Abstract
Introduction
Remote monitoring technology can help patients and healthcare providers to track blood pressure measurements during pregnancy to manage hypertension, but limited evidence from widespread community use is available.
Methods
Among pregnant patients in 2022–2023 with hypertensive disorders of pregnancy, in a setting offering home blood pressure monitoring with blood pressure data collected either via weekly nurse telephone calls or digital remote upload of blood pressures with nurse calls only as‐needed, we compared participant characteristics, monitoring process, and clinical outcomes between remote monitoring participants and non–remote monitoring participants using propensity weighting to account for differences between participant groups.
Results
Among all 2750 pregnant patients with hypertensive disorders of pregnancy, 2366 (86.0%) participated in remote monitoring. After adjustment, compared with patients with telephonic nurse monitoring of home‐recorded measures, patients with remote monitoring had statistically significantly (all p < 0.05) fewer synchronous nurse phone calls (6.4 with remote monitoring vs. 12.6 without remote monitoring), more weeks with blood pressure readings shared with clinicians (measures on 12.7 weeks with remote monitoring vs. 8.3 weeks without remote monitoring,) and more weeks with severe hypertension captured (events on 1.08 weeks with remote monitoring vs. 0.22 weeks without remote monitoring). While the number of medication adjustments was not significantly different between monitoring types, the gestational age at first adjustment was 1.96 weeks earlier (95% confidence interval [CI] 0.22–3.69) in patients with remote monitoring. Emergency department visits and hospitalizations did not differ statistically significantly between monitoring groups, nor did severe maternal morbidity or cesarean or stillbirth outcomes.
Conclusions
Remote blood pressure monitoring uptake and feasibility was high and was associated with higher capture of clinically actionable values, earlier medication adjustment, improved resource utilization, and similar perinatal outcomes compared with a telephone‐based care model for hypertensive disorders of pregnancy.
Keywords: blood pressure control, clinical outcomes, digital health, healthcare utilization, hypertensive disorders of pregnancy, perinatal outcomes, prenatal care, remote patient monitoring, telemedicine
1. INTRODUCTION
Managing high blood pressure (BP) in pregnant patients is key in addressing risk of preterm delivery, preeclampsia, and other perinatal outcomes. Guidelines for safe BP management [1, 2] require patient data collection and sharing of these data with clinicians, a situation that currently available technology can support efficiently. Traditionally, BP measurements may be collected by patients and recorded in a static log that is obtained by clinicians through phone calls or presented in person at a scheduled appointment. This synchronous sharing of data may introduce limitations in quality, quantity, and timeliness of BP monitoring, which may be influenced by patient characteristics including barriers to care associated with social determinants of health. Instead, a Bluetooth‐enabled BP cuff linked to a smartphone app can transmit patient‐collected data asynchronously, whenever the patient uploads their own data for clinician review, potentially providing a more patient‐friendly and resource efficient means for perinatal BP surveillance.
Costs, benefits, and uptake of this remote perinatal monitoring modality across diverse patient groups remain uncertain in real‐world community clinical practice [3, 4]. While remote monitoring of BP has been shown to be feasible, cost‐effective, and potentially to reduce disparities in ascertainment, there is extremely limited prior evidence examining patient health impacts, particularly in large and diverse real‐world community clinical practice, with mixed results [3, 5, 6].
In a large diverse multi‐site integrated health care system managing perinatal BP among those with hypertensive disorders of pregnancy (HDP), we compared scheduled nurse telephone calls to monitor BP and remote BP monitoring (RPBM) with nurse calls only as needed, with regard to patient characteristics and associations with process and clinical outcomes pre‐ and postdelivery. We hypothesized that there may be differences in patient characteristics between RBPM users and non‐users, and that use of remote monitoring would be associated with process improvements.
2. METHODS
2.1. Setting
This study was conducted in a large diverse integrated system across 21 hospitals, serving over 4 million patients including approximately 40,000 deliveries annually.
In this setting, registered nurses (RNs) from a central Regional Perinatal Service Center (RPSC) telehealth program collect home BP monitoring data from all patients seen across any of the 21 study hospitals, with HDP and report actionable data to obstetric clinicians. Eligible patients include any pregnant patients with chronic hypertension who were taking antihypertensive medication any time within the 1 year prior to pregnancy, or with diagnosed gestational hypertension or preeclampsia before 36 weeks of pregnancy. Prior to 2021, RPSC conventional collection of home monitoring data was conducted by scheduled weekly telephone conversation, with patients tracking their BP values on a log at home, then verbally reporting their home‐collected values to a personal nurse manager for documentation, assessment, and follow‐up per protocol. The program includes an algorithm for nurses to send non‐urgent BP information to the referring clinician. And for high values, patients are referred for emergency care or labor and delivery with a warm handoff. Any changes in clinical care resulting from abnormal BPs or other patient reported data, such as antihypertensive medication adjustment, are conducted by the obstetric clinician in at their discretion in response to data collected and documented by the RPSC nurses.
Starting in 2021, patients with HDP were offered enrollment in an opt‐out fashion in a newly developed RBPM program to collect home measured BP data in a more technologically and resource efficient manner while maintaining the other elements of the RPSC program unchanged. For remote monitoring enrolled patients, a Bluetooth‐enabled cuff with paired mobile app supports patients in directly uploading BP data into the medical record. In the RBPM program, daily and weekly reports of patients with BP values or upload adherence out of target ranges are reviewed by RPSC RNs, and patients who require assessment or advice are contacted via an outbound RN call.
All patients in both the conventional nurse telephone monitoring and remote‐monitoring (RBPM) programs are instructed to measure their BP twice daily at home. For patients in the telephone monitoring pathway, nurses contact the prenatal patient to collect BP data by telephone weekly. In the RBPM program, nurse telephone contact frequency instead occurs as needed based on review of BP reports generated. In both the RBPM and non‐RBPM programs, patients are instructed to contact the RPSC via inbound call if they self‐detect abnormal BPs or have any symptoms that requires further assessment and management (or to go to the emergency room as needed).
This study was approved by the setting's institutional review board, which waived the requirement for informed consent in this study of retrospective automated data. Reporting is consistent with strengthening the reporting of observational studies in epidemiology (STROBE) guidelines for observational data.
2.2. Population and study period
We evaluated all pregnancies among English‐ and Spanish‐speaking patients ≥18+ years old in the RPSC program for HDP monitoring January 2022 to December 2023. Study eligibility required at least 3 months of continuous enrollment in the health insurance plan of the setting (for capture of baseline data) and RPSC participation by 37 weeks gestation to allow for observation time to capture outcome impacts.
2.3. Data and exposures
Study datasets were compiled using a combination of program participation and process data from an operational database of RPSC patient contacts, and clinical history and outcome data extracted from the comprehensive inpatient/outpatient electronic health record (EHR).
The primary exposure of the study was RPBM participation versus non‐participation. We defined study exposure groups based on electronic flags for source of BP values, categorizing patients with a mix of BP sources (RBPM or non‐RBPM) according to the source with most BP values. We defined weeks of monitoring exposure from the timing of first RN contact to the date of delivery.
We collected patient covariates from existing automated data, including patient sociodemographics: age, race/ethnicity, primary language English versus Spanish, and residential neighborhood deprivation index [7]. We also categorized the patient's insurance type and captured any prior use of a mobile device to access the patient portal (within the 12 months prior to RPSC participation) as a measure of mobile device access. We examined clinical characteristics at the time of first RPSC participation including gestational age, BP, and obstetric comorbidity score [7].
2.4. Outcomes
Study BP monitoring process measures included the number of synchronous RPSC nurse telephone visits (RN calls) and the volume of recorded BP readings, with the unit of analysis being number of weeks with BP readings present in the medical record. Additional process measures included total number of BP readings, number in severe HTN range (SBP [systolic BP] ≥ 160 mmHg and/or DBP [diastolic BP] ≥ 110 mmHg) and number of weeks with all BPs over the following thresholds: <160/<110, < 150/ < 100, and < 140/ < 90.
Clinical process and outcomes included gestational age at first antihypertensive medication adjustment (as a measure of timeliness of treatment), rate and gestational age at preeclampsia diagnosis among those with gestational hypertension or chronic hypertension who developed superimposed preeclampsia, triage visits to the emergency department or Labor and Delivery unit, antepartum hospitalization, mortality, severe maternal morbidity (SMM, yes/no), stillbirth, and caesarean delivery [8, 9].
2.5. Analysis
We used multivariate logistic regression to examine patient characteristics associated with RBPM participant status, adjusting for calendar month. We calculated adjusted absolute participation rates by patient characteristic via marginal standardization (using Stata's margins post‐estimation command).
To compare study outcomes between RBPM participants and non‐RBPM participants, we used inverse probability treatment weighting (IPTW) to minimize the differences in patient characteristics and clinical characteristics between exposure groups. We first calculated a propensity score for enrolling in RBPM from the multivariable logistic regression model described above. We then used a logistic regression model with IPTW for binary outcomes and linear regression model with IPW for numeric outcomes, also adjusting for each patient's total weeks of monitoring exposure.
3. RESULTS
Overall, the study population included 2750 pregnant patients with HDP who were monitored in the RPSC home BP monitoring program with 2366 RBPM participants and 384 non‐RBPM participants.
Table 1 shows the demographic, socioeconomic, and clinical characteristics of the study populations. Overall, 62.3% of patients began their BP monitoring program at 27 weeks gestation or earlier, 57.5% were less than age 35 years, 24.0% were Asian, 9.1% were Black, 25.0% were Hispanic, 5.0% were Multiracial, and 22.3% were White.
TABLE 1.
Patient characteristics.
| All | RBPM | non‐RBPM | p value | |
|---|---|---|---|---|
| N | 2750 | 2366 | 384 | |
| Age | ||||
| <25 | 140 (5.1%) | 120 (5.1%) | 20 (5.2%) | 0.3216 |
| <30 | 462 (16.8%) | 402 (17%) | 60 (15.6%) | |
| <35 | 979 (35.6%) | 855 (36.1%) | 124 (32.3%) | |
| <40 | 845 (30.7%) | 720 (30.4%) | 125 (32.6%) | |
| 40+ | 324 (11.8%) | 269 (11.4%) | 55 (14.3%) | |
| Race/ethnicity | ||||
| White | 918 (33.4%) | 790 (33.4%) | 128 (33.3%) | 0.3501 |
| Black | 250 (9.1%) | 207 (8.7%) | 43 (11.2%) | |
| Hispanic | 687 (25%) | 596 (25.2%) | 91 (23.7%) | |
| Asian | 660 (24%) | 564 (23.8%) | 96 (25%) | |
| Multiracial | 136 (4.9%) | 118 (5%) | 18 (4.7%) | |
| Other | 99 (3.6%) | 91 (3.8%) | 8 (2.1%) | |
| Language | ||||
| English | 2703 (98.3%) | 2333 (98.6%) | 370 (96.4%) | 0.0016 |
| Spanish | 47 (1.7%) | 33 (1.4%) | 14 (3.6%) | |
| NDI | ||||
| 1 (lowest deprivation) | 307 (11.2%) | 267 (11.3%) | 40 (10.4%) | 0.887 |
| 2 | 520 (18.9%) | 444 (18.8%) | 76 (19.8%) | |
| 3 | 668 (24.3%) | 581 (24.6%) | 87 (22.7%) | |
| 4 | 645 (23.5%) | 553 (23.4%) | 92 (24%) | |
| 5 (highest) | 610 (22.2%) | 521 (22%) | 89 (23.2%) | |
| Insurance type | ||||
| Medicare/Medicaid | 348 (12.7%) | 290 (12.3%) | 58 (15.1%) | 0.1196 |
| Commercial | 2402 (87.3%) | 2076 (87.7%) | 326 (84.9%) | |
| Mobile portal access | ||||
| No | 33 (1.2%) | 4 (0.2%) | 29 (7.6%) | <.0001 |
| Yes | 2717 (98.8%) | 2362 (99.8%) | 355 (92.4%) | |
| Baseline SBP | ||||
| 90–<110 | 74 (2.7%) | 62 (2.6%) | 12 (3.1%) | 0.8355 |
| 110–<140 | 1862 (67.7%) | 1609 (68%) | 253 (65.9%) | |
| 140–<160 | 728 (26.5%) | 621 (26.2%) | 107 (27.9%) | |
| ≥160 | 86 (3.1%) | 74 (3.1%) | 12 (3.1%) | |
| Gestational age | ||||
| ≤12 | 548 (19.9%) | 478 (20.2%) | 70 (18.2%) | 0.3804 |
| ≤27 | 1165 (42.4%) | 1004 (42.4%) | 161 (41.9%) | |
| ≤33 | 499 (18.1%) | 433 (18.3%) | 66 (17.2%) | |
| ≤36 | 538 (19.6%) | 451 (19.1%) | 87 (22.7%) | |
| Obstetric risk score | ||||
| Quartile 1 (lowest) | 692 (25.2%) | 574 (24.3%) | 118 (30.7%) | 0.0439 |
| Quartile 2 | 688 (25%) | 594 (25.1%) | 94 (24.5%) | |
| Quartile 3 | 689 (25.1%) | 599 (25.3%) | 90 (23.4%) | |
| Quartile 4 | 681 (24.8%) | 599 (25.3%) | 82 (21.4%) | |
Abbreviations: NDI, neighborhood deprivation index; RBPM, remote blood pressure monitoring; SBP, systolic blood pressure.
3.1. Characteristics by RBPM participation status
We did not observe any statistically significant differences in age or race associated with RBPM participation versus non‐RBPM participation (Figure 1). After adjustment, patients who were Spanish speakers (74.5% participation among Spanish speakers vs. 86.2% among English speakers), who lived in more deprived neighborhoods (83.8% participation among patients living with the highest neighborhood deprivation vs. 88.7% among patients with the lowest deprivation), or without a history of previously using the patient portal use from a mobile device (15.2% participations among patients with no prior mobile portal use vs. 86.8% among those with prior mobile use) were statistically significantly less likely to participate in RBPM (all p < 0.05). Patients in the highest quartile of obstetric risk at baseline were also significantly more likely to participate in RBPM (88.4% participation among patients at the highest risk vs. 82.9% among patients at the lowest risk, p < 0.05).
FIGURE 1.
Patient characteristics associated with RBPM and adjusted % of RBPM by characteristics. Multivariable logistic regression is also adjusted for calendar month. Adjusted rates of RBPM by patient characteristics were calculated via marginal standardization. RBPM, remote blood pressure monitoring.
3.2. BP monitoring process
Both RBPM participants and non‐RBPM participants had an average of 15 weeks of exposure to the BP management program before delivery (Table S1). RBPM participants had a median of 129 total BPs uploaded during the pregnancy.
Shown in Table 2, after accounting for differences in patient characteristics between RBPM participants and non‐RBPM participants, the number of nurse calls was significantly lower via RBPM, but also spread across a broader portion of the patient's monitoring time period (6.4 total RN calls over 13 weeks with RBPM vs. 13 RN calls over 8 weeks with non‐RBPM, p < 0.05).
TABLE 2.
Blood pressure management process, treatment, and clinical outcomes by remote blood pressure monitoring (RBPM) versus non‐RBPM.
| Adjusted difference between RBPM and non‐RBPM | |||||||
|---|---|---|---|---|---|---|---|
| Outcome | Type of monitoring | Adjusted result | 95% CI | Difference | 95% CI | ||
| Process measures | |||||||
| Number of nurse phone visits | Non‐RBPM | 12.5 | 11.70 | 13.40 | |||
| RBPM | 6.37 | 6.10 | 6.64 | −6.18 | −7.08 | −5.28 | |
| Weeks with recorded BP | Non‐RBPM | 8.29 | 7.50 | 9.08 | |||
| RBPM | 12.71 | 12.49 | 12.93 | 4.42 | 3.59 | 5.25 | |
| Blood pressure values | |||||||
| Weeks with SBP ≥ 160 | Non‐RBPM | 0.23 | 0.15 | 0.32 | |||
| RBPM | 1.07 | 0.99 | 1.15 | 0.83 | 0.71 | 0.96 | |
| Weeks with BP ≥ 160/110 | Non‐RBPM | 0.04 | −0.01 | 0.09 | |||
| RBPM | 0.22 | 0.19 | 0.24 | 0.17 | 0.12 | 0.23 | |
| Weeks with BP ≥ 150/100 | Non‐RBPM | 0.31 | 0.15 | 0.48 | |||
| RBPM | 1.15 | 1.06 | 1.23 | 0.84 | 0.65 | 1.02 | |
| Weeks with BP ≥ 140/90 | Non‐RBPM | 1.76 | 1.24 | 2.27 | |||
| RBPM | 4.38 | 4.18 | 4.58 | 2.63 | 2.07 | 3.19 | |
| Treatment | |||||||
| Medication adjustment | Non‐RBPM | 63.52% | 56.17% | 70.86% | |||
| RBPM | 61.52% | 59.36% | 63.69% | −1.99% | −9.66% | 5.67% | |
| GA at first med adjustment | Non‐RBPM | 25.61 | 23.94 | 27.28 | |||
| RBPM | 23.65 | 23.29 | 24.02 | −1.96 | −3.69 | −0.22 | |
| Pregnancy outcomes | |||||||
| Preeclampsia after index | Non‐RBPM | 16.11% | 8.73% | 23.48% | |||
| RBPM | 12.80% | 10.38% | 15.22% | −3.30% | −11.05% | 4.45% | |
| GA at pre‐eclampsia | Non‐RBPM | 36.03 | 34.61 | 37.45 | |||
| RBPM | 35.50 | 35.05 | 35.95 | −0.53 | −2.03 | 0.97 | |
| ED visit (any) | Non‐RBPM | 11.91% | 6.66% | 17.16% | |||
| RBPM | 11.57% | 10.24% | 12.90% | −0.34% | −5.73% | 5.05% | |
| Hospitalization (any) | Non‐RBPM | 8.25% | 4.14% | 12.37% | |||
| RBPM | 6.85% | 5.82% | 7.88% | −1.40% | −5.62% | 2.82% | |
| SMM | Non‐RBPM | 7.51% | 3.01% | 12.00% | |||
| RBPM | 7.00% | 5.97% | 8.03% | −0.51% | −5.12% | 4.11% | |
| Cesarean | Non‐RBPM | 42.89% | 34.87% | 50.91% | |||
| RBPM | 45.95% | 43.74% | 48.17% | 3.07% | −5.25% | 11.38% | |
| Stillborn | Non‐RBPM | 1.88% | −0.44% | 4.19% | |||
| RBPM | 0.45% | 0.18% | 0.71% | −1.43% | −3.76% | 0.90% | |
Note: Model—logistic regression with IPW adjusted for weeks of exposure for binary outcome and linear regression with IPW adjusted for weeks of exposure for numeric outcome. Propensity score is calculated from multivariable logistic regression with RBPM as outcome and all the covariates in Table 1 and indicator variables for calendar month.
Abbreviations: BP, blood pressure; GA, gestational age (weeks); ED, emergency department; SMM, severe maternal morbidity.
Bold values statistically significance at p < 0.05.
3.3. BP and pregnancy outcomes
Shown in Table 2, after adjusting for patient characteristics, patients using RBPM had a greater number of weeks with high BP measurements captured at each BP threshold than non‐RBPM participants (4.4 vs. 1.8 weeks with BP ≥ 140/90).
While the number of antihypertensive medication adjustments was not significantly different between monitoring types, the gestational age at first adjustment was 1.96 weeks earlier (95% CI, 0.22–3.69) in RBPM participants compared to non‐participants.
Emergency department visits and hospitalizations did not differ significantly between monitoring groups, nor did SMM, cesarean delivery, or stillbirth rates.
4. DISCUSSION
In a study of home‐based BP monitoring among a large cohort of pregnant patients with HDP within a multi‐site integrated health care delivery system, the vast majority of patients participated in the Bluetooth device–based remote monitoring program and uploaded home‐based BP measurements. Patients with lower English language proficiency or who lived in more deprived neighborhoods were less likely to participate in remote monitoring, while patients at higher obstetric risk were more likely to participate. RBPM participation was associated with efficient capture of more high BP values while utilizing fewer RN calls, and with earlier antihypertensive medication adjustments, with comparable perinatal outcomes compared to RBPM nonparticipation.
Prior evidence has supported the effectiveness of remote BP management in obtaining home‐based measures, however, uptake of home BP monitoring programs is often modest at 30%–40%, with variation by patient characteristics, including race [10, 11, 12]. In contrast, the current study did not identify age or race/ethnicity differences, perhaps due to the setting's centralized and systematic management of patients. This health‐system level nurse management program supported high participation rates (86% overall) to overcome differences in patient or physician engagement that might otherwise hinder participation in remote monitoring programs. We did, however, identify differentially lower uptake in patients with limited English proficiency, patients living in lower socioeconomic status neighborhoods, and patients without evidence of prior mobile device use which points to the importance of continuing to further support patients in accessing remote monitoring programs.
Most prior research studies examining RBPM related to pregnancy have focused on postpartum patients [13]. The current analysis is relatively unique in its setting within a post‐COVID pandemic community health care practice population. Although there is some prior research evidence examining prenatal BP monitoring, there have been calls for higher quality studies and further examination of associations with patient outcomes [11, 14, 15]. To our knowledge our study presented here is the first to report that prenatal RBPM was associated with prenatal clinical process improvements in patients with HDP in finding statistically significantly earlier prenatal medication adjustments, a substantial difference of two weeks earlier with remote monitoring. And although perinatal outcome differences did not reach statistical significance, nearly all outcomes were directionally lower in the RBPM patients, suggesting larger trials may be required to attain statistical power to assess differences of small magnitude [14].
Perinatal RBPM programs leverage technology to provide an alternative care delivery model in obstetrics. In part incentivized by the COVID‐19 pandemic associated care delivery changes [16, 17], models of care are currently evolving to include more diverse options and more patient‐directedness. It will be critical to consider data like those described here in determining optimal care pathways and outcomes. Whether these alternate BP monitoring programs will prove clinically superior to more traditional models in the antepartum period and among important patient subgroups is yet undetermined. We provide data in support of improved resource utilization and capture of potentially actionable BPs in the antepartum period among patients with HDP. Regarding cost effectiveness [2], limited data suggests RBPM is cost effective in terms of postpartum management [4]. Little is known about antepartum management. We have demonstrated improved resource utilization even when comparing RBPM to a relatively resource efficient RN‐based telehealth program, which itself may be considered a cost‐effective alternative to in‐person BP assessment and management.
A strength of our study is the community obstetric care setting with a large sample and centrally nurse‐managed BP programs. As such, the findings may not necessarily generalize well to other clinical settings, particularly settings without well‐developed prenatal BP management programs. Using robust electronic health record‐derived study data allowed us to account for demographic characteristics and detailed obstetric clinical risk characteristics, particularly in accounting for a obstetric‐specific comorbidity risk score, to help balance patients who used remote BP monitoring with those who did not [7]. Also, while remote monitoring devices can increase measurement density, including as a key feature, our study design conservatively measured BP weekly to align with the interval of phone‐based monitoring among non‐participants. Still, as an observational, retrospective study there could be unmeasured confounders, including characteristics of patients who declined to participate in remote monitoring (e.g., patient engagement, adherence, etc.) or differences in patients’ underlying type of hypertensive disorder, thus we cannot rule out unmeasured differences between remote BP monitoring participants and non‐participants and these observational data should not be interpreted to be causal. Further exploration is needed to better understand patient reasoning for declining remote monitoring, which may inform patient‐centered approaches to support access.
5. CONCLUSION
In a large multi‐site community setting, we found that widespread use of RBPM for prenatal hypertension was feasible and widely adopted. Still, patients with language barriers or other barriers to care may warrant further outreach or support to participate. Patients using remote monitoring shared more BP values, including high BP readings with their clinicians, and were treated with medication adjustments sooner, however, with comparable pregnancy outcomes. Efficient BP management through remote monitoring may support patient access to care.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
ETHICS STATEMENT
This study was approved by the Kaiser Permanente Northern California Institutional Review Board, which waived the requirement for informed consent in this study of automated data.
Supporting information
Supporting Information
ACKNOWLEDGMENTS
This study was funded by a Kaiser Permanente Community Health Grant.
DATA AVAILABILITY STATEMENT
The data used for this study contain protected health information (PHI) and access is protected by the Kaiser Permanente Northern California Institutional Review Board (IRB). Data are available from the Kaiser Permanente Division of Research for researchers who meet the criteria for access to confidential data.
REFERENCES
- 1. Clinical Guidance for the Integration of the Findings of the Chronic Hypertension and Pregnancy (CHAP) Study. Accessed October 17, 2025. https://www.acog.org/clinical/clinical‐guidance/practice‐advisory/articles/2022/04/clinical‐guidance‐for‐the‐integration‐of‐the‐findings‐of‐the‐chronic‐hypertension‐and‐pregnancy‐chap‐study.
- 2. Countouris, M. , Mahmoud Z., Cohen J. B., Crousillat D., Hameed A. B., Harrington C. M., Hauspurg A., et al. 2025. “Hypertension in Pregnancy and Postpartum: Current Standards and Opportunities to Improve Care.” Circulation 151(7): 490–507. 10.1161/CIRCULATIONAHA.124.073302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Ashworth, D. C. , Maule S. P., Stewart F., Nathan H. L., Shennan A. H., and Chappell L. C.. 2020. “Setting and Techniques for Monitoring Blood Pressure During Pregnancy.” Cochrane Database of Systematic Reviews 8(8): CD012739. 10.1002/14651858.CD012739.pub2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Lewkowitz, A. K. , and Hauspurg A.. 2024. “Perinatal Remote Blood Pressure Monitoring.” Obstetrics & Gynecology. 144(3): 339. 10.1097/AOG.0000000000005690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Chappell, L. C. , Tucker K. L., Galal U., Yu L.‐M., Campbell H., Rivero‐Arias O., Allen J., et al. 2022. “Effect of Self‐Monitoring of Blood Pressure on Blood Pressure Control in Pregnant Individuals With Chronic or Gestational Hypertension: The BUMP 2 Randomized Clinical Trial.” JAMA 327(17): 1666–78. 10.1001/jama.2022.4726. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Tucker, K. L. , Mort S., Yu L. Y.‐M., Campbell H., Rivero‐Arias O., Wilson H. M., Allen J., et al. 2022. “Effect of Self‐Monitoring of Blood Pressure on Diagnosis of Hypertension During Higher‐Risk Pregnancy: The BUMP 1 Randomized Clinical Trial.” JAMA 327(17): 1656–65. 10.1001/jama.2022.4712. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Ruppel, H. , Liu V. X., Kipnis P., Hedderson M. M., Greenberg M., Forquer H., Lawson B., and Escobar G. J., 2021. “Development and Validation of an Obstetric Comorbidity Risk Score for Clinical Use.” Women's Health Reports. 2(1): 507–15. 10.1089/whr.2021.0046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. CDC . 2024. Identifying Severe Maternal Morbidity (SMM). Maternal Infant Health. Accessed October 17, 2025. https://www.cdc.gov/maternal‐infant‐health/php/severe‐maternal‐morbidity/icd.html.
- 9. Gestational Hypertension and Preeclampsia. Accessed October 17, 2025. https://www.acog.org/clinical/clinical‐guidance/practice‐bulletin/articles/2020/06/gestational‐hypertension‐and‐preeclampsia.
- 10. Mehta, S. J. , Volpp K. G., Troxel A. B., Teel J., Reitz C. R., Purcell A., Shen H., McNelis K., Snider C. K., and Asch D. A., 2024. “Remote Blood Pressure Monitoring With Social Support for Patients With Hypertension: A Randomized Clinical Trial.” JAMA Network Open 7(6): e2413515. 10.1001/jamanetworkopen.2024.13515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Rajkumar, T. , Hennessy A., and Makris A.. 2025. “Remote Blood Pressure Monitoring in Women at Risk of or With Hypertensive Disorders of Pregnancy: A Systematic Review and Meta‐Analysis.” International Journal of Gynaecology and Obstetrics. 169(1): 89–104. 10.1002/ijgo.16059. [DOI] [PubMed] [Google Scholar]
- 12. Howard, E. , Emily H., Sherri L., Dorans K., and Biggio J.. 2025. “Evaluation of a Remote Blood Pressure Monitoring Program During Pregnancy: Utilization and Implementation Outcomes.” Pregnancy Hypertension 41: 101251. 10.1016/j.preghy.2025.101251. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Lemon, L. S. , Quinn B., Binstock A., Larkin J. C., Simhan H. N., and Hauspurg A.. 2024. “Clinical Outcomes Associated With a Remote Postpartum Hypertension Monitoring Program.” Obstetrics and Gynecology. 144(3): 377–85. 10.1097/AOG.0000000000005665. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Barshay, M. , Zang C., Bolden T., Rosenfeld L., Marko K., Sudimack A., Sebastian A., Loganathan A., Ma Y., and Meltzer A. C., 2025. “Tracking Engagement With Remote Patient Monitoring in Prenatal Care and Detection of Preeclampsia: A Retrospective Review.” Telemedicine and e‐Health 31(12): 1452–60. 10.1177/15305627251371519. [DOI] [PubMed] [Google Scholar]
- 15. Arkerson, B. J. , Finneran M. M., Harris S. R., Schnorr J., McElwee E. R., Demosthenes L., and Sawyer R.. 2023. “Remote Monitoring Compared With In‐Office Surveillance of Blood Pressure in Patients With Pregnancy‐Related Hypertension: A Randomized Controlled Trial.” Obstetrics and Gynecology. 142(4): 855–61. 10.1097/AOG.0000000000005327. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Ferrara, A. , Greenberg M., Zhu Y., Avalos L. A., Ngo A., Shan J., Hedderson M. M., and Quesenberry C. P., 2023. “Prenatal Health Care Outcomes Before and During the COVID‐19 Pandemic Among Pregnant Individuals and Their Newborns in an Integrated US Health System.” JAMA Network Open. 6(7): e2324011. 10.1001/jamanetworkopen.2023.24011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Tailored Prenatal Care Delivery for Pregnant Individuals. Accessed October 17, 2025. https://www.acog.org/clinical/clinical‐guidance/clinical‐consensus/articles/2025/04/tailored‐prenatal‐care‐delivery‐for‐pregnant‐individuals. [DOI] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Information
Data Availability Statement
The data used for this study contain protected health information (PHI) and access is protected by the Kaiser Permanente Northern California Institutional Review Board (IRB). Data are available from the Kaiser Permanente Division of Research for researchers who meet the criteria for access to confidential data.