Getting what is needed, when it's needed: Sharing information, knowledge, and know-how in a Collaborative Learning Health System.

BACKGROUND: Collaborative Learning Health Systems (CLHS) improve outcomes in part by facilitating collaboration among all stakeholders. One way to facilitate collaboration is by creating conditions for the production and sharing of medical and non-medical resources (information, knowledge, and knowhow [IKK]) so anybody can get "what is needed, when it's needed" (WINWIN) to act in ways that improve health and healthcare. Matching resources to needs can facilitate accurate diagnosis, appropriate prescribing, answered questions, provision of emotional and social support, and uptake of innovations. OBJECTIVES: We describe efforts in ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease (IBD), to increase the number of patients and families creating and accessing IKK, and the challenges faced in that process. METHODS: We applied tactics such as outreach through trusted messengers, community organizing, and digital outreach such as sharing resources on our website, via social media, and email to increase the number of people producing, able to access, and accessing IKK. We applied an existing measurement system to track our progress and supplemented this with community feedback. RESULTS: In August of 2017 we identified and began measuring specific actions to track community growth. The number of patients and families producing IKK has increased by a factor of 2.7, using resources has increased by a factor of 4.1 and aware of resources as increased by a factor of 4.0. We identified challenges to measurement and scaling. CONCLUSIONS: It is possible to intentionally increase the number of patients and caregivers engaged with a CHLS to produce and share resources to improve their health.

Measuring engagement in a collaborative learning health system: The case of ImproveCareNow.

BACKGROUND: Collaborative learning health systems have demonstrated improved outcomes for a range of different chronic conditions. Patient and healthcare provider engagement in these systems is thought to be associated with improved outcomes. We have adapted an observational framework to measure, and track over time, engagement in ImproveCareNow, a collaborative learning health system for children with inflammatory bowel disease. INTRODUCTION: We developed a categorical classification scheme for engagement in ImproveCareNow. Each tier is defined in terms of observable individual behaviors. When an individual completes one or more qualifying behavior, s/he is classified as engaged at that tier. Individuals are entered into a database, which is accessible to care centers throughout the ImproveCareNow network. Database records include fields for individual name, behavior type, time, place, and level of engagement. RESULTS: The resulting system is employed at 79 ImproveCareNow care centers in the United States. The system recognizes four levels of engagement. Behaviors are recorded in a managed vocabulary and recorded in an online database. The database is queried weekly for individual engagement behaviors, which are tracked longitudinally. Center- and network-level statistics are generated and disseminated to stakeholders. CONCLUSION: It is possible to monitor longitudinal engagement in a collaborative learning health system, thereby charting progress toward engagement goals and enabling quantitative evaluation of interventions aimed at increasing engagement.

Feedforward inhibition and synaptic scaling--two sides of the same coin?

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

Rapid convergence to feature layer correspondences.

We describe a neural network able to rapidly establish correspondence between neural feature layers. Each of the network's two layers consists of interconnected cortical columns, and each column consists of inhibitorily coupled subpopulations of excitatory neurons. The dynamics of the system builds on a dynamic model of a single column, which is consistent with recent experimental findings. The network realizes dynamic links between its layers with the help of specialized columns that evaluate similarities between the activity distributions of local feature cell populations, are subject to a topology constraint, and can gate the transfer of feature information between the neural layers. The system can robustly be applied to natural images, and correspondences are found in time intervals estimated to be smaller than 100 ms in physiological terms.