Social Media Learning Collaborative for Public Health Preparedness

A significant challenge for scientific discovery in public health preparedness is how to optimize the translation, dissemination, and implementation of research findings into practice. Current dissemination and implementation methods are largely passive, relying on workers to individually access the scientific literature, translate research findings into their practices, and devise ways to implement changes at both the system and workforce levels. As a result of lack of time and resources; complexities in searching and navigating to the relevant publications, training, and tools; and poor alignment of these materials with learners’ knowledge, experience, or job responsibilities, few practitioners succeed in implementing their research findings.

In the case of local public health workforces in town, city, and county health departments, the problem is even more daunting. Sifting through the immense amount of information to find locally applicable solutions can lead to frustration and ultimately to surrendering the search for best practices. We pilot tested a social media learning collaborative prototype model to address current barriers to the translation, dissemination, and implementation of best practices for public health preparedness.

NEED FOR COLLABORATIVE LEARNING MODELS

Gaps in access to education and training can result in public health workforces having reduced capacity and capability to respond effectively to threats, emergencies, and disasters. Although workforce training content is abundant, choosing the dissemination platform that optimizes the likelihood of implementation in practice is often difficult.¹ Optimally, this platform should (1) translate public health research findings into actionable practice opportunities, (2) lead to dissemination of training resources and tools to the most receptive audience, (3) engage learners by promoting active involvement and social connectedness, and (4) facilitate adoption of education and training resources through commonly shared goals and objectives.

Even when public health practitioners successfully access research findings, translation to practice is often unsuccessful because the material does not correspond to their level of knowledge, experience, or job responsibility. This is especially true for smaller town, city, and county health departments that have few individuals trained in critically evaluating research findings, especially in public health preparedness. In addition, although the numbers of programs and graduates in public health have risen over the past several decades, the estimated number of local health department employees has decreased by nearly 30% since 2008. This trend has created an even greater void with respect to mentorship and collaboration.

As collaborative learning methodology advances, studies of the effectiveness of forming collaborative learning practice communities have followed, especially during the most recent decade. Collaborative learning methods incorporate educational approaches involving cooperative and joint intellectual efforts by instructors and students that could potentially offer a more effective approach to dissemination and implementation.² To clarify, whereas collaborative learning refers to methods for instruction and learning, the “learning collaborative” typically refers to the structure, design, and composition of the collective group that unites researchers, practitioners, and policymakers to create a “community of practice” environment.³

Although institutions of higher learning have been applying collaborative learning methods to improve education for many years,⁴ the fields of public health and health care have only recently embraced collaborative learning methods with applications to improve the practice and quality of care as outlined formally in 2003 by the Institute for Healthcare Improvement.⁵ Learning collaboratives should be organized, structured group learning initiatives involving multidisciplinary teams representing different levels of the organization. They should focus on improving provider practices or outcomes that offer training from experts in specialty practice areas and quality improvement methods. Measurable targets, data collection, and feedback for quality and performance improvement, as well as structured activities and opportunities for learning and cross-site communication, are also critical components of a learning collaborative.

MERGING WITH SOCIAL MEDIA

As information technology, mobile computing, and Web-based applications have become part of everyday life, collaborative learning has been coupled with distance learning methods for improving health and medical care. Although public health training programs are considerably smaller in number than health care training programs, several public health programs have used learning collaborative approaches.

The most prominent use of a learning collaborative in public health practice was the Public Health Robert Wood Johnson Multi-State Learning Collaborative for Accreditation, designed to build quality improvement capacity within state and local health departments with a focus on preparing for accreditation.⁶ The program resulted in 162 mini-collaborative quality improvement projects involving 234 health departments from 16 participating states in nine target areas and substantively increased quality improvement implementation, capacity, and competency. Advances in information technology, data science, computational collective intelligence, and Web design platforms offer an increased potential for using collaborative learning models in public health workforce development.

As a prototype social media learning collaborative, we adapted a self-organizing e-learning community model with award and exchange mechanisms.⁷ The architecture and technical components were designed to conform to a Web 2.0 social media interactive learning collaborative, support tier-based learning targeted to local and regional health department staff, and allow for collective intelligence, social media networking, and smart search engine agents. The prototype design offered several user functions, including selecting training resources (e.g., online courses and toolkits), submitting questions, and undertaking assessments as part of exercises and performance tests. The execution of these user functions also quantifies the interest, intent, and capability of the user, providing metrics of learner interest in a content area that can be assigned to groups. Learner preference is predefined according to an initial self-administered questionnaire and updated through the learner’s keyed searches and time spent on specific topics.

In addition, the content of the questions submitted by the learner represents a request for specific knowledge points. Scores obtained from exercise assessments provide metrics for determining mastery and capability associated with a selected topic that is used to direct tier-based learning. Learning capability is described through a parameter that encodes types of requests. Another parameter encodes the areas of the learning content within the finite set of all possible knowledge points. We developed a WordPress Web site development platform (Figure 1) with the capability to link to SQL internal and external databases. The public access pages of the Web site www.phasevtechnologies.com/preparedness01 were designed to explain the goals of the collaborative and encourage membership. A single e-mail invitation to board of health members in Massachusetts to join the prototype social media learning collaborative for the purpose of evaluating public health preparedness research-based training and tools resulted in 64 acceptances.

FIGURE 1—

Social Media Learning Collaborative Web Site General Architecture

Note. LAMPS = linking assessment and measurement to performance in public health and emergency preparedness systems; LHD = local health department; SVI = social vulnerability index. Shown are social media communication and knowledge interchange pathways between different health departments (circles) interacting within the social media learning collaborative, with its backend structure, process, and outcomes databases containing descriptive regional data, predictive learning and practice performance metrics, and evaluative outcome assessments.

CONCLUSIONS

Disseminating and implementing new knowledge and methods derived from public health preparedness research continues to be a challenge. Our prototype work indicates that learning collaboratives focusing on social interaction, exchange, and communication can bring together “virtually” isolated staff with more experienced colleagues and mentors in planning for and responding to emergencies and disasters.

Coupled with advances in data analytics and information technology, we believe that it is feasible to design social media learning collaborative platforms for engagement and communication that can leverage the current culture of information technology and communication to advance the adoption of emergency preparedness best practices, especially in the case of events that are uncommon yet devastating. As online social networks have become part of everyday life, we believe that the potential for using them to improve the quality of public health preparedness by advancing research findings into practice can become a reality.