Healthcare in the United States of America is a USD $3.2 Trillion industry, yet is often described by experts as a “cottage industry” due to the lack of maturity surrounding its contract management and supply chain practices. Research suggests that up to 40% of funds spent are non-value-added in nature. The dynamics of the sector is complicated by the number of different supply chain participants involved, and the fragmented nature of different systems used in various locations for data capture which operate independently.
A number of potential solutions have been suggested with the rise of social media and e-markets, including provider and supplier consolidation, public websites documenting healthcare costs, reimbursement levels and integration with electronic medical records as part of its elements. In a recent study I published which is forthcoming in the Journal of Strategic Contracting and Negotiation with two former NC State students (Shweta Murthy Jaikishen Venkitaraman), we focus on a singular issue that is an important aspect of medical care which contributes to escalating costs, the need for improved price and cost transparency as a vehicle for greater market competition. While not unique to healthcare, improved control over hospital spending is a significant challenge that is particularly troublesome given the escalating costs of healthcare in the United States.
Many now recognize that supply chain management is a critical link that ties together various parties in the continuum of care: clinicians, suppliers, distributors, and payers, but one of the biggest stumbling blocks is the inability to access good, clean spend data in healthcare. Some would even suggest that this is the root cause of increasing costs of healthcare, and that the lack of cost transparency in the supply chain, due to the multiple exchanges that occur between providers of healthcare services and suppliers, is causing prices to continually explode. Healthcare consumers have been disconnected from cost information in the United States for decades. (Have you ever tried to figure out your hospital bill, and tried to decipher exactly what you are paying for? Get my point?) Provision of price transparency, improved supply chain coordination, and market power are important issues to resolve if hospitals are able to “break-even” given increasing reimbursement pricing pressure from insurance companies and public healthcare. This problem ultimately manifests itself in the words of a healthcare supply chain executive we interviewed, who noted that
“We have no idea if we made money or lost money on a given DRG, because we are unable to understand the true costs of the materials required to perform it. We know we bought materials, but we are unable to trace it to a patient, and often cannot map it to a single unit cost that can be billed to a patient.”
In our study, we accessed a sample of data from three major hospitals in the United States in Chicago, New Jersey, and Florida, in collaboration with SCWorx, which part of a national initiative to drive improved data integrity in hospital acquisition systems. This organization is working with a number of major hospitals across the country, including Maimonides, Mayo Clinic, Baptist Health, University of Chicago, University of Vermont, and others. SCWorx has developed a machine-based learning algorithm to address the gaping hole in data integrity. The system has three essential elements that provide a solution for healthcare providers to deal with the lack of integration between their systems (which they have already invested in), and to enable access to data to drive up utilization rates. These include the following:
- Item master creation. SCWorx provides free toolsets to Providers, to help them create the attributes for the item master. Providers will maintain their own dictionaries of everything they use, using the tool. The toolset allows them to connect into SCWorx’s database, and standardize across 200 data elements and enable each hospital system to join a consortium of other hospitals. This consortium allows hospitals using different systems and different codes to translate and compare standard prices across different units, and collaborate to find new ways to work together. Despite differences in systems and numerical codes, the system creates a standard taxonomy that creates a common system for comparison of prices.
- This feature increases productivity by assigning a General Ledger number, a UNSPSC code, and other critical codes that take “people out of the loop” and ensure standardization of data on an on-going basis using machine-based learning algorithms.
- Item Master standardization. By creating standardization, the database can be used to analyze trends, business intelligence, and key performance indicators, thereby launching the journey into effective data analytics. Hospitals cannot be successful in their quest to drive down costs unless they have data that allow them to address challenges in spend analysis.
SCWorx has been a partner with NC State for several years, and conducted several joint data analytics projects in the Fall 2018 semester with a group of MBA student teams in the first-ever Healthcare Analytics class taught at NC State’s Poole College of Management. In addition, the study corroborates many of the findings from the 2nd Annual Data Governance Study produced in conjunction with IBM.
Using a sample of category item prices for 3000 SKU’s from three major hospitals, our research team concluded that volume aggregation and price reductions do not occur over some of the most expensive supply items in hospitals, primarily because of a strong lack of control and visibility of such items. From our analysis, there were indications that physician preference item prices are more simply a function of the supplier’s proximity to the hospital. The three hospitals considered were not been able reach their cost savings potential, due to a bias that exists in choosing suppliers that provide the greatest value.
Our findings also support the idea that items are purchased multiple times without the hospital being able to assess their spending patterns. Our interviews supported these findings, and suggested that most hospitals validate manually that their prices are accurate and this results in increasing errors such as overpayment, including payments for supplies not consumed. This is reflected in the following comments:
“Data governance is at the heart of this issue. Healthcare providers need to re-focus their efforts to focus on data as the defining component for building an analytics strategy that leads to better care, cost management, and revenue capture.” (Supply Chain Analyst, University Hospital).
“Without an ability to track clinical materials using a common coding system, there are serious problems that arise. Two of the primary symptoms of a bigger problem include lack of productivity and lack of data, as well as massive waste of materials used in clinical practices.” (Physician, Midsize Hospital)
“I feel very frustrated, because I went into nursing because I wanted to help physicians and patients. However, I am spending more time on the computer searching for items than I am working with patients. Many of our nurses are becoming equally frustrated, and we are seeing many of them quitting because they say they can’t stand working at this job any more, as there is too much computer time spent searching for items in the catalogue.” (Nurse, Urban Hospital)
Our analysis also provides insights regarding how such discrepancies can be addressed. By developing improved data governance mechanisms and approaches for establishing cloud-based procurement systems, hospitals can take more control over their spending patterns. Many new procure to pay technologies are available with easy graphical user interfaces that both facilitate improved user experiences in nursing stations, and enable a higher level of procurement data governance. Our results suggest that procurement efforts that rely on the idea that higher volumes of purchases, (pursued through such means as hospital consolidation, GPO contracts, supplier consolidation, and larger volume purchases), are unlikely to produce any meaningful reductions in unit prices for purchased goods and materials. This result is astounding, as it defies all logic associated with the idea of quantity discounts produce lower prices. Our empirical results suggest this law doesn’t work in healthcare!
The solution in this case is for hospitals to get a handle on their data. Improved data governance can potentially create better monitoring of utilization of such products, and produce more efficient usage, thus producing improved savings through more direct means. Data governance and data quality will become important considerations for hospitals investing in new material purchasing systems, and the ability to derive standard taxonomies and data classification systems are important in evaluating the the significant investments in such systems.
 A Diagnosis-Related Group (DRG) is a statistical system of classifying any inpatient stay into groups for the purposes of payment.