Problem

Rural indigenous communities in Guatemala have some of the worst neonatal outcomes in Latin America.1 Many births in rural Guatemala occur in the home, under the care of lay midwives. In addition to encouraging and fostering acceptance of facility-level births among the population, working to improve midwives’ capacity to assess and refer neonates to facility-level care when births do occur in the home is an important area where systems of care could be improved.

Wuqu’ Kawoq | Maya Health Alliance is a nonprofit primary healthcare system based in rural Guatemala. Since 2015, we have been working closely with lay midwives and regional public health authorities to improve workflows around the detection of maternal and neonatal complications and increase referrals of patients in need of high-level care. This work has included the development of mobile health technology to help midwives standardise their workflows and connect them with on-call medical teams who assist with triage. The use of this technology and back-up support has increased rates of obstetrical referrals to hospital care by >50%.2 However, similar improvements in the referral of neonates have not been observed. In fact, in the baseline period prior to the improvement project we report here, the rate of neonatal referral was only 1.5% of all live births. In comparison, high-quality prospective data from the region of Guatemala where we work have shown low birth weight rates of at least 10%, suggested that an ideal detection and referral rate of neonates for paediatric evaluation should be at least this high.3

With the goal of improving the neonatal referral rate among our collaborating midwives, we convened a working group of midwives and clinical and administrative staff from Wuqu’ Kawoq | Maya Health Alliance to investigate key drivers of neonatal care. Several key themes for improvement were discovered. First, both midwives themselves and many of the supporting clinicians providing triage support through mHealth felt insufficiently trained in neonatal assessment techniques and warning signs. Second, midwives’ mHealth interface lumped the neonatal assessment into the maternal intrapartum and postpartum visit, leading to them frequently skipping the assessment due to clinical attention to the mother. Third, many midwives with low numeracy skills felt interpreting birth weights to classify neonates as a low birth weight to be a significant challenge. Finally, as families reimbursed midwives only for maternal care, there was no specific incentive to spend extra time and effort on neonatal assessments.

With these findings in mind, our SMART (Specific, Measurable, Achievable, Relevant, and Timely) aim for this improvement project was to improve the proportion of neonates born at home under the care of midwives who were referred to a higher level of medical care to 10% within 6 months and, subsequently, to show sustained improvement over an additional 6-month period.

Measurement

Our primary quality improvement outcome was to increase the rate of referral of neonates born to participating midwives within the first week of life. Secondary outcomes were the proportion of neonates receiving a documented examination by participating midwives within the first week of life, the proportion of neonates identified as needing referral who completed the referral (rather than remaining at home) and neonatal death. Process outcomes monitored included volume of phone calls and text messages to the on-call medical team.

Given the limited clinical and demographic information collected by participating midwives as part of their routine workflow, community health workers bilingual in Spanish and Kaqchikel Maya performed home visits with new pregnant patients under midwife care as part of this improvement project. During these visits, they obtained signed informed consent for the use of individual-level data and collected demographic and prior obstetrical history, which was documented in each patient’s electronic health record (EHR). They also conducted a second visit, approximately 1–2 weeks after delivery, to collect additional information on neonatal health and any perinatal or postnatal complications. In addition, all calls by midwives to the on-call line, as well as the generation and clinical outcome of all neonatal referrals were documented in the EHR. A physician reviewed all the charts to confirm final outcomes and diagnosis, confirming these through conversation with the clinical staff at the receiving facilities when necessary. Call-backs to each midwife on a monthly basis by the nurse champion were used to verify the accuracy and completeness of birth records, which were all double-checked in the EHR. All data used in this project were subsequently extracted from the EHR. In particular, the monthly neonatal referral rate was calculated as the number of neonatal referrals in the EHR over the number of registered births.

Subsequently, data were extracted from the EHR using a structured query language. Descriptive statistics were generated using STATA V.14 (College Station, Texas, USA). Run charts and proportion control charts (P charts) were constructed using Minitab V.17 (State College, Pennsylvania, USA). For control charts, we used three calendar quarters of data preceding the intervention (January–September 2017) to construct the baseline. We used 3-sigma control limits and defined special cause as data points lying outside the control limits. The study was approved by the Institutional Review Boards of Maya Health Alliance (WK-2017-003) and Partners Healthcare (2017P000122). Individual patients gave signed, informed consent for the use of individual-level data.

Design

Improvement team and activities

We formed a quality improvement team, which included a nurse champion (YJ), physician champion (MJ) at Maya Health Alliance as well as representative lay midwives and institutional leadership (PR). This team began by investigating the key drivers of neonatal care by midwives and related opportunities for improvement, as summarised above under ‘Problem’. Key areas were summarised using a key driver diagram and interventions to address each driver developed (figure 1).

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Figure 1

Driver diagram for neonatal care continuum improvement initiative. Primary drivers and proposed interventions from the project planning phase are shown in relation to the key project objective.

After the start of improvement activities, the team met every 2 weeks to review performance data and evaluate and modify proposed interventions using a ‘Plan-Do-Study-Act’ methodology.23 The physician champion extracted clinical and process outcomes data from Maya Health Alliance’s EHR in advance of each meeting, presenting data in the form of run charts to assist with decision making and intervention evaluation.

To address the need for training in neonatal assessments and triage by midwives and back-up clinical staff, the improvement team reviewed Guatemala’s public health guidelines for primary care, as well as a review of the WHO’s Safe Childbirth Checklist and Essential Newborn Care Course.24–26 This content was distilled into short 1-day refresher courses for clinical staff.

In addition to improve standardised workflows for neonatal assessment by midwives—and to assist with improved decision support around classifying low birth weight—the improvement team proposed several changes to the existing mHealth interface in use by midwives (figure 1). This included a new structured symptom checklist for neonatal warning signs (online supplementary table 1, online supplementary figure 1) as well as a visual interface with automated decision support for entering birth weight (online supplementary figure 1). For these modifications, an agile design process was used whereby a small group of five midwives worked closely with our software engineers to iteratively alpha test changes to the interface.2 After initial design decisions were worked out, a larger group of 15 midwives began a beta test, using the modified interface in their clinical work, with additional feedback and modifications by the software team before implementation with the entire cohort. In the final application modifications, all user screens are pictogram-driven and do not require text entry. In addition, audio prompts in Kaqchikel Maya provide instructions to midwives on each screen (online supplementary figure 1). We have previously shown that these design features lead to applications with excellent usability characteristics, even for lay midwives with limited literacy and no prior exposure to smartphone technology.2 12

Supplemental material

[bmjoq-2019-000775supp001.pdf]

Supplemental material

[bmjoq-2019-000775supp002.pdf]

Implementation strategy

Our general strategy was to roll-out proposed improvement interventions from least to the most complex (table 1). We began with content refresher training on neonatal care, an intervention that was easy to implement in a timely manner while our development team worked on modifications to the mHealth platform. Similarly, performance incentives were delayed until mHealth modifications were complete, as we planned to use device visit logs to verify visit completion. Throughout the improvement initiative, midwives followed standard referral guidelines, referring neonates to local government health posts or to the regional referral hospital, as appropriate. The quality improvement team assisted by documenting all referral outcomes and providing feedback to both midwives and health personnel on neonatal outcomes.27

Lessons and limitations

Rural indigenous communities in Guatemala have some of the worst neonatal outcomes in Latin America. Given the fact that many births in rural Guatemala occur in the home under the care of lay midwives, in this improvement project we sought to identify and overcome barriers to timely neonatal care by lay midwives in rural communities (figure 1). The improvement project occurred within a long-standing practice collaborative with lay midwives and an existing mHealth platform that could be modified relatively easily to introduce new workflows designed by the improvement team.

Specific interventions that we implemented included (a) training both midwives and on-call medical providers on neonatal assessment, warning signs and triage; (b) augmented decision support for neonatal assessments (and, especially, assessment of low birth weight) through an existing mHealth platform; and (c) financial production incentives for midwives conducting neonatal examinations. We found that this package of interventions effectively increased timely neonatal examinations by midwives and the proportion of neonates referred for evaluation at a higher level of care (figure 2A). The initiative seemed highly acceptable to participating communities, as the large majority of neonates identified as needing evaluation were successfully referred (figure 2B).

Our findings can be compared with others from the literature in Guatemala and elsewhere. For example, several studies have demonstrated that lay midwives can be trained to provide some elements of neonatal care with improvements in selected outcomes, including early neonatal referrals.13–17 A large multisite trial that included Guatemala trained lay midwives and other community-based providers on essential newborn care and neonatal resuscitation, it reduced stillbirth but not neonatal death rate and did not assess for changes in neonatal referral patterns.28 Another study in Guatemala provided similar training but added professional midwifery liaisons to advocate for improved perinatal referrals by lay midwives, but the impact on primary outcomes and referral patterns have not yet been reported.29 Finally, a recent metanalysis concluded that training for lay midwives and other frontline providers on essential newborn care and neonatal resuscitation can improve stillbirth and early neonatal mortality, but that gains after the first week of neonatal life require a more comprehensive continuum of care interventions.30

A strength of our improvement study, based on our key drivers analysis (figure 1) is that it addresses multiple steps along the neonatal care continuum, including not just training in neonatal assessment and triage by frontline providers (similar to the training offered in nearly every other neonatal intervention reviewed above), but also real-time mHealth and on-call support for assistance after the decision to refer was made. We used a mHealth design approach that included a close design collaboration with the midwife end users and produced an application usable even by those midwives with limited literacy or prior smartphone technology exposure.2 We also addressed the financial disincentives lay midwives face for taking on additional clinical work with neonates, which many may feel is outside their scope or interest. Multipronged continuum of care interventions like this is especially important in Guatemala where the extensive literature on obstetrical referrals has shown that the decision to refer is complex and often fear-laden, given the prevalence of disrespect and low-quality care that both lay midwives and their indigenous patients often experience within referral facilities.12 31–34

Our improvement study has several limitations. First, birth volume and in-home birth outcomes were self-reported by participating lay midwives. Although the improvement team was in close contact with each midwife, working to verify clinical volume and helping to adjudicate outcomes, the possibility remained for under-reporting both of overall births and, specifically, adverse outcomes. In addition, the overall observed neonatal mortality rate was high (7% among referred neonates, 3% among non-referred). It is possible that this rate may be artificially elevated, as deaths in the home were self-reported by midwives who may not have carefully distinguished between stillbirths and early neonatal deaths. At the same time, these rates do highlight the marked disparities in health outcomes in this rural population. For comparison, a large, representative population-based cohort study in rural Guatemala has also recently reported a 2%–3% mortality rate among neonates.3 35 Along these lines, although our time between rare events control chart showed a trend toward increased time between neonatal deaths, the sample of participating midwives and the birth cohort was small and therefore the data from our improvement project did not have the power to detect a change in mortality over the time course of the project.

Finally, this improvement intervention occurred within the context of a small, motivated group of practising midwives and a nonprofit primary care organisation providing technology support. Additional work will be needed to determine if similar improvement interventions can generalise other practices contexts in Guatemala where lay midwives provide care. As well, the financial sustainability of this and similar mHealth applications is an important area of ongoing exploration, if solutions like this are to be sustainable and scalable. Potential areas we are exploring now include (a) development of a ‘subscription model’ for smartphone applications with individual lay midwives as subscribers; (b) licensing of the application to institutional uses, such as large non-profits; (c) exploring subsidies by telecommunications providers in Guatemala.