Transcript Slide 1

Using the Pediatric Health Information System
and Administrative Data for Research
David Bertoch, Vice President
Matt Hall, Senior Statistician
Presentation Objectives
• To provide an overview of available
administrative data sources, including CHCA’s
Pediatric Health Information System
• To describe how to use these data sources for
research
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Presentation Content
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•
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Administrative Data Overview
Pediatric Health Information System (PHIS)
Other Administrative Data Sources
Reliability of ICD-9 Codes
Types of Research
Analytic Considerations
Resources/Contacts
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What is administrative data and where does it come from?
Patient arrives at
hospital and
demographic info is
collected and
entered into IDX
Provider performs
assessment and
treatment of patient
As treatments are
provided and
ordered, charge
codes are captured
in IDX
Using national
required coding
guidelines, ICD-9-CM
diagnoses and
procedure codes are
assigned
Diagnoses and
procedures are
entered into patient’s
administrative record
(IDX)
Provider documents
some component of
their actions and
thoughts
Trained coders
review all of
medical record
including
documentation
Data pulled to send
bills, quality and
outcomes reporting,
operations/finance,
PHIS/NACHRI
Flowchart designed by the Children’s Hospital of Wisconsin
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Common Administrative Data Sources
• State Databases
• HCUP
– KID 2003
– NIS 2006
• CHCA Pediatric Health Information System
(PHIS)
• Thomson’s National Pediatric Discharge
Database (NPDD)
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Pediatric Health Information System
(PHIS)
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PHIS Data Overview
PHIS Data Repository
PHIS By The Numbers*
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•
INPATIENT
Participating Hospitals: 40
Inpatient Cases: 2.2 million
Inpatient Days: 13.1 million
ED encounters: 6.7 million
Total Charges: $90.7 billion
Total ICD-9 Codes: 33.6 million
Pharmacy Transactions: 116.8 million
Physicians: 297,250
Ambulatory
Surgery
Emergency
Department
Observation
Unit
Medical Records
Billing
System
Systems
All data submitted electronically
(no manual entry) on a quarterly
basis
1
* Since 2002, does not include
available archived data back to 1992
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CHCA: North America’s Leading Children’s Hospitals
Seattle - Children’s Hospital and Regional Medical Center *
Kansas City – The Children’s Mercy *
Orange – Children’s Hospital of Orange County *
Atlanta – Children’s Healthcare of Atlanta *
San Diego – Children’s Hospital and Health Center *
Chicago – The Children’s Memorial Hospital *
Columbus – Children’s Hospital *
Birmingham – The Children’s Hospital of Alabama *
Los Angeles – Children’s Hospital Los Angeles *
Dallas – Children’s Medical Center of Dallas *
Oakland – Children’s Hospital Oakland *
New Orleans – Children’s Hospital *
Memphis – Le Bonheur Children’s Medical Center *
Cincinnati – Children’s Hospital Medical Center *
Palo Alto – Lucile Packard Children’s Hospitals *
Miami – Miami Children’s Hospital *
Dayton – The Children’s Medical Center *
Corpus Christi – Driscoll Children’s Hospital *
Fresno / Madera – Children’s Hospital Central California *
Houston – Texas Children’s Hospital *
Milwaukee – Children’s Hospital of Wisconsin*
Ft. Worth – Cook Children’s Medical Center *
Buffalo – Children’s Hospital of Buffalo*
Boston – Children’s Hospital Boston *
Denver – The Children’s Hospital *
Omaha – Children’s Healthcare Services *
Akron – Children’s Hospital Medical Center of Akron*
Memphis – St. Jude Children’s Research Hospital
Norfolk – Children’s Hospital of The King’s Daughters Health System *
Minneapolis – Children’s Hospitals and Clinics *
Washington D.C. – Children’s National Medical Center *
Phoenix – Phoenix Children’s Hospital *
Pittsburgh – Children’s Hospital of Pittsburgh *
Detroit – Children’s Hospital of Michigan*
Little Rock – Arkansas Children’s Hospital *
Nashville - Vanderbilt Children’s Hospital*
Philadelphia – The Children’s Hospital of Philadelphia *
Hartford – Connecticut Children’s Medical Center*
St. Louis – St. Louis Children’s Hospital*
Toronto – The Hospital for Sick Children
St Petersburg – All Children’s Hospital *
New York – Children’s Hospital of New York Presbyterian*
Indianapolis – Riley Children’s Hospital/Clarian Health Partners*
Listed In Order of Membership
* Submit Data into PHIS
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PHIS Hospitals – 41 CHCA Hospitals Submitting Data
Minnesota
Omaha
Kansas City
Milwaukee
Dayton
Chicago
Columbus
St. Louis
Cincinnati
Detroit
Akron
Indianapolis
Buffalo
Boston
Hartford
New York
Philadelphia
Seattle
DC
Oakland
Norfolk
Pittsburgh
Palo Alto
Phoenix
Memphis
Madera
Denver
Nashville
Los Angeles
Dallas
Little Rock
Atlanta
Orange
Fort Worth
New Orleans
St. Petersburg
San Diego
Corpus Christi
Birmingham
Miami
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Houston
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PHIS Features
• Comparable hospitals
– Largest children’s hospitals in the US
• Unblinded peer selection
– Select hospitals with whom you want to compare
• Ease of networking
– Physicians, clinicians, quality leaders, analysts
• Direct access to data
– Control over report specifications
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Every hospital uses PHIS in different ways
physician
Meet JCAHO requirement
Credentialing
Make better decisions
Improve coding
Manage utilization
Access to database with 6
million pt encounters
Increase revenue
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Reduce manual MR review
Develop new service lines
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Increased Use for Research
• 23 articles published with at least 3 pending and
many more in development
• 25 posters/presentations at Pediatric Academic
Societies meeting since 2003
– 9 in 2007
• Wide variety of topics
– See handouts
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Typical Data Submission Process
Sample for October data load
Data
Quality
Data
Pre-Submission
Tests
Repts
to Hosp
Submitte
d
August 15
Review
&
Correcti
ons to
Sign
Off
Solucien
t
August 29
September 26
October 7
Solucient
Database
Loaded
~ October 18
Groupers
&
Derivatio
ns
~ October 18
Data Quality Management
Audit
Database
October 21
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Data
Quality
Report
Card
Annual (2nd Qtr)
Ad
Hoc
Issue
Resolutio
n
As Needed
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Practical PHIS Data Quality Resources
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Data Quality and Completeness Report Card
Data Quality Alerts
Web Cast: Validating Your Data
Significant Issues List
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PHIS for Research Resources
• Subset of PHIS main web
site
• Includes
– Standard PHIS
methodology text
– Existing articles by topic
– Data Quality resources
– Data content resources
• Register at www.chca.com
and select PHIS as
requested site
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“Level 1” and “Level 2” Breakout
LEVEL 1 – Patient Abstract and ICD-9 Coding
Patient
Abstract
Diagnoses
Procedures
(ICD-9)
(ICD-9)
LEVEL 2 – Billed Transaction/Utilization Data (all items/services billed to the pt)
Pharmacy
Imaging/
Lab
Clinical
Radiology
Other:
* Room/Nursing
Supplies
Other
* Surgical Svcs
* Other misc
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Level 1 – Patient Abstract
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Patient Identification
Demographics
Episode of Care
Physician Profiles
Dx/Px Profiles
Clinical Classification (Groupers)
Payer Source
Charge Summaries
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Patient Abstract
• Episode of Care
– LOS
– Admit Date/Month/Year
– Discharge Date/Month/Year
– Infection Flag
– Surgical and Medical Complication
Flags
• Age in Years
– Disposition
• Age in Months (if less than 2
yrs)
– Pre-Op LOS
• Age in Days (if less than 30
– Post-Op LOS
days)
• Demographics
– Gender
– Birthweight (gms)
– DOB
– Pediatric Age Group
– AAP Age Code
– Age (based on age at
admission)
– Race/Ethnicity
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Patient Abstract
• Physician Profiles
– Attending Physician
– Attending Physician Subspecialty
– Principal Px Physician
– Principal Px Physician Subspecialty
• Dx/Px Profiles
– Principal Dx
– Principal Px
• Clinical Classification (Groupers)
– Major Diagnostic Category (MDC)
– CMS (HCFA) DRG
– APRDRG
• Version 15
• Version 20
• Version 24
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Patient Abstract
• Payer Source (Principal Payer)
– Medicare
– Medicaid
– Title V
– Other gov’t
– Workers comp
– Blue Cross
– Other Ins Co
– Self Pay
– Other
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• Charge Summaries
– Pharmacy Charges
– Supply Charges
– Lab Charges
– Imaging Charges
– Clinical Charges
– Other Charges
– Unmapped Charges
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Pt Abstract Data (Level 1)
Patient
Abstract
We will follow one
patient visit through
different sections of
PHIS:
Discharge ID =
142006763
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Value:

Compare LOS

Readmission
Rates

Stratify patients
based upon
your criteria

Physician
Profiling

Severity Adjust
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Diagnosis Codes (ICD-9) – (Level 1)
Diagnoses
(ICD-9)
Value:
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
Go beyond the
principal dx

Specific
inclusion/
exclusion of
patients
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Procedure Codes (ICD-9) – (Level 1)
Procedures
(ICD-9)
Value:
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
Go beyond the
principal px

Pre vs Post Op
LOS

Analysis by
surgeon
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Charge Master Comparability
Hospital B
Hospital A
35309888
6561447
Vancomycin 125 mg
Tablet 125 mg
Vancomycin
CTC Code
124133.1011552
12  Anti-infectives (Drug Class = 12)
124  Misc antibiotics (Therapeutic Cat = 124)
124133  Vancomycin (Generic Drug=124133)
12413310  oral (Route of Administration=10)
1241331011  tablet (Dosage Form=11)
124133101155  55 (Strength=125)
1241331011552  mg (Unit of Measure=2)
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Pharmacy Data (Level 2)
Pharmacy
Value:
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
Compare drug
utilization by
drug, class, and/
or category

Compare when
drugs were
given (by day)

Compare route
of administration
(IV, PO, etc)
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Lab Data (Level 2)
Lab
Value:
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
Compare lab
tests/pt

Revenue
Enhancement
Opportunity
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Room/Nursing (Level 2)
Room/
Nursing
Value:
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
Compare LOS
by type of room
(med/surg vs
ICU)

Measure Return
to ICU/Direct
Admit to ICU

Analyze
resource
utilization by
room type (eg.
drugs while in
NICU
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Issues/Measures by CTC Area
Area
Good Measures
Limitations
Pharmacy
•Generic Drug and Route of
Administration are commonly used
•% of patients received
• # of days received
• % of patient received by route of
admin (digits 7-8)
• Units – shouldn’t be used
• Can not measure “doses”
• Be aware of “Billed” vs “Actual”
• Respiratory drugs bundled not billed
separately
Supplies
• Tracking high cost supplies – monitor
proper billing of these supplies for
specific patient populations
• Comparability difficult for low cost
supplies – bundling issues
Lab
• Typically 1 unit = 1 test
• Generic lab test fields are commonly
used
• Watch out for panels vs tests,
particularly in Chemistry
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Issues/Measures by CTC Area
Area
Good Measures
Limitations
Imaging
• Area of imaging focus and exam type
used most often
•Typically 1 unit=1 exam
• Be careful of hosp-hosp mapping
consistency on digits1-4; best to
include more codes in filter than limit to
1 or 2 codes
Clinical
• Digits 1-6 are used most often
• Non-respiratory use of units is pretty
good
• Unit of measures involving minutes is
better with CTCs but still not exact
• Some pharmacy chgs will show up in
Respiratory for some hospitals
Other
• # of days spent in ICU vs non-ICU
• Returns to ICU
• Products provided while in ICU
• Track pts that came through the ED
• Use in multi-pass to track products
given on day of surgery
• Nursing unit severity levels not
comparable
• Make sure comparing room-room and
not just “nursing only” (digit 7-8)
• OR and RR time is not exact
(minutes)
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PHIS Direct Access User Agreement
Key Components of Agreement
•
PHIS data, other than the PHIS Member Hospital’s own data, cannot be
shared verbally, in written form or electronically with any individual or group
not acting on the sole behalf of the PHIS Member Hospital without prior
consent from the PHIS External Use of Data committee.
•
The confidentiality statement applies, but is not limited to, the following
situations:
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–
–
–
the publishing of research results in an external publication
using PHIS data in a promotional/advertising campaign
giving PHIS data in any format to a managed care organization
making an external presentation with PHIS data displayed either verbally or
visually in a handout
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HCUP KID / Thomson NPPD
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Healthcare Cost and Utilization Project (HCUP)
Kids’ Inpatient Database (KID)
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•
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Current Data: 2003; updated every three years
Short-term, general, non-federal hospitals
Stratified systematic sample
2.9 million discharges weighted to 7.4 million
– i.e. one row represents multiple discharges
• Data elements similar to Level I data in PHIS
• Purchase for $200 per year from
http://www.hcup-us.ahrq.gov
• Free web tool: http://hcupnet.ahrq.gov/
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Thomson’s National Pediatric Discharge Database
(NPDD)
• Similar to HCUP KID, but updated twice annually
• Uses datasets available to Thomson
–
–
–
–
State hospital associations
Public state data
Individual hospitals contracting with Thomson
Various hospitals systems
• 2.2 million discharges are weighted to 7.1 million
• Not as widely published as HCUP KID
• Accessible through CHCA
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Reliability of ICD-9 Codes
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Reliability of ICD-9 Codes
• Epidemiology, Outcomes, and Costs of Invasive
Aspergillosis in Immunocompromised Children
in the United States, 2000
• Zaoutis, Heydon, Chu, Walsh, Steinbach
• Pediatrics. 2006 Apr; 117(4): e711-e716
– “In general, health services researchers believe
that the use of ICD-9-CM codes to identify cases
in administrative databases has high specificity
(eg, few instances in which patients did not in fact
receive a diagnosis of the condition) but may be
lower in sensitivity (ie, the administrative
diagnosis may fail to detect all true cases).”
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Reliability of ICD-9 Codes
• Differences in Admission Rates of Children With
Bronchiolitis by Pediatric and General
Emergency Departments
• Johnson, Adair, Brant, Holmwood, Mitchell
• Pediatrics. 2002 Oct; 110(4):e49
• Spec: Of 3,091 charts coded as having a
discharge diagnosis of bronchiolitis (ICD-9 code
466.1), 3,054 cases (99%) met clinical definition
• Sen: Additional 43/377 (11%) should have been
coded as bronchiolitis
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Reliability of ICD-9 Codes
• Use of Active Surveillance to Validate International
Classification of Diseases Code Estimates of Rotavirus
Hospitalizations in Children
• Hsu, Staat, Roberts et al.
• Pediatrics. 2005 Jan; 115(1):78-82
• Spec: Discharge coded as rotavirus very specific marker
for true rotavirus disease: 98% of discharge records
coded specifically as rotavirus had a laboratoryconfirmed diagnosis.
• Sen: Discharge records were coded as rotavirus in less
than half of the confirmed rotavirus infections
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Types of Research Using Administrative Data
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1. Epidemiology / Population Estimates
• Population estimates difficult with PHIS
– Convenience sample, lacks “denominator”
– Possible with specific quaternary diagnoses or procedures
• HCUP KID & Thomson’s NPDD
– Weighted for national estimates
• Potential research topics…
– What is the prevalence of a disease in the population
– How frequent is a px done in a population
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1. Epidemiology / Population Estimates (Example 1)
• National hospitalization impact of pediatric all-terrain
vehicle injuries.
• Killingsworth JB, Tilford JM, Parker JG, Graham JJ, Dick RM, Aitken ME
• Pediatrics. 2005 Mar;115(3):e316-21
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–
–
–
HCUP KID 1997 and 2000
5,292 children hospitalized with ATV-related injuries
Hospitalizations increased 79.1% between 1997 and 2000
Rates of ATV-related hospitalization were highest among
adolescent white male
– Total hospital charges: $74,367,677 for the 2-year study
period
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1. Epidemiology / Population Estimates (Example 2)
• Off-label drug use in hospitalized children
• Shah SS, Hall M, Goodman DM, Feuer P, Sharma V, Fargason C Jr, Hyman
D, Jenkins K, White ML, Levy FH, Levin JE, Bertoch D, Slonim AD
• Arch Pediatr Adolesc Med. 2007 Mar;161(3):282-90
– At least 1 drug was used off-label in 297,592 (78.7%) of
355,409 discharges
– Off-label use accounted for $270m (40.5%) of the total
dollars spent on these medications
– Factors associated with off-label use: undergoing a
surgical procedure, age older than 28 days, greater
severity of illness, and all-cause in-hospital mortality.
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2. Cost & Charge Estimation
• Most data sources do not capture costs, but charges
• Typically use ratio of cost-to-charges
– In KID, each hospital has one ratio
– In PHIS, each hospital has 31 ratios categorized into drug,
radiology, etc.
• Potential research topics…
–
–
–
–
Public vs. private expenditures
Incremental charges associated with comorbidities
Compare costs of treating with drug x versus drug y
Identify factors associated with increased charges
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2. Cost & Charge Estimation (Example 1)
• Direct medical cost of influenza-related
hospitalizations in children.
• Keren R, Zaoutis TE, Saddlemire S, Luan XQ, Coffin SE.
• Pediatrics. 2006 Nov;118(5):e1321-7
– 727 patients hospitalized for community-acquired
laboratory-confirmed influenza
– The mean total cost of hospitalization: $13,159
• $39,792 pts admitted to an ICU
• $7,030 pts cared for exclusively on the wards
– Cardiac, metabolic, and neurologic/neuromuscular
diseases and age of 18-21 were independently associated
with the highest hospitalization costs
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2. Cost & Charge Estimation (Example 2)
• Factors associated with increased resource utilization for congenital
heart disease
• Connor JA, Gauvreau K, Jenkins KJ.
• Pediatrics. 2005 Sep;116(3):689-95
– Identify patient, institutional, and regional factors that are
associated with high resource utilization for congenital heart
surgery
– Some states were more likely to have high resource use cases
– Independent predictors of a higher odds of high cost
•
•
•
•
•
•
Risk Adjustment for Congenital Heart Surgery risk category
Age
Prematurity
Presence of other major noncardiac structural anomalies
Medicaid insurance
Admission during a weekend
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3. Longitudinal Data Analysis
• KID and NPDD do not have unique pt identifiers
• PHIS has MRNs that can be tracked across time within
institution (some hospitals have data back to 1992)
• Useful for classifying certain pts underlying disease
• Potential research topics…
– Utilization of chronic populations
– Readmissions, if the case can be made that most patients
don’t go somewhere else
– Time-to-event analysis
– Trends in admissions or seasonality
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3. Longitudinal Data Analysis (Example)
• A multi-center study of factors influencing cerebrospinal
fluid shunt survival in infants and children
– Shah SS, Hall M,Slonim A, Hornig GW, Berry JG, Sharma V.
– J Neurosurg (In Press)
• 7,399 had shunt placement and at least one-year of
follow-up
• 20.2%, 7.5%, and 6.9% of patients required 1, 2, or 3 or
more shunt revisions, respectively
• In multivariable analyses, children undergoing shunt
placement in the Northeast census region had a longer
duration of shunt survival between initial placement and
both the first and second revisions.
• Young age and a pdx of obstructive hydrocephalus were
associated with a higher risk of failure
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4. Utilization / Standards of Care
• Line item utilization is not available in KID or NPDD
• PHIS is the most robust data source
• Look for frequency of utilization (drugs, imaging, labs, etc.) in a
population
– Common (e.g. asthma) or rare (e.g. HLHS)
• Potential research topics…
– Disparities in care
– Impact of specific diagnoses on resources (throughput, supplies,
pharmacy, etc.)
– Adherence to evidence-based guidelines
– Evaluate the effect of clinical care guidelines (pre vs. post)
– Impact of case volume on outcomes
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4. Utilization / Standards of Care (Example 1)
• Racial and economic disparity and the treatment of
pediatric fractures
• Slover J, Gibson J, Tosteson T, Smith B, Koval
• J Pediatr Orthop. 2005 Nov-Dec;25(6):717-21
– Supracondylar humerus (n = 2,957), femoral shaft (n =
1,726) or radius and ulna forearm fracture (n = 828) as
their primary diagnosis
– Hispanic (78%) and black (82%) patients were more likely
to receive closed reduction with internal fixation of
supracondylar humerus fractures than whites (73%, P =
0.02)
– No other differences noted
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4. Utilization / Standards of Care (Example 2)
• The effect of surgical case volume on outcome
after the Norwood procedure
• Checchia PA, McCollegan J, Daher N, Kolovos N, Levy F, Markovitz B
• J Thorac Cardiovasc Surg. 2005 Apr;129(4):754-9
– Twenty-nine hospitals and 87 surgeons
performed 801 Norwood procedures during the
study period
– Survival after the Norwood procedure is
associated with institutional Norwood procedure
volume but not with individual surgeon case
volume
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4. Utilization / Standards of Care (Example 3)
• Institutional variation in ordering complete blood counts
for children hospitalized with bronchiolitis
• Tarini BA, Garrison MM, Christakis DA.
• J Hosp Med. 2007 Mar;2(2):69-73
– Little evidence to support the use of diagnostic testing,
particularly complete blood counts (CBCs)
– 17,397 children were included in the analysis, and 48.2%
had at least 1 CBC, whereas 7.8% had more than 1 CBC
– The proportion of admissions with initial (23.2%-70.2%)
and repeat (0%-18.6%) CBCs varied significantly across
hospitals
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Analytic Considerations
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Risk Adjustment
• Necessary given heterogeneity of hospitals /
patient populations across time
• APR-DRG severity-of-illness or case mix index
may or may not be adequate
• Charge and los weights in PHIS
– Assigned to every discharge based on APR-DRG
and severity level assignment
– Assume resource utilization is correlated with
severity
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Risk Adjustment (Continued)
• Weights compare the average charge (LOS) for
each APR-DRG / severity level combination to
the overall average using a national dataset
• Example:
– APR-DRG=1 (liver transplant), Severity=3 (Major)
– Charge weight is 21.2.
– This means that the average charge for patients
in this group were 21.2 times the average charge
for ALL pediatric discharges, regardless of their
APR-DRG or severity level.
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Risk Adjustment (Continued)
• Ad hoc risk adjustment may be necessary
– Create list of factors that might impact outcome
• Patient and hospital level
– Model for parsimony (remove insignificant
predictors)
– Clearly articulate gaps in factors unavailable in
administrative data
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Modeling Considerations
• Data is clustered by hospital
– All models should be hierarchical
• Varying reliability of estimates across hospitals
– Consider Bayesian shrinkage estimators, also
useful when doing risk adjustment
• Data is retrospective and observational
– Consider matching or propensity scoring to mimic
randomized trials, be sure to verify methods
effect on balancing covariates
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Modeling Considerations (Continued)
• Overwhelming power
– Reduce significance
– P-values can be ineffectual, consider alternate
presentations
• KID and NPDD is survey data
– Use software (e.g. SAS, SUDANN) to account for
sampling frame
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Strengths of Administrative Data in Research
•
•
•
•
•
•
•
Patient level data
Line item utilization (PHIS)
Population size = Power
Multiple institutions for rare conditions
National estimates
Hospital-to-hospital variation
Useful for designing Phase I trials
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Limitations of Administrative Data in Research
• Retrospective and observational
• Substantial factors for risk adjustment might be
missing
• Outcomes are limited
• Unknown Sen / Spec for many ICD-9 codes; dxs
and pxs rely on proper documentation and
coding
• Charges are billed resource, not necessarily
administered
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58
PHIS for Research Resources
• Subset of PHIS main web
site
• Includes
– Standard PHIS
methodology text
– Existing articles by topic
– Data Quality resources
– Data content resources
• Register at www.chca.com
and select PHIS as
requested site
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59
PHIS Contacts
• CHCA
– David Bertoch ([email protected])
– Matt Hall ([email protected])
• CHOP
– Quality Improvement:
• Finnah Escritor
– PHIS-related Research:
• The Center for Pediatric Clinical Effectiveness Theo Zaoutis or Ron Keren
Child Health Corporation of America, All Rights Reserved, 2007
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