BackgroundProper monitoring of labor and childbirth prevents many pregnancy-related complications. However, monitoring is still poor in many places partly due to the usability concerns of support tools such as the partograph. In 2011, the World Health Organization (WHO) called for the development and evaluation of context-adaptable electronic health solutions to health challenges. Computerized tools have penetrated many areas of health care, but their influence in supporting health staff with childbirth seems limited.ObjectiveThe objective of this scoping review was to determine the scope and trends of research on computerized labor monitoring tools that could be used by health care providers in childbirth management.MethodsWe used key terms to search the Web for eligible peer-reviewed and gray literature. Eligibility criteria were a computerized labor monitoring tool for maternity service providers and dated 2006 to mid-2016. Retrieved papers were screened to eliminate ineligible papers, and consensus was reached on the papers included in the final analysis.ResultsWe started with about 380,000 papers, of which 14 papers qualified for the final analysis. Most tools were at the design and implementation stages of development. Three papers addressed post-implementation evaluations of two tools. No documentation on clinical outcome studies was retrieved. The parameters targeted with the tools varied, but they included fetal heart (10 of 11 tools), labor progress (8 of 11), and maternal status (7 of 11). Most tools were designed for use in personal computers in low-resource settings and could be customized for different user needs.ConclusionsResearch on computerized labor monitoring tools is inadequate. Compared with other labor parameters, there was preponderance to fetal heart monitoring and hardly any summative evaluation of the available tools. More research, including clinical outcomes evaluation of computerized childbirth monitoring tools, is needed.

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Objective There is no consensus on the essential parameters to monitor during childbirth, when to start, and the rate of monitoring them. User disagreement contributes to inconsistent use of the twelve-item modified World Health Organization partograph that is started when the cervix is at least 4 cm dilated. The inconsistent use is associated with poor outcomes at birth. Our objective was to identify the perspectives of childbirth experts on what and when to routinely monitor during childbirth in low resource settings as we develop a more acceptable childbirth clinical decision support tool. Method We carried out a Delphi study with two survey rounds in early 2018. The online questionnaire covered the partograph items like foetal heart, cervical dilation, and blood pressure, and their monitoring rates. We invited panellists with experience of childbirth care in sub-Saharan Africa. Consensus was pre-set at 70% panellists rating a parameter and we gathered some qualitative reasons for choices. Results We analysed responses of 76 experts from 13 countries. There was consensus on six important parameters including foetal heart rate, amniotic fluid clearness, cervical dilation, strength of uterine contractions, maternal pulse, and blood pressure. Two in three experts expressed support for changing the monitoring intervals for some parameters in the partograph. 63% experts would raise the partograph starting point while 58% would remove some items from it. Consensus was reached on monitoring the cervical dilation at 4-hourly intervals and there was agreement on monitoring the foetal heart rate one-hourly. However, other parameters only showed majority intervals and without reaching agreement scores. The suggested intervals were two-hourly for strength of uterine contractions, and four-hourly for amniotic fluid thickness, maternal pulse and blood pressure. The commonest reason for their opinions was the more demanding working conditions. Conclusion There was agreement on six partograph items being essential for routine monitoring at birth, but the frequency of monitoring could be changed. To increase acceptability, revisions to birth monitoring guidelines have to be made in consideration of opinions and working conditions of several childbirth experts in low resource settings. Electronic supplementary material The online version of this article (10.1186/s12978-019-0786-6) contains supplementary material, which is available to authorized users.

Background After determining the key childbirth monitoring items from experts, we designed an algorithm (LaD) to represent the experts’ suggestions and validated it. In this paper we describe an abridged algorithm for labor and delivery management and use theoretical case to compare its performance with human childbirth experts. Objective The objective of this study was to describe the LaD algorithm, its development, and its validation. In addition, in the validation phase we wanted to assess if the algorithm was inferior, equivalent, or superior to human experts in recommending the necessary clinical actions during childbirth decision making. Methods The LaD algorithm encompasses the tracking of 6 of the 12 childbirth parameters monitored using the World Health Organization (WHO) partograph. It has recommendations on how to manage a patient when parameters are outside the normal ranges. We validated the algorithm with purposively selected experts selecting actions for a stratified sample of patient case scenarios. The experts’ selections were compared to obtain pairwise sensitivity and false-positive rates (FPRs) between them and the algorithm. Results The mean weighted pairwise sensitivity among experts was 68.2% (SD 6.95; 95% CI 59.6-76.8), whereas that between experts and the LaD algorithm was 69.4% (SD 17.95; 95% CI 47.1-91.7). The pairwise FPR among the experts ranged from 12% to 33% with a mean of 23.9% (SD 9.14; 95% CI 12.6-35.2), whereas that between experts and the algorithm ranged from 18% to 43% (mean 26.3%; SD 10.4; 95% CI 13.3-39.3). The was a correlation (mean 0.67 [SD 0.06]) in the actions selected by the expert pairs for the different patient cases with a reliability coefficient (α) of .91. Conclusions The LaD algorithm was more sensitive, but had a higher FPR than the childbirth experts, although the differences were not statistically significant. An electronic tool for childbirth monitoring with fewer WHO-recommended parameters may not be inferior to human experts in labor and delivery clinical decision support.

BACKGROUND After determining the key childbirth monitoring items from experts in childbirth, we designed an algorithm to represent the experts’ suggestions and we validated it. OBJECTIVE In this paper we describe the abridged algorithm for labour and delivery (LaD) management and use theoretical case to compare its performance to human childbirth experts. METHODS The LaD algorithm encompasses the tracking of six of the 12 childbirth parameters monitored using the World Health Organisation partograph. It has recommendations on how to manage a patient when parameters are outside the normal ranges. We validated the algorithm with purposively selected experts selecting actions for a stratified sample of patient case scenarios. The experts’ selections were compared to get pairwise sensitivity and false positive rates (FPR) between them and the algorithm. RESULTS The mean weighted pairwise sensitivity among experts was 68.2% (StD. 6.95; CI. 59.6, 76.8) while that between the experts and LaD algorithm was 69.4% (StD. 17.95; CI. 47.1, 91.7). The pairwise FPR amongst the experts ranged from 12% to 33% with a mean of 23.9% (CI. 12.6, 35.2) and that between the experts and the algorithm ranged from 18% to 43% (mean 26.3%; CI. 13.3, 39.3). The was a correlation (mean of 0.67) in the actions selected by the expert pairs for the different patient cases with a reliability coefficient 0.91. CONCLUSIONS The LaD algorithm was more sensitive but with a higher FPR than the childbirth experts, although the differences were not statistically significant. An electronic tool for childbirth monitoring with fewer than WHO-recommended parameters may not be inferior to human experts in labour and delivery clinical decision support.