Model Impaired Driving Records Information System NHTSA DTNH22-98-D-45079

1. Model Impaired Driving Records Information System NHTSA DTNH22-98-D-45079

2. Anne T. McCartt, PhD PRG, Inc. COTR: De Carlo Ciccel, NHTSA

3. In almost all jurisdictions in the U.S., basic information on impaired driving offenses is difficult, if not impossible, to obtain.

4. Project Plan Review literature Site visits in four “best practices” states Iowa New York New Jersey North Carolina Review of model by expert panel How-to Guide

5. Key Stakeholders Law Enforcement Agencies (LEAs)* Department of Motor Vehicles (DMVs)* Courts (including prosecution, probation, judges)* Highway Safety Offices Others: Treatment, Corrections, Criminal Justice, MADD, Legislature, etc.

6. Impaired Driving Records Information System Totality of a state’s efforts to generate, transmit, store, update, link, manage, analyze, and report information on impaired driving offenders and citations.

7. Functions Supported Appropriately identify, charge, and sanction impaired driving offenders, based on their driving history and statutes Manage cases from arrest through the completion/non-completion of court and administrative sanctions Identify target populations and trends, evaluate countermeasures, and identify weaknesses of the impaired driving system

8. Functions Supported (cont.) Provide stakeholders with adequate and timely information to fulfill their responsibilities Reduce administrative costs for system stakeholders and increase system efficiencies

9. State Differences Government structure, especially court system Impaired driving laws criminal/civil/traffic offense administrative penalties imposed by DMV definition of repeat offense Access to public records, electronic signatures, etc. Telecommunications infrastructure Uniform traffic citation; number citations per arrest Interagency computer network

10. Features of Model System Statewide coverage Electronic access by LEAs and courts to current information on driver history, licensing status, vehicle, criminal history, and warrants, as applicable Electronic citation system supporting license/registration technologies at roadside/station Electronic citation tracking system – access to stakeholders by citation number and offender Electronic reporting by LEAS and courts to DMVs

11. Features (cont.) Electronic reporting by probation, treatment, corrections to DMVs and courts on compliance/non-compliance with sanctions Linked data to provide a complete record for each offender, including driver history Timely access to statistical aggregate data Flexibility to support additional data and technological innovations Compliance with national standards Security and privacy safeguards

12. Key Benefits to State Cost savings and reduced duplicative efforts by automating data entry and linking data systems More efficient, better managed DMV, court, LEA operations Appropriate and consistent charging and sanctioning Improved management and evaluation of overall impaired driving system Improved targeting of countermeasures Citation accountability Heightened security

13. National Benefits Better understanding of the scope and nature of impaired driving problem Tracking progress Developing better laws and programs Supporting research and evaluation Developing measures of efficiency and effectiveness

14. Core Data Elements from Existing Databases Driver identifying information Driver license class, status, restrictions Vehicle registration information and status Relevant criminal history Outstanding warrants Driver history information – crashes, impaired driving and traffic citations Outstanding citations or arrests

15. Examples of Core Data Elements Generated from Arrest Arrest/citation information, e.g., date, time of day Alcohol and/or drug test Pre-conviction administrative penalties Prosecution/adjudication process and outcomes, e.g., case disposition, penalties, etc. Completion/non-completion of treatment/assessment, other court and administrative sanctions Whether reinstated license

16. Data Entry, Storage, Transmission Courts, DMVs, LEAs primary Totally electronic systems to retrieve, enter, and transmit data Menu pull downs and edit checks when creating records Automatic population of forms Audits of data timeliness, accuracy, completeness Immediate access to DMV and court records Easily understood summary of driver license data

17. Data Entry, Storage, Transmission(continued) For court case tracking system, query capabilities by citation, offender, LEA, court, etc. Ability to match multiple citations for same event E-citation records integrated into court system within 48 business hours of issuance DMV acceptance of citation or court data within 48 business hours of action by officer or court

18. Statistical Report Capabilities Periodic standardized reports Simple ad hoc reports Numbers and rates Current and historical Statewide, by jurisdiction, by court or LEA, etc. First and repeat offenders

19. Examples of Statistical Reporting Characteristics of offenders Arrests and all adjudication outcomes by charge BAC refusals and test results Diversion/deferrals Referrals to treatment Above by 1st and repeat offenders

20. Data Warehouse Draws from various data systems Links offender-based and case-based records User friendly access Single agency oversight Secure access Generates statistical information across agencies and across impaired driving process Periodic standardized reports and special ad hoc reports

21. State should consider: Complexity of laws Resources need for hardware and software, technical support, and training Telecommunications infrastructure Computer network Degree of uniformity across agencies, forms, and processes Inter/intra-organizational issues

22. Designing and Implementing Model Convene interagency coordinating committee Designate lead agency Define agency roles/responsibilities Develop interagency cooperative agreements Develop impaired driving critical path Standardize processes, procedures, etc. Develop long-range plan Develop protocols to protect privacy and security

23. Interstate Efforts Are Key to Take Advantage of Economies of Scale and to Minimize Duplicative Efforts