The 21st century is only twenty years old, yet it is clear that one of the main transformative technologies and enablers for human society of this century will be artificial intelligence (AI).
So much so that McKinsey predicts it will deliver global economic activity to the value of $13 trillion by 2030. More immediately, Gartner predicts the global AI-based economic activity will climb from 2018’s $1.2 trillion to about $3.9 Trillion by 2022.
What’s the power behind this transformation? Machine Learning (ML) tools and techniques such as Linear Methods, Bagging, Gradient Boosted Machine (GBM), Support Vector Machines and Deep Learning, to name a few.
Paradoxically, these highly complex techniques and algorithms that take place behind the scenes, result in quiet simplicity and streamlined operational efficiency.
And the healthcare industry is the ideal candidate for the many applications of AI tools and techniques.
The primary focus of the healthcare industry is patient treatment, care, and payment, but it requires a diversity of support services including administrative, financial, legal, and quality control to name a few. These services involve multiple departments, cross-functional activities, distinct areas of medical expertise, specifically skilled support staff, and shared resources, all working within a tight set of protocols and regulatory controls. The same goes for the pharmaceutical industry which involves a myriad of activities related to the production and distribution of medicines and related products.
Here are four significant ways that healthcare and pharma businesses can raise their level of productivity, efficiency, and decision-making by using AI.
1. Improved and Optimized Planning
Pharma and healthcare organizations need to constantly forecast, plan and adjust if they hope to optimize processes based on current trends, demands, and conditions.
Predictive analytics makes use of AI and machine learning (ML) to highlight patterns in vast amounts of historical data. It quickly and accurately forecasts what is most likely to take place in the future based on this data, and what actions to take for an optimal outcome. More than that, these predictions are updated as new events take place, such as a disease breakout or epidemic, a disruption in the supply chain or a sudden increase in demand for vaccines.
This historical data can also be used to design new processes that will improve and optimize planning for healthcare and pharma organizations. This allows businesses to be agile and stay one step ahead of customer demand. For pharma, this would have a downstream optimization on inventory and manufacturing management. According to McKinsey machine learning and analytics can reduce overall inventory by 20% to 50%.
2. Streamlining Processes and Implementation
Analytics can be used to monitor and benchmark processes at an operational level. As the saying goes, “if you can measure it, you can improve it”. Analytics can also be used to determine strategies for implementing new or optimized processes, such as determining and improving the wait time for patients at various departments, or the average length of stay, and the overall cost to the patient.
For pharmaceutical companies, it could be in the form of AI-powered predictive and preventative maintenance. This is a sure-fire way to improve operating efficiency, with an almost immediate impact on profitability. Integrating internet-connected devices such as sensors placed on the equipment ensures organizations don’t lose time and money to unscheduled downtime due to equipment failure and breakdowns. These IoT connected devices stream data from their server and send to AI applications such as SEDGE. SEDGE’s analytics are then able to quickly and accurately flag which machines or equipment are most likely to break down, so that predictive and preventative maintenance can be carried out, mitigating the risk of total machine failure and operational stand-still.
According to McKinsey, AI-enhanced predictive maintenance of manufacturing machinery and equipment can result in a 10% reduction in annual maintenance costs, as much as a 20% increase in uptime, and a 25% cut in inspection costs.
3. Cost Control
McKinsey estimates that machine learning and analytics can lower supply chain forecasting errors by 50% and cut costs related to logistics and warehousing by 5% and 10%, and supply chain admin by 25% to 40%.
For those in healthcare, AI-powered systems analyze historical data and provide actions that will result in the best possible outcome. This supports decision-making for hospital administrators, healthcare providers, and operations managers.
Whether AI is used to optimize machine use, reduce bottlenecks and delays, streamline procurement and supplier management, or give clarity on inventory levels and demand, manufacturing can be data-driven and make full use of available resources.
It might not be paper anymore, but the daily admin of emails, messages, tasks, files and meetings results in hospital and clinic staff spending nearly 20 percent of their week processing information. AI can automate much of the admin work associated with back-office, allowing staff to focus on the work itself.
4. Data-driven Decision-making
AI improves analytics efficiency and effectiveness and provides the data required for healthcare leaders to make strategic decisions quickly and accurately. AI can provide answers to fundamental questions such as, “Is there more opportunity in readmission or depression?” or “How many staff do we need in the ED on weekends?” This analysis provides undeniable data that can help leaders make decisions that benefit the business as a whole.
The two biggest areas where AI assists leaders, by leveraging data to accomplish both, are:
Distinguishing signal from noise – Cutting through the hubbub of the marketplace and demands of work, AI gives leaders the measurable, actionable insights they need to make strategic decisions.
Making decisions that impact the future – Predictive analytics and data analytics give leaders a glimpse of the future based on the past. This empowers them to set measurable goals that are realistic and achievable.
An example of how SEDGE uses AI and ML to predict heart disease in patients for improved decision making
SEDGE, an AI and ML-powered application that is used for predictive analytics can successfully be used to predict heart disease in a list of patients. In this example, patient data is uploaded, sorted, and stored on SEDGE. The application allows for various algorithms or models to be run against the data (Fig. 1). This is important to check for accuracy.
Figure 1. SEDGE's prediction page showcases available models/algorithms.
SEDGE Model Explainability helps the user understand how much percentage each variable contributes to a single prediction. This is why the predicted value differs from the actual value.
Figure 2. Model Explainability - the variables that supports/contradicts a single prediction.
SEDGE gives the prediction of each observation simply by saving the prediction model, uploading the data, and clicking “Predict”. In a few simple clicks, you can see the predicted outputs of those most likely to suffer from heart disease.
Figure 3. SEDGE allows the new data set to run on the Stored Model. The predicted outputs are visible in the last column.
This could then show healthcare leaders what areas show higher inequality for their attention. Similar to how data scientists and AI tools like SEDGE can predict which patients are more susceptible to heart disease, or what patients will be readmitted, they can also forecast how equitably the health system can control a specific measure across the patient demographic, empowering leaders to activate strategies of improvement.
SEDGE data science and analytics for optimized business operations
In today’s modern healthcare and pharma industries, digitized medical data is available from public hospitals, nursing homes, doctors’ clinics, pathology labs, and more. However, this data can be messy and unstructured, making it difficult to extract actionable insights.
SEDGE provides multiple solutions for healthcare and pharma businesses who want to improve business operations.
Automation and Predictive Analytics: The cloud-based AI tool – SEDGE – is able to access and connect multiple patient databases to quickly and accurately analyze diverse data types. It can then discern deeper patterns and use these for accurate forecasting towards optimizing business operations.
Complex Analytics Requirements: The highly skilled and committed Data Science team at SEDGE is standing by to provide customized assistance for your business operations needs.
In addition, SEDGE is able to translate and visually represent these findings in a format that is easy to assimilate by leaders and stakeholders, doctors and healthcare professionals, who can then activate their output with greater confidence, authenticity, and transparency.
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