Healthcare organizations in the U.S. have a hard time controlling costs while following rules and keeping good patient care. The COVID-19 pandemic showed how weak supply chains can be, causing shortages and problems. A 2024 survey by GHX-AHVAP found that one big challenge is balancing cost control with long-term efficiency and patient care results.
Getting doctors involved is another problem. The survey showed that over 85% of healthcare groups said doctors were only somewhat or not much involved in value analysis. This process looks at products and vendors to make better buying choices. Not having doctors involved enough can lead to supply decisions that don’t fit clinical needs.
There is also a gap in knowledge in value analysis teams. Almost 30% of workers have less than three years of experience. Many experienced workers are retiring soon. If new staff are not trained well, important knowledge might be lost.
Bad data quality is a basic problem. Tom Swapp, Vice President at Direct Supply® DSSI™, says messy and inconsistent data is a big pain in hospital buying. It is hard to make good plans or conclusions with poor data. Many hospital ERP systems are not set up well for supply chain tasks. This causes trouble in managing spending and working with vendors.
Data-driven decision-making means collecting, studying, and using numbers and facts to guide buying and supply chain plans. In healthcare, it means using good and timely data to manage stock, predict needs, and pick suppliers that balance price and quality.
More healthcare groups are starting to use advanced data analysis. The 2024 GHX survey shows that about 34% of organizations use advanced analytics in their value analysis, a small increase from the previous year. This is progress but shows many still find data hard to use well.
Data-driven buying helps see spending clearly. It shows patterns of purchases, which helps find waste and save money. Tom Swapp says clean data and good ERP systems are keys to this. Better visibility also helps organizations handle vendor relationships and make contract deals with real-time facts.
Unlike old methods that guess or use past habits, data-driven methods help hospitals guess changes in demand. This prevents too much or too little stock. For example, Uganda’s Quantification and Procurement Planning Unit used data on use and disease trends and got $139 million more for health supplies. This shows data can help with funding and supply plans, even in the U.S.
Using data also helps build strong supply chains. Between 2023 and 2024, the average strength of healthcare supply chains rose from 3.45 to 3.74 on a five-point scale. This shows efforts to make systems that can adjust and handle problems better.
Getting doctors involved is very important so buying fits with what’s needed for patients. But many healthcare groups say doctors have low or medium involvement in deciding what to buy. Steve Haas, a healthcare expert, points out this is a problem. Without doctors’ input, buying teams might pick products that don’t meet what doctors need, which can hurt patient care and money management.
One fix from GHX is giving doctors clear clinical and financial facts. This helps doctors understand how buying choices affect care and cost. It tries to put doctors’ voices into supply chain talks. This way, buying decisions make sense for both medical and money reasons.
To get doctors more involved, teams need training, mentoring, and changes that help doctors and buying groups work together. That is the only way value analysis teams can link money reports with patient-centered choices.
New technology, like artificial intelligence (AI) and automation, is changing healthcare buying and supply chain work a lot. AI helps hospitals and clinics make work smoother, more exact, and find ways to spend less.
AI can do boring tasks like making purchase orders, handling invoices, and talking to suppliers. This cuts mistakes and speeds up buying work. It lets staff spend time on plans and ideas. AI spend analysis tools can look at lots of spending data and suggest cheaper options.
Russell Rosario, a leader in supply chain and buying, says AI gives “power of foresight.” AI tools warn about risks like supplier fraud or market changes before they cause trouble. This helps handle risks and makes supply chains stronger.
Sumanto Bhandari says AI not only saves time but also finds hidden chances to save money. AI helps manage supplier relations by showing real-time data on delivery, quality, and rules compliance.
In U.S. healthcare, using AI and automation means better contract handling and faster buying processes. Satyanarayana Kotnala notes AI can check contracts, spot risks, and track renewals. This cuts work for buying teams.
But for AI to work well, organizations must focus on good data, invest in IT tools, and train buying teams to understand AI insights. Also, humans must still check AI decisions to avoid costly mistakes.
More healthcare supply chain experts use models like convolutional neural networks (CNNs) and bidirectional long short-term memory networks (BiLSTMs) to improve buying work and make processes greener. These AI methods study big data sets to find spending trends, use resources better, and predict needs well.
Surjeet Dalal’s work shows CNNs help find location patterns in supply networks. This leads to better delivery and less spending. Umesh Kumar Lilhore explains BiLSTMs capture timing patterns to help predict future needs and plan orders.
Using these AI models gives real-time tracking and flexible decision-making. This is important for supply chains that must respond quickly to sudden events like disease outbreaks or equipment shortages.
Besides making work more efficient, these models help meet environmental goals by planning routes to cut pollution. This matches rising demands for healthcare groups to buy in ways that protect the environment.
Though focused on U.S. healthcare, managers can learn from programs like Uganda’s Strengthening Supply Chain Systems. This USAID project uses data integration, real-time stock tracking, and forecasting based on use and disease trends.
In Uganda, over 1,900 health centers order supplies electronically from the National Medical Store. Private nonprofit centers have a 90% order fulfillment rate. The program uses data to move supplies where needed, like shifting extra oxygen during outbreaks. This cuts waste a lot.
Problems like weak digital tools and data quality issues from staff changes are challenges Uganda faces. These also matter in the U.S. as healthcare groups try to improve supply chains. The experience shows the need to invest in people, digital tools, and data rules to keep buying systems working well.
Dr. Victoria Nganda from USAID says that without strong data, healthcare supply chains would work blindly. This is a clear reminder of how important full data is for supply chains.
Invest in Clean, Accessible Data: Accurate and steady data is the base of good procurement plans. Organizations should focus on fixing ERP systems and cleaning data. Tools like Direct Supply’s Order Guide Management and DSSI Analyze help provide clean data for better spending control.
Incorporate Advanced Analytics: Using analytics software helps find trends, predict supply needs, and check supplier work. This allows active buying and lowers emergency buys that cost more.
Engage Clinical Stakeholders: Get doctors and clinicians involved in value analysis talks. Giving them clear clinical and money facts can build teamwork for buying that balances quality and cost.
Expand Use of AI and Automation: Automate routine buying tasks to cut errors and save staff time. Use AI insights to spot risks and improve contract work.
Train and Support Staff: Fill knowledge gaps with training for value analysis and buying pros. This is needed as experienced staff retire and to keep knowledge going.
Focus on Resilience: Build flexible buying systems that quickly adjust to supply problems, backed by real-time data and risk tools.
Today, healthcare buying and supply chain work can’t depend on guesses or old data alone. Data-driven decision-making, with AI and automation, helps make healthcare operations smarter and stronger, while controlling costs and keeping good quality.
Medical practices across the U.S. will need to understand and use these methods as supply chains face more challenges. By using clean data, advanced analytics, AI tools, and teamwork between admin and clinical teams, healthcare providers can handle resources better, cut waste, and help improve care for patients.
Healthcare supply chains face challenges including rising costs, regulatory demands, the need for improved resilience post-COVID-19, and low physician engagement in value analysis processes.
The average resilience score increased from 3.45 in 2023 to 3.74 in 2024, reflecting a greater emphasis on adaptability to disruptions.
Data-driven decision-making has grown, with 34.04% of respondents in 2024 using advanced analytics to enhance value analysis, emphasizing the need for data integration.
In 2024, 85.11% of organizations reported low to moderate physician engagement, which is crucial for aligning clinical practices with financial outcomes.
Almost 30% of professionals have less than three years of experience, and the expected retirement of seasoned experts presents a risk to institutional knowledge.
Respondents in 2023 highlighted the need for value analysis to go beyond cost-cutting, focusing on long-term operational efficiency and patient outcomes.
Standardizing products helps reduce clinical variation but balancing it with clinical effectiveness remains challenging for healthcare organizations.
Despite some progress, slow technology adoption persists, with many organizations lacking the necessary infrastructure or expertise for advanced analytics.
Key recommendations include investing in technology adoption, enhancing physician engagement, addressing the knowledge gap, and building resilience in supply chains.
GHX offers solutions like VASS that provide real-time data, streamline decision-making processes, and support physician engagement, addressing key challenges in healthcare procurement.
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