Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
Phase: NA
Conditions: Influenza, Vaccination, Health Promotion, Health Behavior, Risk Reduction
Sponsor: National Bureau of Economic Research, Inc.

Full Study Protocol  
GENERAL INFORMATION 
Using AI Risk Predictions to Nudge Flu Vaccination 
Christopher Chabris, Ph.D. 
cfchabris@geisinger.edu 
June 28, 2021 
IRB#: 2021-0483 
BACKGROUND AND OBJECTIVES
On average, 8% of the US population gets sick from flu each flu season (Tokars et al. 2018). 
Since 2010, the annual disease burden of influenza has included 9-45 million illnesses, 140,000-
810,000 hospitalizations, and 12,000-61,000 deaths (CDC 2020). The CDC recommends the flu 
vaccination to everyone aged 6+ months, with rare exception; almost anyone can benefit from 
the vaccine, which can reduce illnesses, missed work, hospitalizations, and death (CDC 2019). 
Flu vaccination will be especially important for high-risk patients during the COVID-19 
pandemic so that flu cases are reduced and resources conserved. 
While most recover from influenza without treatment, the elderly, those with comorbidities, and 
other high-risk individuals can experience complications such as pneumonia, other respiratory 
illness, and death. Geisinger has partnered with Medial EarlySign (Medial; www.earlysign.com) 
to develop a machine learning (ML) algorithm to identify patients at risk for serious (moderate to 
severe) flu-associated complications on the basis of their existing electronic health record (EHR) 
data. The development of this algorithm was already approved through a separate Geisinger IRB 
application (IRB number 2020-0211). Additionally, Geisinger deployed this system in a field 
study during the 2020–21 flu season (IRB number 2020-0290) and contacted the identified 
patients with special messages (in addition to standard interventions conducted by the health 
system every flu season) to encourage vaccination. Geisinger will again deploy this system 
during the 2021-22 flu season.  
Medial's algorithm is an example of how interoperable health information exchange (HIE)—the 
ability for health information technology to share patient data—can improve the efficiency and 
effectiveness of healthcare. However, patients may not appreciate these benefits or the fact that 
healthcare has become substantially more integrated and collaborative. A systematic review of 
patient privacy concerns about HIE found that 15-74% of patients expressed privacy concerns, 
depending on the study, and concluded that patient perspectives remain poorly understood. A flu 
outreach message that explicitly references a review of patient medical records might backfire as 
patients react poorly to a sense they have lost control of their health records, even though in this 

case the use is intended to benefit their care. 
There is conflicting evidence on how people respond to advice or information that comes from 
an algorithm or machine. Dietvorst et al. (2015) documented a pattern of "algorithm aversion," in 
which people choose inferior human over superior algorithmic forecasts, especially after they 
observed the algorithm make an error. In contrast, Logg et al. (2018) described "algorithm 
appreciation," in which people followed advice more when they thought it came from algorithms 
than when they thought it came from human beings. Finally, Bigman and Gray (2019) found 
aversion to algorithms that make "moral decisions," including a (fictitious) medical decision of 
choosing whether or not to operate on a high-risk patient. In our field experiment from the 2020-
21 flu season, we found evidence of neither aversion nor appreciation: High-risk patients 
obtained vaccination at similar rates whether they were told their risk was determined by a 
computer algorithm or simply a review of their medical records (Shermohammed et al., 2021). 
At the same time, informing these patients about their risk status caused an increase and 
acceleration in flu vaccination compared with a care-as-usual control group. This suggests that 
informing patients about their risk status can be effective, but it is not evident that the increased 
vaccination rate went beyond what would have occurred with a flu vaccination message devoid 
of risk information. Therefore, the present study includes an active control message that 
encourages flu vaccination without revealing risk. The prior work varied the general description 
of how risk was identified, but it did not identify specific, personalized factors contributing to 
risk for individual patients. Providing such information increases the transparency, quantity, and 
prominence of risk factors, and it can build trust in the messages and their source. But it may also 
backfire, if patients have difficulty perceiving sensible connections between the explanations and 
the risk determination (Miller, 2019; Stubbs et al., 2007; Mercado et al., 2016). Next season's 
high-risk messages will include personalized risk factors identified as having the greatest 
predictive utility for patients’ risk scores generated by the Medial ML model. The extent to 
which such explanatory information contributes to algorithm appreciation or aversion remains an 
open question. 
Our specific aims are: 
1.
Evaluate whether providing influenza risk information to high-risk patients has a larger effect on
vaccination rates than simple reminders (with no risk information).
2.
Evaluate whether providing an explanation of the personalized factors that contributed to risk
assessment specifically boosts vaccination.
3.
Evaluate whether providing an explanation of the personalized factors that contributed to risk
assessment results in aversion (as revealed in decreased vaccination) or appreciation (increased
vaccination) of algorithm-based assessment, relative to a simple review of medical records.

PROCEDURES 
Research Design 
Patients from the high-risk sample (primary target population) will be randomly assigned to one 
of 5 study arms. Outcomes during the 2021-2022 flu season will be compared between study 
arms. 
Study Population 
There are 3 separate populations to consider in the current study: the primary target population 
that will be randomized into an experimental condition and potentially contacted, and two 
secondary populations who will not experience an intervention and will only be included for data 
analysis purposes. 
1) Primary target population
Inclusion Criteria: 
•
Aged 18 or older
•
Have been determined as high-risk through Medial’s ML algorithm
Exclusion Criteria: 
•
Has contraindications for flu vaccination
•
Note: we will respect opt-out communication preferences, but will include patients in the study if
they haven’t opted out of at least one communication modality employed
2) Secondary population A (data analysis only): Household members
Inclusion Criteria: 
•
Household members of primary target population
•
Have data in Geisinger’s electronic health records
3) Secondary population B (data analysis only): Sub-threshold risk
Inclusion Criteria: 
•
Same as primary target population, except that these patients’ algorithmic risk scores fall just
below the high-risk cutoff
Exclusion Criteria: 
•
Same as primary target population

Recruitment and Enrollment. All patients will be recruited and enrolled from Geisinger; 
MIT/NBER collaborators will not be involved in recruitment or enrollment. Patients meeting the 
primary target population criteria will be enrolled into one of the 5 experimental conditions. 
Anticipated enrollment number for this primary target population is 47,000. Only patients from 
this primary target population will be contacted. However, health record data will be accessed to 
assess secondary outcomes for household members of this target population (expected N of 
234,000) and for patients whose risk scores were calculated by Medial’s ML algorithm and 
determined to fall just below the high-risk cutoff (expected N of 47,000). This brings the total 
planned enrollment to 346,000 participants. 
Detailed Study Procedures. 
Eligible patients will be randomly assigned to one of 5 experimental conditions: 
1. No-Contact Control: In this condition, patients will receive no additional pro-
vaccination intervention beyond Geisinger's normal efforts. Note that these efforts
include a variety of Marketing and other system campaigns designed to encourage all
patients and members to get their flu shot. In addition, the Care Gaps team has an annual
campaign to encourage flu shots that targets patients who are determined by a non-ML
assessment that they are at high risk for complications and we expect there to be overlap
between this group of patients and the ML high-risk group we will be contacting.
(Because the Care Gaps team’s high-risk patients are not told that they are at high risk or
that they have been targeted, we are not concerned that this ongoing campaign will
interfere with our ability to measure the effects of high-risk communication.)
2. Reminder Control: In this condition, patients will receive messages reminding them to
get the flu shot without being advised of their risk status.
3. High Risk Only: In this condition, patients will receive messages telling them they are at
high risk for flu complications without specifying how/why Geisinger believes this to be
the case.
4.
High Risk Based on Medical Records: In this condition, patients will receive messages telling
them they have been identified to be at high risk for flu complications via analysis or review of
their medical records, and they will be given one or more contributing risk factors from their
medical record. This is an accurate statement, since it does not specify that a human conducted
this review or analysis. However, we anticipate that most readers will assume it was a human
rather than an algorithm, allowing us to compare attitudes towards human versus machine risk
determination.
5. High Risk Based on Algorithm: In this condition, patients will receive messages telling
them they have been identified to be at high risk for flu complications via analysis of
their medical records by AI/ML, and they will be given one or more contributing risk
factors from their medical record.
Risk factors includes in messages for Arms 4 and 5 may vary in their level of specificity. Some 
factors may be specific, in that they mention a particular medical function or process (e.g. 
“medical condition(s) or procedure(s) related to breathing”). Other factors may be general, 
without mentioning the medical function or process (e.g. “medical condition(s) or 
procedure(s)”). We may test whether outcomes vary as a function of factor specificity. We 

included examples of specific and general messages in this protocol, along with a dictionary of 
all factors that may be included in messages. 
In the 2020–21 study, messages were signed “Your Geisinger Health Team.” In the present 
2021–22 study, messages may be signed by the individual’s PCP of record or by a clinical 
leader. 
The copy for risk messages is nearly identical to messages sent during the 2020-2021 flu season 
(IRB number 2020-0290) with 2 main differences: 1) explanatory factors are added to messages 
for patients in Arms 4 and 5, and 2) the messages are written as if from a PCP rather than a team 
(e.g., “We are writing…” was changed to “I am writing…”). 
Because this intervention is timed to the 2021–22 flu season, the intervention period will begin 
~9/1/21. Subjects in treatment Arms #2–5 will receive the same type of communication via up to 
three modalities—printed letters to their mailing addresses, SMS to their mobile phones, and/or 
secure messages via Geisinger’s patient portal—depending on what consent and information is 
on file for each patient. Messages will be staggered by modality, starting with postal letters, then 
sending the same message via the patient portal two weeks later, and a final message via SMS 
after four weeks.  
The primary outcome will be whether the patient was vaccinated during the first six weeks after 
the intervention begins. A secondary outcome will be vaccination within three months after the 
intervention begins. Additional secondary outcomes to be measured through the end of the flu 
season (start of intervention through 3/31/22) will include: rates of flu diagnoses (both using the 
most rigorous biological tests, "high confidence flu", and using broader criteria that also include 
diagnosis codes and treatment information, "likely flu"), flu complications, and rates of other 
relevant healthcare utilization outcomes such as ER visits, hospitalizations, insurance claims (for 
an expected ~40% of participants who are members of Geisinger Health Plan) by patients, 
household members of patients, and those at sub-threshold risk. We will also measure rates of flu 
vaccination by fellow household members of targeted patients and non-targeted patients at sub-
threshold risk. Finally, we will measure rates of COVID-19 vaccination in targeted patients, 
household members, and those at sub-threshold risk.  
Data Sources 
In order to identify patients in the primary target population, Business Intelligence & Advanced 
Analytics (BIAA), working with Geisinger’s Phenomics and Clinical Data Core (PACDC), 
will provide a list of patients (including patient identifiers such as Medical Record Number) that 
were assessed by the Medial and each patient’s associated risks. 
We will also obtain contact information (address, phone number, email address) to message 
participants in the primary target population from BIAA & PACDC. 
After the intervention is complete, we will obtain experimental outcome data for all 3 population 
groups from BIAA & PACDC. This will include data on patient flu vaccination, diagnosis of flu, 
diagnosis of flu-like symptoms, presence of flu-associated complications, hospital visits, 

emergency department visits, and covid vaccination status. This data set will additionally include 
patient characteristics that will be useful as analysis covariates, such as: age, patient primary care 
provider, and flu-related behavior and outcomes during previous flu seasons. 
STUDY DATA DETAILS 
Data Management Procedures and Confidentiality. All data will be electronic. Datasets with 
full identifying information will only be stored on Geisinger-managed, password-protected 
computers of the data brokers (Gail Rosenbaum and Amir Goren) for the purpose of linking 
datasets from different sources.  
A limited data set containing dates of service and ZIP codes will be shared with any non-
Geisinger collaborators (e.g., Additional Principal Investigator Doyle and his team) under a Data 
Use Agreement (pending approval) in compliance with HIPAA’s Privacy Rule, and using data 
security protocols reviewed and approved by the Geisinger Security Office, Privacy Office, and 
Information Technology department. 
After all data have been linked in a de-identified, coded file and analyzed, the datasets with 
identifiable information and any codes needed to link the identifiable information will be deleted. 
After the de-identified data have been fully analyzed, the de-identified dataset will be shared 
along with publications from this study. The deidentified data will not be destroyed or removed 
after any prespecified period of time has elapsed. We intend to permanently and securely archive 
the deidentified dataset at a research repository such as Open Science Framework (OSF) in order 
to be consistent with the best practices for open and reproducible science, as well as our 
obligation to the public as NIA-funded researchers. 
The only study team members who will have access to fully identifiable and protected health 
information will be Gail Rosenbaum and Amir Goren. Non-Geisinger collaborators will have 
access to a limited data set containing dates of service and ZIP codes. The remaining 
investigators will have access to the de-identified, coded data during data analysis. 
All data analysis will be conducted by Gail Rosenbaum, Amir Goren and our non-Geisinger 
Collaborators. We will analyze the data using standard behavioral research analysis methods, 
including computing bivariate correlations, using generalized linear models, using non-
parametric models for non-normally distributed data, and entering variables as independent 
predictors in regression models to attempt to predict desired outcomes.   
RESEARCH ACTIVITIES AT OTHER SITES 
Geisinger is the lead research team for this multi-site, single IRB study. Massachusetts Institute 
of Technology (MIT) and National Bureau of Economic Research (NBER) will cede to 
Geisinger’s IRB. Research activities conducted at MIT and NBER will be limited to data 
analysis, using limited data and de-identified data, as described above. MIT and NBER will not 
be involved in study recruitment/enrollment or intervention administration. 
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