In the era of value-based care provision, automated benchmarking technology in hospitals enables real-world, reliable data insights for understanding, improving and promoting provider performance.
Value-based healthcare is here to stay. Hospitals across the U.S. are on a quest to deliver successful outcomes at the lowest overall cost, improving care delivery and patient wellness. Nationwide, 34 percent of total healthcare payments were tied to alternative payment models in 2017, a steady improvement from 23 percent in 2015.1
According to panelists at Becker’s Hospital Review 7th Annual CEO + CFO Roundtable in Chicago in November 2018, when transitioning from fee-for-service to value-based care delivery, applying data effectively is critical to making key decisions.2 Using the right information is a vital component for success, but choosing the specific data to focus on and deciphering it can be daunting, the panelists reported.
Indeed, the path to the affordable provision of high-quality care for large hospitals and smaller institutions alike is an uncertain and complicated one. Amid trends of health system consolidation and partnerships, and the linking of various care facilities and subspecialty care groups, provider performance transparency has never been more imperative.
As providers seek to understand not only their own performance but that of competing hospitals or potential partners, automated benchmarking can provide comparisons to help decision-makers uncover paths to performance improvement. Deciphering the data does not have to be daunting nor cognitively burdensome3: in today’s age of the fourth industrial revolution, artificial intelligence (AI)-based technology can provide a comprehensive and repeatable method for revealing performance insights about hospitals in plain, straightforward English with rich supporting context. Benchmarking insights reveal specific areas of both successes and deficiencies, along with related measures of interest, offering guidance on areas of focus.
A successful marriage of data and technology
Automated benchmarking technology can work on both private and public healthcare data sets. A good example of a public data source on hospital performance is the Centers for Medicare & Medicaid Services’ (CMS) Hospital Compare that seeks to “help improve hospitals’ quality of care through easy to understand data on hospital performance, and quality information from patient perspectives.”
When automated benchmarking technology is applied to this rich yet cumbersome-to-use dataset, it explores all possible comparisons among nearly 5,000 institutions seeking noteworthy, action-enabling insights. By evaluating certain quality measures such as star ratings, median time for emergency department actions, surgical complication rates, mortality rates for specific patient populations, readmission rates, and communications ratings, among other attributes, decision-makers can assess their own performance and compare measures to those of nearby, national, and similar hospitals for a thorough evaluation.
Traditional hospital benchmarking technology is limiting in that human beings can only manually acquire and compare so much data and so many attribute combinations within a dataset. By automating the process, the AI technology reshapes benchmarking to include a larger realm of possibilities from both groups and measures perspectives. The technology provides answers to simple but critical questions, such as how each hospital is doing over time (improved or worsened), how it’s performing compared to others like it, and what other highly related measures are significant in a data evaluation.
Here is an example of an automated benchmarking insight that highlights the positive achievement of a New Jersey hospital:
Our Lady of Lourdes Medical Center in Camden, N.J., has the lowest median time patients spent in the emergency department (ED) before being seen by a healthcare professional (14 mins) of all the 44 hospitals within 25 miles. Those 14 mins are 56% lower than the average of 31.6 mins across the 44 hospitals.
The automated method of benchmarking also produces related “bonus” insights that give both specific times and performance-based percentages, revealing how the hospital stacks up on other measures among that same peer group of 44 hospitals within 25 miles:
– Median time from emergency department arrival to emergency department departure for discharged patients = 135 mins (5th-lowest)
– Patients who left the emergency department before being seen = 1% (4th-fewest, tied)
(Source: OnlyBoth, Inc. available at www.BenchMine.com, based on the 10/31/18 data update at Hospital Compare.)
Performance transparency enables providers to track and standardize levels of clinical and patient-centered care, without the demand for frequent, manual benchmarking audits and surveys. It’s essential that hospital performance is measured over time to determine where there is room for improvement. The automated benchmarking technology updates these hospital measures regularly.
Traditional qualitative hospital benchmarking solutions present yes/no questions and numerical output, yielding only a narrow field of insights, often in complicated formats. This type of output does not motivate the user to take action on value-based care measures since it fails to facilitate an understanding of the results, does not spotlight what really deserves attention, nor indicates what actions are required in response to certain revelations.
Fact-based marketing of healthcare services
In addition to using benchmarking insights to guide performance improvement, positive comparative facts can help shape the marketing of services to potential patients.
Providers can consider how they are meeting performance objectives and how the quality of care can be upheld, improved, or attained. Today’s patients aren’t the patients of yesteryear: they are proactive consumers who expect high-quality service for what has become a high-price-tag offering. Hospitals are in fierce competition to secure patients from both a commitment and referral perspective.
Many patients today have tangible means to evaluate numerous hospitals and physicians. Some are limited by such criteria as geography, finances, or a health plan’s in-network directories, but for many patients, the level of performance is a major factor impacting where they go for care. As patients proactively seek digestible and credible information that highlights how a hospital is doing, each hospital should, likewise, access their own and competitor performance.
Positive performance outcomes can then be shared with relevant stakeholders: administrators and other hospital decision-makers, in-house physicians, referring physicians, and patients. Sharing the insights internally facilitates discussion about patient outcomes, the efficiency of processes and ways to achieve success amid changing payment models. Sharing positive insights externally is a marketing opportunity for hospitals to demonstrate how they are excelling compared to others. While traditional benchmarking dashboards do not translate well for broad consumption, objective, factual comparisons in plain English grab the attention of current and potential patients.
Consider the impact that Camden’s Our Lady of Lourdes Medical Center could make with a billboard showcasing its 14-minute wait time—the lowest ED wait within 25 miles, even among all the hospitals in nearby Philadelphia!
As the shift to value-based payment models continues to drive hospitals to reconsider their strategies for success and even possibilities of consolidation, measures of cost, quality and outcome become paramount. A commitment to regular benchmarking processes is a simple, affordable and forceful strategy for generating more value in care.
References
Raul Valdes-Perez, Ph.D., is co-founder and CEO of OnlyBoth, a provider of benchmarking insights that enhance understanding, transparency, and performance improvement. Prior to OnlyBoth, he co-founded Vivisimo in 2000, a search software company that provided enterprise products and web-based consumer services, serving as its CEO for nine years and as Chairman for twelve until its acquisition in 2012 by IBM.
