Beyond the Pilot: New Technologies that Work

BEYOND THE PILOT: NEW TECHNOLOGIES THAT WORK

Moderator: Laurie M. Orlov, Principal Analyst and Founder, Aging in Place Technology Watch Case Studies By: Â

Schon Alkire, Innovative Solutions Developer, LifeWell Senior Living

Timothy Reilly, Vice President of Associate Experience, Benchmark

LifeWell’s Health Care Monitoring Rollout Case Study Presenter: Schon Alkire

“Houston, we have a problem!”

 How do we help seniors to live longer, stronger, and more full and meaningful lives?  How do we measure improvement and/or decline?  The meeting…

The Pilot:    

One Community 20 Residents/35 Staff Members 6-7 months Success!

Implementation:    

Rollout at 5 New Communities (86 Suites Each) 64 Assisted Living 22 Connections (Memory Care) 25-65 Employees (Management and Staff, Depending on Census)

“So, what is CarePredict?”

•Tempo (Wearable Worn on Dominant the Hand) •Emergency Alert System •Two-Way Audio Communication •Uses AI (Artificial Intelligence), Machine Learning, and RTLS (Real Time Location System) to Create Predictive Analytics • Sleep • Steps/Activity Levels • Eating Habits • Bathroom Visits • Fall Risk • Personal Interactions (with Residents and Staff) • Time Being Cared For •Geofencing and Wander Alerts •RFID (Radio Frequency Identification) •Fall Detection

Results So Far (A Two-Year Study):    

27% Reduction in Response Times 37% Lower Average Fall Rate 21% Lower Hospitalization Rate On Average, Residents Stay is 85 Days Longer in Communities with CarePredict  21% Higher Occupancy Rate

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The Realities of Implementation Some    Some 

Don’ts Don’t expect communities to implement on their own.

Don’t set it and forget it and assume things will run smoothly.

Don’t rollout at multiple communities at the same time (if you can help it).

Do’s Make sure the proper infrastructure is in place for the technology you are implementing.  If large install is necessary, plan this out, so as to have the least impact on staff and residents.  Educate and train everyone involved!

Be a Product Developer  Observe  Talk to and Read People (Residents and Staff)  Solve Problems  Provide Feedback  Innovate!

Next Steps:  Socialization  Engagement  Mad Science Dream (CareMerge/CarePredict Integration)

Benchmark’s AI Solution for Hiring Case Study Presenter: Timothy Reilly

The Challenges of Front-Line Staffing  Hiring  Retention/competition in/out of industry  Turnover  Cost of turnover eroding the bottom line  Turnover effecting quality & customer satisfaction

argentum.org/webinars

At Benchmark, turnover challenges our ability to “transform lives through human connection”

 Our turnover rate = Better than industry average which is not good!  Previous strategies to reduce turnover:  Applicant Tracking System (ATS) implementation  Redesigning our on-boarding program/process  Providing additional training  Restructuring regional teams  Additional leadership training  Surveys, surveys, and more surveys (year one 5+ surveys)  All had positive impact, but we wanted more….

Arena Software Arena predicts RETENTION using massive amounts of applicant and third-party data which becomes customized by community

Arena in Benchmark’s Hiring Process Candidate completes the Benchmark application online

Candidate completes the Arena questionnai re

Arena generates a Retention Prediction

Use prediction to identify candidates to interview

Interview and make hiring decision

The Questionnaire Personal preference questions Timed sections including reading and math Behind the scenes BEHAVIOR PATTERNS are assessed

Have you ever had any job working in a healthcare environment?

Have you ever run a newspaper route?

Results 15 months into our implementation:

Over 1500 associates have completed the questionnaire prior to hire. We monitor their retention relative to their Arena “Recommended” vs “Not Recommended” prediction.  Early terminations (60 to 90 days) are often the most painful. Here’s how our hires perform relative to their Arena predictions in their first 90 days: Recommended by Arena – First 90 day turnover: 20.6% Not Recommended by Arena – First 90 day turnover: 32.6% That’s 12 Percentage points & ~37% lower turnover for Recommended hires Nursing & Resident Care are two of our most important roles. Here’s how hires into those roles perform relative to their Arena predictions in their first 60 days of hire: Recommended by Arena – First 60 Day turnover: 14.5% Not Recommended by Arena – First 60 Day turnover: 26.2%

Successes 15 months into our implementation:  Process has forced adoption of the Applicant Tracking System (ATS)  Seeing better-quality candidates at the interview stage  Hiring Managers reporting time savings in their recruitment screening process  Hiring Managers reporting higher satisfaction with the Arena Recommend candidates

Keys to Success  Leadership buy-in & ongoing support  Strong IT support for system integrations and ongoing data feeds  Establish user champion group to drive adoption  Frequent meetings and check-ins with vendor and key stakeholder groups

Supplemental Outcomes to be Watched  Resident Satisfaction Net Promoter Score (NPS)  Associate Engagement (eNPS and surveys)  Improved Safety Protocols  Improved Time/Attendance/Absenteeism  Increased Sales  Increased Career Pathing/Promotions  Additional Quality Metrics TBD