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HELP Data for Quality Improvement

This resource explores the importance of collecting data in multiple hospital constituencies to enhance care for older adults, improve metrics on adverse events, refine practices, and communicate outcomes effectively. It highlights the principles and types of data collection, linking them to clinical outcomes, process outcomes, and system outcomes, with real-life examples showcasing financial gains and enhanced stakeholder satisfaction. Learn about the impact of data on staff turnover, patient experience, and hospital reputation.

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HELP Data for Quality Improvement

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  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.

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