Bringing Value-Based Perspectives to Care: Including Patient and Family Members in Decision-Making Processes

Background: Recent evidence shows that patient engagement is an important strategy in achieving a high performing healthcare system. While there is considerable evidence of implementation initiatives in direct care context, there is limited investigation of implementation initiatives in decision-making context as it relates to program planning, service delivery and developing policies. Research has also shown a gap in consistent application of system-level strategies that can effectively translate organizational policies around patient and family engagement into practice. Methods: The broad objective of this initiative was to develop a system-level implementation strategy to include patient and family advisors (PFAs) at decision-making points in primary healthcare (PHC) based on wellestablished evidence and literature. In this opportunity sponsored by the Canadian Foundation for Healthcare Improvement (CFHI) a co-design methodology, also well-established was applied in identifying and developing a suitable implementation strategy to engage PFAs as members of quality teams in PHC. Diabetes management centres (DMCs) was selected as the pilot site to develop the strategy. Key steps in the process included review of evidence, review of the current state in PHC through engagement of key stakeholders and a co-design approach. Results: The project team included a diverse representation of members from the PHC system including patient advisors, DMC team members, system leads, providers, Public Engagement team members and CFHI improvement coaches. Key outcomes of this 18-month long initiative included development of a working definition of patient and family engagement, development of a Patient and Family Engagement Resource Guide and evaluation of the resource guide. Conclusion: This novel initiative provided us an opportunity to develop a supportive system-wide implementation plan and a strategy to include PFAs in decision-making processes in PHC. The well-established co-design methodology further allowed us to include value-based (customer driven quality and experience of care) perspectives of several important stakeholders including patient advisors. The next step will be to implement the strategy within DMCs, spread the strategy PHC, both locally and provincially with a focus on sustainabilit

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, can not keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, cannot keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone

Capturing highfrequency phenomena using a bandwidth-limited sensor network. Sensys

Small-form-factor, low-power wireless sensors—motes—are convenient to deploy, but lack the bandwidth to capture and transmit raw high-frequency data, such as human voices or neural signals, in real time. Local filtering can help, but we show that the right filter settings depend on changing ambient conditions and network effects such as congestion, which makes them dynamic and unpredictable. Mote collection systems for high-frequency data must support iteratively-tuned, deployment-specific filter settings as well as fast sampling. VANGO, our software system for high-frequency data collection, achieves these goals via integrated processing across network tiers. Bandwidth-limited sensor nodes reduce data in network but rely on microservers, which have greater computational capabilities and a wider scope of observation, to plan how. VANGO provides a cross-platform library for data transformation, measurement, and classification; a fast and low-jitter data acquisition system for motes; and a mechanism to control mote and microserver signal processing. With VANGO we have developed new applications: the first acoustic collection system for motes responsive to changing environmental conditions and user interests, and the first neural spike acquisition application capable of supporting a network of nodes. Categories and Subject Descriptors

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, cannot keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone

Collecting High-Rate Data Over Low-Rate Sensor Network Radios

Embedded systems can already capture data produced at high rates, and embedded CPU and sensor performance are still rapidly improving. Radio technology, however, can not keep pace, and will not in the future due to known physical limits of shared communication channels. This leads to a fundamental gap between the data a sensor network node can collect and the data it can transmit back for analysis. VanGo, our software system for data collection, uses flexible transcoding to narrow this gap. To make effective use of channel bandwidth, data reduction software must run on sensor nodes. However, to calibrate how data reduction software should run, that same software should be capable of running on the back end on real data received from the network. In VanGo, users decide where data processing occurs. To show that transcoding helps, we evaluate two radically different applications: acoustic collection and the measurement of neural activity. Among our findings is that in bandwidth-limited environments, proactive filtering of some of our signal can result in collecting three times the signal energy than we could by removing silent periods alone

Capturing High-Frequency Phenomena Using a Bandwidth-Limited Sensor Network

Small-form-factor, low-power wireless sensors—motes—are convenient to deploy, but lack the bandwidth to capture and transmit raw high-frequency data, such as human voices or neural signals, in real time. Local filtering can help, but we show that the right filter settings depend on changing ambient conditions and network effects such as congestion, which makes them dynamic and unpredictable. Mote collection systems for high-frequency data must support iteratively-tuned, deployment-specific filter settings as well as fast sampling.VANGO, our software system for high-frequency data collection, achieves these goals via integrated processing across network tiers. Bandwidth-limited sensor nodes reduce data in network but rely on microservers, which have greater computational capabilities and a wider scope of observation, to plan how. VANGO provides a cross-platform library for data transformation, measurement, and classification; a fast and low-jitter data acquisition system for motes; and a mechanism to control mote and microserver signal processing. With VANGO we have developed new applications: the first acoustic collection system for motes responsive to changing environmental conditions and user interests, and the first neural spike acquisition application capable of supporting a network of nodes

Correspondence

Presents letters to the editor referencing articles and topics discussed in previous issues. "The Power of Pets," which discussed the passion of U.S. citizen for pets; "Not Two Cents," which focused on foreign policy writings by Thomas Friedman; "Kinder Cuts," which suggested the need for the U.S. administration to focus on balancing the budget and practicing fiscal responsibility