• Nursing Innovation Grant

Artificial Intelligence in Health Care Certificate – Massachusetts Institute of Technology- Sloan School of Management – 2024
Quality Improvement Fellowship Certificate – University of Toronto – 2012
Masters of Nursing – University of Toronto – 2009
Bachelor of Science in Nursing, Honours – Brock University – 2005

The Workload Intensity Matrix (WIM) AI algorithm analyzes copious amounts of data from the electronic health record and assigns a time-based workload value. Charge nurses, managers, and nursing leaders will use this tool to enable load-balanced nursing assignments and precision staffing.

Many workload tools to date have required users to input time-based values as an additional task in their workday – Often, this is a. inaccurate, and b. used solely for financial reporting. The goal of our WIM algorithm drives a new focus which impacts nursing at the local and systems level. Locally, data is visualized in real-time bringing visibility to nursing practice, workload, and patient care complexity.

By looking at hundreds of constructs (many unique pediatric context) and outputs within the patients record, our algorithm will return a specialized value of total anticipated direct nursing time required for the next 12 hours at the individual patient level. This new approach will enable the practice of objective, load-balanced assignments, improves global, data-driven decision-making and allows matching of the right care provider to the patient’s needs. Future opportunities allow for acuity, operational, and workload metrics to be used together to inform planning and decision-making.

Increase objectivity in nursing assignments and resource allocation – Building an organizational understanding of score logic and category-based thresholds (i.e., high, moderate, low) will enable an increase in objectively describing care needs at the individual, unit, and organizational level. This effort occurs alongside operational efforts on patient flow and clinical manager workload process improvements. Improving objective output will contribute to greater sense of advocacy by nurses and managers, enabling a greater sense of equity and fairness for resource allocation and load balancing.

Articulating often “invisible” nursing care – Often described as work that may go unnoticed or undervalued, capturing workload intensity elements of items such as education, play and developmental care, care coordination, language and translation service utilization will enable nurses to articulate and embed the relational aspects of nursing more readily. Further, these areas will be captured as data to better inform patient care needs.

Precision Staffing – Through increased objectivity in describing patient care needs and accompanying workload thresholds, WIM will enable load-balanced assignments by empowering charge nurses to make data-driven decisions and build opportunities for nurses to provide input on what informs safe and reliable care. Future iterations will overlay an option for nurses to indicate their perceptions of workload, giving voice to the data ongoing.

Shared governance informed AI technology development – Nurses are present in all levels of project governance. Nursing validation groups will directly inform WIM requirements and sustain future revisions. As we work towards a vision for “Our Kind of Nursing Technology” at SickKids, WIM embodies a culture where technology for nurses is made by nurses. Driven by informatics and practice, fully realizing the potential of WIM leverages the strengths of an over 80% digitally acclimatized workforce. This engagement enables nurses to innovate by directly engaging in this digital transformation.

There are four distinct phases for implementation across our organization:

1. Build: Algorithm conversion and refinements. To take timely action in this phase, an external consultant will partner with our electronic health record team to both complete build requirements and build capacity to continue its maintenance post go-live. Point-of-care nurses, charge nurses, and managers will attend validation sessions to review, validate, and inform rule decisions. Validation will be finalized through a silent trial, during which background data will be analyzed and outputs assessed to determine level of specificity and sensitivity prior to go-live.

2. Ready: Change engagement. Further to the above point of care engagement, multiple strategies will be leveraged to address education and knowledge translation as part of the implementation approach. Additional tools will be created to enable daily practice, including reports and integrated bed management tools. Ongoing work is tailored to each end-user persona and design is underway. Shared governance nursing committees will further engage to inform requirements, socialization, and knowledge translation strategies.

3. Go-live: Implementation. Following validation, knowledge translation and tool development, all end users will be live with available tools to support their daily workflows. Charge nurses, clinical managers, bed flow administrators, and other clinical operations leaders will have integrated the WIM into their daily bed flow decision making and articulate their average workload scores when advocating for resources. The project team will be at the elbow providing support for the use of tools and coaching through decision-making.

4. Sustainability: Ongoing Plan Do Study Act (PDSA) and Evaluation. Based on best practices, an annual review will occur to sustain WIM accuracy and account for practice changes. Collaboration will involve professional practice, clinical informatics, finance, decision support, and Epic. End user engagement will be enabled ongoing and mechanisms to support rule library updates and infrastructure will be determined and enacted as part of go-live, ensuring nurses are engaged in all aspects of sustainability.

In addition to existing partnerships and collaborations with other paediatric acute-care centres, and post implementation and evaluation, publication will be sought in nursing leadership and clinical informatics literature. Publishing the model, implementation process, successes, and challenges will enable organizations to transfer strategies and benefit nurses across the healthcare sector. Conference presentation opportunities, such as the International Conference on Nursing Informatics and the Canadian Nursing Informatics Association virtual will further enable knowledge transfer. Opportunities to influence and impact the electronic health record vendor’s strategy are underway and efforts will seek to improve the foundational build, so that other organizations can realize the benefits of WIM “out of the box”.

Connie Cameron, RN, Senior Manager Professional Practice & Clinical Extern Program, SickKids
Cameron Irani, RN, Clinical Informatics Specialist, SickKids