Machine learning and blockchain-backed optimized assignment matching for PSWs to improve understaffing and risk during the COVID-19 outbreak

机器学习和区块链支持的 PSW 优化分配匹配，以改善 COVID-19 爆发期间的人手不足和风险

• 批准号: 554458-2020
• 负责人: Leaver, Chad
• 金额: $ 4.71万
• 依托单位: Seneca College of Applied Arts and Technology
• 依托单位国家: 加拿大
• 项目类别: Applied Research Rapid Response to COVID-19
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=696262
• 关键词: Machine; learning; blockchain; backed; optimized

The COVID-19 pandemic exposes systemic weakness in how the Personal Support Worker (PSW) industry is operated here in Canada. Lack of validated proof of certifications and microcredentials (first aid, safety training, background checks, etc.) leads to hiring delays, understaffing, and potential risks to patients. This in turn causes issues for PSWs, including low pay, inefficient scheduling, underemployment, and has resulted in an unsustainable PSW "gig economy." TriNetra is collaborating with ConnexHealth to implement a system for verifying PSW qualifications, achievements, and certifications. They are proposing to collaborate with Seneca to extend the features of this system to include capabilities to match candidates to job assignments based on their entire profiles, including certifications, training, geography, work history, and availability. This will help the COVID crisis by reducing understaffing at care facilities due to difficulty in validating the credentials of potential workers, eliminating travel and skills mismatches between PSWs and their given assignments; and improving PSW quality of life through better assignment matching, recognition for training, and less travel. This project will initially create a full-featured PSW community portal with blockchain-based credentialing and a machine learning/artificial intelligence-based matching system to address the current need for better and more reliable credentialing and placement software for PSWs. The portal will automatically create individual PSW assignments and schedules to encompass all aspects of their training, availability, and geography. The first two phases of the project will result in a requirements report and optimized matching system to be deployed within the first 12 weeks of the project, and a fully functional system at completion of the 24 week project. This would provide immediate benefit to help reduce understaffing in long-term care facilities and assist PSWs in faster employment at better-matched assignments.

COVID-19 大流行暴露了加拿大个人支持工作者 (PSW) 行业运营方式的系统性弱点。缺乏经过验证的认证和微证书证明（急救、安全培训、背景调查等）会导致招聘延误、人手不足以及给患者带来潜在风险。这反过来又给私营工人带来了问题，包括工资低、调度效率低、就业不足，并导致了不可持续的私营工人“零工经济”。 TriNetra 正在与 ConnexHealth 合作实施一个验证 PSW 资格、成就和认证的系统。他们提议与 Seneca 合作，扩展该系统的功能，包括根据候选人的完整资料（包括认证、培训、地理位置、工作经历和可用性）将候选人与工作分配相匹配的功能。这将有助于缓解新冠危机，减少护理机构因难以验证潜在工人的资格而造成的人手不足，消除私人护理工作者与其指定任务之间的旅行和技能不匹配；通过更好的任务匹配、培训认可和减少出差来提高 PSW 的生活质量。 该项目最初将创建一个功能齐全的 PSW 社区门户，具有基于区块链的认证和基于机器学习/人工智能的匹配系统，以满足当前 PSW 对更好、更可靠的认证和安置软件的需求。该门户将自动创建个人 PSW 分配和时间表，以涵盖其培训、可用性和地理位置的各个方面。该项目的前两个阶段将在项目的前 12 周内生成一份需求报告和优化的匹配系统，并在 24 周的项目完成时生成一个功能齐全的系统。这将带来立竿见影的好处，有助于减少长期护理机构的人手不足，并帮助私人护理工作者更快地找到更匹配的工作岗位。