Transcript HELP Data for Quality Improvement

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 52:1875-1882, 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
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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
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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
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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.