A Roadmap to Improving Efficiencies and Removing Costs at Hospital Labs
Laboratory services play a critical role in the healthcare ecosystem. According to the National Library of Medicine, approximately 70% of clinical decisions are based on an initial lab result that highly influences downstream medical care. Despite this crucial role, the lab is often overlooked when it comes to spend management.
Based on recent payment trends, there is a strong need for labs to access costs and implement proactive strategies to improve efficiencies. Medicare spent $7.68 billion on clinical laboratory tests in 2019, an increase of $93 million from 2018. However, payment rates in 2019 were lower for 73% of lab tests than in 2018.
Other pressures are mounting for labs, including high labor costs, increasing test volumes (in many cases unneeded tests) and higher costs for materials. Staffing shortages can lead to delayed testing turnaround, which increases length of stay (LOS) and drives up hospital costs, especially for patients receiving costly ICU care.
It is crucial for labs to improve quality, financial performance and processes, and to transition (when necessary) from transactional assets to strategic assets while reducing costs and improving lab performance. With careful program evaluation, Vizient estimates that many labs can trim 7-10% of costs annually.
Begin by reducing unnecessary lab tests
Providers that operate their own labs can work with physicians to reduce the number of unnecessary (and often obsolete) tests that raise costs and delay turnaround times through increased volume. For example, a national study found that testing for patients in the ICU increases over time, even as most results move closer to normal values. Unnecessary testing consumes limited labor and supply resources and increases the potential for additional (and costly) interventions, which can lead to reduced patient outcomes.
Forward-thinking lab leaders should increase collaboration with physicians when it comes to protocol development to ensure that the tests administered align with the expected diagnostic solution. This can help to eliminate unnecessary, obsolete or antiquated tests through the use of superior scientific methods and technology, such as Creatine Kinase MB (CK-MB) testing. Click here for a full list of obsolete tests .
One meta-analysis estimated the rate of inappropriate hospital laboratory testing to be 43.9% at the time of admission and 7.4% for subsequent testing. Aside from utilizing precious labor resources, inappropriate testing increases the potential for false positives and additional interventions. Learn more about how to begin a vibrant hospital laboratory demand management assessment to identify and decrease inappropriate testing.
As part of a demand management assessment, a clinical laboratory utilization committee can provide the governance framework to help establish a partnership with medical staff so that lab leaders can leverage their expertise to help prioritize clinical relevance. Typically, the group will include physicians and scientists such as the chief pathologist/chief science officer, microbiologist, chemist or another scientist.
Though there is a wide range of models for these committees, most have the support of hospital leadership, the active involvement of medical staff and include representation from multiple specialties. In addition, it is crucial to secure IT resources to access utilization data and implement any changes. Leverage available sources of such information, including laboratory information system orders, reference lab usage, HIS reports or Vizient tools. Look for red flags such as:
- Obsolete tests on the lab’s test menu
- Frequent ordering of older tests when a better option (with respect to cost and/or accuracy) is available
- Ordering of daily routine labs
- Order sets for broad disease test panels that have not been updated for diagnostic utility
Traditional cost-saving strategies must be enhanced with emerging technologies
Although collaboration with physicians and demand management strategies can drive proven lab savings, emerging technologies promise to provide transformational change within labs. As artificial intelligence (AI) gains acceptance, the technology will improve efficiencies and could significantly reduce labor constraints, which are at a critical stage within labs. However, health systems face major deployment obstacles, including obtaining funding for AI technology and lab testing platforms and information systems that do not support AI. Overcoming these obstacles could take years for some providers.
However, some forward-thinking health systems are already implementing AI for early detection of sepsis and for analyzing radiology images for diagnosis of prostate cancer and other conditions. Laboratory professionals should carefully examine the benefits of AI, as data generated by lab results is a major component incorporated into AI tools to generate clinical decisions.
According to the American Association for Clinical Chemistry (AACC), chemistry and immunology laboratories are prime candidates for AI and machine learning because they generate large, highly structured data sets. Labor-intensive processes used for interpretation and quality control of electrophoresis traces and mass spectra could benefit from automation as the technology improves. Clinical chemistry laboratories also generate digital images — such as urine sediment analysis — that may be highly conducive to semiautomated analyses, given advances in computer vision.
The digitalization of biopsy slide images is driving further lab transformation when it comes to pathology, as AI techniques can assist pathologists with slide analysis. The first FDA-cleared AI for assistance in core needle biopsy detection of prostate cancer uses AI to output a "yes or no" binary classification and highlights a single image location displaying the highest probability of cancer.
- Subanalysis showed this was due mostly to the identification of smaller, lower-grade tumors that are often harder to diagnose. AI use also resulted in less diagnostic variation across a range of pathologist experience levels, patient populations and laboratory practices. A future use case may involve AI as a first-read tool to eliminate negative slides that do not require further review, which could significantly reduce pathology workload.
Other potential use cases for AI include:
- Faster lab sample risk analysis/diagnosis, improving turnaround time for earlier intervention
- Using generative AI to produce lab test results and facilitate documentation/reporting to clinicians and/or patients (such as AI-enabled interpretation services)
- Case triage to ensure higher risk patients receive timely care compared to lower-risk cases
- Supporting physicians in evaluating patient records to suggest necessary labs
- Interpreting lab data to the specifications of each patient to determine their unique risk profiles and likelihood to respond to different therapies (i.e., precision medicine)
- Monitoring lab equipment performance, utilization and inventory management
- Monitoring environmental factors (like room temperature, lighting or electricity use)
The three key benefits of AI revolve around high-value work prioritization, hyper efficiency and unmatched depth. These features translate into AI that handles repetitive, mundane, data-intensive tasks; provides continuous monitoring and 24/7 availability; and enables greater accuracy and speed in tasks, reduced errors and the ability for distributed learning and evaluation of huge datasets in a fraction of the time it takes humans. Considering the pace at which medical knowledge and clinical data is expanding, it’s unrealistic to assume humans can keep up with algorithms in not only analyzing the data, but then synthesizing it to draw out (often unintuitive) correlations in the data and predict what it means.
Recommendations for reducing costs in the lab:
- Collaborate with physicians to reduce unnecessary tests.
- Become more engaged in protocol development to ensure that the right test is being administered.
- Examine labor costs to ensure that you have right-sized the organization in terms of matching skills and licensing requirements with the tests being performed.
- Enhance revenue collection by making sure that the proper tests are billed for.
- Consider reducing the scope of services conducted in the lab and outsourcing more complicated and less frequent testing. Outsourcing esoteric testing can decrease the need for staffing on off-shifts and lead to quality control downtime reductions and even real-time results in the future.
- Seek out provider-friendly financing with suppliers. Some suppliers are providing financing credits for capital purchases to help hospitals with current capital restraints.
- Analyze how AI is being used throughout the health system, and how use cases translate to the lab environment.
- Pursue innovation funding opportunities, such as partnering with AI startups and investment groups that will enable you to prove initial use cases and build a case for funding.
By taking these steps, cost-strapped labs are likely to see much-needed — and substantial — savings across the board.