Hospital, health system leaders address continued cost pressures

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Health systems are making swift changes to regain footing in 2024 as industry challenges made things difficult last year, and according to an analysis by healthcare technology company Q-Centrix, one area of focus is clinical data, which can be harnessed to implement improvements and reduce expenses.

"More and more leaders realize that improving clinical data management processes can drive cost savings across the board," said Raghu Bukkapatnam, chief growth officer at Q-Centrix. "Depending on its size, a health system can spend anywhere from $10 million to more than $50 million on clinical data management activities every year. Assessing what they spend, understanding what kind of return they get from their investments, and identifying inefficiencies are simple ways to improve financial performance."

To that end, Q-Centrix executives identified a number of key strategies hospital and health system leaders are exploring to mitigate financial pressures throughout the rest of 2024.

WHAT'S THE IMPACT?

Hospitals are seeking refuge from continued cost pressure with AI solutions, but the safe integration of generative AI is a long-term endeavor, not a quick fix to shrink spending, the analysis said.

The report notes that 79% of healthcare facilities use or plan to use AI technologies to curate clinical data, whether internally or through a third party – and for nearly half of these respondents, their primary goal for using AI in this way is reducing administrative burden on staff. With administrative tasks costing the U.S. healthcare system $60 billion dollars each year, AI has great potential to save time and lower costs, but the risk an AI model introduces compared to an individual's impact is exponential, authors said.

Brian Foy, chief product officer at Q-Centrix, anticipates that many leaders within the hospital setting are looking to AI to solve their budgetary concerns but are underestimating its risks.

"The potential that AI presents is huge, but it comes at a cost: substantive risk," he said. "Models create model-sized problems; inevitably, someone is going to be in the headlines because LLMs broke something that negatively impacted a large patient population. The only way to avoid the risk is with precision – but precision at scale is extraordinarily complex, and it requires a combination of access, technology, and human intervention. Unless hospitals are willing to take a thoughtful, long-term AI approach that includes all these elements – or partner with an organization that has a proven track record for exploring AI safely – they may need to consider other ways to reduce costs."

Alternatively, to address rising expenses, hospitals are exploring new growth options in areas traditionally seen as cost centers, such as quality. Leveraging clinical data sets from registries for research, the study said, offers new opportunities to drive advancements and generate revenue.

Three-quarters of hospital leaders surveyed said their facility is currently sharing or plans to share de-identified clinical data with other organizations for research purposes. Yet almost two-thirds of clinical trials fail to enroll enough patients for an effective study.

The ability to access structured, flexible and custom clinical data sets is essential for health system leaders seeking innovative ways to leverage their data, the report found. But when most clinical data is unstructured and siloed throughout the health system, centralizing data management is paramount.

The report showed that nearly eight in 10 hospitals are currently centralizing or planning to centralize clinical data management throughout a region, service line or facility. This strategy allows healthcare facilities to save costs, drive efficiencies and gain better access to their data.

THE LARGER TREND

In March, the Congressional Budget Office determined that the evidence on the usefulness of AI technology is mixed, particularly when it comes to costs.

AI and machine learning tools might affect healthcare costs in the future in many ways, the CBO said, including by detecting illness earlier or identifying patients who might benefit from preventive interventions. But while some uses of those tools might reduce costs by preventing the need for costlier care or eliminating unnecessary care, others might increase costs by spurring the development of expensive new technologies with meaningful health benefits, or by identifying additional patients who might benefit from certain medical services.

The practical application of these technologies is still inconsistent at this nascent phase – showing usefulness in predicting cancer mortality, but falling short when predicting heart failure outcomes. The CBO said it will need to see more empirical evidence before determining the overall effect on healthcare spending.
 

Jeff Lagasse is editor of Healthcare Finance News.
Email: jlagasse@himss.org
Healthcare Finance News is a HIMSS Media publication.