Efficiency Differences Between Critical A

Efficiency Efficiency Differences Differences Between Between Critical Critical A

Hospitals and Non Hospitals and Non-Converting Rural Hos

Converting Rural Hos

Ryan Mutter, Ph.D.

Agency for Healthcare Research and Quality

Rockville, MD

Michael Rosko, Ph.D.

School of Business Administration

Widener Universit

y

Chester, PA
Background

• Enactment Enactment of the Balanced Balanced Budget Act of 19

(BBA) created the Critical Access Hospital (C

p g ro ram

• Program subsequently modified by addition

legislation

– Medicare, Prescription, Drug, Improveme

Modernization Act of 2003 (MMA)

• Seeks to enhance financial viability of small,

isolated rural and “necessary provider hosp
Background (continued)

• Under the CAH program program, hospitals hospitals accept a 

of restrictions

– Limits on the number of acute care patie

patie

treated at one time (25)

– Limits on average average patient patient LOS (4 days)

• In exchange, they receive 101 percent of cos
Background (continued)

• Medicare Medicare payments payments to CAHs rose at an annu

annu

growth rate of 9.5 percent from 1998 to 200

• The growth rate for similar similar non‐converting converting h

h

was 3.3 percent.

• Medicare Medicare paid an estimated estimated $1 million million more 

CAH in 2006 than they would have had paym

increased at the rate of non‐converting com

hospitals.
Background (continued)

• Q lit ua

y i t mprovemen

t was one

o

f th

e main go

the CAH program.

• Man

y of these extra resources have

gone into

quality improvement and quality assurance

activities.

• See Li et al. (2007) for

a review of the impact 

CAH status on quality.
Motivation

• Program Program has succeeded succeeded in its aim of halting halting th

closure of hospitals providing care to under‐s

pp , o ulations, but there are concerns about theefficiency impact of the program
Motivation (continued)

“Although the CAH program has helped to preserve access to emergency and inpatiecare in isolated areas it may not have care in isolated areas, it may not have

accomplished this goal in an efficient mann

- MedPAC (20
Aim

• Use stochastic stochastic frontier frontier analysis analysis (SFA) to estim

estim

efficiency impact of the CAH program.
Stochastic Frontier Analysis 

• Econometric technique

– Generates provider‐level (i.e., hospital‐leve

estimates of inefficiency

– Inefficiency estimates are measured as dep

from a statistically statistically derived, derived, theoretical theoretical bestpractice frontier that takes input prices, out

product mix, quality, case mix, and market f

into account
SFA (continued)

Total 

Expenses

SFA 

Inefficiency

Random Error Random Error

Output
SFA (continued)

• Measures Measures cost inefficiency inefficiency (i e. ., the percentage percentage 

which observed costs exceed minimum costs pfor a given level of outp , uts input p , rices etc.)

• Particularly useful for determining the relative

performance of hospitals

– Hospital A is among the top 40 percent mos

efficient hospitals in its peer group.

• Folland and Hofler (2001) demonstrate its usef

for comparing the efficiency of groups of hosp
SFA (continued)

• Specified generally as

TCi = f(Yi, Wi) + ei

where TC represents total costs; Y is a vector 

outputs; W is a vector of input prices; and e i

error term, which can be decomposed as foll

ei = vi + ui

where v is statistical noise ~ N(0, σ2) and u co

of positive departures from the cost‐frontier
SFA (continued)

• Byproduct Byproduct of the analysis analysis is information information abou

hospital‐level variables on cost and environm

pressure variables on inefficiency
SFA (continued)

• Featured Featured in the National National Healthcare Healthcare Quality Quality R

(NHQR), a product of the U.S. Department of

Health and Human Services

 See Rosko and Mutter (2008) for an ove
Data

• Using 1997‐2004 panel data for 543 hospita

hospita

located in 16 states, employ SFA to estimate

inefficiency of CAH‐designated hosp , itals as 

a comparison group of prospectively‐paid, n

converting hospitals located in rural areas

1997 12 CAHs 531 non‐converting, r

comparison hospitals

2004 286 CAHs 257 non‐converting, r

comparison hospitals
Data Sources

• American Hospital Association ( ) AHA Annual 

of Hospitals

• Medicare Cost Reports

• AHRQ Healthcare Healthcare Cost and Utilization Utilization Project Project • Area Resource File
Methods

• Time‐varying varying cost SFA with product product mix des

des

controls for quality, etc.

• Frontier 4 1. program program
Intuition

• Compare Compare the performance performance of CAHs to similar

similar

prospectively paid rural hospitals and to thei

previous, pp y ros ectively paid rural selves.
Variables

• Input prices

– Price of labor– Price of capital capital

• Outputs

– Ad i i m ss ons

– Outpatient visits

– Post‐admission inpatient days
Variables (continued)

• Product mix

– Acute care beds / total beds– Births / total admissions– ED visits / total outpatient visits

– Outpatient surgical operations / total outp

visits
Variables (continued)

• HCUP

– A family of health care databases and relate

software software tools and products products developed developed thro

thro

Federal‐State‐Industry partnership and spon

AHRQ.

– Includes State Inpatient Databases (SID), wh

contain the universe of inpatient discharge a

from participating states.

• Data from 24 states available to the publi

the HCUP Central Distributor
Variables (continued)

• The AHRQ Quality Indicators (QIs) are measur

h lh ea t care quality that make use of readily av

hospital inpatient administrative data, such as 

• Free software tools available online

• Includes

– Inpatient Quality Indicators (IQIs), which r

quality quality of care inside hospitals hospitals including including in

in

mortality for medical conditions and surgic

procedures.

– Patient Patient Safety Indicators Indicators (PSIs), which refl

refl

of care inside hospitals, but focus on poten

avoidable complications and iatrogenic ev
Variables (continued)

• Quality Quality measured measured by the application application of the Q

software to HCUP data

• Analysis includes the following, risk‐adj , usted 

hospital rates:

– Mortality for congestive heart failure (CHF)– Mortality for pneumonia

– Iatrogenic pneumothorax

– Infection due to medical care– Accidental puncture laceration
Variables (continued)

• The Comorbidity Comorbidity Software Software assigns assigns variables variables t

identify comorbidities in hospital discharge re

using the diagnosis coding of ICD‐9‐CM.

• Available for free online
Variables (continued)

• Patient burden of illness controlled by the inc

of hospital‐level rates per discharge of the

following comorbidities identified by the

Comorbidity Software

Congestive heart failure Cardiac arrhythmias Valvular diseasePulmonary circulation disordersPeripheral vascular disorders HypertensionParalysis Other neurological disorders Other neurological disorders Chronic pulmon

Chronic pulmon

Diabetes, uncomplicated Diabetes, complicated HypothyroidismRenal failure Liver disease Peptic ulcerAIDS Lymphoma Metastatic ulcerSolid tumor without metastasis Rheumatoid arthritis CoagulopathyObesity Weight loss Fluid and electroBlood loss anemia Deficiency anemias Alcohol abuseDrug abuse Psychoses Depression 

 See Mutter et al. (2008) for details.
Market Forces

• The model controls controls for

– hospital ownership, hospital competition

(county HHI), median family income,

unemployment rate, Medicare HMOpenetration, and % admissions paid for b

Medicare, Medicaid

– Time trend

– CAH program participant (0, 1)– Years in CAH program
Results – Market Forces

Variable Variable Coefficient Coefficient Mu -2.5530** 

Years in CAH Program Years in CAH Program 0.0722** 

FP Ownership FP Ownership -1.5269** 

Gov’t Ownership Gov’t Ownership 0.2148** 

Hospital Competition Hospital Competition 0.4163** 

CAH 0 1958** CAH 0.1958 Medicaid Admissions % Medicaid Admissions % 0.0029** 

Medicare Admissions % Medicare Admissions % -0.0037** 

Median Income Median Income 0.00004 0.00004** 

Medicare HMO % Medicare HMO % -0.0452** 

Time Trend Time Trend -0.0180** 

Unemployment Rate Unemployment Rate 0.0024 

** Significant at the 1% level.* Significant at the 5% level.
Results – Mean Inefficie

All H it l CAH

Non-C

C

All Hospitals CAHs C 

Comp

Year N Mean Std. Dev. Std. Dev. N Mean Std. Dev. Std. Dev. N 1997 543 0.1130 0.0664 12 0.1776 0.0861 531 1998 543 0.1146 0.0647 15 0.1643 0.0673 528 

1999 543 0.1140 0.0649 17 0.1831 0.0929 526 2000 543 0.1224 0.0752 48 0.1791 0.1201 495 2001 543 0.1212 0.0952 110 0.1501 0.0804 433 2002 543 0.1287 0.0845 186 0.1633 0.1073 357 2003 543 0.1432 0.0929 242 0.1770 0.1096 301 2004 543 0.1575 0.1057 286 0.1938 0.1184 257 All years 4,344 0.1268 0.0838 916 0.1763 0.1091 3,428 0
Results – Mean Inefficiency by tim

CAH program

Year in CAH Program Year in CAH Program Program N Mean St

St

1 278 0.1484 

2 238 0.1659 

3 185 0.1879 

4 110 0.2061 

5 48 0 2108 . 6 16 0.2180 7 15 0.2414 8+ 26 0.2329 All years 916 0.1763 
Results – Correlations

 Pearson Correlations between SFA Estim

and Performance Measures

Variable All Hospitals CAHs

Non-Conve

Conve

Compariso

EXP/ADJUSTED ADMIT EXP/ADJUSTED ADMIT 0.477* 0. 409* 0. 409* OPERATING MARGIN OPERATING MARGIN -0 159* . -0 082* . FTE/ADJUSTED ADMIT 0.295* 0.256* 

 Note that CAHs have an average Medica

of admissions of about 61%; correspondifor non for non-converting rural hospitals is abou

converting rural hospitals is abou
Results – Correlations (continue

• Estimated Estimated Pearson Pearson correlation correlation coefficients coefficients (

between SFA‐derived inefficiency measures 

commonly used hospital performance meas

– Positive and significant coefficients for ex

per adjusted admission and FTE personneadjusted admission

– Negative and significant correlation betw

inefficiency and op g erating margin

• Correlation twice as strong in compari

group sub‐sample in 2004 than in CAH

f l aci ities
Results ‐ Quality

• In the cost function

– Coefficient on risk‐adjusted mortality rate fo

was negative and significant.

• Lower mort lit a y rates associtd a e with hi 

g 

costs

– Coefficient on iatrogenic iatrogenic pneumothorax pneumothorax wa

positive and significant.

• Occurrence of this patient safety event is 

to hospitals

• Zhan and Miller (2003) estimate it is asso

with excess costs of $17 312 $17,312.
Discussion

• Findings Findings suggest suggest that CAH facilities facilities tend to b

cost‐inefficient than non‐CAH rural facilities
Discussion (continued)

• Results Results could be a reflection reflection of more cost‐

inefficient hospitals choosing to convert to C

status

• However,

– Methodology not only compares CAHs to 

prospecti l ve y paid rural h it l ospitals, it also

compares CAHs to their previous, prospe

paid selves

– Findings suggest that CAHs tend to be mo

cost‐inefficient over time
Discussion (continued)

• Among non‐converting hospitals, stronger ne

correl i at on coeffi i c ent between SFA‐did er ve

inefficiency measure and operating margin is 

surprising

– PPS is i dd nten ed to reward h il osp tals fi 

financ

cost containment

• Although highly significant, coefficient was sm

0 204) .

– Variety of factors, among which is efficienc

determine profitability

– Cost equation is an abstraction from realit

limiting accuracy of inefficiency estimates
Discussion (continued)

• The CAH p g ro ram has succeeded in its aims

– Has kept hospitals providing care to under‐se

populations open

• Holmes et al. (2006) find that rural hospita

leads to non‐trivial decline in per‐capita in

employment

– Conversion to CAH status is associated with th

provision of higher q y ualit care

• But possible result of cost‐based payment soluti

increased inefficiency, which can fuel already es

expenditures
Discussion (continued)

• The challenge is to find a p ya ment mechanism 

provides incentives for efficiency yet allows pto maintain fiscal viability.

– Why did small rural hospitals hospitals suffer so mu

under PPS?

• Is PPS inherently ill‐suited for these typ

h it l ? hospitals?

• Could PPS work for these hospitals if pa

levels were higher?

– Perhaps the solution is to retain cost‐base

payment but to pay on the basis of histori