Agenda - Day 1

• Start your day off right and make connections with healthcare data leaders.
• Join “drop-in” roundtables discussing best practices and innovative strategies.
• Schedule one-to-one video meetings with peers or vendors.
• Gather essential content and get exclusive offers at virtual exhibit booths.

Gain insight into today’s sessions so you can get the most out of your conference experience and maximize your value.

• Setting the context with discussing what the challenges facing healthcare are at the moment, the evolving data ecosystem we live and work in, and lessons learned from the pandemic;
• Understanding what a Data & Analytics strategy for a healthcare organization looks like, why it’s needed, and strategy drivers and components for consideration;
• Providing examples of what we are doing at UHN.

Evolve your data strategy with lessons learnt from the pandemic

In comparison to other fields of medicine, applying advanced analytics to strengthen care delivery in a mental health context has been relatively limited. Deliver deeper patient insights by advancing your AI/ML capabilities and expanding your data sources to include consumer health data. Create an action plan to:

• Streamline your service delivery and patient flow by leveraging ML to gain key insights, including prediction of no shows and readmissions
• Enhance your ability to identify signals of relapse with new data sources from consumer wearables
• Optimize treatment selection by predicting case complexity and optimal service delivery modality

Revolutionize your mental health service delivery and outcomes with data.

What can Canadian healthcare leaders learn from regional innovators who are accelerating technology innovations in data analytics and AI in a post pandemic world while responding to the changing expectations of patient and practitioner experiences? Join us to learn how the Microsoft Cloud for Healthcare is empowering healthcare providers with a platform to be more agile in their innovation efforts.

In this session you’ll learn how healthcare providers are:

• Empowering new health team collaboration models
• Connecting data from across systems, creating insights to predict risk and helping to improve patient care
• Synchronizing operational data across clinical and administrative staff to improve patient experience and accelerate decision-making
• Reducing time spent documenting patient encounters and alleviating provider burnout through AI-powered solutions that drive more personal healthcare.

Expand your network and exchange expertise with your peers. Take advantage of our intuitive platform to deepen your knowledge and connect with healthcare data experts.

• Pose your biggest questions to leading solution providers through one-to-one video conversations or chat.
• Join “drop-in” roundtables in the networking lounge and make connections that last beyond these two days.
• Experience live demos and test drive new technology. Source leading-edge content from virtual booths.

Big data and analytical tools provide various solutions like detection of existing COVID-19 cases, prediction of future outbreak, anticipation of potential preventive and therapeutic agents, and assistance in informed decision-making. Discover learnings from North York General Hospital that have informed their future organizational direction and how they have responded to insights during the pandemic. Source practical tips to:

• Analyze the shifts in medicine use and spending
• Allocation of resources for organizational direction and effective performance
• Evaluate the impact on the delivery of non-covid 19 related healthcare

Maximize the efficiency of your response

There are many challenges in the fast-moving data analytics field of the changing healthcare industry. Our biggest challenge is not data, not technologies, not even budget but the right talent and a sustainable high-performance team. There are 30+ team members in the London Health Sciences Centre (LHSC) Decision Support and Business Intelligence Team, with highly specialized expertise in hospital internal and external reporting, data analysis, knowledge creation, knowledge distribution, as well as internal consulting. There was a need for course correction, as although staff were working very hard and produced large quantities of reports and BI products, customers were dissatisfied and disconnected. In order to create capacity, we needed to prioritize automation by leveraging our BI data warehouse, standardizing our toolsets, empowering people, and really listening to clients. Our initial deep-dive analysis pinpointed root causes; for example, it was apparent that our traditional hospital leadership structure and approach were ineffectively engaging knowledge workers, which contributed to the lack of innovation and inspiration. We have since recognized that technical subject matter experts should be formally included in our leader team structure, where they have accountability to contribute to and help develop our departmental vision, business strategies, proactive planning for training, capacity building, team building, and mentoring team member professional development. A Team Lead model was established with the recognition of the daily technical and operational workload and the fast-growing professional and technical knowledge needs for data analytics healthcare. Three Team Leads (Decision Support and Knowledge Transfer, Data Analytics and Community, BI Services and Products) were promoted within the department to establish a core professional team structure to work on team vision, strategies, branding, promotion, and operational priorities. The following were the key deliverables of the new team structure in place for 12 months:  

1. Developed a Strategy Roadmap with five key initiatives (Reporting Infrastructure, Corporate Reporting, Re-Branding & Operational Models, Building Data Community, Research, and Innovation). Several projects with timelines fall under each category.
2. Implemented Power BI as a reporting infrastructure to effectively and efficiently deliver comprehensive clinical and operational data to clinical units daily to support the enterprise-wide initiative of Continuous Improvement of Care
3. Implemented projects that aim to automate and maintain regular reports and expand self-serve reporting to leverage Power BI to efficiently deliver the right information to the right people at the right time
4. Established a centralized intake and task assignment tool process to allow measuring activities and workforce efforts, ensure knowledge distribution among team members and improve communications with clients.
5. Established regular meetings, retreats, formal regular check-ins, and focus group discussions to systematically analyze the evolving situation and challenge the leaders, the team, and the hospital to do better and continuously improve

Achieving effective analytics starts with improved data literacy. However, there is still confusion surrounding what organizations must do to not only capture the necessary data but to mobilize it. Boost your productivity and create a sustainable digital environment with democratized data. Source practical tips to:

• • Establish clear goals and boundaries for determining what your data should accomplish
  • Create an action plan to educate teams on appropriate data acquisition and use
  • Understand the ‘power plant’ of data and information management

Develop an integrated approach to improve your organization’s data literacy.

Medication reviews are an important part of healthcare and safe prescribing for older adults, and in Long Term Care settings, these reviews largely remain a pen-and-paper process. Development of a digital process creates a more efficient workflow for clinicians, but also allows us to rethink how data can be collected and applied in this healthcare setting. We discuss our approach to: 

• Successfully collect and use data to improve care. 
• Engage clinicians to be part of a new digital process. 
• Utilize insights made possible from the data collected. 

There are many existing data sources that could be potentially used for real-world evidence generation purposes.  However, majority of them are broad datasets with limited focus on rare diseases.  This study gathers information on existing data sources in the indications and geography of interest, and compares the coverage, time span and capabilities of datasets. Evaluation of data quantity and quality in terms of rare diseases of interest is then performed to estimate potential bias in rare disease analysis by comparing demographics across datasets. Gain deeper knowledge to; 

• Evaluate real-world datasets to provide assessment for fit for RWE generation in rare diseases 
• Gather information on coverage, capacities, and characteristics of 10+ real-world datasets 
• Provide insights on potential bias of broad datasets for rare disease analysis 

Review the key solutions and takeaways from today’s sessions. Source a summary of action points to implement in your work. Discuss tomorrow’s highlights!

• Get to know fellow delegates in “drop-in” virtual roundtables.
• Engage in one-to-one video conversation with peers or vendors.
• Connect with other attendees based on similar interests and business objectives using our “recommend matches” tool.