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Data for dummies Harnessing the power of ePCR

Data for dummies Harnessing the power of ePCR. Chris Rosa, B.A., Paramedic. “It’s OK, I know you’re just a computer geek…”. Impacts of data Defining data Current trends and implications Bringing it all together. Topics. Impacts. “We can’t keep talking about data, data, data…”.

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Data for dummies Harnessing the power of ePCR

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  1. Data for dummiesHarnessing the power of ePCR Chris Rosa, B.A., Paramedic

  2. “It’s OK, I know you’re just a computer geek…”

  3. Impacts of data • Defining data • Current trends and implications • Bringing it all together Topics

  4. Impacts

  5. “We can’t keep talking about data, data, data…”

  6. Software Hardware Significant cost Ongoing maintenance and emerging technology can be problematic Ease of use and compatibility to ePCR systems Mobility and security • EMS documentation still on paper or semi-electronic in many systems (Shavarini, 2013). • Systems that are not on electronic system by 2015 will be penalized. • Significant up front cost, require extensive ongoing support, and training. • How will new systems impact existing processes? Electronic Patient Care Reporting

  7. New/additional data requirements can be overwhelming for staff • Education and training should focus on quality documentation versus penalties • Corrective action should be supportive early, but expectations should be outlined in policy • Staff should be included in data system development and decision-making process Staff

  8. Modifying existing workflow • Leveraging features within new system • Ensuring data is clean and accurate • Rolling out new programs to personnel QA / QI

  9. “0 to 100” • Organizing the information • Evaluating current priorities • Gap analysis and applying changes System Impacts

  10. Defining Data…

  11. Data is any piece of information that can be collected and/or interpreted. • Quantitative (measurable) • Qualitative (observable) • The decisions we make everyday are based on some sort of data. What is data?

  12. QA/QI • Clinical/operational surveillance • Benchmarking • Accountability • Budgeting and Resource Allocation • Workforce composition Why does data matter?

  13. In order to be effective, data must be accurate and reliable, verifiable, controlled, and recorded (ISO, 2004) • Standardization and common understanding of terms are crucial Data Definitions

  14. Offer a method of defining and organizing numerous terms and elements across systems • Limit variability and increase reliability • Examples • National EMS Information System • California EMS Information System • National Fire Incident Reporting System • National Trauma Data Bank • Get With The Guidelines Data Dictionaries

  15. Data standardization (Goolsby & Levin, 2012) • Creates new opportunities • Increases effectiveness and cost-saving • Improves delivery of services • Benchmarking (Byrne, Schreiner, Rizk & Sokolowski, 1998) • Driven through standardized data • Compare across systems internally/regionally/nationally • Improve services based on best practices • CalEMSA Core Measures Benefits of Standardized Data

  16. Trends and Implications

  17. NEMSIS CEMSIS Fully aligned with NEMSIS 3.0 Evidence based! Tied in with other CalEMSA quality measures and programs Aligns pre-hospital care with outcome measures from registry data • Central repository for all EMS data • EMS systems in the process of understanding current system AND implementing NEMSIS 3 • The standard by which States and locals set their data definitions EMS Information Systems

  18. QA/QI system to benchmark current position based on EMS data • Helps to ensure data remains consistent and reliable • Measures and definitions are improving year over year • EMS system data should be aligned based on the identified core measures CalEMSA Core Measures

  19. Community Paramedicine Health Information Exchange Links patient records multiple levels in the healthcare chain Shifts focus of data from the agency or hospital to the patient Increased need for consistent data collection Increased responsibility of field personnel related to data collection and retrieval • Will increase need for consistent data, accessible by stakeholders • Creates a need for linked data at every level of healthcare • Additional fields / study questions not included in NEMSIS/CEMSIS Emerging Programs

  20. Bringing it all Together

  21. Consistent data definitions before documentation system implementation • Deviation from data standards should be clearly communicated to stakeholders • New programs and changes to data systems will require significant shifts in standards and requirements

  22. All data systems should be aligned and linked whenever possible • “Cradle to Grave” approach to data system development • 911 to hospital discharge • Focus on quality of documentation versus a score or length of report

  23. Caution with custom fields or re-tasking existing fields • Adapt to new data requirements, don’t force data elements into your existing system • Collaboration and development of an open, adaptable system is crucial to success in data collection

  24. Thank You! Questions?

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