This study aimed (1) to examine the association between patient engagement with a bidirectional, semiautomated postdischarge texting programme and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey outcomes, readmissions and revisit rates in a large health system and (2) to describe operational and clinical flow considerations for implementing a postdischarge texting programme.

The study involved 1 main academic hospital (beds: 2500+) and 6 community hospitals (beds: 190–400, averaging 300 beds per hospital) in Houston, Texas.

Retrospective, observational cohort study between non-engaged patients (responded with 0–2 incoming text messages) and engaged patients (responded with 3+ incoming, patient-initiated text messages) between December 2022 and May 2023. We used the two-tailed t-test for continuous variables and ² test for categorical variables to compare the baseline characteristics between the two cohorts. For the binary outcomes, such as the revisit (1=yes, vs 0=no) and readmissions (1=yes vs 0=no), we constructed mixed effect logistic regression models with the random effects to account for repeated measurements from the hospitals. For the continuous outcome, such as the case mix index (CMI), a generalised linear quantile mixed effect model was built. All tests for significance were two tailed, using an alpha level of 0.05, and 95% CIs were provided. Significance tests were performed to evaluate the CMI and readmissions and revisit rates.

From 78 883 patients who were contacted over the course of this pilot implementation, 49 222 (62.4%) responded, with 39 442 (50%) responded with 3+ incoming text messages. The engaged cohort had higher HCAHPS scores in all domains compared with the non-engaged cohort. The engaged cohort used significantly fewer 30-day acute care resources, experiencing 29% fewer overall readmissions and 20% fewer revisit rates (23% less likely to revisit) and were 27% less likely to be readmitted. The results were statistically significant for all but two hospitals.

This study builds on the few postdischarge texting studies, and also builds on the patient engagement literature, finding that patient engagement with postdischarge texting can be associated with fewer acute care resources. To our knowledge, this is the only study that documented an association between a text-based postdischarge programme and HCAHPS scores, perhaps owing to the bidirectionality and ease with which patients could interact with nurses. Future research should explore the texting paradigms to evaluate their associated outcomes in a variety of postdischarge applications.

During the COVID-19 pandemic, most essential services experienced some level of disruption. Disruption in LMICs was more severe than in HICs. Early reports suggested that services for maternal and newborn health were disproportionately affected, raising concerns about health equity. Most disruption indicators measure demand-side disruption, or they conflate demand-side and supply-side disruption. There is currently no published guidance on measuring supply-side disruption. The primary objective of this review was to identify methods and approaches used to measure supply-side service disruptions to maternal and newborn health services in the context of COVID-19.

We carried out a systematic review and have created a typology of measurement methods and approaches using narrative synthesis.

We searched MEDLINE, EMBASE and Global Health in January 2023. We also searched the grey literature.

We included empirical studies describing the measurement of supply-side service disruption of maternal and newborn health services in LMICs in the context of COVID-19.

We extracted the aim, method(s), setting, and study outcome(s) from included studies. We synthesised findings by type of measure (ie, provision or quality of services) and methodological approach (ie, qualitative or quantitative).

We identified 28 studies describing 5 approaches to measuring supply-side disruption: (1) cross-sectional surveys of the nature and experience of supply-side disruption, (2) surveys to measure temporal changes in service provision or quality, (3) surveys to create composite disruption scores, (4) surveys of service users to measure receipt of services, and (5) clinical observation of the provision and quality of services.

Our review identified methods and approaches for measuring supply-side service disruption of maternal and newborn health services. These indicators provide important information about the causes and extent of supply-side disruption and provide a useful starting point for developing specific guidance on the measurement of service disruption in LMICs.