HELP Data for Quality Improvement

1. HELP Data for Quality Improvement

2. Why collect data Gaining support: Making the case for multiple hospital constituencies • We want care of older adults to be better! (Clinical staff) • We want improved metrics on hospital acquired adverse events (quality, safety, cost) • We want an enhanced reputation in the community (PR) and internally • We want to refine our practice • A need to proactively communicate HELP outcomes to administration

3. More than scientific evidence is needed ….. • Characteristics of the innovation and social/organizational structure predicts the pace and success of adoption • The Hospital Elder Life Program has been studied as an example of human technology diffusion and uptake • Study results can guide implementation and sustainability Bradley EH, Schlesinger M, Webster TR, Baker D, Inouye SK. "Translating research into clinical practice: making change happen." Journal of the American Geriatrics Society 52:1875-1882, 2004

4. The HELP Model of Care as a Human Technology Human technologies: innovations that are • multifaceted • require coordination across disciplines • are not traceable to a specific new technology • involve substantial attitudinal shifts among staff • human resource intensive-investing in human capital Bradley EH et al. Translating research into Clinical Practice. Making change happen JAGS 52:1875–1882, 2004.

5. Types of data- Clinical Outcomes What clinical outcomes resonate with your hospital’s priorities? • Delirium • Functional decline /mobility • Fall rate • Pressure ulcer rate • Restraint usage • CA-UTI rate • Inappropriate medication usage • Length of stay • Patient satisfaction/experience

6. Principles of data collection • Resource it! Data collection, entry and analysis take time • Use existing data through partnerships ie: Informatics, Decision Support, Pharmacy, Practice Chiefs • Only collect what you will use. • Use it to refine your service- review 2-4 weeks • If resources are limited, target one indicator and track for 3-6 months. • Review the HELP manuals for more ideas

7. Types of data: Process Outcomes • numbers of patients eligible on HELP unit/ numbers of patients enrolled • Number of volunteers recruited/ turnover rate • Number of geriatric educational events offered to staff • Percentage of assigned volunteer interventions completed • ELS interventions/nursing intervention assigned/completed

8. Types of Data -System outcomes • Staffing turnover/ staffing burden • Readmission rates • Lawsuits rates/ complaints • Inappropriate medication usage • Donation rates

9. Example 1- Shadyside, Pittsburgh Financial Gains • Decreased LOS- increases patient turnover • Decreased variable costs (supplies, personnel) • Increased staff satisfaction • Prevent hospital acquired conditions -> less litigation Revenue generation/preservation • Decreased LOS allows more new admissions • Prevent HACs (x: falls), which are not reimbursed • Reduced readmissions • Increased patient satisfaction

10. Example 2 - Trillium Health Care • Clinical Indicators: ADL Score, Cognitive Score, incidence of hospital acquired delirium, pressure ulcers, falls, use of anti‐psychotic medications • Stakeholder Satisfaction: Patient experience, volunteer experience, staff satisfaction • Process Indicators: Program volumes (number of patients screened, Percentage of patients screened, number enrolled in program), number of interventions completed, intervention adherence rate • Financial Indicators: Cost savings from hospital acquired delirium, length of stay, bed days saved • Educational Indicators: %staff who reported increased clinical knowledge of acute delirium, and HELP post training. • Volunteer Development: Number of volunteers HELP trained

11. Final thoughts ………. • HELP data collection requires investment of time and resources from both clinical and administrative staff. Who will enter what and review when with who? • Data collection can be difficult. Hospital partnerships are key to ensure the resources for metrics.