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Senior Care Trending: Tech Case Study for Population HealthSession #125, February 21, 2017

J Patrick Bewley, Chief Executive Officer, Big Cloud Analytics

Moulay Elalamy, Vice President, Information Technology, Benchmark Senior Living

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Speakers

J Patrick Bewley

Chief Executive Officer

Big Cloud Analytics

Moulay Elalamy

Vice President, Information Technology Benchmark Senior Living

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Conflict of Interest

J Patrick Bewley

Moulay Elalamy

Have no real or apparent conflicts of interest to report.

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Agenda

• Introduction - Wearables for Advanced Senior Care Project

• Learning Objectives

• Implementation Findings and Keys to Pilot Success

• Early Learnings

• Application

• Findings

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Introduction

Value of IT in Senior Living

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Resident Engagement and Population Management in Senior Living

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Pilot Perspective

• Goals and Learning Objectives

• Introduction to data and correlations to data insights

• Use Cases

How can the data and insights be implemented into workflow and used by the company?

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Learning Objectives1. Assess overall wellness across the senior care continuum leveraging

digital health tools and intervention

2. Identify seniors experiencing major quality of life changes through digital health tools

3. Deliver not previously available insights and actions

4. Evaluate value propositions across the senior care continuum for providers, seniors and their families

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Implementation Findings and Keys to Success

1. Define Pilot Team Roles and Responsibilities

2. Infrastructure

3. Finding the right wearable /syncing solution for your environment

4. Support of Activities Directors

5. Resident engagement

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Early Learnings: Challenges

Wearables for Seniors present new challenges

Charging routine

Automating data

synchronization

Simplifying data

interpretation

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Early Learnings: Establishing a Baseline

What’s normal?

Wearables data for seniors looks different…

MEDICATION AND DISEASE CONDITION OVERLAYS

We segmented the population by age, gender, medications

and disease conditions to understand very discretely what a

“Normal” baseline should look like

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Early Learnings: Intervening Influences

Steps aren’t the best proxy for activity in seniors

Mobility aids effect wearables measurement

MOBILITY AIDS OVERLAID TO STEPS AND SLEEP

We segmented the population by age, gender, and mobility

aids to understand very discretely what a “Normal” baseline

should look like for each cohort

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Application: Activity Levels

Can wearable devices be used to measure participation and effectiveness of activity calendars? FINDING PATTERNS IN MILLIONS OF HEART BEATS

We enabled views of heart rate as a proxy for activity. This

was organized at the community and resident level in a similar

format as the monthly activity calendar… down to the minute

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Application: Predictions

Can we help identify potential for adverse events?

Meet Mrs. Jeanie

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Application: Predictions (cont’d)

FINDING DEVIATIONS IN RESIDENTS

BASELINE BEHAVIORS1

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Significant reduction in sleep when compared with normal

range

Significant decline in activity levels

Major spike in BCA sleep index four days before

hospitalization

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Findings: Falls

Falls

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Findings: Decease

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Findings: Blood Clot in Lungs

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Findings: Stomach Bleed

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Findings: UTI, Slurred Speech

UTI, Slurred Speech

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Care Coordination - Business Use Cases

Wearables and Engagement with Technology Improve Senior Living by:

• Improving Care – Use Predictive Analytics to provide proactive care and lower healthcare costs

• Improving Lives – Increase Resident Engagement through Health and Wellness program activities, contests, gamification and data visualization

• Family and Caregiver Advocacy – informing and engaging a senior’s community of family and caregivers improves overall health and wellness

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