Are Big Data Analytics a Panacea for the Healthcare Supply Chain?
Big data has an unmistakable and irrefutable future. Machine learning, the Internet of Things, artificial intelligence, and various other technologies are making inroads into our everyday lives.
Data analytics is the act of evaluating unstructured and structured information sets to generate real-time data that can help healthcare decision-makers and improve supply chain efficacy.
Despite growing research and interest in big data analytics, one of the primary problems for academics and practitioners is determining its importance in applying supply chain management strategies in healthcare facilities.
What are Big Data Analytics, and How Do They Work?
Data analytics is often used to improve operational efficiency by automating operations that would otherwise be done manually.
Executives in the supply chain can concentrate on producing more excellent value in other areas of the organization, such as productivity and expansion.
Analytics solutions make it simple to make use of corporate data. The expert should be well-versed in the many forms of analytics, including predictive, descriptive, prescriptive, and diagnostic analytics.
KPI Dashboard for the Hospital Supply Chain
Almost any aspect of healthcare operations can be improved using business analytics. Equipment and Supplies, Revenue Cycle, Hospital Operations, and Patient Demographics are all included in the KPI Dashboard of the Supply Chain.
Hospitals can improve their staffing plans and identify how many nurses are needed throughout different shifts by examining variations in Emergency Department treatment.
Furthermore, the very same analytics may be used to figure out how many facilities need to be cleaned and how many maintenance workers are needed for each shift. This is relevant for the employment of resources in all areas of patient safety.
Implementing Healthcare Supply Chain Insights
Supply chain management, also known as Supply Chain Triangle, is all about finding the right balance between offering a service and managing costs within a set of Capital or Cash budgets.
Predictive analytics is used by healthcare supply chain professionals to produce a Material Requirements Plan (MRP) based on existing hospital data and to generate a dashboard for Supply Chain Analytics.
When building the Supply Chain dashboard, keep in mind that a well-balanced Supply Chain Triangle approach is crucial in the institution. Customer intimacy, product leadership, and operational effectiveness are all critical.
For predictive analytics, algorithm development and big data are used, and the outcome is not simply statistics and graphs but something that can be used.
Using Data from the Supply Chain to Find Cost-Cutting Alternatives
The healthcare supply chain produces a lot of data, from supply utilization to practitioner preference sheets to costs. Department heads entrusted with creating and delivering responsible and sustainable supply chain improvement projects need this information.
On the other hand, large amounts of data can sometimes be exorbitant. The institution’s capacity to generate transparency between the revenue and expense sides of the organization is hampered by the absence of compatibility between the organizational revenue cycle and resource planning management systems.
Charge capture is also imprecise due to a lack of compatibility. In case the chargemaster does not list the products, suppliers cannot charge consumers for their supply usage.
To this end, the Cambridge Health Alliance, for example, delegated data cleaning and optimization to a team of experts who work solely in the supply chain department.
Four supply chain analysts work for the Alliance, tackling supply chain analytics, contract administration, disbursement supervision, and profitability analysis.
Integrating a Quality Dimension in Supply Chain Analytics
Executives in the supply chain are also looking into incorporating quality data to change the supply network from a cost centre to a value-adding asset.
One physician may be less costly than another, but the relatively expensive surgeon may be employing a product or procedure that results in better performance or a shorter length of stay, resulting in savings significantly beyond the price of the more expensive item.
By overlaying quality information atop healthcare supply chain expenditure and utilization statistics, health organizations have the opportunity to gain considerable downstream savings.
However, hospitals must ensure that the correct price is paid for those high-quality supplies. AI solutions could be able to assist healthcare organizations in striking the best balance between upfront costs and long-term consequences.
According to supply chain specialists, artificial intelligence and many other advanced analytics tools are now too expensive for healthcare supply chain cost estimates to accommodate.
Prices are projected to reduce as artificial intelligence solutions become more embedded into the healthcare IT market, making them accessible for supply chain specialists.
Meanwhile, more conventional data analytics techniques will continue to prove their worth by identifying ways to cut expenses and improve the purchase process.
Big data can help healthcare supply chains solve formerly impossible challenges with traditional systems or algorithms.
It can aid innovation by lowering its time to create and commercialize an item or service.
Moreover, it can also help reduce operating costs, enhance patient outcomes, and encourage innovation.
Every healthcare supply chain in today’s environment should utilize big data. There is a threat of operational inefficiencies if this does not happen. Big data analytics serves everyone in the healthcare industry.