Midwives’ competence in providing continuity of care using mobile health (mHealth) applications is limited in developing countries. This study identified and explored midwives’ competency and service needs to develop mHealth in Midwifery Continuity of Care (MCOC) education and training. It used an explanatory sequential mixed method, and was conducted from August to December 2021. A cross-sectional approach was used to find the characteristics and competency scope of 373 midwives in West Java, and continued with a qualitative design through a Focus Group Discussion (FGD) of 13 midwives. Descriptive data analysis (frequency, mean, deviation standard) and qualitative data analysis (coding, sub-themes, and theme) were conducted. In terms of the midwives who participated in this study, more than half were aged ≤ 35 years (58.98%), with a working period > 10 years (56.30%), had diploma degrees (71.12%), and used smartphones on average 1–12 h/day (78.28%). Most midwives needed to develop competency in the MCOC scope, including its early detection of the risk factor of complications and treatment management. They were concerned about the purposes, benefits, and design of mHealth. In summary, midwives’ competency indicators for early detection are more needed in MCOC using mHealth. Further research is required to evaluate midwives’ competence in MCOC using mHealth.

With the widespread application of digital healthcare, mobile health (mHealth) services are also developing in maternal and child health, primarily through community-based services, such as Posyandu in Indonesia. Patients need media for consultation and decision-making, while health workers are constrained in responding quickly. This study aimed to obtain information from pregnant women and midwives in developing a decision tree model as material for building a semi-automated chatbot. Using an exploratory qualitative approach, semi-structured interviews were conducted through focus group discussions (FGD) with pregnant women (n = 10) and midwives (n = 12) in March 2022. The results showed 38 codes, 15 categories, and 7 subthemes that generated 3 major themes: maternal health education, information on maternal health services, and health monitoring. The decision tree method was applied from these themes based on the needs of users, evidence, and expert sources to ensure quality. In summary, the need to use a semi-automated chatbot can be applied to education about maternal health and monitoring, where severe cases should be provided with non-automated communication with midwives. Applying the decision tree method ensured quality content, supported a clinical decision, and assisted in early detection. Furthermore, future research needs to measure user evaluation.

Posyandu is an Indonesian mother-child health, community-based healthcare. The provision of the Posyandu data quality map is crucial for analyzing results but is limited. This research aimed to (a) demonstrate data quality analysis on its completeness, accuracy, and consistency and (b) map the data quality in Indonesia for evaluation and improvement. An observational study was conducted using the Posyandu application. We observed data in Indonesia from 2019 to 2021. Data completeness was identified using children’s visits/year. Data accuracy was analyzed using WHO anthropometry z-score and implausible z-score values analyzing the outliers. Cronbach’s α of variables was used to know data consistency. STATA 15.1 SE and QGIS 3.10 was used to analyze and map the quality. Data completeness and accuracy in three years show a good start for the pilot project area, continued with declines in pandemic time, while some other areas demonstrated a small start, then slightly increased. The overall consistency decreased through the study period. A good report on data completeness can occur initially in a pilot project area, followed by others. Data accuracy and consistency can decrease during the pandemic. The app can be promising when synchronized with the government health information system.

The community’s mother and child health (MCH) and nutrition problems can be overcome through evidence-based health policy. Posyandu is an implementation of community empowerment in health promotion strategies. The iPosyandu application (app) is one of the health informatics tools, in which data quality should be considered before any Posyandu health interventions are made. This study aims to describe and assess differences in data quality based on the dimensions (completeness, accuracy, and consistency) of the secondary data collected from the app in Purwakarta Regency in 2019–2021. Obstacles and suggestions for improving its implementation were explored. This research applies a mixed-method explanatory approach. Data completeness was identified as the number of reported visits of children under five per year. Data accuracy was analyzed using WHO Z-score anthropometry and implausible Z-score values. Data consistency was measured using Cronbach’s alpha coefficient, followed by qualitative research with focus group discussions, in-depth interviews, and field observation notes. The quantitative study results found that some of the data were of good quality. The qualitative research identified the obstacles experienced using the iPosyandu app, one of them being that there were no regulations governing the use of iPosyandu to bridge the needs of the government, and provided suggestions from the field to improve its implementation.

The midwifery continuity-of-care model improves the quality and safety of midwifery services and is highly dependent on the quality of communication and information. The service uses a semi-automated chatbot-based digital health media service defined with the new term “telemidwifery”. This study aimed to explore the telemidwifery menu content for village midwives and pregnant women in the Purwakarta Regency, West Java, Indonesia. The qualitative research method was used to explore with focus group discussion (FGD). The data collection technique was purposive sampling. The research subjects were 15 village midwives and 6 multiparous pregnant women. The results of this study involved 15 characteristics of menu content: (1) Naming, (2) Digital Communication, (3) Digital Health Services, (4) Telemidwifery Features, (5) Digital Check Features, (6) Media Services, (7) Attractiveness, (8) Display, (9) Ease of Use, (10) Clarity of Instructions, (11) Use of Language, (12) Substances, (13) Benefits, (14) Appropriateness of Values, and (15) Supporting Components. The content characteristics of this telemidwifery menu were assigned to the ISO 9126 Model standards for usability, functionality, and efficiency. The conclusion is that the 15 themes constitute the characteristic menu content required within the initiation of telemidwifery.

Introduction: High-risk pregnancies are found in pregnant women who are too old, too young, too many, and too close (4T). Many risk factors become screening/early detection of pregnant women in the Poedji Rochjati scorecard, including maternal age 35 years, and mothers who have children 4 or more. Objective: The aim of this study was to describe the 4T risk incidence in pregnant women at the Jatinangor Community Health Center in 2020. Methods: The type of research used was descriptive-analytic with a quantitative approach and cross-sectional design. The population in this study were all pregnant women who were in Jatinangor Health Center. January-September 2020 period, amounting to 2357 people, using accidental sampling technique and obtained a total sample of 149 people. Data analysis using descriptive data analysis with frequency distribution. Results: The results showed that respondents who had the highest risk of 4T were 63.19% at the age of more than 35 years, and multiparity of 43.62%. Conclusion: In this study, it was found that the incidence of 4T risk in pregnant women at the Jatinangor Health Center in 2020 was mostly at the risk of being too old by 58.3% which was dominated by multiparity.