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      OB Nest randomized controlled trial: a cost comparison of reduced visit compared to traditional prenatal care

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          Abstract

          Background

          Traditional prenatal care includes up to 13 in person office visits, and the cost of this care is not well-described. Alternative models are being explored to better meet the needs of patients and providers. OB Nest is a telemedicine-enhanced program with a reduced frequency of in-person prenatal visits. The cost implications of connected care services added to prenatal care packages are unclear.

          Methods

          Using data from the OB Nest randomized, controlled trial we analyzed the provider and staff time associated with prenatal care in the traditional and OB Nest models. Fewer visits were required for OB Nest, but given the compensatory increase in connected care activity and supplies, the actual cost difference is not known. Nursing and provider staff time was prospectively recorded for all patients enrolled in the OB Nest clinical trial. Published 2015 national wages for healthcare workers were used to calculate the actual labor cost of providing either traditional or OB Nest prenatal care in 2015 US dollars. Overhead expenses and opportunity costs were not considered.

          Results

          Total provider cost was decreased caring for the OB Nest participants, but nursing cost was increased. OB Nest care required an average of 160.8 (+/− 45.0) minutes provider time and 237 (+/− 25.1) minutes nursing time, compared to 215.0 (+/− 71.6) and 99.6 (+/− 29.7) minutes for traditional prenatal care ( P < 0.01). This translated into decreased provider cost and increased nursing cost (P < 0.01). Supply costs increased, travel costs declined, and overhead costs declined in the OB Nest model.

          Conclusions

          In this trial, labor cost for OB Nest prenatal care was 34% higher than for traditional prenatal care. The increased cost is largely attributable to additional nursing connected care time, and in some practice settings may be offset by decreased overhead costs and increased provider billing opportunities. Future efforts will be focused on development of digital solutions for some routine nursing tasks to decrease the overall cost of the model.

          Trial registrations

          ClinicalTrials.gov Identifier: NCT02082275.

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          Most cited references7

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          US Health Care Spending by Payer and Health Condition, 1996-2016

          Joseph L. Dieleman, Jackie Cao, Abby Chapin … (2020)
          How does spending on different health conditions vary by payer (public insurance, private insurance, or out-of-pocket payments) and how has this spending changed over time? From 1996 to 2016, total health care spending increased from an estimated $1.4 trillion to an estimated $3.1 trillion. In 2016, private insurance accounted for 48.0% (95% CI, 48.0%-48.0%) of health care spending, public insurance for 42.6% (95% CI, 42.5%-42.6%) of health care spending, and out-of-pocket payments for 9.4% (95% CI, 9.4%-9.4%) of health care spending. After adjusting for population size and aging, the annualized spending growth rate was 2.6% (95% CI, 2.6%-2.6%) for private insurance, 2.9% (95% CI, 2.9%-2.9%) for public insurance, and 1.1% (95% CI, 1.0%-1.1%) for out-of-pocket payments. Understanding how much each payer spent on each health condition and how these amounts have changed over time can inform health care policy. US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these amounts have changed over time. To estimate US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition. Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting. National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016. Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product [GDP]; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion [95% CI, $116.3-$149.7 billion]) and most had private insurance (56.4% [95% CI, 52.6%-59.3%]). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion [95% CI, $105.7-$115.9 billion]) and most had public insurance (49.8% [95% CI, 44.4%-56.0%]). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion [95% CI, $81.1-$95.5 billion]), falls ($87.4 billion [95% CI, $75.0-$100.1 billion]), urinary diseases ($86.0 billion [95% CI, $76.3-$95.9 billion]), skin and subcutaneous diseases ($85.0 billion [95% CI, $80.5-$90.2 billion]), osteoarthritis ($80.0 billion [95% CI, $72.2-$86.1 billion]), dementias ($79.2 billion [95% CI, $67.6-$90.8 billion]), and hypertension ($79.0 billion [95% CI, $72.6-$86.8 billion]). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%). Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably. This study estimates health care spending for the most common health conditions in the United States, including low back pain and musculoskeletal disorders, diabetes, and ischemic heart disease, between 1996 and 2016.
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            OB Nest: Reimagining Low-Risk Prenatal Care

            Mekayla Beaver, Marnie de Mooij, Barbara A. Barry … (2018)
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              Implementation of a new prenatal care model to reduce office visits and increase connectivity and continuity of care: protocol for a mixed-methods study

              Jennifer L Ridgeway, Annie LeBlanc, Megan E. Branda … (2015)
              Background Most low-risk pregnant women receive the standard model of prenatal care with frequent office visits. Research suggests that a reduced schedule of visits among low-risk women could be implemented without increasing adverse maternal or fetal outcomes, but patient satisfaction with these models varies. We aim to determine the effectiveness and feasibility of a new prenatal care model (OB Nest) that enhances a reduced visit model by adding virtual connections that improve continuity of care and patient-directed access to care. Methods and design This mixed-methods study uses a hybrid effectiveness-implementation design in a single center randomized controlled trial (RCT). Embedding process evaluation in an experimental design like an RCT allows researchers to answer both “Did it work?” and “How or why did it work (or not work)?” when studying complex interventions, as well as providing knowledge for translation into practice after the study. The RE-AIM framework was used to ensure attention to evaluating program components in terms of sustainable adoption and implementation. Low-risk patients recruited from the Obstetrics Division at Mayo Clinic (Rochester, MN) will be randomized to OB Nest or usual care. OB Nest patients will be assigned to a dedicated nursing team, scheduled for 8 pre-planned office visits with a physician or midwife and 6 telephone or online nurse visits (compared to 12 pre-planned physician or midwife office visits in the usual care group), and provided fetal heart rate and blood pressure home monitoring equipment and information on joining an online care community. Quantitative methods will include patient surveys and medical record abstraction. The primary quantitative outcome is patient-reported satisfaction. Other outcomes include fidelity to items on the American Congress of Obstetricians and Gynecologists standards of care list, health care utilization (e.g. numbers of antenatal office visits), and maternal and fetal outcomes (e.g. gestational age at delivery), as well as validated patient-reported measures of pregnancy-related stress and perceived quality of care. Quantitative analysis will be performed according to the intention to treat principle. Qualitative methods will include interviews and focus groups with providers, staff, and patients, and will explore satisfaction, intervention adoption, and implementation feasibility. We will use methods of qualitative thematic analysis at three stages. Mixed methods analysis will involve the use of qualitative data to lend insight to quantitative findings. Discussion This study will make important contributions to the literature on reduced visit models by evaluating a novel prenatal care model with components to increase patient connectedness (even with fewer pre-scheduled office visits), as demonstrated on a range of patient-important outcomes. The use of a hybrid effectiveness-implementation approach, as well as attention to patient and provider perspectives on program components and implementation, may uncover important information that can inform long-term feasibility and potentially speed future translation. Trial registration Trial registration identifier: NCT02082275 Submitted: March 6, 2014 Electronic supplementary material The online version of this article (doi:10.1186/s12884-015-0762-2) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Regan N. Theiler: Theiler.Regan@mayo.edu
                Journal
                Journal ID (nlm-ta): BMC Pregnancy Childbirth
                Journal ID (iso-abbrev): BMC Pregnancy Childbirth
                Title: BMC Pregnancy and Childbirth
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1471-2393
                Publication date (Electronic): 21 January 2021
                Publication date PMC-release: 21 January 2021
                Publication date Collection: 2021
                Volume: 21
                Electronic Location Identifier: 71
                Affiliations
                [1 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Department of Obstetrics and Gynecology, Mayo Clinic, ; 200 First Street SW, Rochester, MN 55905 USA
                [2 ]GRID grid.66875.3a, ISNI 0000 0004 0459 167X, Biomedical Statistics and Informatics, Mayo Clinic, ; Rochester, MN 55905 USA
                Article
                Publisher ID: 3557
                DOI: 10.1186/s12884-021-03557-3
                PMC ID: 7818056
                PubMed ID: 33478433
                SO-VID: bb0100ca-9b37-4407-9687-a01a35c6d30f
                Copyright © © The Author(s) 2021
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 23 November 2020
                Date accepted : 30 December 2020
                Categories
                Subject: Research Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                ScienceOpen disciplines: Obstetrics & Gynecology
                Keywords: pregnancy,prenatal,obstetrics,midwifery,obstetrician,nest,telemedicine
                Data availability:
                ScienceOpen disciplines: Obstetrics & Gynecology
                Keywords: pregnancy, prenatal, obstetrics, midwifery, obstetrician, nest, telemedicine

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