Deb Koester Joseph Thomas Sudeshna Paul Bruce Craig Michael Weiner Laura Sands Healthcare Utilization in Preschool-Age Children.
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• Deb Koester• Joseph Thomas• Sudeshna Paul• Bruce Craig• Michael Weiner• Laura Sands
Healthcare Utilization in Preschool-Age Children
• 5 year olds in 2003, 2004• the 18-month period before September 1st • well-child visits• compare utilization among those with and
without well child visits• Indiana Medicaid data• continuous enrollment• not in a managed care organization
Healthcare Utilization in Preschool-Age Children
• 5 year olds in 2003, 2004• the 18-month period before September 1st • well-child visits• compare utilization among those with and
without well child visits• Indiana Medicaid data• continuous enrollment• not in a managed care organization
Healthcare Utilization in Preschool-Age Children
N = 9618
• Dx’s = pyloric stenosis• ICD9 = 750.5• ICD9 = 537.0• Radiology• Transfers to Riley?
Pyloric stenosis
• Dx’s = pyloric stenosis• ICD9 = 750.5• ICD9 = 537.0• Radiology• Transfers to Riley?• Riley
Pyloric stenosis
• Dx’s = pyloric stenosis• ICD9 = 750.5• ICD9 = 537.0• Radiology• Transfers to Riley?• Riley• Text Reports
• Discharge Summaries• Op Notes**
Pyloric stenosis
Exposure to Erythromycin
• 483 patients were identified
• Upon review of all 483 in Inquiry, 14 (3%) were removed from drug group
•ordered and then d/c’d immediately (8)
•“250 mg tabs” (6)
• 469 patients in the exposure group
Infant systemic erythromycin
Pyloric Stenosis
Erythromycin
RR =4.98(2.11, 11.74)
6
37
463
14,370 14,407
469
yes
yes
no
no
43 14,833
(1.28%)
(0.26%)
Ageat Rx
Infant systemic erythromycin
#infants with PS#infants with Rx
#infants with PS#infants without Rx RR
<3 months 1.28%(6/469)
0.26%(38/14,407)
4.98(2.1, 11.7)
Ageat Rx
Infant systemic erythromycin
#infants with PS#infants with Rx
#infants with PS#infants without Rx RR
<2 weeks
<3 months
2.65%(6/226)
1.28%(6/469)
0.25%(37/14,650)
0.26%(38/14,407)
10.51(4.5, 24.7)
4.98(2.1, 11.7)
0
10,00020,000
30,00040,000
50,00060,000
70,00080,000
90,000100,000
110,000
2003
CC = "SICK" Total N of CC
Wishard Chief Complaints
Chief Complaints
“SICK”“sick”“SICK’”“SICK’;”“SICK”“SICK AS 3 DOGS”“SICKO”“SIDK”“SIICK”“SIKC”“SIKCK”“SIC”
0
10,00020,000
30,00040,000
50,00060,000
70,00080,000
90,000100,000
110,000
2000 2002 2004 2006
CC = "SICK" Total N of CC
Wishard Chief Complaints
• Estimate the N with ________Quick Checks, and Discussion of Feasibility
• Generate a List for Chart Review
• Generate a List for Recruitment (e.g., by ResNet)
• Help Investigators with their own databases
• Full Retrospective Study Aggregate Data Individual-Level Analytic Data Set
Deidentified Limited Identified
Types of Projects
• Estimate the N with ________Quick Checks, and Discussion of Feasibility
N of patients with coronary artery disease in 2006,and ≥ 1 visit to an IUMG primary care
medicine clinic
Types of Projects
• Estimate the N with ________Quick Checks, and Discussion of Feasibility
• Generate a List for Chart Review
Patients with HIV and other criteria
Types of Projects
• Estimate the N with ________Quick Checks, and Discussion of Feasibility
• Generate a List for Chart Review
• Generate a List for Recruitment
Patients > 50 years old in IUMG primary care medicine
Types of Projects
• Estimate the N with ________Quick Checks, and Discussion of Feasibility
• Generate a List for Chart Review
• Generate a List for Recruitment (e.g., by ResNet)
• Help Investigators with their own databases
setting up forms for data entry into Microsoft Access
Types of Projects
• RMRS• INPC Hospitals
• Wishard• Clarian• Community• St. Francis• St. Vincent• a growing list…
• Medicaid etc.
Data Sources
Existing
• RMRS• INPC Hospitals
• Wishard• Clarian• Community• St. Francis• St. Vincent• a growing list…
• Medicaid etc.
Data Sources
Existing
Global ID
• RMRS• INPC Hospitals
• Wishard• Clarian• Community• St. Francis• St. Vincent• a growing list…
• Medicaid etc.
Data Sources
Existing
• you can bring us data you’ve collected
• &/or we can help you build a database
New
Global ID
• Lab Results• Imaging Studies• Orders• Visits: Inpatient, ER, and Outpatient• Rx• Age, Sex, Race• Insurance status at check-in• Text Reports, e.g.
Discharge Summaries Op Notes
• Charges
Types of Electronic Data
• The Data are a Work-in-Progress• Shaped by the Real World
• clinical and billing considerations• work flows• interfaces
• History• Shelf Life• Need for Triangulation
• scrutiny• >1 method• written charts
• Perspectives
About the Data
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