Part 3
This post is part three of a new series featuring healthcare visionary and thought leader Andy Dé. In this series, Dé discusses how COVID-19 has triggered remarkable digital transformation and uncovers five, long-term innovation implications that providers, healthcare leaders, and payers need to consider.
The past two weeks, Dé led us through the first two implications (you can catch up part 1 and part 2 ), and today he dives into today he dives into numbers three and four.
Innovation implication #3: Artificial intelligence and machine learning for operations, revenue cycle and supply change management innovation
There is a growing need to enable advanced predictive, prescriptive and spatial analytics to empower executives, line of business (LOB) leaders, clinicians and nurses at the point of care. As a result, we’re seeing a convergence of AI (machine learning, deep learning, natural language processing) and descriptive and spatial analytics with actionable, forward looking insights to power critical decisions.
I predict health systems and hospitals leveraging these advanced technologies and analytics for operations, revenue cycle and supply chain innovation to:
- Locate and design new facilities, testing centers and supply chain warehouses
- Optimizes access to care and convenience for patients and their families
- Expands capacity for cardiovascular procedures, hip and knee replacements, cancer care and more, with the ability to align capacity with demand
- Optimize supply chain network (especially for overseas suppliers)
- Enables accurate and reliable demand forecasts
- Supports potential partnerships with Emergency Response Inventory Services Providers to ensure ample supply of N-95 masks, PPE, ventilators, testing equipment and supplies, critical therapeutics and vaccines, and medical devices and equipment
- Forecast demand for elective procedures and services from their patient population
- Leverages clinical, financial and socio-economic determinants of health data
- Accounts for hospital and provider capacity, as well as profitability from a revenue and margin perspective
- Prioritizes treatment and services at hospital locations aligned with that demand for optimal patient access
Innovation implication #4: Medical robotics
I expect visionary leaders at top healthcare organizations and health systems to embark on a journey to embrace medical robotics. Robotics can enable process automation and higher efficiencies while assuring safety for healthcare personnel and front-line workers.
The framework below shows four primary areas and corresponding applications that are already being enabled by robotics – and will continue to be developed and explored in the post-pandemic era.
![](https://medeanalytics.com/wp-content/uploads/2021/05/blog-De-figure3.jpg)
Countries including Japan, China, South Korea, and Taiwan are already testing the use of multiple applications ranging from improvement of manual process efficiency to protection of care teams through non-contact diagnosis enablement and support of clinical procedures to enabling post-discharge patient care and recovery.
That wraps up our dive into AI, machine learning, and robotics. Tune in next week for our fifth and final innovation implication: convergence of AI and analytics for healthcare payers. If you have any questions about what I’ve covered in this series so far, don’t hesitate to reach out.
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