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The Role of Military Ambulatory Care Nurses in Facilitating the Best Evidence-based Care

The Role of Military Ambulatory Care Nurses in Facilitating the Best Evidence-based Care. AAACN April 2008 Mary Ramos, PhD, RN. Military Health System New to Provision of Broad-based Care. Until 1880s cared only for Soldiers

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The Role of Military Ambulatory Care Nurses in Facilitating the Best Evidence-based Care

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  1. The Role of Military Ambulatory Care Nurses in Facilitating the Best Evidence-based Care AAACN April 2008 Mary Ramos, PhD, RN

  2. Military Health System New to Provision of Broad-based Care • Until 1880s cared only for Soldiers • After 1884 Medical Officers and Contract Surgeons cared for families free of charge as possible • World War II presented huge burden of obstetrical care • December 7, 1956: Dependents Medical Care Act • CHAMPUS: 1967

  3. The 1960s • CHAMPUS provided ambulatory and psychiatric care for all family members and retirees by 1967: Modified HMO/PPO • About 30 HMOs in commercial sector • Beginning awareness of financial risk in provision of health care • Utilization Review • Cost Containment

  4. 1980s • Fee for Service Decreasingly Feasible in Community-based Care • Growth of Managed Care Environment • CHAMPUS Reform Initiative in Military • TRICARE

  5. Military Health Care in 2008 • TRICARE-contracted • Network-dependent • Ambulatory • Cost-focused • Resource-strained • Complex Systems • High Technology • High Expectations • Outcomes Measured and Compared

  6. Key to Maximizing Quality of Care • Attention to Processes • Balanced Team Approach • Patient Focus • Data-based Decision-making • Evidence-based Practices • Clinical Practice Guidelines • Using Available Data

  7. The Best Position to Facilitate Change: Nurses • Providers Look at One Patient at a Time • Providers Should be Seeing Patients • Providers Need System Support • Nurses Can Be System Thinkers • Nurses Can Provide Data • Nurses Can Support Providers in Improving Population Health Through Data-based Decision-making

  8. Clinical Practice Guidelines • Evidence-based • Algorithmic: Decision Matrices • Outcomes-focused • Bring Best Practices to the Patient • Decrease Unwanted Variation • Free Providers to Concentrate on What Cannot be “Automatic”

  9. https://qmo.amedd.army.mil/pguide.htm

  10. https://qmo.amedd.army.mil/CPGShoppingCart/entry.aspx?link=/CPGShoppingCart/default.aspxhttps://qmo.amedd.army.mil/CPGShoppingCart/entry.aspx?link=/CPGShoppingCart/default.aspx

  11. Diabetes Clinical Practice Guideline Recommendations • A1C At Least Annually • Foot Exam Annually • Dilated Eye Exam Annually • Track Lipids • Influenza/PNX Vaccine • Microalbumin Check

  12. Provider Decisions? • Research-based • Universally Accepted • Patient Should be Involved in Planning • Timing for A1C • Relationship of Diet and Exercise to A1C • Patient Ability to Assess Feet • Importance of Eye Exam • Diabetes and Kidney Function

  13. Using Data in Clinical Settings • Aggregation • Analysis • Action

  14. Aggregation • Aggregation is putting individual numbers into sets that “fit” questions • The questions, then, must come first

  15. Know Your Enrollees How Many Enrollees • By Benefit Category • By Age Group and Gender • By Function and Activity • By Condition

  16. Know Your Providers How Many Providers • By Category • Primary Care vs. Specialty • Physician vs. NP and others • By Population Benefit Category • By Population Age Group and Gender • By Deployment Status • By Population Condition

  17. Formulate Questions • How many patients with asthma? • How many of those have had ED visits for asthma? • How many of them are on inhaled steroid medication? All answers readily available through the MHSPHP

  18. Patient-level Data https://pophealth.afms.mil/tsphp/login/login.cfm

  19. MTF-level Data https://cms.mods.army.mil/cms/secured/stoplights_frameset.aspx?metricCategoryCd=POPH

  20. Analysis • Without analysis, numbers are just data – not information • Without analysis, we DRIP • Analysis is interpreting numbers to answer questions • The questions, then, must come first

  21. Analysis • Analyze data sets separately • ORYX • HEDIS • Other Applicable Data • Analyze data sets together • ED Utilization against Portal Asthma data • Inpatient days for diabetes patients • Readmission for CHF and ORYX discharge education data

  22. ORYX Analysis Tools • www.mhs-cqm.info • MTF comparison charts • Can be edited to address local issues • www.hcdinc.com • Process Control Charts (Consistency) • Comparison Charts (Observed vs. Benchmark) • Quality Reports (TJC Quality Check)

  23. Action • Having data is not enough • Everyone is involved in incorporating data into every day decision-making and strategic planning to modify processes to enhance and optimize patient care and patient safety

  24. Get your findings out of the office and into the field • Talk to the delivery teams about creative ways to improve numbers • Use data to feed and nourish quality improvement efforts • Cross-pollinate

  25. Ambulatory Nursing Interventions • Design Systems to Support Care Delivery • Huddles • Order Sets • Availability of Data • Integrated Teams • Patient Partnerships • Case Management and Continuity of Care

  26. Best Practices • Mammography Self-referral • Integrated Diabetes Care • Classes to Fit Patient Preferences • Comprehensive Cardiac Risk Management • Interdisciplinary Approaches • Comprehensive Asthma Care • Integration with School Nurses • Involvement of ED • Diagnostic and Coding Issues Addressed

  27. Expanded Roles for Nurses in Ambulatory Care • Active Partnerships with Case and Disease Managers • Identifying and Eliminating “Silos” in Preventative Care Services • Richer Mix of Professional Nurses • Nurses More Involved in Direct Patient Care • Larger Voice in Administrative Decision-making and Systems Design

  28. Golden Opportunities for Nurses • Advocate for Patient-friendly Systems • Expand Responsibility and Partnerships • Assume Responsibility for Optimal Patient Wellness • Assist Military in Designing Delivery Systems for the 21st Century

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