Hospital Clinical Asset Optimization and Cost Management

Introduction

Across the U.S., hospitals are overspending millions each year on mobile assets that are not utilized effectively. Despite more than adequate inventories, equipment often is not available when needed. As a result, more units are bought, leased, or rented — and those units, in turn, get lost in the system and therefore underutilized. In fact, the number of mobile devices per U.S. hospital bed has increased 60% in the past 15 years while costs have doubled. Yet in most hospitals, device utilization is approximately 45%. In the present study, the need for optimization and efficiency methods with clinical assets is investigated.

Hospitals in the U.S. must incur increased expenses for the acquisition of medical equipment used in their normal operations. The cost of equipment purchased is high, and hospitals are required to maintain a backup inventory in order to efficiently carry out their daily operations. Hospitals utilize equipment based on their needs, and an increased number of devices are reported missing at various times. The misuse, theft, wastage, and unavailability of medical equipment when required pose an economic challenge for these institutions. The cumulative result of these issues manifests as significant annual losses, damaged reputation, and inefficiency in hospital operations.

The primary research objective is to perform a detailed analysis of the elements contributing to the high cost of hospital operations and to present a framework for the optimization of clinical assets. The cost of operations is further increased through losses of clinical assets, and the research will explore and identify the possible reasons for such losses.

Literature Review

The secondary objective of the research is to provide a framework for rectifying these problems. The research also focuses on providing strategies that can facilitate improved hospital operations, inventory management, and security of clinical assets. The third objective is to propose recommendations for reducing the mishandling, theft, and misuse of clinical resources. The use of technology options will also be explored for clinical asset traceability, allocation, and optimization (Pflaum, Meier, Muench, Fluegel, Gehrmann, Hupp, & Sedlmayr, 2010). The research will also address clinical asset optimization issues particularly in the United States and, more generally, in other parts of the world.

One common source of financial stress for hospital executives is equipment replacement. While new technology is paramount to providing quality patient care, its cost can often be measured in the millions of dollars. With most healthcare delivery systems already feeling strained by operational costs, requests to reduce spending are a constant part of the budget conversation. Across the U.S., hospitals are overspending millions each year on mobile assets that are not utilized effectively. Despite more than adequate inventories, equipment often is not available when needed. As a result, more units are bought, leased, or rented — and those units, in turn, get lost in the system and become underutilized.

Research by Kelly (2009) for Thomson Reuters suggests there is anywhere from $75 billion to $100 billion of waste in healthcare attributable to what it labels "Provider Inefficiency and Errors," a category that specifically describes inefficiencies in the utilization of equipment. In the same article, Kelly referenced a May 2009 interview with NPR in which Peter Orszag, director of the White House Office of Management and Budget, stated: "Estimates suggest that $700 billion a year in healthcare costs do not improve health outcomes. They occur because we pay for more care rather than better care. We need to be moving towards a system in which doctors and hospitals have incentives to provide the care that makes you better, rather than the care that just results in more tests and more days in the hospital."

According to Baretich (2004), hospitals procure devices and clinical assets for use in critical situations. Procurement is made in advance, and additional assets are kept in adequate quantities to respond to emergencies and support normal operations. However, when needed, these assets are often difficult to locate, disrupting normal hospital operations (Nabelsi, 2012). A review of the literature highlights significant gaps in asset management and allocation. The three major issues contributing to non-availability are theft, misplacement, and inefficient retrieval from inventory. Addressing these issues is essential for eliminating non-availability at critical times.

Operations management in hospitals — particularly with respect to inventory control and resource allocation — is the primary driver of inflated clinical asset costs. Optimizing the use of clinical assets is the second stage, following the resolution of inventory management and allocation problems. Hospital management must implement effective, technology-assisted procedures to identify and locate required assets (Christe, Rogers, & Cooney, 2010; Castro, Lefebvre, & Lefebvre, 2013). The issuance, retrieval, and collection of clinical assets must be managed efficiently, along with regular stock-takes, to eliminate the economic and operational damage caused by inefficient handling of valuable clinical assets.

It has been reported that mobile equipment — such as IV pumps, ventilators, and physiological patient monitors — typically makes up more than 95% of a hospital's clinical assets, and that inventory represents thousands of devices worth tens of millions of dollars. Yet results of a recent study conducted by GE Healthcare disclose that the average utilization of mobile devices is only 42%, meaning that more than half of the fleet is idle at any given time. Despite the seeming oversupply, availability is inconsistent; for example, nurses spend an average of 21 minutes per shift searching for lost equipment.

According to analysis by DeGraff (2013), the average number of mobile devices per staffed bed increased 62% on average between 1995 and 2010. In the mid-to-late 1990s, the typical staffed bed had eight devices; today, there are thirteen devices per bed. This finding, coupled with low asset utilization, indicates a serious problem and a need for asset intervention. With the number of devices increasing 62% over the 15-year period, overall maintenance costs have risen even more sharply — at a rate of 90%. DeGraff (2013) suggests that an average 200-bed hospital saw service and maintenance costs for clinical devices increase from $331,200 to $628,800. However, the actual per-unit cost to service equipment only increased approximately 19%. Synthesizing this data points to the conclusion that there has been an influx of technology over the 15-year period, thereby driving up operational costs.

The prevailing attitude at many hospitals — held in error — is that it is less costly to address equipment availability issues by leasing, renting, or buying more units rather than by optimizing how existing devices are managed and distributed. This "purchase more" strategy backfires, as the additional equipment simply gets swallowed up in the system, further driving up costs. Therefore, when it comes to assets, most hospitals do not have a maintenance cost problem; they have an excess inventory problem.

This excess inventory problem is also documented by Horblyuk (2013). Data show that while the average cost of maintaining a device has seen a very moderate increase, the average number of devices per bed has increased significantly, driving the overall cost per bed to nearly double in recent years.

Figure: Average Service Cost, Devices, and Cost per Bed
Source: Horblyuk (2013)

The data for this study were collected by the GE Healthcare Asset Management team in 1995–97 ("1995") and 2008–10 ("2010"), and included the number of staffed beds and mobile device inventory counts. In an article on RFID benefits and barriers, Yao (2012) identifies another dimension of clinical asset inefficiency: theft loss. The article estimates that equipment and supply theft costs hospitals $4,000 per bed each year, representing a potential loss of $3.9 billion annually — a factor not emphasized in previous studies.

In a study performed at Bon Secours Health System in Richmond, VA, the health system realized savings of more than $5 million annually through reductions in equipment costs after adopting an RFID system. Eighty percent of those savings were attributed to better utilization, which resulted in a reduction in unnecessary equipment. For example, the St. Mary's campus was able to reduce its IV pump inventory from 520 to 392 units — a 25% reduction. This reduction also improved the facility's utilization rate. Prior to RFID implementation, St. Mary's was at approximately 60% utilization of IV pumps; after implementation, utilization rose to 92%.

To address the excess inventory challenge, hospital systems need an integrated strategy that drives productivity across the entire process — from ensuring maintenance and repairs are performed efficiently to tracking, monitoring, and managing every device in order to maximize utilization throughout the useful life of the asset, while ideally improving care delivery as well as patient and staff satisfaction.

Research Methodology

To answer where an institution should start with a clinical optimization program, DeGraff (2013) proposes a five-step process: (1) conduct a physical inventory, (2) optimize workflow processes, (3) consider deployment of an asset-tracking technology such as RFID, (4) audit all service-related costs, and (5) develop a device replacement strategy. By redesigning distribution and management processes and, in some cases, adding real-time location technologies, hospitals are able to reduce inventory, lower or eliminate rental and lease expenditures, and decrease maintenance and service costs — all of which can amount to hundreds of thousands, or in some cases millions, of dollars in savings each year.

In summary, the U.S. healthcare system is experiencing significant operational waste. To ensure long-term viability, individual healthcare organizations must identify operational inefficiencies and activate strategies to address and ultimately correct the factors that contribute cost rather than value to care delivery.

The basic research process involves four stages: formulation, execution, analysis, and decision-making (Myers, 2013). The research methodology addresses both general and study-specific aspects of research methods and design. The population and sample are described in relation to the current research. Data collection methods — qualitative in nature — as well as the theoretical assumptions, research approach, and data collection techniques are addressed throughout the research design.

The current study is designed to take a qualitative approach to identifying the economic impact of hospital asset optimization. The interpretive research adopts a philosophical assumption of exploration — examining the problem statement and, to an extent, the consequences of inefficiency for hospitals. An action research method is also selected, as this approach enables exploration of the subject from positive, interpretive, and critical perspectives. The data collection method is aligned with the qualitative research paradigm and employs the use of documents and participatory observation.

The large number of hospitals and clinics operating in the United States can benefit from the research findings in terms of optimizing their clinical assets (Stantchev, Schulz, Hoang, & Ratchinski, 2008). Moreover, hospitals facing challenges related to increased operational costs can use the results to identify sources of asset leakage and misuse. Healthcare units operating outside the United States facing similar conditions may also benefit. Accordingly, medium- to large-scale hospitals are considered the population of the current study.

The data collection method must conform to the research design and the selected approach to addressing the problem. Various methods are used by researchers to collect data within a qualitative paradigm. It is noted that medium- to large-scale U.S. hospitals are particularly relevant to the study's focus on reducing operational costs and increasing clinical asset optimization. The method chosen is predominantly dependent on the research method, the topic of research, and data availability. The current research uses document-based data collection to achieve its objectives.

The research takes an approach of collecting and using secondary data. Data published in journals, books, news articles, and peer-reviewed reports will be used to interpret and explore the economic impact of clinical asset optimization. Comparing current data with historical data will enable the researcher to investigate changes over time. The interpretation of those changes will further support the development of an applicable model for improvement.

According to Munhall (2011), ethical considerations are highly important in research that examines organizational impact and then transfers findings to individuals. Ethical aspects of the research must be highlighted before the study begins. Research that is unguarded in terms of ethics leads to dishonesty and damages both personal integrity and institutional reputation. The ethical process also requires clear explanation of the research process so that the public can understand the relevance of the methods used to reach certain results.