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Measurement

Measurement. Stephen McPhee, MD Kathleen Puntillo, RN, DNS, FAAN. Structure of session. Entire group (20 minutes) Break-out sessions (60 minutes) Individual hospital/hospice team members: Complete worksheet 4.1, Discussion Q&As (50 minutes)

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Measurement

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  1. Measurement Stephen McPhee, MD Kathleen Puntillo, RN, DNS, FAAN

  2. Structure of session • Entire group (20 minutes) • Break-out sessions (60 minutes) • Individual hospital/hospice team members: Complete worksheet 4.1, Discussion Q&As (50 minutes) • Small groups (~3 hospitals/hospices, 2 faculty each): Debrief Review menu of measurement tools in Appendix 4.8 (adopt or adapt?) (10 minutes)

  3. Objectives of session 1. Discuss measurement why and how : what elements to collect, measure, analyze, report 2. Create process for measuring 4 types of outcomes in your institution: a. Clinical outcomes (e.g., pain, other symptoms) b. Operational measures (e.g., volume and type of referrals, team workload) c. Financial outcomes (e.g., billing revenues, hospital, ICU LOS, cost per day) d. Family, clinician satisfaction 3. Adopt or adapt existing measurement resources

  4. PCS Data March, 1999 - February, 2005 n = 1,209 Patient Volume • March 99 - Feb 00 102 • March 00 - Feb 01 139 • March 01 - Feb 02 209 • March 02 - Feb 03 261 • March 03 - Feb 04 234 • March 04 - Feb 05 264 6 Year Total 1,209

  5. PCS Data March, 1999 - February, 2005 n = 1,209 Patient Age • Average Age 70.1 years • Age Range 24 - 104 years

  6. PCS Data March, 1999 - February, 2005 n = 781 Life Expectancy • < 6 months 5% • < 1 month 16% • < 1 week 64% • < 24 hours 14%

  7. PCS Data March, 1999 - February, 2005 n = 1,165 Diagnosis Group # Pts % Pts • Cancer 498 43% • Neurological 252 22% • Cardiac 126 11% • Pulmonary 80 7% • Hepatic 64 5%

  8. PCS Data March, 1999 - February, 2005 n = 1,165 Diagnosis Group (continued) # Pts % Pts • Infection/Sepsis 64 5% • Renal 44 4% • Other 26 2% • Gastric 11 1%

  9. PCS Data March, 1999 - February, 2005 n = 1,165 Attending Service # Pts % Pts • Medicine 689 59% • Neurology 124 11% • Cardiology 96 8% • CRI/Oncology 80 7% • Neurosurgery 71 6% • LTU 41 4% • General Surgery 26 2%

  10. PCS Data March, 1999 - February, 2005 n = 1,165 Attending Service (continued) # Pts % Pts • CT Surgery 12 1% • Gyn/Onc 9 1% • Vascular Surgery 8 1% • ENT 5 0% • KTU 4 0%

  11. PCS Data March, 1999 - February, 2005 n = 1,165 Acuity at PCS Service Admit # Pts % Pts • Acute Care 434 37% • Intensive Care 379 33% • Step Down 157 13% • ED 123 11% • Direct 33 3% • Unknown 39 3%

  12. PCS Data March, 1999 - February, 2005 n = 1,165 Disposition at Discharge # Pts % Pts • Death on PCS 904 78% • Hospice 125 11% • SNF 71 6% • Home 46 4% • Non-comfort care 15 1% • Other 4 0%

  13. PCS Data March, 1999 - February, 2005 Length of Stay • Hospital ALOS 10.64 days • ALOS before PCS admit 7.83 days • Range pre-PCS LOS 1-126 days • ALOS on PCS 2.63 days • Range PCS LOS 1-19 days

  14. PCS Data March, 1999 - February, 2005 Length of Stay (continued) • Total inpatient bed days 12,400 days • Total PCS bed days 3,064 days • Percent LOS on PCS 25%

  15. PCS Data Collection Procedures • Intake Form (Appendix 4.2) • Physician • Nurse • SW • PCS Consult Record Rounding Tool (Appendix 4.3) • Demographics • Clinical • PCS • Billing

  16. Why do we need to collect data? • Data needed both for palliative care (PC) service (internal) purposes and for institutional (external) purposes • Importance of various types of data differs for different stakeholders (e.g., financial data for CFO) • Data needed for PC service: 1. Planning 2. Sustaining 3. Improving

  17. Why do we need to collect data? 1. Planning: Needs/opportunities to start/expand PC service? a. Hospital/hospice data (e.g., number of Medicare patients, ICU and hospital LOS) b. Areas of need, referral base, educational deficits, etc.

  18. Why do we need to collect data? 2. Sustaining: How do we justify our service? What impact are we having? a. Validate program to hospital, hospice, insurers, etc. b. Impact on quality of care, financial stability, satisfaction

  19. Why do we need to collect data? 3. Improving: How are we doing? How can we improve? a. Process changes needed to optimize clinical outcomes

  20. Measurement is critical • Start before day #1 of PC service • Decide what you want to know on day #365 • Realize that data needs may evolve over time

  21. Measurement is critical, but… • Data collection/analysis costs time and money • Take advantage of data already being collected • Prioritize types of data to be collected • Only collect data you will use • Integrate data collection into ongoing clinical care

  22. What might be useful data to collect? (Appendix 4.4) • What might be useful data to collect before establishment of PC service? • What are data needed during conduct of PC service, e.g., to measure quality of care (clinical and financial outcomes)? • Where can this data be found?

  23. What data will you collect? • Align data collection to mission: consider your needs assessment, goals, strategic plan • Collect at least rudimentary measures in each outcome category: clinical; operational; financial; satisfaction • Put effort into measures of primary importance to you and to your institution, perhaps especially to program continuation decisions (team staff, beds, budget, etc.) • Plan for resources to gather and analyze data

  24. Consider what your data needs are

  25. What data will you collect? • What will you want to know? • What will your adversaries want to know? • What will your administrators want to know? • What will you be judged by?

  26. How will you create a process to collect data? • Determine who will collect data • Hospital data: information technology (IT), billing, and/or medical records dept. staff • Clinical data: PC service team members • Patient/family satisfaction data: quality assurance or compliance staff • Clinician satisfaction data: PC service team or human resources staff

  27. How will you collect data? (cont.) • Clinical outcomes: use a daily rounding tool (Appendix 4.3) • Not burdensome • Standardized, reduces variance among clinicians • Sequential, enables tracking of effects of interventions and quality improvement • Useful reminder for documentation, billing purposes

  28. How will you collect data? (cont.) • Patient/family (“customer”) satisfaction survey (Appendix 4.5) • JCAHO mandates post-discharge consumer satisfaction surveys • Existing hospital surveys often do not include palliative care • Determine key questions for your hospital and offer items to surveyors • Use results in marketing PC service • Clinician (“customer”) satisfaction survey (Appendix 4.6)

  29. How will you store and analyze data? • Maintain data in computerized database (e.g., Access database or Excel spreadsheet) • Consider how and when you will report data: • Dashboard? Benchmarks? • Quarterly reports on clinical and operational outcomes can yield quality improvement • Annual reports on financial and satisfaction outcomes can help justify program

  30. How will you report data?“The Dashboard” (Appendix 4.7) Dashboard = useful reporting tool for key clinical, utilization and financial data Displays key data, making obvious when outcomes are out of expected/desired ranges, assisting in improving performance Displays different but related data, making visible the effects of changes in one area on another Displays data sequentially, making obvious changes from period to period, assisting in sustaining improvement Populated late, but structured early

  31. What tools exist for you to adopt or adapt? • See Appendix 4.8: CAPC Measurement Tools Web Site and Titles

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