Find out why supply chain analytics helps healthcare organizations maintain patient loyalty and avoid unnecessary costs.
In any industry, supplies need to flow smoothly. But in healthcare, lives depend on medications and supplies that arrive on time. Researchers surveyed 400 healthcare workers, from nurses to supply chain managers. More than half of those surveyed said they could recall when a doctor didn’t have a medical product for a patient’s procedure. And twenty-four percent of respondents had seen or heard of a situation where an expired or recalled product was used on a patient.
Some nurses and doctors spend twenty percent of their time locating supplies when they could be doing their job and treating patients. Data analytics helps organize the supply chain so that healthcare workers have the resources to treat patients at their fingertips.
What Is Supply Chain Analytics?
The supply chain consists of different processes: procurement, distribution, inventory management, etc. The system analyzes, interprets, and suggests improvement strategies for these processes. Managers who manage the supply chain can use the system reports for optimization. What can they optimize with supply chain analytics software? Via manufacturing processes, warehouse management, transportation, etc. And managers can also forecast demand.
An analytics system copes with unstructured big data. For example, a hospital works with electronic health records (EHR) and enterprise resource planning (ERP). Different departments of the hospital manage these databases. Members of the clinical team work closely with the EHR. They enter data on how they use clinical products. The ERP tool is useful for the supply chain team and leaders when they manage the hospital’s finances or make a strategic plan. EPR contains data on products, prices, suppliers, contracts, procurement, and payment. It is also important to consider the data of the supply chain management (SCM) system. E.g., the data from barcodes and RFID tags. They are needed to track the stock of medical equipment or consumables. Another example is data on the expiration dates of drugs.
The forms of this data may vary. If the clinical and supply chain teams rely on manual processes to fill out paperwork, they may get lost in the data. But it doesn’t confuse the analytics system. It integrates the SCM system with the EHR and ERP and offers a single system of record for financial transactions and patient care.
Supply chain analytics software can be thought of as a set of tools. These tools use big data to answer questions and offer solutions.
There are four types of big data analytics in healthcare that apply to the supply chain as well.
Descriptive analytics answers the question, «Which events happened?» For example, algorithms show the demand for basic personal protective equipment (PPE) has increased. These are hospital gowns, surgical masks, gloves, respirators, etc.
Diagnostic analytics explains, «Why did these events happen?» The reason is an epidemic in a certain area. It spreads widely and quickly.
Predictive analytics answers the question, «What can happen?» The epidemic can unfold quickly, and the demand for PPE grows. Algorithms show that healthcare organizations may experience a shortage of PPE. Detailed forecasts are possible because developers train analytics models so that they can detect patterns. The model is trained on a wide ecosystem of libraries specifically for healthcare analytics software. One example of such an ecosystem is Python libraries. Belitsoft expert Dmitry Baraishuk emphasizes that Python development services are not only about model training. Before that, developers clean the client datasets and study them to select the features most closely related to the predicted variable. After that, the developers split the dataset into training and test data. They evaluate the performance of the resulting model and only then deploy it in a real app.
Prescriptive analytics answer the question, «What should we do?» For example, analytics tools calculate the potential stock of PPE for a healthcare company, to eliminate a shortage. So, it can replenish PPE stocks from local suppliers. It may be that the stocks of local suppliers vanish due to increased demand from several healthcare organizations at once. So, the healthcare company can establish a supply chain with a third-party supplier.
What Are the Benefits of Supply Chain Analytics for Healthcare?
A supply chain analytics system in healthcare helps solve many problems. It analyzes how resilient suppliers are and how available supplies are. It checks how efficient the logistics network is and estimates costs. Supply chain analytics algorithms also allow healthcare organizations to predict future demand.
There are several key benefits when a healthcare organization uses the supply chain analytics software.
Improved Customer Service
The patient experience is guaranteed to be worse when critical supplies are unavailable to physicians and patients. For example, a physician and patient are scheduled for a knee replacement. But there is a supply chain disruption. The knee implant does not arrive on time or is in stock but is too big or small. The patient is forced to reschedule the procedure. And it means more time off from work, more asking a friend or relative to drive them to or from the hospital. More babysitting or pet sitting. And of course, it means the patient will have to live with knee pain longer.
What is the main goal of supply chains in healthcare? To meet demand. Healthcare workers and patients want to ensure that all the medical supplies and consumables they need are available without gaps. There are several ways to monitor supply availability. One way is to examine patient data in the EHR. Analytics tools reveal patterns and trends in how doctors and nurses use certain supplies to treat a patient. They also provide data to ensure that the medical supplies ordered from the supplier match the patient’s needs.
Reduced Costs
If a provider studies the analytics reports on procurement and use of supplies, they can direct the supply chain to reduce costs. For example, the analytics software tracks the expiration dates of consumables. It offers a report about the number of consumables the healthcare organization needs. It considers how quickly the providers usually use these materials and which materials they use more and less often. This way the organization can avoid waste. Analytics tools can also identify which supplier or suppliers offer these materials at the best prices.
Analytics algorithms link supply chain data and clinical data from doctors. The goal is to determine whether specific supplies lead to adverse events that ultimately «cost a pretty penny» and rehospitalizations. For example, there are two similar medical products for surgical interventions. They have a price difference. The higher quality product is more expensive, but its use provokes fewer complications at the place of surgical intervention. In this case, the healthcare organization studies the analytics reports and can choose the more expensive medical product from the supplier. It’s a more cost-effective solution because treating frequent complications via a cheaper product will cost even more.
Improved Operational Efficiency
Algorithms perform statistical analysis of processes. Analytics reports enable managers to identify and eliminate inefficiencies. For example, analytics tools study procurement and delivery. The reports show that customers in different departments of a healthcare organization place separate orders with the same supplier on average once every two weeks. It forces the procurement team to process multiple purchase orders. The accounts payable (AP) team also has more work to do: they have to process numerous invoices. The supplier receives these separate orders from one healthcare organization and sends many shipments monthly. The warehouse team must receive and process each of them. The analytics software combines orders for the supplier and shipments. As a result, procurement, AP, and the warehouse do not waste time and energy on unnecessary work.
Last but not Least
The supply chain analytics system allows the healthcare organization to be flexible when it faces order disruptions. Algorithms identify alternative clinically equivalent products that have been pre-approved by the supplier in case of availability issues. It allows you to redesign your procurement process quickly. And you are guaranteed to have the product on time. The analytics system can also look at past supply consumption trends and predict future demand. It makes the supply chain more flexible in delivering what providers need in the near term.
About The Author:
Dmitry Baraishuk is a partner and Chief Innovation Officer at the software development company Belitsoft (a Noventiq company) with 20 years of expertise in digital healthcare, custom e-learning software development, and Business Intelligence (BI) implementation.
