By Korina Fischer
As the ability to amplify human intelligence with AI-driven decision support continues to evolve in the healthcare supply chain, the tide of transformation is rising.
According to a recent analysis in the Harvard Business Review, “Across many industries, digitally transforming the supply chain has been shown to reduce process costs by 50% and increase revenue by 20%; hospitals are no exception.” The authors note that a growing number of hospitals today are seeking solutions for clearer windows into inventory, pricing, lead times and demand trends.
Many are finding it with AI-as-a-Service (AIaaS) solutions. Intelligent machine learning and artificial intelligence platforms can uniquely deliver what hospital supply chains need most – the ability to predict the future and respond swiftly and skillfully to changes in patient demand.
Exploring that notion of “patient demand” is important, so I will return to it later in this article. But first, a look at the techniques that deliver the “crystal ball” hospital supply chain leaders need:
- Predictive Modeling
Last year, nine of every 10 healthcare provider executives surveyed by PwC researchers experienced pandemic-related supply chain shortages and/or disruption. Among them, 8 in 10 (81 percent) said they plan to invest in predictive modeling (up from 74 percent in 2020).
“Pairing predictive analytics models with AI [is] crucial in improving forecast accuracy post-pandemic,” writes Kevin Beasley of the Forbes Technology Council. The pandemic spotlighted how ill-equipped the supply chain has been to predict and plan for such events, but “an AI system could proactively flag likely events, resulting in more informed decision-making.”
Using algorithms that compute a much more complete data set, predictive analytics “think” far beyond historical patterns to forecast future trends. When combined with AI that predicts the most likely future pattern by continuously computing internal data (e.g., billing/consumption, EHR/ERP, ordering/inventory history, volume changes from service line growth, M&As, consolidations, etc.) alongside several other external data sets, predictive modeling becomes sharper. Predictive modeling expands the value of forecasting by bridging gaps between predictions and real-world clinical and operational interventions.
- Scenario Planning
Nearly one-third (31 percent) of the healthcare executives surveyed by PwC said they plan to invest more in scenario planning as well. Scenario planning shows how certain strategies and actions hold up under different futures. The resulting insight empowers organizations to plan more comprehensively and effectively.
While scenario planning has long been used to assess possibilities years in advance, today’s technology has evolved into tools capable of visualizing and planning future events that may be just months ahead – or even less, as this article from The Economist points out:
“Rather than dusting off their scenarios annually, businesses told us they were using them in weekly decision-making. … Companies seem to be bringing in a wider range of voices than usual to create their scenarios, as the pandemic disrupts everything from supply chains to human resources. This can also improve the buy-in around the resulting decisions.”
PwC’s research shows a smaller but still significant number of healthcare provider executives plan to invest in simulation technologies: 23 percent fall in this camp, or nearly one in four.
While modeling explores behaviors and scenario planning helps develop frameworks for decision-making, simulation helps to see how various “players” will act and react within that framework. The authors of this research published in The Scientific World Journal explain it well:
“Having defined the parameters of the future and the ‘What if’ elements that need consideration, the organisation needs to “Play out” the landscape to understand the future actions of competitors and stakeholders and how they can be responded to, shaped or exploited. This is where the simulation process is used.”
Findings from that same study show that even before the pandemic, the potential value of an efficiently managed healthcare supply chain could be as high as 12 percent of a hospital’s entire operating costs. Simulation provides important windows into possible results of changes across various conditions, enabling better planning, readiness and overall supply chain management.
Driving Better Decisions by Focusing on Patient Demand
With the accessibility of cloud-based AI and machine learning platforms, the technology for all the above is here and quickly gaining traction. For the technology’s value to be maximized, however, the ability to integrate and enrich each calculation with the right data is, without question, equally important. That brings me back to a point I mentioned earlier – the importance of patient demand.
Industry guru Vance Moore, Healthcare Advisor at RocketStop and Former President of Business Integration at Mercy, is a long-time advocate for reconsidering the paradigm that supply chain begins with product flow.
“If you work from the premise that the healthcare supply chain starts with a diagnosis – not a product – then you can begin to see your supply chain from an actionable, procedural point of view,” Moore explains. Making that leap requires in-depth understanding of actual patient consumption as well as clinician preferences (across current and future entrants, including new drugs, therapies, devices and physicians). “For the supply chain to help rather than hinder success under increasingly value-based models, supply chain managers need to shift their mindset from products first to patients first.”
Bringing the Benefits Home: Where to Go from Here
At MUUTAA, we agree with Vance Moore and have built the first and only AI solution for healthcare supply chains that uses patient demand to drive forecasting. It’s truly an exciting time to be in healthcare AI because, for the first time ever, hospital supply chain professionals can see forecasts and view suggested order quantities based on best-fit projections that factor patient protocols, consumption and planned growth into every computation.
As the world looks to stabilize from and ultimately overcome the pandemic, a range of business advantages await hospitals that embrace AI and machine learning now for supply chain optimization and clinical integration. “Successfully implementing AI-enabled supply-chain management has enabled early adopters to improve logistics costs by 15 percent, inventory levels by 35 percent, and service levels by 65 percent, compared with slower-moving competitors,” according to McKinsey & Company.
By helping to ensure surgeries, procedures and therapies can proceed on time and as planned, AI-driven technologies support a wide range of health system objectives, including higher quality care at lower total costs, as well as improved clinician and patient satisfaction. Through cloud-based AI, machine learning and rich data integration, DemandAMP+ is healthcare supply chain AI that provides self-learning cognitive assistance for predictive modeling, scenario planning and simulation.
To learn more and see how DemandAMP+ works across different use cases, contact MUUTAA.
About the author
Korina Fischer is the Chief Executive Officer and Cofounder of MUUTAA, a healthcare AI company focused on patient-driven demand for clinically integrated supply chains. She is a forward-thinking entrepreneur with 20+ years’ proven leadership in health IT ecosystems, pharmaceutical and medical device supply chains, clinical workflow, and relationship management. To contact Korina, email firstname.lastname@example.org or call 581-398-0068