Improving Patient Outcomes With Analytics

The sheer number of health IT solutions promising to improve outcomes and solve the healthcare industry’s most pressing issues is staggering. It is very easy to get lured into thinking that the solution to an organization’s challenges is just a technology purchase away.

The reality, however, is not quite that simple.

Accumulating data through EHRs and using an analytics solution to track improvement efforts doesn’t make an organization data-driven. The organization must turn the data into action to improve outcomes, and it must also possess the organizational skills necessary to empower clinicians to drive change.

Change Management

To make information actionable and improve outcomes, an organization must combine analytics with clinical content and organizational supports designed to promote the uptake of best practices. For example, suppose a health system discovers that the number of caesarian sections performed in its facilities is above the national average. Leaders can review the literature and best-practice guidelines to provide clear direction for scheduling early caesarian section deliveries. They can then build those guidelines into EMR order sets and can measure compliance with those guidelines using the analytics application.

With such strong change management in place, staff are empowered to promote best practices. For example, if an OB physician requests that an early cesarean section be scheduled (contrary to guidelines), the scheduling nurse will feel safe declining the request. The physician can appeal to the medical director to explain why an exception should be made. Such organizational supports provide a necessary and solid foundation for data-driven improvement work.

Improvement Efforts

Often the long-term success of a quality improvement program is determined by the success of the first few projects. That is why it is important to choose the right projects to get started with-and analytics applications can help. Applications can analyze combined clinical and financial data to highlight the best opportunities for quality improvement and cost reduction. Effective applications analyze the data to identify high-volume clinical processes with the highest cost and quality variance.

Having such an analysis on hand makes it easier to bring physicians on board with improvement efforts. The data can convince them that the care process should indeed be improved. Once the organization gains consensus on this point, physicians can help determine the best ways to reduce variation while improving care at the same time.

SEE ALSO: Increasing Importance of Data Analytics

Performance Initiatives

Having clinical, financial, claims, patient satisfaction, and other data available for analysis-as well as the organizational supports in place to make it actionable-enables organizations to leverage analytics to drive change in multiple areas (clinical processes, revenue cycle management, operations, and more). Since this article has already touched on some ways in which analytics applications can help improve clinical processes, the following are examples of other areas where such applications can help drive better performance.

Analyzing Patient Satisfaction

With the high level of importance now being placed on patient satisfaction measures, analyzing patient satisfaction data in conjunction with utilization is more important than ever. One opportunity to increase patient satisfaction is by improving patient access. The ability to provide shorter wait times in emergency departments, as well as same-day or 24-hour appointments with primary care physicians, could provide an organization with a distinct advantage in an increasingly competitive market. Using analytics applications, hospital leadership can view data about patient movement and ADT logs and find patterns or trends to help improve workflow throughout the department and thus improve access.

Applications for Hospital Finances

Analytics can deliver profound improvement in another operational area: no-pays. Hospital-acquired conditions such as catheter-associated urinary tract infections (CAUTI) or central line-associated bloodstream infections (CLABSI) must be treated without reimbursement (no-pay), which negatively impacts the organization’s bottom line.

Using analytics applications, health system leadership can address these issues, beginning with an analysis of infections across departments to uncover if similar procedures or best practices are being implemented in areas where instances are low. Once a best practice for reducing CAUTI or CLABSI has been determined, it can be implemented across the organization. Then, the analytics applications can track compliance with this best practice and any resulting decrease in infection rates.

Patient Risk & Population Health Management

Using data from multiple systems is critical for population health management and shared-risk arrangements, such as ACOs. These organizations can use analytics to identify improvement opportunities for quality, outcome, and costs among their most expensive patient populations using applications that stratify a patient population by risk. Without this information, the ACO will be hard pressed to implement necessary changes and improve performance.

Long-term Improvement

The examples above represent only a small sample of the benefits that analytics can bring to an organization. While there is no technological silver bullet for performance improvement, analytics applications empower organizations with the insights they need to improve quality and cost today and for many years to come.

Dan Soule is Vice President, Product Management, for Health Catalyst, a Salt Lake City-based developer of healthcare data warehousing and analytics solutions.

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