Abstract
Objectives:
To identify factors associated with enrollment, retention, and patient engagement in the antenatal remote blood pressure (BP) monitoring program, Connected MOM, and to evaluate program implementation by race and residence.
Study Design:
Retrospective cohort.
Main Outcome Measures:
Implementation outcomes (adoption, reach, implementation, sustainability, acceptability) and rates of patient enrollment, retention and engagement.
Results:
34,387 Connected MOM-eligible pregnancy episodes (29,897 unique patients) were identified between November 1, 2016 and October 1, 2023. Of those, 8,471 pregnancies (24.6%) had no offer made by the provider; 1,371 pregnancies (4.0%) declined participation; 7,509 pregnancies (21.8%) had an order placed but did not sign the consent form; 6,471 pregnancies (18.8%) consented and onboarded, but did not submit a remote BP reading required to become enrolled; and 10,565 pregnancies (30.7%) enrolled. Of those who were offered participation, approximately 40% of eligible patients enrolled in the program; however, White (44.3%) and urban patients (40.0%) were more likely to enroll than Black (35.3%) and rural patients (24.5%) (p<.0001). Program attrition was very low (1.3%) and patients averaged at least one BP reading per week about 50% of the weeks enrolled. Lower patient engagement was associated with Black race (p<.0001), rural residence (p=0.030), and having a public insurance payer (p<.0001). Highly engaged patients were more likely to have highly engaged providers (RR=1.06, 95% CI, 1.02–1.11).
Conclusion:
Identifying and addressing barriers at each step along the enrollment pathway is required for equitable implementation and improvement in maternal outcomes. Provider engagement in the program should be encouraged to improve patient engagement once enrolled.
Keywords: blood pressure, digital health, remote monitoring, health equity, obstetrics
INTRODUCTION
Hypertensive disorders of pregnancy (HDP) remain a leading cause of maternal morbidity and mortality, with a 15.9% incidence in the United States in 2019.1 Louisiana is a state fraught with poor health outcomes, high economic hardship, and low high school graduation rates, with the greatest disparities due to race and rurality.2 Black patients, who comprise roughly 1/3 of the state’s population 3, are more likely to experience preeclampsia,4 eclampsia,5 and severe maternal morbidity4–8 compared to their White counterparts.9–14 Rural women experience higher rates of new-onset HDP compared to urban women,15 as well as higher maternal mortality.12,16 Black women living in economically and racially segregated areas are more likely to develop HDP.17
Remote blood pressure monitoring (RBPM) has been proposed as a method to improve equity in maternal outcomes through prompt diagnosis and treatment of hypertension during pregnancy18,19 with continued monitoring in the postpartum period.20–22 RBPM is recommended by national and international obstetric societies for women with chronic or gestational hypertension.23,24 Previous studies indicate that RBPM is a feasible, valid, acceptable, safe, and cost-effective way to diagnose hypertension and monitor blood pressure (BP) during pregnancy to reduce prenatal hospital admissions, induction of labor, and antenatal visits.25–28
Ochsner Health developed Maternity Online Monitoring (Connected MOM), a free of charge, clinical RBPM program that interfaces directly with the patient’s electronic health record (EHR) and is available to expectant patients across Louisiana. Previous research found the program is beneficial for BP monitoring for all patients and improves, but does not eliminate, racial disparities in BP evaluation.29 This study aims to identify factors associated with enrollment, retention, and patient engagement, and to evaluate program implementation outcomes in the context of racial and geographic disparities.
MATERIALS AND METHODS
This retrospective observational cohort study included all pregnancies in patients who received prenatal care at an Ochsner facility and were eligible for program participation between November 1, 2016 and October 1, 2023. Ochsner Health is the largest non-profit, academic healthcare system operating in Louisiana with centers across the Gulf South with eight OB locations enrolling patients in Connected MOM. The study was deemed exempt by the Ochsner Institutional Review Board citing educational setting and secondary research on data.
Program eligibility criteria included: prenatal care with an Ochsner-employed obstetric provider whose clinic offered Connected MOM, at least 18 years of age, active pregnancy episode in the EHR, weight less than 400 pounds (BP cuff limit), and possession of a Bluetooth-enabled personal smartphone. Patients enrolled in the program receive a no-cost, Bluetooth-enabled BP measurement device. Prior to late 2019, patients with government-funded insurance were not eligible for participation due to concern for federal regulatory compliance. Additionally, prior to May 2022, patients with a BP>140/90 at the initial visit were ineligible due to their high-risk status; thereafter, they were eligible to enroll.
The enrollment process usually begins with a face-to-face conversation following a best practice alert (BPA) that appears for the obstetric provider at clinic visits for eligible patients between 10–20 weeks’ gestation, although Connected MOM can be offered outside the BPA at an enrolling providers’ discretion (Figure 1). Patients who consent to participation install two smartphone applications, iHealth MyVitals and MyChart, and set up their Connected MOM kit, which includes a Bluetooth-enabled BP cuff (iHealth Ease BP Cuff), and technical support is available by telephone. Patients are considered enrolled after the first remote BP submission. Participants are expected to obtain at least one BP reading per week and text reminders are sent if no data is received in the last 8 days. The data from the remote BP unit transmits directly to the EHR and provides real-time feedback to the patient. The patient’s obstetric care team monitors the data, and when alerted to high BP readings (≥140/90) uses the data to drive predictive and personalized interventions. The program continues until the 4–6-week postpartum visit.
Figure 1.
Connected MOM Enrollment Process
All data, including Connected MOM program utilization data, BP readings, maternal demographics, comorbidities, and outcomes, were obtained from the EHR. Race and ethnicity were self-reported. Home residences were classified according to Rural-Urban Commuting Area (RUCA) codes using maternal address zip codes to differentiate urban from rural.30 Connected MOM-eligible pregnancies were grouped into mutually exclusive categories: 1) eligible but no offer made by the provider, 2) eligible but patient declined participation, 3) program order was placed, but patient did not consent, 4) patient consented and onboarded but did not submit a remote BP required to enroll, or 5) enrolled in Connected MOM.
Adoption, reach, implementation, sustainability, and acceptability were assessed as implementation outcomes (Table 1). 31,32 Provider engagement assessed the Connected MOM offer rate and was defined as the number of offers to eligible pregnancies attributed to the provider divided by the number of eligible pregnancies attributed to the provider, dichotomized at the median offer rate. Providers with fewer than 50 eligible pregnancies during the study period were not included in this calculation. Patient retention was defined as participation in the program until at least delivery. Patient engagement was defined as the percentage of Connected MOM-eligible weeks (enrolled weeks until delivery) in which a remote BP measurement was submitted and was dichotomized at the median. Program acceptability included three five-point Likert scale questions on satisfaction, ease of use, and recommendation to others, self-administered through an online platform following the pregnancy.
Table 1.
Implementation Outcomes, Level of analysis, and Application.
| Outcome | Level of Analysis | Description |
|---|---|---|
| Adoption28 | Organization | • Number of providers who make ≥1 Connected MOM offer during pregnancy episode, divided by the total number of Connected MOM-eligible providers • Number of eligible providers enrolling ≥1 pregnancy in Connected MOM, divided by the total number of eligible providers who offered the program to ≥1 pregnancy |
| Reach28 | Patient | • Rates during the enrollment process (Figure 3) • Characteristics of those who enroll vs those who do not enroll (Table 2) |
| Implementation28 | Patient | • Gestational age at BPA, program initiation/enrollment • Engagement: % of eligible weeks with a BP measurement (Table 3) • Retention (Table 3) |
| Provider | ▪ Provider engagement defined as the number of offers to eligible pregnancies attributed to the provider divided by the number of eligible pregnancies attributed to the provider ▪ % of providers with high engagement, defined as > 59% of their eligible patients offered Connected MOM |
|
| Sustainability29 | Patients | • Number of Connected MOM enrolled pregnancies in a given year / Number of eligible pregnancies in a given year by geographic residence and race (Table 4) |
| Provider | • Number of Connected MOM eligible providers / Number of OB providers at a Connected MOM OB location in a given year (Table 4) | |
| Organization | • Number of OB primary locations with Connected MOM enrolling providers seeing OB patients in a given year / Number of OB primary locations seeing OB patients in a given year (Table 4) | |
| Acceptability29 | Patient | • Participant satisfaction, ease of use, and recommendation to others were asked following the conclusion of program via online survey (Fig. 3a-c) |
Comparisons were performed using the chi-square, Fisher’s exact, and ANOVA tests with control for multiple comparisons as appropriate. Risk ratios were calculated to compare the proportion of Black, White, urban, and rural patients who enrolled in the program to those who did not. Race and rural-urban status were examined independently; where there were interactions between these factors, they are mentioned in the text. Among the urban patients, 35.1% were Black, 58.0% White, 6.9% other race; among the rural patients, 18.6% were Black, 76.6% were White, 4.8% other race. A sensitivity analysis of patients eligible from May 1, 2022-October 1, 2023 was completed to account for changes in program eligibility to include patients on public insurance and high-risk patients (baseline BP >140/90). A 5% level of significance was used (SAS version 9.4).
RESULTS
Adoption
There were 177 Connected MOM-eligible providers during the study period; 87.0% of the providers who received a BPA offered the program to at least one eligible patient, up to 714 patients (median: 44; IQR: 3–229). Of those who did make an offer through the BPA process or outside it (n=175), 33 providers (18.9%) did not onboard anyone and 37 (21.1%) did not enroll any patients in Connected MOM. Providers onboarded a median of 36 pregnancies (IQR: 2–162) and enrolled 19 pregnancies into the program (IQR: 1–89).
Reach
After inclusion and exclusion criteria were applied, 34,387 Connected MOM-eligible pregnancy episodes (29,897 unique patients) were identified. Of those, 8,471 pregnancies (24.6%) had no offer made by the provider; 1,371 pregnancies (4.0%) declined participation; 7,509 pregnancies (21.8%) had an order placed but the patient did not sign the consent form; 6,471 pregnancies (18.8%) consented and onboarded, but did not submit a remote BP reading required for enrollment; and 10,565 pregnancies (30.7%) enrolled in Connected MOM (Table 2).
Table 2.
Patient characteristics of pregnancies eligible for Connected MOM (N=34,387).
| OFFER | ONBOARD | ENROLL | p | |||
|---|---|---|---|---|---|---|
| Characteristics | Pregnancies where No Offer was Made | Pregnancies Eligible, but Refused Order | Pregnancies with Order Placed, but did not Consent | Pregnancies with Order Placed, Consented, but did not Enroll | Pregnancies participating in Connected MOM | |
| 8471(24.63%) | 1371(3.99%) | 7509(21.84%) | 6471(18.82%) | 10565(30.72%) | ||
| Demographics | ||||||
| Age (years), median(q1,q3) | 29(25,33) | 31(27,34) | 29(24,33) | 29(25,33) | 31(27,34) | <.0001 |
| Race | ||||||
| Black | 2983(36.0) | 385(28.7) | 2691(36.7) | 2764(43.3) | 2890(27.5) | <.0001 |
| White | 4697(56.7) | 820(61.1) | 4059(55.3) | 3305(51.8) | 6960(66.3) | |
| Other | 600(7.3) | 137(10.2) | 593(8.1) | 316(5.0) | 650(6.2) | |
| Ethnicity | ||||||
| Hispanic | 891(10.8) | 154(11.5) | 801(10.9) | 334(5.2) | 552(5.3) | <.0001 |
| Non-Hispanic | 7387(89.2) | 1187(88.5) | 6540(89.1) | 6036(94.8) | 9933(94.7) | |
| Mother’s Language | ||||||
| English | 7870(92.9) | 1234(90.1) | 6922(92.2) | 6377(98.6) | 10489(99.3) | <.0001 |
| Spanish | 528(6.2) | 106(7.7) | 424(5.7) | 54(0.8) | 35(0.33) | |
| Other | 73(0.9) | 31(2.3) | 163(2.2) | 40(0.6) | 39(0.4) | |
| Social History | ||||||
| Marital Status | ||||||
| Never married | 3651(44.3) | 400(29.8) | 3651(44.3) | 3017(47.3) | 2875(27.4) | <.0001 |
| Divorced/separated/widowed | 179(2.2) | 29(2.2) | 112(1.5) | 123(1.9) | 162(1.5) | |
| Married/significant other | 4413(53.5) | 914(68.1) | 3697(50.6) | 3234(50.7) | 7451(71.0) | |
| Smoking Status | ||||||
| Current smoker | 719(10.4) | 91(7.4) | 605(9.6) | 530(9.6) | 662(7.3) | <.0001 |
| Non-smoker | 6170(89.6) | 1135(92.6) | 5695(90.4) | 4991(90.4) | 8473(92.8) | |
| Health Insurance | ||||||
| Commercial | 4951(60.4) | 1009(76.0) | 4167(57.5) | 3779(59.6) | 7957(76.4) | <.0001 |
| Public | 2904(35.4) | 277(20.9) | 2813(38.8) | 2358(37.2) | 2133(20.5) | |
| Other | 339(4.2) | 42(3.2) | 272(3.8) | 200(3.2) | 323(3.1) | |
| Geographic Residence | ||||||
| Rural | 459(5.6) | 16(1.2) | 318(4.3) | 185(2.9) | 190(1.8) | <.0001 |
| Urban | 7792(94.4) | 1333(98.8) | 7002(95.7) | 6167(97.1) | 10249(98.2) | |
| Participated in another digital medicine program | 30(0.4) | 8(0.6) | 13(0.2) | 19(0.3) | 75(0.7) | <.0001 |
Other race includes American Indian or Alaska Native, Asian, Multi-race, or Other not specified.
Other mother’s language includes Arabic, Vietnamese, or Other not specified.
Public health insurance includes Medicaid and Medicare coverage; Other health insurance includes Worker’s Compensation, Self-Pay, or Other not specified.
Urban residence included one of the following RUCA codes: 1, 1.1, 2, 2.1, 3, 4.1, 5.1, 7.1, 8.1, 10.1. The remainder of large rural, small rural, and isolated codes were considered rural residences.
Other digital medicine (DM) programs currently offered at Ochsner Health include DM Hypertension, DM Type 2 Diabetes, DM Hyperlipidemia, DM weight management.
Of those pregnancies offered participation by their provider, the enrollment rate was 39.3%. White patients were significantly more likely to enroll than Black patients (44.3% vs 35.3%, p<0.0001). Pregnant patients residing in urban areas were also more likely to enroll if offered than their rural counterparts (40.0% vs 24.5%, p<0.0001). Racial and place-based differences persist along the enrollment pathway (Figure 2). There was a small but significant (4.3%, p<0.0001) difference in the provider rate of offer by race with White patients more likely to be offered to participate than Black patients, which persisted into onboarding (4.6%, p<0.0001). The greatest difference by race was seen between the onboarding and enrollment phase where 16% fewer eligible Black patients enrolled than White (p<0.0001). Rural patients were 12–15% less likely than urban patients to be offered the program, accept the offer, and enroll (p<0.0001). Black patients in rural areas were not less likely to be offered the program (55.4%), but were the least likely to onboard (41.6%), and enroll (41.3%) compared to White patients in rural areas (53.7%, 71.1%, 51.7%, respectively), although the differences were non-significant (p>0.05).
Figure 2.
Connected MOM loss to enrollment during the enrollment process (offer, onboarding, and enrollment) by race and residence from November 2016-October 2023 (N=34,387 pregnancies).
Note: Offer rate denominator is all eligible pregnancies where a BPA fired by 20 GA weeks. Onboarding rate denominator is all pregnancies with an offer to participate made. Enrollment rate denominator is all pregnancies with an onboarding start. All differences were significant at p<.0001. Black patients in rural areas were not less likely to be offered the program (55.4%), but were the least likely to onboard (41.6%), and enroll (41.3%) compared to White patients in rural areas (53.7%, 71.1%, 51.7%, respectively), although the differences were non-significant (p>0.05).
Implementation
Over half (56.5%) of all BPAs did not result in an offer to participate in Connected MOM. During the study period, 52,832 BPAs fired at eligible patient visits with the majority targeting physicians (73.2%). Nearly 40% of the BPAs (n=21,101) ended in an order for Connected MOM while the remainder refused participation. The average gestational age at the time of the order triggered by the BPA was 14.0 weeks (IQR: 12.0–19.0), while the average gestational age of enrollment was 16 weeks (IQR: 14.0–19.0). Patients were enrolled for an average of 23 weeks (IQR: 19.0–25.0) and remotely submitted at least one BP measurement 9 (IQR: 1.0–19.0) of those weeks. Provider engagement averaged 59.3% (IQR: 27.2%–83.4%).
Program retention was high (98.7%) and did not vary by race, insurance provider, geographic residence, pregnancy comorbidity, or provider engagement (p>0.05, Table 3). However, program engagement differed by many factors (Table 3). Black patients, those on public insurance, and rural patients were significantly less engaged in the program compared to White, non-governmentally insured, or urban counterparts (weeks with a remote measurement submitted: 60.0% vs 43.8%, p<.0001; 67.4% vs 43.1% and 54.5%, p<.0001; 56.4% vs 48.4%, p=0.030, respectively). There was no race by residence interaction (p=0.74). Provider engagement was associated with patient engagement, where patients with highly engaged providers were more likely to be highly engaged in the program themselves (RR=1.06, 95% CI, 1.02–1.11), a result that held for Black patients (RR=1.17, 95% CI, 1.06–1.30) and urban patients (RR=1.05, 95% CI, 1.01–1.10), specifically.
Table 3.
Connected MOM Retention and Engagement
| Dropped Out (N=119) |
Did Not Drop Out (N=9,156) |
p | High Engagement* (N=5,428) |
Low Engagement* (N=5,108) |
p | |
|---|---|---|---|---|---|---|
| Median(q1,q3) | Median(q1,q3) | Median(q1,q3) | Median(q1,q3) | |||
| GA at enrollment (weeks) | 15(13,18) | 16(14,20) | <.001 | 16(14,19) | 16(14,20) | 0.693 |
| Length of gestation (weeks) | 39(38,40) | 39(38,40) | 0.232 | 39(38,40) | 39(38,40) | <.0001 |
| Gravidity | 2(1,3) | 2(1,3) | 0.455 | 2(1,3) | 2(1,3) | <.0001 |
| Parity | 1(1,2) | 1(1,2) | 0.309 | 1(1,2) | 1(1,2) | <.0001 |
| Maternal age (years) | 31(27,34) | 31(27,34) | 0.868 | 31(28,35) | 30(26,34) | <.0001 |
| Weeks with a Connected MOM reading | 8(4,13) | 14(7,21) | <.0001 | 18(14,22) | 1(0,5) | <.0001 |
| Patient Engagement (% of weeks)** | 23.1(0,50.0) | 61.5 (18.5,92.9) | <.0001 | n/a | n/a | - |
| # (%) | # (%) | # (%) | # (%) | |||
| Race | 0.077 | <.0001 | ||||
| Black | 41(1.6) | 2479(98.4) | 1154(40.0) | 1728(60.0) | ||
| White | 73(1.2) | 6088(98.8) | 3899(56.2) | 3042(43.8) | ||
| Payer | 0.802 | <.0001 | ||||
| Public | 25(1.4) | 1775(98.6) | 693(32.6) | 1430(67.4) | ||
| Commercial | 87(1.2) | 6992(98.8) | 4520(56.9) | 3420(43.1) | ||
| Other | 4(1.5) | 257(98.5) | 147(45.5) | 176(54.5) | ||
| Geographic Residence | 0.454 | 0.030 | ||||
| Rural | 3(1.9) | 156(98.1) | 82(43.6) | 106(56.4) | ||
| Urban | 112(1.2) | 8885(98.8) | 5277(51.6) | 4946(48.4) | ||
| Provider Engagement† | 0.846 | 0.005 | ||||
| High | 61(1.1) | 5344(98.9) | 3177(54.7) | 2634(45.3) | ||
| Low | 28(1.1) | 2565(98.9) | 1432(51.4) | 1352(48.6) |
Chi-square and Fisher’s exact, T-test
Only patients who identified as solely Black or White race were included due to small numbers in the other diverse race categories (n=650).
High engagement defined as the patients who submitted a remote measurement for ≥50% of the Connected MOM-enrolled weeks. Low engagement defined as the patients who submitted a remote measurement for <50% of the Connected MOM-enrolled weeks.
Patient engagement defined as the % of Connected MOM-eligible weeks (enrolled week until delivery) where a Connected MOM remote measurement was submitted.
Provider engagement was defined as the number of offers attributed to the provider divided by the number of eligible pregnancies attributed to the provider. High provider engagement is defined as an enrollment from onboarding rate ≥59% for providers with >50 eligible pregnancies; low provider engagement is defined as an enrollment from onboarding rate <59% for providers with >50 eligible pregnancies.
Sustainability
Patient participation began at the end of 2016 with only 12 patients enrolling in Connected MOM. By 2022 (the last full year with results), 1,998 patients enrolled in the program (Table 4). Participation rates for both rural and urban patients peaked in 2019 at 29.0% and 33.5%, respectively, which also represented the narrowest rural-urban gap in participation. Of eligible Black patients, participation rates dipped in 2020–2021, but improved in recent years. The difference in enrollment between Black and White patients was also greatest in 2020 and 2021 (14.5% and 14.4%) with improvement in racial differences in 2023 (6.1%). Provider and organizational participation steadily increased since 2016 with 94% of OB providers at Connected MOM participating sites and 80% of OB locations eligible to enroll patients in the program in 2023.
Table 4.
Connected MOM Sustainability and Expansion Metrics by Year
| 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023* | |
|---|---|---|---|---|---|---|---|---|
| Patient participation | ||||||||
| N | 12 | 193 | 475 | 749 | 1769 | 1633 | 1998 | 1921 |
| % | 48.0% | 23.2% | 26.8% | 33.2% | 25.2% | 27.2% | 30.6% | 28.7% |
| Rural patients | 0% | 9.1% | 6.7% | 29.0% | 17.0% | 9.4% | 12.7% | 14.9% |
| Urban patients | 48% | 23.4% | 26.9% | 33.5% | 25.9% | 28.2% | 31.5% | 29.6% |
| Black patients | 50% | 21.0% | 23.0% | 27.9% | 17.7% | 19.0% | 25.0% | 25.8% |
| White patients | 45% | 24.2% | 27.8% | 35.2% | 32.2% | 33.4% | 34.6% | 31.9% |
| Provider participation | 21.4% | 52.3% | 75.0% | 78.2% | 95.9% | 92.8% | 95.7% | 94.0% |
| Organizational participation | 16.7% | 33.3% | 71.4% | 55.6% | 50.0% | 50.0% | 80.0% | 80.0% |
Data only through October 1, 2023
Column % are presented.
Patient Participation is defined as the proportion of Connected MOM-enrolled pregnancies in a given year of those Connected MOM-eligible pregnancies in a given year. In late 2019, enrollment was opened for all patients regardless of insurer. In May 2022, patients with a BP>140/90 at the initial visit became eligible to enroll.
Provider participation is defined as the proportion of Ochsner providers eligible to enroll patients in the Connected MOM program of the OB providers attributed to the Connected MOM OB location. The group includes providers who (1) spend the majority of their work in a year at a clinic that is Connected Mom eligible, (2) have at least one encounter in the year with patients that is “routine prenatal” or “initial prenatal”, and (3) are a midwife, nurse practitioner, physician, physician assistant, or resident.
Organizational Participation is defined as the proportion of OB primary locations with Connected MOM enrolling providers seeing OB patients in a given year of those OB primary locations seeing OB patients in a given year.
Acceptability
Survey participation was low (n=620, 5.9%). However, of those who responded, the average Connected MOM participant rated the program 5 out of 5 for each metric at the conclusion of the program (following the 6-week post-partum period). Most participants were satisfied with participating in Ochsner’s Connected MOM program, agreed that the technology was convenient and easy to use, and would recommend the program to others (Figure 3).
Figure 3.
Connected MOM program patient satisfaction
Sensitivity Analysis: Effects of Changes to Program Eligibility
We assembled a cohort limited to only those eligible May 1, 2022 and after (n=10,873) in order to eliminate potential confounding from early program restrictions. More Black patients enrolled during this recent period, but still fewer than White patients (26.3% vs 33.4%, respectively). Patients using public insurance made up a higher percentage of the enrolled group, but only 21.0% of eligible patients on public insurance enrolled, compared to 38.1% of patients with commercial insurance. Rural patients made up a slightly higher percentage of the enrolled group compared to the original analysis, but still only 14.8% of eligible patients with rural residences enrolled compared to 31.0% of urban patients.
The overall enrollment rate of eligible patients was 30.1% (Table 5). White patients who were offered the program were 1.3 times more likely to enroll than Black patients (RR=1.32, 1.25–1.40). Rural patients who were offered the program were 2.3 times less likely to enroll than urban patients (RR=2.32, 1.80–3.00). Offer rates improved with Black patients exceeding White patients (78.5% vs 75.3%, p=0.0002); however, rural patients were still offered the program less often (67.8% vs 76.1%, p<.0001). Importantly, enrollment from onboarding rates continued to show disparities, with Black patients 18.5% and rural patients 11.9% less likely to enroll than White and urban patients (p<.0001 and p=0.0037, respectively).
Table 5.
Sensitivity analysis summary of Connected MOM Offer, Onboarding, and Enrollment Rates, 5/1/2022 – 10/1/2023
| Rate | Definition | n/N (%) | Black (N=3832) | White (N=6065) | p | Rural (N=493) | Urban (N=10220) | p |
|---|---|---|---|---|---|---|---|---|
| Overall Enrollment Rate | # enrolled/total cohort | 3274/10873 (30.11%) | 1006/3832 (26.25%) | 2025/6065 (33.39%) | <.0001 | 73/493 (14.81%) | 3168/10,220 (31.00%) | <.0001 |
| Enrollment from Offer Rate | # episodes with an offer by 20 GA weeks that enrolled /# episodes with an offer by 20 GA weeks | 3,227/8,235 (39.19%) | 993/3,008 (33.01%) | 1,998/4565 (43.77%) | <.0001 | 73/334 (21.86%) | 3,121/7,779 (40.12%) | <.0001 |
| Offer Rate | # episodes that received an offer by 20 GA weeks/# episodes where BPA Fired by 20 GA weeks | 8,235/10,873 (75.74%) | 3,008/3,832 (78.50%) | 4,565/6,065 (75.27%) | 0.0002 | 334/493 (67.75%) | 7779/10,220 (76.12%) | <.0001 |
| Onboarding from Offer Rate | # episodes with an offer by 20 GA weeks that onboarded /# episodes with an offer by 20 GA weeks | 5185/8235 (62.96%) | 1956/3008 (65.03%) | 2891/4565 (63.33%) | 0.1322 | 141/334 (42.22%) | 4981/7779 (64.03%) | <.0001 |
| Enrollment from Onboarding Rate | # episodes enrolled /# episodes that had an onboarding start | 3274/5268 (62.15%) | 1006/1990 (50.55%) | 2025/2931 (69.09%) | <.0001 | 73/144 (50.69%) | 3168/5061 (62.60%) | 0.0037 |
Note: race by residence interactions were non-significant for all rates (p>0.05).
Engagement was lower overall in this period, with only 24.5% being highly engaged compared to the larger cohort findings of 51.5%. White patients were more engaged than Black patients (26.3% vs 20.0%, p=0.0002), while rural and urban patients showed similar engagement (p=0.35). Even after restricting the analysis to the time period when Medicaid patients were eligible to enroll, patients on public insurance had lower enrollment and engagement than patients with other types of insurance (both p<.0001).
DISCUSSION
Principal Findings
Provider and organization participation rates in the RBPM program Connected MOM were high, especially in recent years. Overall, approximately 40% of eligible patients enrolled in the program, with White and urban patients being more likely to enroll than Black and rural patients. Racial and place-based differences persisted at each phase of the enrollment process, including the offer, onboarding and enrollment phases. Once enrolled, program attrition was very low and patients averaged at least one BP reading per week about 50% of the weeks enrolled in Connected MOM. Lower patient engagement was associated with identifying as Black race, rural residence, and public insurance payer.
Results in the context of what is known
This study emphasizes that patients at high risk for HDP (rural, Black, lower SES) who may benefit from an intervention most, are not enrolled and do not utilize the program to the same extent as other patients. This result corroborates a previous study’s findings that all patients benefit from RBPM, but disparities persist: Black patients were less likely to be enrolled in Connected MOM than White patients and submitted fewer BP measurements during pregnancy with longer BP intervals than their White counterparts using Connected MOM.29 Real-world implementation of RBPM has lower levels of participation among Black patients.20 Black and low-income patients may face more barriers (policy, community, health system, interpersonal, individual) to complete the multistep process necessary to enroll and have higher levels of engagement in Connected MOM.18 Policy and payer concerns should be minimal since Ochsner offers the program at no cost to patients; however, access to Internet may be an issue, especially in rural areas. This analysis focused on implementation at the individual and health system levels, but the role of community and caregiver support may be equally important in successful adoption of RBPM (discussed below).
Clinical implications
Provider program adoption in the offer and order phases was generally high, although lower for Black and rural patients. Provider participation steadily increased with a jump in 2020 reflecting provider incentives to place orders. Providers should consider individual barriers to enrollment with high-risk patients who express initial interest but do not complete the enrollment process. Given the high number of consented patients in the onboarding phase who never enroll, a mechanism for assistance at prenatal appointments by a medical assistant or patient navigator may improve enrollment rates.
High provider engagement was associated with high patient engagement, where enrolled patients were significantly more likely to submit remote BP readings each week if they had a highly engaged provider. While the correlation between provider and patient engagement in e-Health interventions is not well documented, successful remote monitoring interventions that include clinical support and education to increase self-management skills and improve communication between healthcare staff and patients can improve clinical outcomes in all populations.18,33 This relationship is especially important for Black patients who were significantly less engaged, despite similar retention in the program, but showed more engagement when they had a provider with high engagement.
Research implications
Technology barriers are often a concern with digital interventions. While there is no racial difference in smartphone ownership,34 rural adults are less likely than urban adults to own a smartphone.35 In our study, not having a smartphone accounted for only a small portion of the reasons why a patient did not have an order placed, but we were not able to assess whether access to home broadband internet or data plans were a barrier. Individuals with lower incomes and Black individuals are less likely to have home access to this service.34,36 Twenty-seven percent of adults earning less than $30,000 a year and 11% of adults earning $30-$100,000 a year own a smartphone, but do not have broadband internet at home.36 This scenario may impact enrollment and engagement, as participants would be required to use their cellular data plan to submit remote measurements. Some rural locations may also encounter poor cellular reception.
Program eligibility criteria, such as comorbidities, weight, gestational age, and insurance provider, should be minimized or eliminated in order to maximize participation. The sensitivity analysis showed an improvement in enrollment disparities for Black, rural, and low SES patients once eligibility was eased. Furthermore, significant change in maternal health outcomes related to HDP that are possible with RBPM can only be achieved by targeting populations at highest risk.33
Strengths and Limitations
EHR data combined with program registries allowed investigation of multiple steps along the enrollment pathway in order to dissect the offer, onboarding and enrollment by race and geographic residence. The rate of offer by providers was slightly differential by race and more differential by residence. In light of this potential provider bias in offering the program, the Connected MOM program has since began an auto-invite feature to introduce the program directly to the patient via a MyChart message and allow a self-order placement request. The effect of this recruitment strategy in combination with the traditional provider recruitment requires evaluation but emphasizes the potential for program change based on research findings.
The major limitation to this study is the inability to answer why these inequities exist solely with retrospective EHR data. This study focused mainly on individual and interpersonal influences within the health care system. However, the explanation is complicated, multi-factorial, may be different for each inequity (race and geographic residence), and vary by individual. Future work should utilize the National Institute on Minority Health and Health Disparities research framework to assess health disparities by domains and levels of influence.37 Qualitative research with multiple stakeholders is required to elucidate specific reasons for non-participation and lower levels of engagement.
The process for acquiring and connecting the Connected MOM device is currently mail order with phone support, or at certain locations, pickup at the “O Bar”, an onsite, in-person technical support service. This analysis was not able to assess which kit collection method onboarded patients utilized. Further understanding of this potential barrier to device setup is critical to adapting the program.
Currently, Connected MOM is not available in any language other than English, although plans are underway to provide a Spanish language version. This will extend the program to at least 10% more eligible Hispanic, Latina and non-English primary language patients who we found to be less likely to onboard or enroll in Connected MOM.
Conclusions
We have identified opportunities for improvement in maternal health equity through enhancements to the RBPM program Connected MOM enrollment process. Equitable implementation requires identifying and addressing barriers at each step along the enrollment pathway using each patient-facing opportunity to introduce and revisit the benefits of program participation and address any barriers the patient may face. Focus groups and interviews with patients, community members offering social support services, providers, and health systems administrators would be useful to further understand these barriers and improve patient recruitment.
Highlights.
Provider and organization participation rates in Connected MOM were high.
Of those offered, 40% of eligible patients enrolled in the program.
White and urban patients more likely to enroll and engage than Black and rural.
Connected MOM is adoptable, implementable, acceptable and sustainable.
Strategies are needed to address the barriers to enrollment among racial minority and rural patients at high risk for hypertensive disorders of pregnancy.
ACKNOWLEDGMENTS
Thank you to the Ochsner Digital Medicine team who was instrumental in acquiring and understanding the data in the patient registries. Thanks to Sabrina Alam and Surabhee Eswaran for assisting with literature review.
FUNDING
Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number U54HD113159. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
The authors report no conflict of interest.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Contributor Information
Elizabeth J. HOWARD, Ochsner Xavier Institute for Health Equity Research, Ochsner Clinic Foundation, New Orleans, LA.
Emily W. HARVILLE, Tulane University Celia Scott Weatherhead School of Public Health and Tropical Medicine.
Sherri LONGO, Department of Obstetrics and Gynecology, Section of Maternal-Fetal Medicine, Ochsner Health, New Orleans, LA.
Kirsten S. DORANS, Tulane University Celia Scott Weatherhead School of Public Health and Tropical Medicine.
Joseph R. BIGGIO, University of Queensland, Ochsner Clinical School; System Chair and Service Line Leader, Women’s Services, Ochsner Health, New Orleans, LA.
REFERENCES
- 1.Ford ND, Cox S, Ko JY, et al. Hypertensive Disorders in Pregnancy and Mortality at Delivery Hospitalization - United States, 2017–2019. MMWR Morb Mortal Wkly Rep Apr 29 2022;71(17):585–591. doi: 10.15585/mmwr.mm7117a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Foundation UH. America’s Health Rankings analysis of America’s Health Rankings composite measure. 2023. Accessed May 29, 2025. AmericasHealthRankings.org
- 3.QuickFacts Louisiana (2024). [Google Scholar]
- 4.Minhas AS, Ogunwole SM, Vaught AJ, et al. Racial Disparities in Cardiovascular Complications With Pregnancy-Induced Hypertension in the United States. Hypertension. Aug 2021;78(2):480–488. doi: 10.1161/HYPERTENSIONAHA.121.17104 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Dongarwar D, Tahseen D, Wang L, Aliyu MH, Salihu HM. Trends and predictors of eclampsia among singleton and multiple gestations in the United States, 1989–2018. Pregnancy Hypertens. Aug 2021;25:56–61. doi: 10.1016/j.preghy.2021.05.013 [DOI] [PubMed] [Google Scholar]
- 6.Admon LK, Winkelman TNA, Zivin K, Terplan M, Mhyre JM, Dalton VK. Racial and Ethnic Disparities in the Incidence of Severe Maternal Morbidity in the United States, 2012–2015. Obstet Gynecol Nov 2018;132(5):1158–1166. doi: 10.1097/aog.0000000000002937 [DOI] [PubMed] [Google Scholar]
- 7.Gyamfi-Bannerman C, Pandita A, Miller EC, et al. Preeclampsia outcomes at delivery and race. J Matern Fetal Neonatal Med Nov 2020;33(21):3619–3626. doi: 10.1080/14767058.2019.1581522 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Leonard SA, Main EK, Scott KA, Profit J, Carmichael SL. Racial and ethnic disparities in severe maternal morbidity prevalence and trends. Ann Epidemiol May 2019;33:30–36. doi: 10.1016/j.annepidem.2019.02.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Burris HH, Passarella M, Handley SC, Srinivas SK, Lorch SA. Black-White disparities in maternal in-hospital mortality according to teaching and Black-serving hospital status. Am J Obstet Gynecol Jul 2021;225(1):83.e1–83.e9. doi: 10.1016/j.ajog.2021.01.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.MacDorman MF, Thoma M, Declcerq E, Howell EA. Racial and Ethnic Disparities in Maternal Mortality in the United States Using Enhanced Vital Records, 2016–2017. Am J Public Health. Sep 2021;111(9):1673–1681. doi: 10.2105/ajph.2021.306375 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Mehta PK, Kieltyka L, Bachhuber MA, et al. Racial Inequities in Preventable Pregnancy-Related Deaths in Louisiana, 2011–2016. Obstet Gynecol Feb 2020;135(2):276–283. doi: 10.1097/aog.0000000000003591 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Singh GK. Trends and Social Inequalities in Maternal Mortality in the United States, 1969–2018. Int J MCH AIDS 2021;10(1):29–42. doi: 10.21106/ijma.444 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Zhang M, Wan P, Ng K, et al. Preeclampsia Among African American Pregnant Women: An Update on Prevalence, Complications, Etiology, and Biomarkers. Obstet Gynecol Surv Feb 2020;75(2):111–120. doi: 10.1097/ogx.0000000000000747 [DOI] [PubMed] [Google Scholar]
- 14.Hoyert D Maternal mortality rates in the United States, 2021. NCHS Health E-Stats. 2023. doi: 10.15620/cdc:124678 [DOI] [PubMed] [Google Scholar]
- 15.Cameron NA, Everitt I, Seegmiller LE, Yee LM, Grobman WA, Khan SS. Trends in the Incidence of New-Onset Hypertensive Disorders of Pregnancy Among Rural and Urban Areas in the United States, 2007 to 2019. J Am Heart Assoc Jan 18 2022;11(2):e023791. doi: 10.1161/jaha.121.023791 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kozhimannil KB, Interrante JD, Henning-Smith C, Admon LK. Rural-Urban Differences In Severe Maternal Morbidity And Mortality In The US, 2007–15. Health Aff (Millwood). Dec 2019;38(12):2077–2085. doi: 10.1377/hlthaff.2019.00805 [DOI] [PubMed] [Google Scholar]
- 17.Francis B, Pearl M, Colen C, Shoben A, Sealy-Jefferson S. Racial and Economic Segregation Over the Life Course and Incident Hypertensive Disorders of Pregnancy Among Black Women in California. American Journal of Epidemiology. 2023;193(2):277–284. doi: 10.1093/aje/kwad192 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Khoong EC, Commodore-Mensah Y, Lyles CR, Fontil V. Use of Self-Measured Blood Pressure Monitoring to Improve Hypertension Equity. Curr Hypertens Rep Nov 2022;24(11):599–613. doi: 10.1007/s11906-022-01218-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ganapathy R, Grewal A, Castleman JS. Remote monitoring of blood pressure to reduce the risk of preeclampsia related complications with an innovative use of mobile technology. Pregnancy Hypertens Oct 2016;6(4):263–265. doi: 10.1016/j.preghy.2016.04.005 [DOI] [PubMed] [Google Scholar]
- 20.Triebwasser JE, Janssen MK, Hirshberg A, Srinivas SK. Successful implementation of text-based blood pressure monitoring for postpartum hypertension. Pregnancy Hypertens Oct 2020;22:156–159. doi: 10.1016/j.preghy.2020.09.001 [DOI] [PubMed] [Google Scholar]
- 21.Hirshberg A, Sammel MD, Srinivas SK. Text message remote monitoring reduced racial disparities in postpartum blood pressure ascertainment. Am J Obstet Gynecol Sep 2019;221(3):283–285. doi: 10.1016/j.ajog.2019.05.011 [DOI] [PubMed] [Google Scholar]
- 22.Arkerson BJ, Finneran MM, Harris SR, et al. Remote Monitoring Compared With In-Office Surveillance of Blood Pressure in Patients With Pregnancy-Related Hypertension: A Randomized Controlled Trial. Obstet Gynecol Oct 1 2023;142(4):855–861. doi: 10.1097/aog.0000000000005327 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hypertension in pregnancy. Report of the American College of Obstetricians and Gynecologists’ Task Force on Hypertension in Pregnancy. Obstet Gynecol Nov 2013;122(5):1122–1131. doi: 10.1097/01.Aog.0000437382.03963.88 [DOI] [PubMed] [Google Scholar]
- 24.Brown MA, Magee LA, Kenny LC, et al. Hypertensive Disorders of Pregnancy: ISSHP Classification, Diagnosis, and Management Recommendations for International Practice. Hypertension Jul 2018;72(1):24–43. doi: 10.1161/hypertensionaha.117.10803 [DOI] [PubMed] [Google Scholar]
- 25.Kalafat E, Benlioglu C, Thilaganathan B, Khalil A. Home blood pressure monitoring in the antenatal and postpartum period: A systematic review meta-analysis. Pregnancy Hypertens Jan 2020;19:44–51. doi: 10.1016/j.preghy.2019.12.001 [DOI] [PubMed] [Google Scholar]
- 26.Perry H, Sheehan E, Thilaganathan B, Khalil A. Home blood-pressure monitoring in a hypertensive pregnant population. Ultrasound Obstet Gynecol Apr 2018;51(4):524–530. doi: 10.1002/uog.19023 [DOI] [PubMed] [Google Scholar]
- 27.Tran K, Padwal R, Khan N, Wright MD, Chan WS. Home blood pressure monitoring in the diagnosis and treatment of hypertension in pregnancy: a systematic review and meta-analysis. CMAJ Open. Apr-Jun 2021;9(2):E642–e650. doi: 10.9778/cmajo.20200099 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Usuzaki T, Ishikuro M, Metoki H, et al. Comparison among research, home, and office blood pressure measurements for pregnant women: The TMM BirThree Cohort Study. J Clin Hypertens (Greenwich). Nov 2020;22(11):2004–2013. doi: 10.1111/jch.14050 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Howard E, Gillispie-Bell V, Olet S, Glenn B, Ammar N, Price-Haywood EG. Evaluating Racial Disparities in Implementation and Monitoring of a Remote Blood Pressure Program in a Pregnant Population-A Retrospective Cohort Study. Ochsner J Spring 2024;24(1):22–30. doi: 10.31486/toj.23.0111 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Service ER. Rural-Urban Continuum Codes. U.S. Department of Agriculture. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/
- 31.Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. Sep 1999;89(9):1322–7. doi: 10.2105/ajph.89.9.1322 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Proctor E, Silmere H, Raghavan R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health. Mar 2011;38(2):65–76. doi: 10.1007/s10488-010-0319-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Thomas EE, Taylor ML, Banbury A, et al. Factors influencing the effectiveness of remote patient monitoring interventions: a realist review. BMJ Open. 2021;11(8):e051844. doi: 10.1136/bmjopen-2021-051844 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Atske S, Perrin A. Pew Research Center: Home broadband adoption, computer ownership vary by race, ethnicity in the U.S. Library of Congress. Updated July 16, 2021. Accessed January 24, 2023, 2023. https://www.pewresearch.org/fact-tank/2021/07/16/home-broadband-adoption-computer-ownership-vary-by-race-ethnicity-in-the-u-s/ [Google Scholar]
- 35.Vogels EA. Pew Research Center: Some digital divides persist between rural, urban and suburban America. Library of Congress. Updated August 19, 2021. Accessed October 1, 2024, 2024. https://www.pewresearch.org/short-reads/2021/08/19/some-digital-divides-persist-between-rural-urban-and-suburban-america/ [Google Scholar]
- 36.Vogels EA. Pew Research Center: Digital divide persists even as Americans with lower incomes make gains in tech adoption. Library of Congress. Updated June 22, 2021. Accessed January 24, 2023, 2023. https://www.pewresearch.org/fact-tank/2021/06/22/digital-divide-persists-even-as-americans-with-lower-incomes-make-gains-in-tech-adoption/ [Google Scholar]
- 37.Alvidrez J, Castille D, Laude-Sharp M, Rosario A, Tabor D. The National Institute on Minority Health and Health Disparities Research Framework. Am J Public Health. Jan 2019;109(S1):S16–s20. doi: 10.2105/ajph.2018.304883 [DOI] [PMC free article] [PubMed] [Google Scholar]