Quality and satisfaction with care following changes to the structure of obstetric care during the COVID-19 pandemic in a safety-net hospital in Georgia: Results from a mixed-methods study

Telehealth in antenatal care: recent insights and advances

BACKGROUND

For decades, antenatal care in high-resource settings has involved 12-14 face-to-face visits across pregnancy. The COVID-19 pandemic forced many care providers to rapidly embrace telehealth to reduce face-to-face visits. Here we review recent advances in telehealth used to provide antenatal care.

MAIN BODY

We conducted a narrative review examining the impact of telehealth on obstetric care. Two broad types of telehealth are used in antenatal care. The first is real-time telehealth, where consultations are done virtually instead of face-to-face. The second is remote monitoring, where in-clinic physical examinations are replaced with at-home alternatives. These can include blood pressure monitoring, fetal heart rate monitoring, and emerging technologies such as tele-ultrasound. Large cohort studies conducted during the pandemic era have shown that telehealth appears not to have increased adverse clinical outcomes for mothers or babies. However, further studies may be required to confidently conclude rare outcomes are unchanged, such as maternal mortality, serious morbidity, or stillbirth. Health economic studies suggest telehealth has the potential to reduce the financial cost of care provision. Telehealth in antenatal care seems to be acceptable to both pregnant women and healthcare providers.

CONCLUSION

Adoption of telehealth technologies may improve the antenatal care experience for women and reduce healthcare expenditure without adversely impacting health outcomes for the mother or baby. More studies are warranted to confirm telehealth does not alter the risk of rare outcomes such as maternal or neonatal mortality.

Changes to Prenatal Care Visit Frequency and Telehealth: A Systematic Review of Qualitative Evidence

OBJECTIVE

To systematically review patient, partner or family, and clinician perspectives, preferences, and experiences related to prenatal care visit schedules and televisits for routine prenatal care.

DATA SOURCES

PubMed, the Cochrane databases, EMBASE, CINAHL, ClinicalTrials.gov , PsycINFO, and SocINDEX from inception through February 12, 2022.

METHODS OF STUDY SELECTION

This review of qualitative research is a subset of a larger review on both the qualitative experiences and quantitative benefits and harms of reduced prenatal care visit schedules and televisits for routine prenatal care that was produced by the Brown Evidence-based Practice Center for the Agency for Healthcare Research and Quality. For the qualitative review, we included qualitative research studies that examined perspectives, preferences, and experiences about the number of scheduled visits and about televisits for routine prenatal care.

TABULATION, INTEGRATION, AND RESULTS

We synthesized barriers and facilitators to the implementation of reduced care visits or of televisits into 1 of 14 domains defined by the Theoretical Domains Framework (TDF) and a Best Fit Framework approach. We summarized themes within TDF domains. We assessed our confidence in the summary statements using the GRADE-CERQual (Grading of Recommendations Assessment, Development and Evaluation-Confidence in Evidence from Reviews of Qualitative research) tool. Four studies addressed the number of scheduled routine prenatal visits, and five studies addressed televisits. Across studies, health care professionals believed fewer routine visits may be more convenient for patients and may increase clinic capacity to provide additional care for patients with high-risk pregnancies. However, both patients and clinicians had concerns about potential lesser care with fewer visits, including concerns about quality of care and challenges with implementing new delivery-of-care models.

CONCLUSION

Although health care professionals and patients had some concerns about reduced visit schedules and use of televisits, several potential benefits were also noted. Our synthesis of qualitative evidence provides helpful insights into the perspectives, preferences, and experiences of important stakeholders with respect to implementing changes to prenatal care delivery that may complement findings of traditional quantitative evidence syntheses.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42021272287.

Health Services and Patient Satisfaction in IRAN during the COVID-19 Pandemic: A Methodology Based on Analytic Hierarchy Process and Artificial Neural Network

The aim of this study is to identify and classify the most important factors affecting patient satisfaction in the COVID-19 pandemic crisis considering economic effects. This is an analytical study using the analytic hierarchy process (AHP) method and ANN-MLP (Artificial neural network based on multilayer perceptron model as a supervised learning algorithm) as an innovative methodology. The questionnaire was completed by 72 healthcare experts (N = 72). The inter-class correlation (ICC) coefficient value was confirmed in terms of consistency to determine sampling reliability. The findings show that interpersonal care and organizational characteristics have the greatest and least influence, respectively. Furthermore, the observations confirm that the highest and lowest effective sub-criteria, respectively, are patient safety climate and accessibility. Based on the study’s objective and general context, it can be claimed that private hospitals outperformed public hospitals in terms of patient satisfaction during the COVID-19 pandemic. Focusing on performance sensitivity analysis shows that, among the proposed criteria to achieve the study objective, the physical environment criterion had the highest difference in private and public hospitals, followed by the interpersonal care criterion. Furthermore, we used a multilayer perceptron algorithm to assess the accuracy of the model and distinguish private and public hospitals as a novelty approach. Overfitting results in finding an MLP model which is reliable, and the accuracy of the model is acceptable.