Improving communication and teamwork during labor: A feasibility, acceptability, and safety study

BACKGROUND

TeamBirth was designed to promote best practices in shared decision making (SDM) among care teams for people giving birth. Although leading health organizations recommend SDM to address gaps in quality of care, these recommendations are not consistently implemented in labor and delivery.

METHODS

We conducted a mixed-methods trial of TeamBirth among eligible laboring patients and all clinicians (nurses, midwives, and obstetricians) at four high-volume hospitals during April 2018 to September 2019. We used patient and clinician surveys, abstracted clinical data, and administrative claims to evaluate the feasibility, acceptability, and safety of TeamBirth.

RESULTS

A total of 2,669 patients (approximately 28% of eligible delivery volume) and 375 clinicians (78% response rate) responded to surveys on their experiences with TeamBirth. Among patients surveyed, 89% reported experiencing at least one structured full care team conversation ("huddle") during labor and 77% reported experiencing multiple huddles. There was a significant relationship between the number of reported huddles and patient acceptability (P < 0.001), suggestive of a dose response. Among clinicians surveyed, 90% would recommend TeamBirth for use in other labor and delivery units. There were no significant changes in maternal and newborn safety measures.

CONCLUSIONS

Implementing a care process that aims to improve communication and teamwork during labor with high fidelity is feasible. The process is acceptable to patients and clinicians and shows no negative effects on patient safety. Future work should evaluate the effectiveness of TeamBirth in improving care experience and health outcomes.

Development, Implementation, and Outcomes of a Serious Illness Care Community of Practice

CONTEXT

Communities of Practice (CoP) can help geographically separated individuals who share a joint enterprise, mutual engagement, and a repertoire of tools to gain, maintain, and implement new skills, including serious illness communication.

OBJECTIVES

To investigate the health system uptake, implementation and outcomes of the Serious Illness Community of Practice (SICoP).

METHODS

Participants included members of the online SICoP, including participants from all 50 states in the United States and 44 countries, interested in implementation of the Serious Illness Care Program. Yearly surveys asked members about their program's composition, completed trainings, number of serious illness conversations, and utilization of the online SICoP tools and resources.

RESULTS

Over four years, membership in the SICoP increased from 429 to 1,912, with an estimated 17,785 clinicians trained and 38,945 serious illness conversations conducted. Members have continued to utilize and modify the SICoP resources.

CONCLUSIONS

Utilizing a CoP has contributed to improving the health care system implementation and process outcomes of serious illness communication training.

KEY MESSAGE

This article describes the implementation and health system outcomes of a Community of Practice developed to support serious illness communication. The results indicate that the membership of the community grew and that the community supported growth in the number of trained clinicians and the number of serious illness conversations conducted.

Just-in-time postnatal education programees to improve newborn care practices: needs and opportunities in low-resource settings

Worldwide, many newborns die in the first month of life, with most deaths happening in low/middle-income countries (LMICs). Families’ use of evidence-based newborn care practices in the home and timely care-seeking for illness can save newborn lives. Postnatal education is an important investment to improve families’ use of evidence-based newborn care practices, yet there are gaps in the literature on postnatal education programmes that have been evaluated to date. Recent findings from a 13 000+ person survey in 3 states in India show opportunities for improvement in postnatal education for mothers and families and their use of newborn care practices in the home. Our survey data and the literature suggest the need to incorporate the following strategies into future postnatal education programming: implement structured predischarge education with postdischarge reinforcement, using a multipronged teaching approach to reach whole families with education on multiple newborn care practices. Researchers need to conduct robust evaluation on postnatal education models incorporating these programee elements in the LMIC context, as well as explore whether this type of education model can work for other health areas that are critical for families to survive and thrive.

Segmenting clinicians’ usage patterns of a digital health tool in limited resource settings: methodology and initial results (Preprint)

Background

Evidence-based digital health tools allow clinicians to keep up with the expanding medical literature and provide safer and more accurate care. Understanding users’ online behavior in low-resource settings can inform programs that encourage the use of such tools. Our program collaborates with digital tool providers, including UpToDate, to facilitate free subscriptions for clinicians serving in low-resource settings globally.

Objective

We aimed to define segments of clinicians based on their usage patterns of UpToDate, describe the demographics of those segments, and relate the segments to self-reported professional climate measures.

Methods

We collected 12 months of clickstream data (a record of users’ clicks within the tool) as well as repeated surveys. We calculated the total number of sessions, time spent online, type of activity (navigating, reading, or account management), calendar period of use, percentage of days active online, and minutes of use per active day. We defined behavioral segments based on the distributions of these statistics and related them to survey data.

Results

We enrolled 1681 clinicians from 75 countries over a 9-week period. We based the following five behavioral segments on the length and intensity of use: short-term, light users (420/1681, 25%); short-term, heavy users (252/1681, 15%); long-term, heavy users (403/1681, 24%); long-term, light users (370/1681, 22%); and never-users (252/1681, 15%). Users spent a median of 5 hours using the tool over the year. On days when users logged on, they spent a median of 4.4 minutes online and an average of 71% of their time reading medical content as opposed to navigating or managing their account. Over half (773/1432, 54%) of the users actively used the tool for 48 weeks or more during the 52-week study period. The distribution of segments varied by age, with lighter and less use among those aged 35 years or older compared to that among younger users. The speciality of medicine had the heaviest use, and emergency medicine had the lightest use. Segments varied strongly by geographic region. As for professional climate, most respondents (1429/1681, 85%) reported that clinicians in their area would view the use of a online tool positively, and compared to those who reported other views, these respondents were less likely to be never-users (286/1681, 17% vs 387/1681, 23%) and more likely to be long-term users (655/1681, 39% vs 370/1681, 22%).

Conclusions

We believe that these behavioral segments can help inform the implementation of digital health tools, identify users who may need assistance, tailor training and messaging for users, and support research on digital health efforts. Methods for combining clickstream data with demographic and survey data have the potential to inform global health implementation. Our forthcoming analysis will use these methods to better elucidate what drives digital health tool use.

Who's your coach? The relationship between coach characteristics and birth attendants' adherence to the WHO Safe Childbirth Checklist

Background: Research demonstrates that coaching is an effective method for promoting behavior change, yet little is known about which attributes of a coach make them more or less effective. This post hoc, sub-analysis of the BetterBirth trial used observational data to explore whether specific coaches' and team leaders' characteristics were associated with improved adherence to essential birth practices listed on the World Health Organization Safe Childbirth Checklist. Methods: A descriptive analysis was conducted on the coach characteristics from the 50 BetterBirth coaches and team leaders. Data on adherence to essential birth practices by birth attendants who received coaching were collected by independent observers. Bivariate linear regression models were constructed, accounting for clustering by site, to examine the association between coach characteristics and attendants' adherence to practices.  Results: All of the coaches were female and the majority were nurses. Team leaders were comprised of both males and females; half had clinical backgrounds. There was no association between coaches' or team leaders' characteristics, namely gender, type of degree, or years of clinical training, and attendants' adherence to essential birth practices. However, a significant inverse relationship was detected between the coach or team leader's age and years of experience and the birth attendants' adherence to the checklist.  Conclusion: Younger, less experienced coaches were more successful in promoting essential birth practices adherence in this population. More data is needed to fully understand the relationship between coaches and birth attendants.

Using electronic health records to identify candidates for human immunodeficiency virus pre-exposure prophylaxis: An application of super learning to risk prediction when the outcome is rare

Human immunodeficiency virus (HIV) pre-exposure prophylaxis (PrEP) protects high risk patients from becoming infected with HIV. Clinicians need help to identify candidates for PrEP based on information routinely collected in electronic health records (EHRs). The greatest statistical challenge in developing a risk prediction model is that acquisition is extremely rare.

METHODS

Data consisted of 180 covariates (demographic, diagnoses, treatments, prescriptions) extracted from records on 399 385 patient (150 cases) seen at Atrius Health (2007-2015), a clinical network in Massachusetts. Super learner is an ensemble machine learning algorithm that uses k-fold cross validation to evaluate and combine predictions from a collection of algorithms. We trained 42 variants of sophisticated algorithms, using different sampling schemes that more evenly balanced the ratio of cases to controls. We compared super learner's cross validated area under the receiver operating curve (cv-AUC) with that of each individual algorithm.

RESULTS

The least absolute shrinkage and selection operator (lasso) using a 1:20 class ratio outperformed the super learner (cv-AUC = 0.86 vs 0.84). A traditional logistic regression model restricted to 23 clinician-selected main terms was slightly inferior (cv-AUC = 0.81).

CONCLUSION

Machine learning was successful at developing a model to predict 1-year risk of acquiring HIV based on a physician-curated set of predictors extracted from EHRs.

Using HIV Surveillance Laboratory Data to Identify Out-of-Care Patients

HIV-associated laboratory tests reported to public health surveillance have been used as a proxy measure of care engagement of HIV+ individuals. As part of a Health Resources and Services Administration (HRSA) Special Projects of National Significance (SPNS) Initiative, the Massachusetts Department of Public Health (MDPH) worked with three pilot clinical facilities to identify HIV+ patients whose last HIV laboratory test occurred at the participating facility but who then appeared to be out of care, defined as an absence of HIV laboratory test results reported to MDPH for at least 6 months. The clinical facilities then reviewed medical records to determine whether these patients were actually not in care, or if there was another reason that they did not have a laboratory test performed, and provided feedback to MDPH on each of the presumed out-of-care patients. In the first year of the pilot project, 37% of patients who appeared to be out of care based on laboratory data were confirmed to be out of care after review of clinical health records. Of those patients who were confirmed to be out of care, 55% had a subsequent laboratory test within 3 months, and 72% had a laboratory test within 6 months, indicating that they had re-engaged with a care provider. MDPH found that it was essential to have clinical staff confirm the care status of patients who were presumed to be out of care based on surveillance data.