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

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: , 2004

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.

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

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

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

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

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

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

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.