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Supplementing Community Public Health Surveillance With Data From Electronic Healthcare Claims

This article explores the use of electronic healthcare claims data to supplement public health surveillance efforts. It discusses the benefits of using data from Verispan's Data Warehouse, including its practicality, uniformity, and rapidity. The article also provides examples of outbreak detection using non-traditional approaches to surveillance.

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Supplementing Community Public Health Surveillance With Data From Electronic Healthcare Claims

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  1. Supplementing Community Public Health Surveillance With Data From Electronic Healthcare Claims October 2002 Thomas Balzer, Ph.D.Chief Scientific Officer, Verispan

  2. Who is Verispan? • Scott-Levin • SMG Marketing Group • Synergy Healthcare • Amaxis • Kelly-Waldron • data sources

  3. Public Health Surveillance “Surveillance: Continuous analysis, interpretation, and feedback of systematically collected data, generally using methods distinguished by their practicality, uniformity, and rapidity rather than by accuracy or completeness.” John M. Last, A Dictionary of Epidemiology, 3rd Edition

  4. Critical Attributes ofPublic Health Surveillance Systems Simple (in concept) Stable (in operation) Acceptable (to providers) Standardized high-quality data Timely (in reporting healthcare events) Representative (of all areas) Sensitive (to outbreaks & other changes over time by applying traditional & non-traditional approaches to surveillance) Flexible (to changing surveillance needs) 4

  5. Verispan Data Warehouse:“Practicality, Uniformity, Rapidity” The largest, broadest real-time and longitudinal sample of patient-centric pharmacy and medical transactions in the world.

  6. The Verispan Data Warehouse Began in 1998 “A Simple & Stable System Already Working” • 50,000+ Pharmacies • 640,000+ Unique Subscribers • 5+ Million Claims Loaded Daily • 100+ Million Unique Patients • 1.7 Billion Annual Rx or Mx Claims • 5 Billion Claims - total in warehouse

  7. Verispan Harnesses Routine Billing Practices“Acceptable to Providers” Verispan Patient Data Base De-Identification Physicians Pharmacies & Prescription Services Hospitals & Facilities MX RX HX Daily Claims Volume Health Care Clearinghouse Payors BCBS Commercial (PBM, HMO) Government (Medicare/Medicaid)

  8. Medical, Hospital, and Pharmacy Data are Available “Verispan Has Standardized, High-Quality Data” HX Facility Data (UB-92) RX Pharmacy Data (NCPDP) MX Provider Data (HCFA 1500) • Patient ID • Patient Age & Gender • Date Written • Date Filled • NDC Code • Quantity Dispensed • Days Supply • Refill Flag • Prescribing Physician • Pharmacy • Payor Type • Patient ID • Patient Age & Gender • Diagnosis Codes (ICD9) • Procedure Codes (CPT) • DRG • Admit Date • Discharge Date • Physician/Provider ID • Location of Care • Payor Type • Patient ID • Patient Age & Gender • Diagnosis Codes (ICD9) • Procedure Codes (CPT) • Service Dates • Physician/Provider ID • Location of Care • Payor Type Pharmacy Data Medical Data Jan ‘98 - to date July ‘98 - to date

  9. Providers are Motivated to File Timely Electronic Claims “The Verispan Data Warehouse Is Updated Daily Lag Days for enteric illness among children, 2000 - 2001

  10. Excellent Geographic Distribution“All Areas of the U.S. Are Represented” Every State Every MSA Every 3 Digit Zip Code

  11. Examples of Outbreak Detection Using Non-traditional Approaches To Surveillance (NTAS) For Additional Information: Thomas.Balzer@Verispan.com 919-998-2547, Fax 919-998-7263

  12. CDC Outbreak Detection Challenge • CDC developed three case studies for evaluating supplementary data bases for their ability to identify outbreaks. CDC provided only limited information about these known 2001 outbreaks: • Case 1: Shigella sonnei gastroenteritis in Ohio • Case 2: Neisseria meningitides meningitis in Ohio in school-age children • Case 3: Histoplasma capsulatum in multiple states among travelers to Acapulco Only Verispan rose to the challenge of identifying the outbreak footprints in existing data bases.

  13. First_Claim Y Patient_State OH PROVIDER_COUNTY Hamilton AGE_GROUP_10YR 00 to 09 SHIGELLOSIS DISEASE SHIGELLOSIS Count of PATIENT_ID 10 5 0 2000_01 2000_03 2000_05 2000_07 2000_09 2000_11 2000_13 2000_15 2000_17 2000_19 2000_21 2000_23 2000_25 2000_27 2000_29 2000_31 2000_33 2000_35 2000_37 2000_39 2000_41 2000_43 2000_45 2000_47 2000_49 2000_51 2001_01 2001_03 2001_05 2001_07 2001_09 2001_11 2001_13 2001_15 2001_17 2001_19 2001_21 2001_23 2001_25 2001_27 2001_29 2001_31 2001_33 2001_35 2001_37 2001_39 2001_41 2001_43 2001_45 2001_47 2001_49 2001_51 SERVICE_EPI_WEEK A Traditional, Diagnosis-based Approach to Detecting an Enteric Illness Outbreak in Children, 2001

  14. First_Claim Y Patient_State OH PROVIDER_COUNTY Hamilton AGE_GROUP_10YR 00 to 09 DISEASE Diarrhea Enteritis Infectious_Diarrhea Count of PATIENT_ID 60 50 40 30 20 10 0 2000_01 2000_03 2000_05 2000_07 2000_09 2000_11 2000_13 2000_15 2000_17 2000_19 2000_21 2000_23 2000_25 2000_27 2000_29 2000_31 2000_33 2000_35 2000_37 2000_39 2000_41 2000_43 2000_45 2000_47 2000_49 2000_51 2001_01 2001_03 2001_05 2001_07 2001_09 2001_11 2001_13 2001_15 2001_17 2001_19 2001_21 2001_23 2001_25 2001_27 2001_29 2001_31 2001_33 2001_35 2001_37 2001_39 2001_41 2001_43 2001_45 2001_47 2001_49 2001_51 SERVICE_EPI_WEEK A Non-Traditional Approach to Outbreak Detection Using Surveillance of Enteric Syndromes Two Previously The Outbreak Unknown Outbreaks

  15. First_Claim Y Patient_State (All) DISEASE Meningococcal infection Place_of_Visit (All) PROVIDER_COUNTY AGE_GROUP_10YR Cuyahoga - 00 to 09 Cuyahoga - 10 to 19 Cuyahoga - 20 to 29 Hancock - 20 to 29 Summit - 10 to 19 Count of PATIENT_ID 5 4 3 2 1 0 2000_01 2000_03 2000_05 2000_07 2000_09 2000_11 2000_13 2000_15 2000_17 2000_19 2000_21 2000_23 2000_25 2000_27 2000_29 2000_31 2000_33 2000_35 2000_37 2000_39 2000_41 2000_43 2000_45 2000_47 2000_49 2000_51 2001_01 2001_03 2001_05 2001_07 2001_09 2001_11 2001_13 2001_15 2001_17 2001_19 2001_21 2001_23 2001_25 2001_27 2001_29 2001_31 2001_33 2001_35 2001_37 2001_39 2001_41 2001_43 2001_45 2001_47 2001_49 2001_51 SERVICE_EPI_WEEK A Traditional, Diagnosis-based Approach to Detecting a Community Meningitis Outbreak, 2001 The Outbreak

  16. First_Claim Y Patient_State OH Proc Code 90733 Place_of_Visit (All) Meningococcal Vaccination AGE_GROUP_10YR PROVIDER_COUNTY 10 to 19 - Cuyahoga 10 to 19 - Lake 10 to 19 - Mahoning 10 to 19 - Portage 10 to 19 - Stark 10 to 19 - Summit 20 to 29 - Cuyahoga 20 to 29 - Lake 20 to 29 - Mahoning 20 to 29 - Stark 20 to 29 - Summit Count of PATIENT_ID 70 60 50 40 30 20 10 0 2000_01 2000_03 2000_05 2000_07 2000_09 2000_11 2000_13 2000_15 2000_17 2000_19 2000_21 2000_23 2000_25 2000_27 2000_29 2000_31 2000_33 2000_35 2000_37 2000_39 2000_41 2000_43 2000_45 2000_47 2000_49 2000_51 2001_01 2001_04 2001_06 2001_08 2001_10 2001_12 2001_14 2001_17 2001_19 2001_21 2001_23 2001_25 2001_27 2001_29 2001_31 2001_33 2001_35 2001_37 2001_39 2001_41 2001_43 2001_45 2001_47 2001_49 2001_51 PROCESS_EPI_WEEK A Non-Traditional Approach to Outbreak Detection Using Surveillance of Vaccination Procedures Unexpected Vaccination Pattern Expected Vaccination From the Outbreak Pattern in Students Entering College

  17. First_Claim Y Histo_Endemic (blank) DISEASE Histoplasmosis AGE_GROUP_5YR (All) Patient_State AZ CT DE FL GA MA ME MI MN NC NE NJ NY OK OR PA SC SD TX VA WI WV Count of PATIENT_ID PA, NY, NJ, MI, TX, DE, MN, NE 10 8 6 4 2 0 (blank) 2000_02 2000_04 2000_06 2000_08 2000_10 2000_12 2000_14 2000_16 2000_18 2000_20 2000_22 2000_24 2000_26 2000_28 2000_30 2000_32 2000_34 2000_36 2000_38 2000_40 2000_42 2000_44 2000_46 2000_48 2000_50 2000_52 2001_02 2001_04 2001_06 2001_08 2001_10 2001_12 2001_14 2001_16 2001_18 2001_20 2001_22 2001_24 2001_26 2001_28 2001_30 2001_32 2001_34 2001_36 2001_38 2001_40 2001_42 2001_44 2001_46 2001_48 2001_50 2001_52 PROCESS_EPI_WEEK2 A Traditional, Diagnosis-based Approach to Detecting a National Histoplasmosis Outbreak, 2001 -

  18. A Non-Traditional Approach to Outbreak Detection Using Surveillance of Ketoconazole Prescriptions First RX Y HISTO_ENDEMIC (blank) NDC 51672402606 DRUGNAME KETOCONAZOLE PHARMACY_STATE (All) AGE_IN_YEARS 18 19 20 21 22 23 24 Count of PATIENT_ID 25 20 15 10 5 0 2000_02 2000_06 2000_10 2000_14 2000_18 2000_22 2000_26 2000_30 2000_34 2000_38 2000_42 2000_46 2000_50 2001_02 2001_06 2001_10 2001_14 2001_18 2001_22 2001_26 2001_30 2001_34 2001_38 2001_42 2001_46 2001_50 SERVICE_EPI_WEEK2 Prescription “Footprint” of the Outbreak

  19. Combining Traditional and Non-Traditional Approaches to Detect Outbreaks” County Fair E. coli 0157:H7 Outbreak*, NY State, 1999 Using the Informatics Mx Data Base 80 5 70 14 60 50 Cases (by EPI-week of healthcare visit) 29 40 3 6 30 7 9 5 5 4 6 5 17 13 5 20 10 13 10 14 14 29 6 18 11 5 10 16 15 12 11 10 9 9 6 6 5 0 30 31 32 33 34 35 36 37 38 39 40 (Aug) (Sep) (Oct) Wash. Co. Saratoga Co. Rensselaer Co. Warren Co. Source: Outbreak of Escherichia coli O157:H7 and Campylobacter among attendees of the Washington County Fair - New York, 1999 (MMWR 48(36); 803) *Based on ICD•9•CM codes: 008.00, 008.04, 008.43, 009, 283.11, 787.91 [Values are raw and unadjusted] 19

  20. Examples of Unique Daily Public Health Reports of Syndromes, Rxs, and Reportable and Non-Reportable Conditions That Are Available From Verispan Through the Web For Additional Information: Thomas.Balzer@Verispan.com 919-998-2547, Fax 919-998-7263

  21. Syndromic Surveillance: Influenza-Like-IllnessAlbany, NY, MSA, Jan 01 to Aug 02

  22. Syndromic Surveillance: SepticemiaFairfield County, CT, Jan 01 to Aug 02

  23. Syndromic Surveillance: SepticemiaGeographic View

  24. Syndromic Surveillance: Enteric IllnessHarrisburg, PA, MSA, Jan 01 to Aug 02

  25. Prescription Surveillance: Anti-Influenza DrugsNew York City, Jan 01 to Aug 02

  26. Surveillance of Non-Reportable Infectious Diseases:Influenza, Pittsburgh, MSA, Jan 01 to Aug 02

  27. Surveillance of Reportable Infectious Diseases:Lyme Disease, CT, Jan 01 to Aug 02

  28. Lyme Disease as Tracked by States & CDC in 2000 “Verispan Data Are Sensitive to Reportable Diseases” Color Code Key: # of Cases Color 3000+ 100-2999 20-99 1-19 No Cases 9 0 0 2 39 393 84 15 291 4,027 1,098 4 0 0 9 RI 590 1,276 CT 2,550 34 4 NJ 1,467 89 4 32 DE 142 11 3 34 MD 559 11 146 DC 11 45 104 17 13 46 28 1 4 17 0 0 7 0 1 36 4 2 50 0 Cumulative Cases Reported to CDC from State Health Departments: 2000 Lyme Disease: ICD-9-CM: 088.81 CDC preliminary case count: n = 13,309 (MMWR 49 [52]) 28

  29. Lyme Disease as Tracked by Quintiles, 2000“Quintiles Data Are Sensitive to Reportable Diseases” Color Code Key: # of Cases Color 3000+ 100-2999 20-99 1-19 No Cases 24 56 1 0 34 122 17 66 332 411 1 2 8,527 214 RI 56 2 CT 6,268 761 14 7 NJ 5,554 112 DE 603 16 190 131 2 MD 1,376 40 383 469 DC 30 38 113 79 238 82 18 29 40 10 60 15 127 29 209 29 2 205 0 Cumulative Cases Reported in Informatics Data: 2000 Lyme Disease: ICD-9-CM: 088.81 Quintiles case count: n = 27,184 CDC preliminary case count: n = 13,309 (MMWR 49 [52]) 29

  30. Evaluate Community Responses to Emergencies “Verispan Data Are Flexible at the Local Level” Cipro Daily Variation - WTC Area (60-Mile Radius) 9/11 10/12 - Anthrax 500% 400% 300% 200% Variance From Expected Activity 100% 0% -100% 08/01/2001 08/08/2001 08/15/2001 08/22/2001 08/29/2001 09/05/2001 09/12/2001 09/19/2001 09/26/2001 10/03/2001 10/10/2001 10/17/2001 Departure from Expectation Lower Limit Upper Limit 30

  31. 9/11 Generate Hypotheses for Further Study“Verispan Data Are Flexible at the Local Level” Source: Verispan Mx Database 31

  32. Other Surveillance Opportunities Control Group: All Prilosec and Amaryl NDC’s

  33. Verispan’ Unique Factors • Proven technology and systems in use for several years • Extensive database of over 100M de-identified patients • Prescription / Medical data integration processes • Daily receipt of ~5 million health claims • Data modeling and statistical strengths • Access to neural networking technology for detection • Existing broadcast technology for alert messages 33

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