Email Campaign to Encourage Patients to Complete Sign-up Process for Geisinger's Patient Portal
Conditions: Telehealth, Health Behavior
Sponsor: Geisinger Clinic

Email Campaign to Encourage Patients to Complete Sign-up Process for Geisinger's Patient 
Portal 
 
NCT04332614 
 
April 5, 2021 
 
 

Study Protocol 
 
Purpose 
The purpose of the current study was to assess what kind of messaging improves the 
effectiveness of an email campaign targeted at enrolling patients into Geisinger's online patient 
portal, called myGeisinger. Currently, Geisinger sends an email every month to patients who 
have started but not completed the enrollment process (they have had an activation code 
generated but have not yet used that code to enroll). This study A/B tested 5 email messages to 
assess if they performed better than the current standard email message. 
 
Background 
In order to enroll in the myGeisinger patient portal, an activation code has to first be generated 
for a patient, which the patient subsequently uses to enroll in the portal. This code can be 
requested by the patient or generated by healthcare staff. Any patient who has had an 
activation code generated but has not yet enrolled is assigned a myGeisinger status of 
“Pending.” Every month, patients with this status are sent an email encouraging them to enroll. 
 
Method 
 
Sample 
The study population consisted of patients aged 18-88 who had an email address on file, a 
myGeisinger status of “Pending”, and had not received emails from this monthly myGeisinger 
marketing campaign within the last 2 months. These eligibility criteria were used to match the 
ongoing marketing campaign already in place. 
Based on the above criteria, N = 14,099 patients were randomized to a study condition 
and sent an email on April 6, 2020. Electronic Health Records for these patients were 
subsequently examined ~1 week and ~1 month after the campaign. We were only able to 
match unique records for 13,523 patients after ~1 week and for 13,344 patients after ~1 
month, which served as the final sample for those respective analyses.   
 
Intervention 
Patients were randomly assigned to receive one of six emails. Each email intervention is named 
and described in the pre-registration under “Groups and Interventions.” The precise email 
content is included in the Appendix below. 
 
Outcomes 
As described in the “Outcome Measures” section of the pre-registration, three outcome 
measures were assessed: Email opened, Link clicked, and Enrollment. The main purpose of 
examining Email opened was to evaluate the impact of the subject line; therefore, all patients 
whose emails were not delivered (i.e. bounced) were excluded from this analysis. The main 
purpose of Link clicked and Enrollment was to evaluate the impact of the full email (subject line 
and body); therefore, these outcomes were only assessed among patients who opened the 
email. 
 

Data Extraction 
The goal of this study was to evaluate outcome measures both 1 week and 1 month after the 
launch of the intervention. Technical issues resulted in delays in these data extractions. In 
addition, Enrollment data resided in a different database than the remaining email engagement 
data and was therefore extracted separately and subject to different delays. Below is the exact 
timeline of data extraction: 
 
04/14/20 [08 days post-launch]: “1-week” engagement data 
04/16/20 [10 days post-launch]: “1-week” enrollment data 
05/08/20 [32 days post-launch]: “1-month” enrollment data 
05/11/20 [35 days post-launch]: “1-month” engagement data 
 
 
 
 

Statistical Analysis Plan 
 
The primary questions of interest were whether each of the high-level interventions – less-is-
better, social proof, or endowment – performed better than the standard email, which served 
as the control group. This was evaluated for all three outcome measures using a logistic 
regression (a generalized linear model with a binary distribution and log-link function) with the 
Standard Email condition dummy coded as the reference level. This dummy coding allowed for 
the comparison of each high-level intervention vs. control within a single model. 
 
A secondary question of interest was: which less-is-better condition – provider communication, 
managing appointments, or medical information access – performed best? This was evaluated 
for all three outcome measures using a logistic regression (a generalized linear model with a 
binary distribution and log-link function). Each pair-wise comparison was evaluated with the 
appropriate correction for multiple corrections using a Tukey's test. 
 
For both evaluations described above, a primary analysis was conducted for the outcome 
approximately 1 week after the intervention, and a secondary analysis was conducted 
approximately 1 month after the intervention. See the Data Extraction section of the Study 
Protocol above for the exact time frame. 
 
For all tests, odds ratios (ORs) were calculated, along with 95% confidence intervals (CIs); two-
tailed p-values < 0.05 were used to determine statistical significance.