill. Is there 

hope that interventions can improve 

readmission rates?

1. Yes, studies consistently show 

readmission rates can be improved.

2. No, evidence is mixed (interventions 

both increase and decrease readmits)

3. Doesn’t matter, floggings will continue 

until readmission rates improve.

30
 NEJM, 1996

 N=1,396 randomized to usual care or an intensive MD/RN intervention 

before discharge and continuing for 6 months.

 Intervention: higher readmission rates (19% vs. 14%; p=.005)

 More days of re-hospitalization (10.2 vs. 8.8; p=.04)

 Intervention patients were more satisfied with care

 In 2013: Primary Care Medical Home?

 No data to suggest it reduces readmission

[Durham VA]

31
Frequently Cited Positive Studies

 Interventions at the time of discharge:

 Project BOOST. Society of Hospital Medicine

 The Care Transitions Intervention. Coleman, et al, Arch Int Med, 2006

 Transitional Care of Older Adults Hospitalized with Heart Failure. Naylor 

et al, JAGS, 2004

 A Re-engineered Hospital Discharge Program to Decrease Rehospitalization. Jack, et al, Ann Int Med, 2009 (aka Project RED)

 N=749; mean age 49.9 yrs

 Readmissions: 0.21 vs. 0.15 visits/pt/mo (p=.09)

 ED Visits: 0.25 vs. 0.17 visits/pt/mo (p=.014)

 Readmissions + ED Visits: 0.45 vs. 0.31 visits/pt/mo (p=.009)

32
Taxonomy of interventions:

Pre-Discharge

• Patient Education

• Medication Reconciliation

• Discharge Planning and Schedule Appointment

Post-Discharge

• Timely Follow-up and PCP Communication

• Follow-up Telephone Call

• Patient Hotline and Home Visit

Bridging the Transition

• Transition Coach

• Patient Centered Discharge Instructions

• Provider Continuity

Ann Int Med. 155 (8). Oct 18, 2011

33
Burke, et al. Moving Beyond Readmission Penalties: Creating an 

Ideal Process to Improve Transitional Care. JHM. 8(2) Feb 2013 

 Key Components

34
Interventions: Take Home Points

 Represent a “bundle” of interventions

 Likely represent “good” things that we should be 

doing anyway; some things for everyone

 Determine local rates, then determine goals for any 

intervention

35
Audience Response Question

 CMS is reducing payments for hospitals 

with higher than expected readmissions 

rates. This is going to:

1. Decrease readmissions and save money

2. Increase readmissions and cost money

3. Will have no net effect, but will result in 

gaming the system 

36
Incentives to Reduce Readmissions

 Who wants patients readmitted? 

 Hospitals may have an “incentive” to readmit.

 DRG-based payment for re-hospitalization are only 4% lower 

 NOT patients, physicians, or payors of health care. 

 If nobody is incentivized to readmit, can we be incentivized to 

NOT readmit them?

 Medicare Payment Advisory Commission (MedPac) Report, June 2008

 2 yrs confidential readmission rate reporting to hospitals/providers

 Reduce payment for readmission (readmissions cost less)

 “Bundle” payment for hospitalization and subsequent care

 Requires hospital and clinic coordination with payment sharing

37
CMS Hospital Readmission Reduction 

Program (HRRP)

 “Beginning October 1, 2012, the HRRP will lower payment 

rates for all Medicare discharges if acute care hospitals 

experience higher-than-average readmission rates for certain 

"applicable conditions.": 

 AMI, CHF, CAP

 “For the first year of the program, payment reductions will be 

capped at a maximum of 1% of inpatient payments. The 

payment reduction rates will subsequently increase by 1% 

each year before being capped at 3% for fiscal year 2015 

and beyond.” 

38
Cost of Implementation and 

Reducing Readmissions

 Estimate for Project BOOST: $170,000

 Hospital payments may go down if reduce 

readmissions [$5,000 pneumonia DRG payment]:

 400 admits + 25% readmits=500 admits

 @$5,000 each = $2.5M

 400 admits + 20% readmits=480 admits

 @$5,000 each = $2.4M

 Hospital loss of $100,000 plus $170,000 to implement

W. Witcomb. Readmission penalties start now. The Hospitalist, 

Oct 2012.

39
Future for Research

 Prediction Rules: only if it can change practice

 Kansagara, Systematic Review. JAMA, 2011 (306)15.

 Interventions: enough already; implement them

 Hansen, Systematic Review, Ann Int Med. 155 (8). Oct 18, 2011

 Burke, et al. Ideal Process to Improve Transitional Care. JHM. 8(2) Feb 2013 

 Preventability: potential for real-time learning

 van Walraven, et al, CMAJ, 2011

 Rates and Associations for Medicine and Surgery: 

 Not if it tells us something we already know

40
5 Summary Bullets: Readmissions

 Not a good quality metric, but has gotten attention 

for care transitions

 Identifying preventable readmissions is difficult, 

but good to try to prevent them

 Preventability limited, but may result from an 

error/adverse event or system failures

 Prediction models stink, but opportunity to figure 

out how to use them

 Intervention “bundles” are out there, but we need 

wide-spread implementation.

41
And a final point...

 Floggings will continue until readmission rates 

improve (or people lose interest)

42
Your host, Dr. Todd Wagner

Palo Alto VAMC

43 Final Break for Questions