How hospitals can identify areas for improvement by merging clinical and operational data
Fiona McNaughton is in Offering Management at IBM Watson Health.
The pressure to deliver high quality care at an affordable cost is here to stay. Whether a provider is in a fee-for-service or value-based environment, or straddling both, the relationship between cost and quality is now intimately linked.
This era of value-based payment models has put tremendous pressure on hospital administrators to understand the drivers behind what’s contributing to their costs, quality outcomes and risk exposures. After all, if providers don’t know what’s driving their costs and outcomes, they’re unlikely to understand where they have opportunities to improve and how to effect change.
Unfortunately, getting this deep level understanding of the individual drivers behind current and evolving measures of performance is not as simple as a basic audit. The Centers for Medicare and Medicaid Services (CMS) has broad authority to test new payment and delivery models and hospitals are being evaluated across several fronts. Initiatives such as the Bundled Payments for Care Improvement (BPCI), Merit-Based Incentive Payment System (MIPS), and Comprehensive Primary Care Plus (CPC+) programs, and several generations of Accountable Care initiatives, have been rolled out, refined, and expanded. A shift to value has spurred a laser focus on hospital readmissions, infection control, and hospital-acquired conditions by penalizing hospitals with above-average rates.
In order for an organization to thrive in this environment, technologies that incorporate clinical and operational data to illustrate performance against external peer benchmarks have become invaluable.
To quantify how well an organization is doing, measuring performance alone doesn’t paint the whole picture. Organizations need to evaluate their performance against comparable benchmarks, such as operating costs or quality measure performance against a benchmark based on their organizational characteristics and patient populations, to get a true snapshot of where they can improve.
For example, consider the experience of Schneck Medical Center, an independent, 93-bed community hospital in Seymour, Indiana. By deploying powerful healthcare data analytics, the hospital was able to conduct a risk- and severity-adjusted comparison of its clinical performance against the performance of peer hospitals around the country. This data quickly revealed that the hospital had a raw readmission rate of nearly 14 percent, a mark which was continuing to trend upward. What’s more, 10 percent of the facility’s readmissions were due to COPD. These readmissions had cost the hospital in excess of nearly $300,000 that year alone.
Based on this data, the hospital could pull together teams that were able to focus on closing those gaps.
These steps reduced the hospital’s raw readmission rate by 55%, and also reduced the number of COPD patients being admitted to the hospital in the first place. This happened because the Schneck Medical Center was taking better care of its patients in the community – through efforts such as assigning care managers, weekly respiratory care visits and dedicated care focused on COPD.
Only by taking a hard look at their performance across a wide range of clinical and comparing that performance to other, similar hospitals and populations could Schneck administrators build a performance improvement action plan rooted in hard numbers.
As we talk to organizations about the challenge of balancing the cost/quality equation, it often becomes a question of execution. Many complications, while well-intentioned, are self-inflicted, where organizations have tried to make improvements, struggled to realize results, and ultimately, have come out more confused than when they started. It’s not uncommon for us to see hospitals that have gaps try to supplement existing systems with additional technologies. But that can get murky if a hospital’s analytic insights aren’t coming from a consistent and reliable source, or if they don’t have a centralized analytic strategy. Providers need those actionable and very specific targeted insights brought together in a consistent way, so that they have a single, reliable source which they can use to make confident decision. Then, once providers have that intelligence, they require the support services to help achieve positive change.
Those who are succeeding in the shift to value-based care are pulling together different dimensions of how they think about performance – and the drivers behind that performance – to get a more comprehensive picture of the discreet drivers of quality care. Along with that, they are incorporating quality and financial metrics that are available in an actionable, acceptable and consumable way for various stakeholders throughout an organization.
What that shows is a concerted effort among both administrators and clinicians to carve out action plans that recognize the complicated link between cost and quality, the need to clearly communicate those links throughout the organization, and to make confident data driven change.
To learn how healthcare organizations are improving performance through comprehensive, centralized data, click here.