Exploration of Knee and Hip Replacement based on HCUP dataset

Purdue University July 24,2015

wu393@purdue.edu zhan1185@purdue.edu yang626@purdue.edu zhan1110@purdue.edu

Tianzhao Wu

Xiaofei Zhang

Mengying Yang

Tianyu Zhang

Acknowledgements

• Arthur and Kathryn Lorenz (Sponsors)

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Department of Statistics • Hao Zhang • Rebecca Doerge

• Lingsong Zhang

• Mark D. Ward

• Jeffrey A. Beckley

Regenstrief Center

for Healthcare Engineering

• Marietta Harrison

• Steven M. Witz

• Kenneth J. Musselman

• Kit Klutzke

Outline

• Background of Bundle Payment

• Research Problem

• Methods and Results

• Summary and Discussion

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Why is bundle payment?

•2013, US spent >$8,700/person on healthcare

services, twice than other developed countries.

(OECD, 2015)

•The implementation of Patient Protection and

Affordable Care Act

– Replacing the Fee-for-Service payment method with

Bundle payment method

– Decreasing the cost without lowering the quality

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What is Bundle Payment?

All the standard treatments costs are all included, from

inpatient and outpatient hospital services, medication

costs, and post-discharge services (Feder, 2011)

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• Post-Acute Services such as physical therapy and nurse visits at home

• Physician services delivered in and outside the hospital

How does Bundle Payment work?

•Enhancing the collaboration among the

hospitals

–“over 50% of hip fracture treatment need 4 or more

different hospital visiting”(Calsyn,2012)

•Providing money incentives to improve the

quality

–“Episode Price=Typical Price + 50% Complications”

(HCI3,2011)

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Why is total knee and hip replacement?

• Complication rate fall in the middle and close to the national average

• Complexity makes total knee replacement have more procedures

• Higher market demand

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Research question

• Analyze the hospital total expense for hip and

knee replacements and compare variation

across different variables.

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Data

• Data source:

Healthcare Cost and Utilization Project (HCUP)

(https://www.hcup-us.ahrq.gov)

• Data field:

National Inpatient Sample (NIS) (2008 & 2009)

State Inpatient Database (SID) (WA 2009)

• Data Feature:

Over 8 million observations

More than 230 variables

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1) CORE: • Primary and secondary diagnoses and procedures • Patient demographic characteristics 2) HOSPITAL: • Hospital characteristics 3) SEVERITY: • Severity measures • Comorbidity measures

National Inpatient Sample (NIS)

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Research Flow Chart

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Raw Data Understand

Data Data Cleaning

Variable Selection

One Way ANOVA

Correlation

Categorical Numerical

Selected Variables

Model Planning

Result Analysis

Pricing Bundle Payment(optional)

Data Cleaning

Integrated Healthcare Association, 2012 • Procedure: (ICD-9 code)

– Knee: Total knee replacement

– Hip: Total hip replacement; Partial hip replacement; Resurfacing hip acetabulum and femoral head

• Severity: (Level 0-4)

– Level 1 and 2

• Diagnosis Exclusion:

– Rheumatoid Arthritis, Other acquired deformities, Crushing injuring. 12

Data Cleaning of Knee Replacement

• Data definition:

13 Integrated Healthcare Association, 2012

Data Cleaning of Hip Replacement

14 Integrated Healthcare Association, 2012

Histogram of total charges (2009 knee)

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Histogram of total charges after taking log (2009 knee)

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Quantiles (Definition 5)

Level Quantile

100% Max 12.72859

99% 11.66067

1% 9.57220

0% Min 5.41610

Histogram of total charges (2009 Hip)

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Quantiles (Definition 5)

Level Quantile

100% Max 13.02399

99% 11.72254

1% 9.62338

0% Min 5.85793

Data Exploration for Each State

In the NIS dataset, there are missing values for some states: 2009 Missing States Data:

Alaska, Alabama, Washington D.C., Delaware, Idaho, Mississippi, North Dakota

2008 Missing States Data:

Alaska, Alabama, Washington D.C., Delaware, Idaho, Mississippi, North Dakota, Montana, New Mexico

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1. (a)Boxplot of log total charge of different hospital states (2009 Knee)

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1. (b)Boxplot of log total charge of different hospital states(2009 Knee)

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1. (b)Scatter Plot of Population vs. mean of log total charge of each state

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1.(c)Population density does not influence the total charge of different county

22 2009 KNEE

Scatter plot of population density vs. log total charge of each county

1.(d)Some Counties with large (>11)

Log Total Charge

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2009 Knee

1. (e)Scatter Plot of GDP per Capita vs. mean of log total charge of each state

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2009 KNEE

Merging data from Bureau of Economic Analysis

Variables Selection

• Selecting Criteria:

– Miss value less than 40%;

– P-value < 0.05;

• Selecting Methods:

– ANOVA for categorical variables

– Correlation for numerical variables

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Result: Selected Variables

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1. Control/ownership of hospital

2. Multihospital system membership

3. Median household income national quartiles

for patient's ZIP Code

4. Length of Stay

5. Race

6. Admission type

1. Control/Ownership of hospital

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(0) government or private,

collapsed category

(1) government, nonfederal,

public

(2) private, non-profit, voluntary

(3) private, invest-own

(4) private, collapsed category

2009 Knee

Means with the same letter are not significantly different.

Scheffe

Grouping

Mean N HOSP_CONTROL

A 10.822950 6913 private, invest-own

B 10.621279 11342 private, non-profit, voluntary

C 10.555834 3748 government, nonfederal,

public

D 10.465235 26638 government or private,

collapsed category

E 10.413152 2537 private, collapsed category

1. Control/Ownership of hospital

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2. Multi-hospital system membership

(mean / sample size) comparison

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(0) non-member

(1) member

2009 Knee

2. Multi-hospital system membership

(mean / sample size) comparison

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Means with the same letter are not significantly different.

Scheffe

Grouping

Mean N HOSP_MHS

MEMBER

A 10.615991 29534 Member

B 10.470943 16227 Non-Member

3. Median household income national quartiles for patient's ZIP Code

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House Hold Median

Income

(1) $1 - $39,999

(2) $40,000 - $49,999

(3) $50,000 - $65,999

(4) $66,000+

2009 Hip

Means with the same letter

are not significantly different.

Scheffe

Grouping

Mean N ZIPINC_QRTL

A 10.629795 8300 3 ($50,000 - $65,999)

A 10.626948 9542 4 ($66,000+)

B 10.570360 6623 1 ($1 - $39,999)

B 10.566940 8911 2($40,000 - $49,999)

3. Median household income national quartiles for patient's ZIP Code

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4. Length of stay

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2009 Knee

Length

of Stay

Mean Obser

-vation

0 10.38 17

1 10.41 1609

… … …

14 10.88 4

15 11.00 5

5. Race

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(1) White

(2) Black

(3) Hispanic

(4) Asian or Pacific

Islander

(5) Native American

(6) Other

2009 Hip

Means with the same letter are not significantly different.

Scheffe

Grouping

Mean N RACE

A 10.91637 979 Hispanic

B 10.75915 370 Asian or Pacific

Islander

B 10.70530 1351 Black

B 10.70352 689 Other

C 10.59921 24933 White

D 10.49265 136 Native American

5. Race

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6. Admission type

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(1) Emergency

(2) Urgent

(3) Elective

(4) Newborn

(5) Trauma center

2009 Hip

Means with the same letter

are not significantly different.

Scheffe

Grouping

Mean N ATYPE

A 10.84406 165 Trauma

Center

B 10.55698 7330 Emergency

B 10.54072 20473 Elective

B 10.53635 2744 Urgent

6. Admission type

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7. Choices of selected variables in each data set

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Variable 2009 Knee 2009 Hip 2008 Knee 2008 Hip

State postal code for the hospital ✓ ✓ ✓ ✓

Control/ownership of hospital ✓ ✓ ✓ ✓

Length of stay ✓ ✓ ✓ ✓

Multi-hospital system membership ✓ ✓ ✓ ✓ Nurse aides per 1000 adjusted

inpatient days ✓ ✓ ✓ ✓

Total number of discharges from this hospital

✓ ✓ ✓ ✓

Square miles by each county ✓ ✓ ✓ ✓

Bed size of hospital ✗ ✓ ✓ ✓

Patient Location ✗ ✓ ✓ ✓ Licensed Pratical Nurse Ful-Time

Equivalents per 1000 adjusted inpatient days

✓ ✓ ✓ ✗

Teaching status of hospital ✓ ✓ ✓ ✗

Location: rural or urban ✓ ✓ ✗ ✓

Admission type ✗ ✓ ✓ ✗

gender ✓ ✓ ✗ ✗

Basic Linear Model (2008 Knee)

•Natural Log total charge (Adj R^2=0.4643, Intercept=10.48)

- Categorical Variables (Reference Level) • Discharge record includes evidence of emergency department (ED)

services---- (No emergency room involve meeting)

• Control/ownership of hospital---- (Government, non federal)

• Control/ownership of hospital---- (Government, nonfederal, public)

• Location ---- (Rural)

• Location/teaching status of hospital---- (Rural or Urban teaching)

• Multi-hospital system membership---- (Non-Member)

• Teaching status of hospital----(Teach)

• Patient Location----(Micropolitan counties)

• Bed size of hospital---(Small)

• State postal code for the hospital---- (Wyoming)

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Basic Linear Model (2008 Knee)

• Log total charge

–Numerical Variables (relationship with log total charge)

•Nurse aides per 1000 adjusted inpatient days (-)

•RN FTEs per 1000 adjusted inpatient days (-)

•Percentage of RNs among all nurses (RNs and LPNs) (-)

•Length of stay (+)

•Total number of discharges from this hospital in the NIS (+)

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RN: Registered nurse; LPNs: Licensed practical nurse

FTEs: Full time equivalent, 2080 hours per year

1. HCUP_ED

• 1. Discharge record includes evidence of emergency department (ED) services

-Reference: -(0) Record does not meet any HCUP Emergency Department criteria

- Above: -(1) Emergency Department revenue code on record

-(4) Admission source of ED

- Below: -(2) Positive Emergency Department charge (when revenue center codes are not available)

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2. H_Contrl

•2. Control/ownership of hospital

-Reference:

-(1) government, nonfederal

- Above:

-(3) private, invest-own

-Below:

-(2) private, non-profit

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3. Hosp_Control

•3. Control/ownership of hospital

-Reference:

-(1) government, nonfederal, public

-Above:

-(3) private, invest-own

-(4) private, collapsed category

-Below:

-(0) government or private, collapsed category

-(2) private, non-profit, voluntary

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4. Hosp_Location

•4. Location

-Reference:

-(0) Rural

-Above:

-(1) Urban

-Below:

-None

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5. Hosp_Locteach

•5. Location/teaching status of hospital:

-Reference:

-(1) rural

-(3) urban teaching

-Above:

-None

-Below:

-(2) urban non-teaching

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6. Hosp_Mhsmember

•6. Multi-hospital system membership

-Reference:

-(0) Non-Member

-Above:

-(1) Member

-Below:

-None

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7. Hosp_Teach

•7. Teaching status of hospital

-Reference:

-(1)Teach

-Above:

-(0) Non-Teach

-Below:

-None

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8. PL_NCHS2006

•8. Patient Location

-Reference -(5) Micropolitan counties (<50,000 people)

-Above -(1) "Central" counties of metro areas of >=1 million population

-Below -(2) "Fringe" counties of metro areas of >=1 million population

-(3) Counties in metro areas of 250,000-999,999 population

-(4) Counties in metro areas of 50,000-249,999 population

-(6) Not metropolitan or micropolitan counties

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9. Hosp_Bedsize

•9. Bed size of hospital

-Reference

-(1) Small

-Above

-None

-Below

-(2) Medium

-(3) Large

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10. Hospst

•10. State postal code for the hospital

-Reference -Wyoming

-Above -Arizona, Indiana, Kentucky, Louisiana, New Hampshire, New Jersey, Pennsylvania, Texas, Vermont, Washington

-Below -Arkansas, Hawaii, Iowa, Kansas, Michigan, Missouri, North Carolina, Nebraska, Nevada, New York, Oklahoma, Oregon, Rhode Island, South Dakota, Tennessee, Utah, Virginia, Wisconsin

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Summary

• Total charge is not normally distributed. Logarithm transformed total

charge is normal

• We have found some important variables that associated with total charge

– Geographical information impacts the total charge (e.g. state, county,

square miles by each county, population, rural vs urban)

– Hospital characteristic (hospital ownership, multi-hospital system

membership; nurse aides per 1000 adjusted inpatient days)

– Care characteristic (length of stay, comorbidity)

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Discussion

• Some limitation of our study:

– HCUP is not designed for bundle payment study. Hospital or insurance

transaction data may be better

– Our model is still preliminary, such as excluding higher level

interactions, and correlation structures.

– Time restriction. Some research can be continued after this

presentation.

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Reference Page

[1] Organization for Economic Cooperation and Development, “OECD Health Data 2015” (2015), available at http://www.oecd.org/health/health-systems

[2] Judy Feder, Paul Ginsburg, Harriet Komisar, “’Bundling’ Payment for Episodes of Hospital Care” (2011), available at https://www.americanprogress.org

[3] Mayra Calsyn, Emily Lee, “Alternatives to Fee-for-Service Payments in Health Care” (2012), available at https://www.americanprogress.org

[4]Health Care Incentives Improvement Institute, “Bundle Payment Case Study”

[5]Integrated Healthcare Association, “Bundled Episode Payment and Gainsharing Demonstration Total Hip Replacement Definition”

[6] Integrated Healthcare Association, “Bundled Episode Payment and Gainsharing Demonstration Total Knee Replacement Definition”

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Appendix 1:

7(b). Choices of selected variables in each data set

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Variable 2009 Knee 2009 Hip 2008 Knee 2008 Hip Location/teaching status of hospital ✓ ✓ ✗ ✗

RN FTEs per 1000 adjusted inpatient days ✓ ✓ ✗ ✗ Percentage of RNs among all nurses (RNs

and LPNs) ✓ ✓ ✗ ✗

Population ✓ ✓ ✗ ✗ Population Density ✓ ✓ ✗ ✗

Race ✓ ✓ ✗ ✗

LRN FTEs per 1000 adjusted inpatient days ✓ ✓ ✗ ✗

Number of procedures coded on the original record

✗ ✓ ✗ ✓

Percentage of all surgeries performed in outpatient setting

✓ ✗ ✗ ✓

AHRQ comorbidity measure: Deficiency anemias

✗ ✗ ✓ ✗

Disease Staging: Principal Disease Category ✗ ✗ ✓ ✗ Indicates elective admission ✗ ✗ ✓ ✗

Number of E codes coded on the original record

✓ ✗ ✗ ✗

Procedure 1 ✗ ✗ ✗ ✓ Point of origin for admission or visit ✗ ✓ ✗ ✗

Discharge record includes evidence of emergency department (ED) services

✗ ✓ ✗ ✗

Appendix 2: P-Value

•State:

–1. Population----significant (p=0.0078)

–2. GDP Per Capita----not significant (p=0.3555)

•County

–1. Population---significant(p=0.0001)

–2. Population Density---not significant(p=0.4273)

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