If People Were Kaleidoscopes: Sharpening the Resolution on Patient Engagement in Safety

Healthcare is unacceptably error prone. The question remains why, with 20 years of evidence, is error and harm reduction not being effective? While precise numbers may be debated, all stakeholders recognize the frequency of healthcare errors is unacceptable, and greater efforts to ensure safety must occur. In recent years, one of these strategies has been the inclusion of the patient and their family as partners in safety, and has been a required organizational practice of Accreditation Canada in many of their standard sets. Existing patient advisories created to encourage engagement, have typically not included patient perspectives in their development or been comprehensively evaluated. There are no existing tools to determine if and how a patient wants to be involved in safety engagement. As such, a multi-phased study was undertaken to advance our knowledge about the client’s and family’s role in promoting safety. Phase 1 of the study was a scoping review to methodically review the existing literature about patients’ and families’ attitudes, beliefs and behaviours about their involvement in healthcare safety. Phase 2 was designed to inductively explore how a group of patients in an Ontario, Canada, community hospital, describe healthcare safety and see their role in preventing error. The study findings, which include the narratives of 30 patients and 4 family members, indicate that although there are shared themes that influence a patient’s engagement in patient safety, every individual has unique, changing beliefs, experiences and reasons for involvement. Five conceptual themes emerged from their narratives: Personal Capacity, Experiential Knowledge, Personal Character, Relationships, and Meaning of Safety. These study results will be used to develop and test a pragmatic, accessible tool to enable providers a way to collaborate with patients for determining their personal level and type of safety involvement. The most ethical and responsible approach to healthcare safety is to consider every facet and potential way for improvement. This exploratory study provides fundamental insights into the complexity of patient engagement in safety, and evidence for future steps.

Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.