Can predictive capabilities get front-line health services off the back foot?
The unexpected COVID-19 has stretched the capacity of hospitals and health services to treat those in need.
As service demand increases, healthcare professionals are looking for opportunities to relieve pressure on front-line staff and the back office. To effectively identify these opportunities heavily relies on the intelligent use of your organisation's data, and collaboration between IT, data and technology team and the field services team.
On Tuesday 16th June, I had the pleasure of joining a live webinar discussion: “Can predictive capabilities get front-line health services off the back foot?”, with several thought leaders from across the healthcare space.
These included Karen Taylor, Director at Deloitte Centre for Health Solutions, George Kapetanakis, Chief Technology Officer at CareRooms and Sam Shah, Global Digital Adviser and NHS Clinician.
We were brought together to discuss the impact of technology in the healthcare system, and what healthcare organisations can and should be doing with their data, to create forward-looking insights that minimise the current stretch in workload.
The agenda focused on 5 key themes:
· The role technology and AI has played in the current pandemic, and technology changes introduced to the healthcare system
· The current landscape of predictive analytics in healthcare, and what we can learn from other industries that have more experience using machine learning
· How the healthcare system today embraces new technologies like tele-consultation and remote monitoring-caring of patients
· The challenges and barriers to implement technology and AI in healthcare and how to address them
· Ethics in using patients’ data for analysis and prediction
Lastly, I shared 4 main takeaways for practitioners that are interested in implementing Predictive Analytics / Machine Learning / Data Science solutions in Healthcare (or other industries), which I hope you find useful:
1. Data foundation is important, in healthcare more than other industries where the margin for error is lower. Data integrity directly affects the validity of the analyses and the impact of the conclusions drawn.
2. Think about where you are at the Analytics Maturity journey. Get the Data Consolidation and Quality Assurance, Reporting and Descriptive/Diagnostic Analytics in shape if you haven't already before diving into Predictive Analytics (but it doesn't mean you can't start thinking about it - see point 3).
3. Identify business use cases (you can look at the application in other industries for inspiration), and try to get validation on the approach and data requirement (start thinking about this early - it might take months or even years to collect the relevant data but you need to know what to collect first!)
4. Build the business case, and think through the implications and actions driven by the results. This is where the real impact takes place, and is also where Ethics topics become most pressing and substantial.
If you're interested in hearing what the expert panel had to say, watch the on-demand recording of the webinar! Get free access here: https://bit.ly/PredictiveInHealthcare
If you'd like to brainstorm, get ideas, or validate your approach in using machine learning in your work, or if you have use cases of Predictive Analytics in Healthcare, feel free to comment or drop me a message!
