Care.Coach Avatars for Improvement of Outcomes in Hospitalized Elders, Including Mitigation of Falls and Delirium, Through Psychosocial Support and Protocol-Driven Interventions Based on the Hospital Elder Life Program: a Multi-Site Clinical Study
Phase: PHASE2
Conditions: Delirium, Fall Injury
Sponsor: Victor Wang

 
 
 
COVER PAGE 
Care.Coach Avatars for Improvement of Outcomes in Hospitalized Elders, Including 
Mitigation of Falls and Delirium: a Multi-Site Clinical Study 
NCT03832192 
December 28, 2018 

 
 
Protocol Name: 
A Protocol-Driven, Digital Conversational Agent at the Hospital Bedside to Support 
Nurse Teams and to Mitigate Delirium and Decrease Falls Risk 
Principal Investigator: 
Sharon Stahl Wexler, PhD,RN 
Primary Contact 
Name/Contact Info: 
Sharon Wexler, PhD, RN; swexler@pace.edu; swexler@jhmc.org 
914-246-7202 
  
ABSTRACT  
 
Delirium occurrence rates for hospitalized older adults range from 29-64%.  Although 30-50% of 
delirium is preventable, it frequently goes unrecognized until acute and potentially devastating.  The 
Hospital Elder Life Program (HELP) is a multidimensional clinical strategy that is effective in 
preventing delirium, but labor intensive.  This randomized controlled study will test an innovative 
method to implement the HELP approach:  a tablet-based digital avatar that can see, hear, and talk with 
a patient using speech-to-text technology.  The avatar platform has already been validated at Jamaica 
Hospital in a previous study in 2016: “GeriJoy: The Use of an Avatar Virtual Service Animal to 
Improve Outcomes in Hospitalized Older Adults” as well as in multiple published studies to effectively 
provide psychosocial support to older adults across varied clinical settings. This study will focus on 
discerning the relative impact of implementing HELP through this technology. Participants aged 65+ 
will be randomly assigned to receive one of the following interventions in addition to usual care 
throughout their hospitalization: 1) an avatar delivering HELP protocols and socialization; or 2) no 
intervention beyond usual care. Participants will be assessed by the investigators upon admission to the 
study and reassessed upon 24-hour discharge notification using the Short Portable Mental Status 
Questionnaire (SPMSQ), Memorial Delirium Assessment Scale, and Short CAM (Confusion 
Assessment Method).  Based on patient medical records of sedation, restraints and incidence of falls and 
delirium, pre- and post-assessment scores will be compared using paired t-tests to determine outcomes 
among the groups.  If the avatar is effective in improving outcomes by providing HELP interventions, 
the number of patients who benefit from HELP could increase greatly due to technology-enabled cost 
efficiencies, and protocol and data collection standardization across many sites. 
 
 
 
 
 
 
 
 
 

 
Specific aims/hypotheses 
 
For hospitalized older adults, will the use of an avatar delivering HELP protocols… 
1. decrease delirium incidence and severity, 
2. decrease falls and falls with injury, 
..more than usual care? 
 
Background and significance 
 
Delirium strikes more than 5 people every minute, totaling over 2.6 million people annually in the U.S. (U. S. 
Department of Health & Human Services, 2011).  It is under-recognized, leading to inappropriate use of 
sedation, sitters, and even restraints in the hospital setting (Adams & Kaplow, 2013; Harding, 2010).  Delirium 
contributes to falls, cognitive decline, disability, morbidity, and mortality, increasing health care costs over 
$164 billion a year (Inouye, Westendorp, & Saczynski, 2014; Labella, Merel & Phelan, 2011; Leslie & Inouye, 
2011).  Delirium can also initiate a “cascade of events” leading to an overall decline in function and 
independence (Fong, Tulebaev, & Inouye, 2009).  These risks are greatest among older patients with limited 
English comprehension, low health literacy, and socioeconomic disadvantage (Tow, et al, 2016). Acute 
delirium may become chronic and have serious permanent sequelae (Fong, Tulebaev, & Inouye, 2009). 
 
HELP is based on a geriatric assessment and integrates trained volunteers with hospital nursing staff to deliver 
protocols tailored to patients’ individual needs.  The protocols target delirium risk factors including orientation, 
activities, mobilization, vision, hearing, hydration, nutrition, and natural sleep. Use of the tailored protocols is 
tracked daily for quality assurance.  HELP is used in more than 200 hospitals worldwide and prevents delirium, 
cognitive and functional decline, falls, sitter use, institutionalization, and reduces length of hospital stay 
(Inouye, Bogardus, Baker, et al., 2000; Rubin, Williams, Lescisin, et al, 2006; Rubin, Neal, Fenlon, et al., 
2011).  Although HELP is cost effective, it requires extensive training and monitoring of a large team of 
volunteers, which can deter adoption, especially in safety net facilities that serve patients at highest risk (Rubin, 
Neal, Fenlon, 2011; Caplan & Harper, 2007).    
This study will measure the effectiveness of implementing a subset of the HELP protocols using a novel type of 
relational agent, which is a technological entity capable of building social relationships with patients (Wang & 
Osborne, 2017). The relational agent platform, called “care.coach,” is provided by a California-based company 
founded by researchers from the Massachusetts Institute of Technology, and has been shown in multiple 
published studies to effectively provide psychosocial support through conversation and socialization with older 
adults in varied clinical settings (Machesney, Wexler, Chen & Coppola, 2014; Demiris, Thompson, Lazar & 
Lin, 2016; Chi, Demiris, Thompson, Lazar & Lin, 2016; Wexler, Drury& Pollak, 2016; Wexler, Drury & 
Pollak, 2017a; Wexler, Drury & Pollak, 2017b; Wang, Luxenberg & Morey, 2016; Chi, Sparks, Lin, Lazar & 
Thompson, in press). Uniquely, the relational agent is embodied as an animated animal displayed on a tablet 
device. This “avatar” represents a real-time fusion of human and software intelligence, powered by a live, 24x7 
staff of health advocates who see, hear, and speak with each patient through the avatar, and who are guided by 
software algorithms to implement clinical protocols. Because the team is distributed across the world, they can 
cost-effectively provide unlimited compassionate psychosocial support, conversation in English and Spanish, 
and supervision: typically, when used for inpatient care, 1-6 hours/patient of direct attention, depending on 
patient need/risk, across 60-80 “check-ins” per 24 hours. 
A study was conducted at JHMC in 2016 which enrolled 95 patients, of which half had a care.coach avatar.  
Patients were enrolled from 3 inpatient medical-surgical units.  The avatar was used as a social conversational 
agent.  The study found that patients who had the avatar experienced less delirium, less loneliness and 
experienced fewer falls (Wexler, Drury, Pollak, Scher, Iovino, Narducci, & Wang, in press).  
Unlike common digital assistants such as Apple’s Siri that are limited by simplistic artificial intelligence and 
poor understanding of the speech of frail older adults, the care.coach avatar connects one human being to 

 
another through an engaging animated dog or cat that responds to touch and demonstrates emotions appropriate 
to the conversation or other interaction state, e.g. through facial expressions, bodily reaction to touch, heart 
symbols, tears, sleeping, snoring, etc. Conversations and observations about the patient’s state are automatically 
recorded and referenced in future interactions, providing both continuity in the growing relationship and quality 
control in the delivery of clinical protocols.  In addition to conversation, the avatar can also display photos, play 
music, and read or pray with the patient.  The staff undergo extensive non-medical training and for this study 
will also complete the HELP online training for volunteers.  
The software system guiding the staff can be programmed to deliver complex sets of protocols, and has 
previously been used to implement coaching programs for managing multiple chronic conditions at home 
(Wang, Luxenberg & Morey, 2016), as well as in our prior exploratory work for preventing falls (e.g. through 
frequent toileting checks) and delirium (e.g. through simple re-orientation tasks) in the hospital.  For this study, 
the system will be programmed to deliver a robust suite of adapted HELP volunteer protocols including re-
orientation, vision/hearing aid checks, hydration and meal consumption encouragement, cognition and attention 
assessment for early delirium detection, and so on, including an automatic guidance and triage protocol for 
when to phone the nurses station (e.g. if a bed-bound patient is getting out of bed or tugging on IV lines). 
If the avatar is effective in improving outcomes by providing HELP interventions, the number of patients who 
benefit from HELP could increase greatly due to 1) the low cost and ease of deployment of the avatar platform, 
not only enabling hospitals that have yet to secure adequate budget and staff for a typical HELP program to 
begin seeing HELP benefits/outcomes data by deploying this technology-enabled variant, but also enabling 
existing HELP sites to easily expand coverage to nights and weekends, and to additional hospital units and 
patients, and 2) the ability of the avatar to standardize best practices and automate data collection and reporting 
(e.g. all survey instruments can be administered by the avatar itself), enabling large scale, highly consistent data 
aggregation and outcomes reporting across many such HELP sites.)  
Study design:  This randomized controlled trial will compare outcomes on delirium, falls, sitter utilization 
(1:1’s) and restraints between the two study groups: HELP-protocolized avatars, and usual care. Each day of 
the study, enrollment of subjects will proceed with randomization of each subject between groups (the 
randomization primarily effects delirium data, which is assessed by subject) until there are no more available 
intervention devices. However, falls and other unit-level grouping are non-randomized (see below). 
 
Sample Size: A total of 3,650  patients (avatar and usual care) are expected to be enrolled at JHMC over the 
course of two years, based on an estimated average length of stay of 4 days. 
 
Study Units: Patients will be recruited from the following units at JHMC: 3S, 3N, 4S, IMCU, 5N, 6S, and 
Hospice. Because the hospital only collects/reports unit-by-unit quarterly falls data, we will take the following 
measures to ensure that falls data collected for study purposes are a valid indicator of possible intervention 
effect: 
• There are expected to be protocol and technical adaptations made during the first quarter of the study, 
and the study will not begin enrolling subjects exactly at the start of a calendar quarter. Therefore, the 
first calendar quarter of falls data is not intended to provide useful data regarding unit-level falls 
outcomes, and enrollment of patients may be performed on select units by convenience. 
• Starting on the first day of the first full calendar quarter, a unit-equitable enrollment protocol will be 
implemented in which one or more of the study units at any time may be formally designated for active 
subject enrollment, and such designated study units will have their enrollment numbers monitored and 
rounding adjusted as needed to ensure that, on average, each designated unit receives a similar number 
of intervention patients at any time. Which units are designated or not designated for enrollment may be 
changed at the start of each calendar quarter to compare the effect of the intervention and to control for 
annual changes in fall rates. Research assistants will not enroll any patients on units not designated for 
enrollment, but will continue to round currently enrolled patients until they are dis-enrolled. 
 

 
Sample inclusion and exclusion criteria: All patients over age 65 on the pilot units who can provide informed 
consent or who have a proxy who can provide informed consent will be assessed by the investigators. Those 
who consent will be randomly assigned to one of the two treatment conditions unless they meet any of the 
exclusion criteria: a.) aggression or combativeness with intent to harm self or others, b.) alcohol or drug 
withdrawal, c.) severe hearing impairment and simultaneous severe vision impairment, despite assistive 
devices, d.) cannot verbalize fluently in either Spanish or English, e). on 1:1;if a study subject is put on 1:1 
during the duration of the study, he/she will be unenrolled from the study, f) expected to be discharged within 
the next 24 hours.  
 
Instruments 
Pre- and post-intervention: 
1. Short Portable Mental Status Questionnaire  
2. Short CAM (Confusion Assessment Method) – also conducted during each daily round 
3. Memorial Delirium Assessment Scale – if CAM screens positive 
 
Statistical analyses: Demographic variables including age, gender, education, race/ethnicity, and living 
arrangements will be collected to determine equivalency of study groups.  Pre-and post-assessment scores will 
be compared using paired t-tests to compare means at baseline and at hospital discharge within each group, and 
between groups.  All data will be analyzed using the Statistical Package for Social Sciences software (SPSS, 
Inc. Chicago, IL).  Statistical significance will be defined as p ≤ 0.05.  
 
 

 
Additional Information 
Photos of Avatar  
 
Figure D.8.1: A hospitalized older adult in South Carolina enjoys the company of an avatar as it plays music 
and shows a photo. A “fisheye lens” mounted to the top of the avatar device enables wide-angle visual 
monitoring, to enable appropriate reaction by the avatar to not only the patient’s facial and body language 
cues, but also any dangerous unassisted transfers, tugging on IV lines, etc. To help with re-orientation, the 
window behind the avatar reflects the actual weather report, and the lighting of the avatar’s environment 
reflects day or night status. A video showing the patient talking to the avatar and singing along is available at 
https://www.youtube.com/watch?v=WfWjazOmKUk 
 
Figure D.8.2: The avatar is available in dog or cat form, depending on patient preference. Patients name their 
avatar as further personalization. On the left, the dog avatar is showing a photo from a web-based Family 
Portal, which helps with engagement and reminiscence. 
 
 

 
Avatar Staff Training  
The health advocates who staff the avatar system must each complete the following screening mechanisms and 
training certifications (or their equivalent as deemed by care.coach, as specific training courses and providers 
become outdated/available from time-to-time), through several online learning platforms, over the course of 
approximately 65 hours: 
1. care.coach psychometric, technical, skills-based & face-to-face interview assessment 
2. InfoCubic international background check 
3. Registered Nurses' Association of Ontario delirium, dementia and depression course 
4. National Institutes of Health privacy & ethics course for human subjects research 
5. Partners Healthcare basic motivational interviewing class 
6. care.coach technology, patient support, information security, and job shadowing training 
The qualification process is very stringent, and the proportion of health advocate job applicants who 
successfully complete all the required certifications and are hired is only about 1%. Health advocates also 
receive ongoing training and professional development after hiring, including programs such as the 
Alzheimer’s Association dementia care course (essentiALZ®). 
For the proposed project, each health advocate will also complete the HELP volunteer training, and a one-hour 
module using resources from the American Delirium Society  and American Nurses Association . 
 
Clarification Regarding Data and Safety Monitoring Plan (DSMP) 
In reference to the DSMP, under the physical risk mitigation section, the spirit and effectiveness of the 
described mitigation techniques will be preserved, but as of December 28, 2018, we are aware of the following 
minor technical and practical issues that affect the details of implementing the described mitigation techniques: 
• The voice recording that plays automatically when a device is unplugged is standardized to say that the 
device is an important healthcare tool that needs to be kept plugged in to power, but the comment about 
“the device must be sanitized before placing it back into its designated storage cabinet” may instead be 
implemented with the following variations: 
o The reminder to sanitize the device may be performed by the avatar itself as part of the dis-
enrollment and/or enrollment protocol, combined with a visual reminder on a label on each 
device. 
o For many technical and practical reasons, the avatar is now designed to stay online, plugged in 
to power even while on standby between patients. So instead of going back to a storage cabinet 
it will go to a charging area in Dr. Wexler’s office, and a visual reminder about this will be 
placed on a label on each device. 
• Due to potential technical limitations of the EHR/Epic integration the practical nature of the varied data 
in each patient record, we may not be able to rely on specific Epic data for each patient to indicate 
suitability for self-ambulation protocols, and we may never be able to push/write to the patient record. 
Therefore, the spirit of the risk mitigation techniques may be alternatively implemented as follows: 
o Instead of relying on Epic data, we may rely on nursing judgement when the avatar is verbally 
told to place the patient on a self-ambulation encouragement protocol. 
o Instead of confirming the assignment of a self-ambulation protocol to a patient through an Epic 
integration, we may phone call the nursing station to confirm. 
 
 

 
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