Electronic surveillance systems increase efficiency

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Technological advancements in data-mining systems are worth the expense

After reading this article, you will be able to:

  • Explain how data-mining systems can improve your IC program
  • Justify ways in which this technology can save money
  • Recognize how automated hand hygiene will increase compliance

The demands on today’s IP are extensive. In addition to managing and educating frontline staff members, IPs are asked to conduct risk assessments for a variety of infections, initiate facilitywide goals, and stay on top of IC measures within their facilities.

However, doing so requires a tremendous amount of infection data tracking, whether it’s by patient, by infection, or by unit. Before elaborate computer software became available to medical facilities, IPs had to go through microbiology reports by hand, manually sorting and calculating rates and trends, says Joan Hebden, RN, MS, CIC, director of IC at the University of Maryland Medical Center in Baltimore and author of “Leveraging Surveillance Technology to Benefit the Practice and Profession of Infection Control,” published in the April 2008 American Journal of Infection Control.

“The major pro to using data-mining software is ef-ficiency,” Hebden says. “You’ll have a perspective on what was done historically—and when I say his-torically, there are some ICPs that are still doing this—where you literally are printing out a report from your microbiology laboratory from all your abnormal results, and then you have to go through that and sort it depending on what type of surveillance you are doing.”

However, the recently released 2009 APIC Economic Survey found that technology use associated with IC programs is lagging. Only one in five of the nearly 2,000 survey respondents used data-mining technology to track infections.

This means IPs need to be able to bring a business case to administration to obtain technology such as data-mining systems, says Linda R. Greene, RN, MPS, CIC, director of IP at Rochester (NY) General Health System and author of APIC Position Paper: The Importance of Surveillance Technologies in the Prevention of Healthcare-Associated Infections (HAIs).

“As preventionists, part of our job is coaching, mentoring, working with the folks on the units, and manual surveillance, unfortunately, ties us to our desk,” says Greene. “The technology is very, very important.” (See p. 4 for a related story.)

Balancing the cost

The trick to achieving leadership buy-in with data-mining technology is being able to show that investment in the system will balance the bottom line. However, Greene concedes that it’s difficult for many IPs to present a business case since they are often more clinically minded.

“It’s probably something we haven’t done very well because we’re pretty altruistic and we are thinking about patients, and they are the most important thing,” Greene says. “But what’s important is that we make our administrators realize that when we prevent an infection, we not only save a life, which is the most important thing, but we also decrease length of stay and decrease the bottom line, and that’s a really important message in these economic times.”

Hebden notes that calculating even basic cost-savings numbers is relatively simple. For example, if the average IP makes $35 per hour and spends eight hours per week on manual surveillance, that hospital could save nearly $15,000 per year with the use of data mining, and the IP could better invest his or her time elsewhere.

“And that has nothing to do with the actual reviewing of patient records that might have to be done, like aggregating your reports for presentation to clinicians and all the other work that goes along with the process of surveillance,” Hebden says. “Because surveillance is not just simply data collection; surveillance is data collection, data aggregation, data reporting, and evaluation of data to determine if outcomes of interventions are working. So the data collection piece should be streamlined as much as possible.”

If you show how purchasing a data-mining system can offset the skyrocketing cost of HAIs, administration may be more likely to invest. For example, making the argument that automated data collection helps eliminate patient days may be convincing.

“Most hospitals in the country, we are running at 100% capacity,” Greene says. “And part of what the CEO and the people in the administration want to see is that throughput, so that if we can move patients out of an ICU bed, get people waiting in the emergency department into those beds, and decrease the length of stay, it’s really important.”

Leveraging regulatory agencies

Another keyword that might make hospital administrators perk up is “reimbursement.”

With the announcement that the Centers for Medicare & Medicaid Services (CMS) will not reimburse “never events,” including catheter-associated urinary tract infections, vascular catheter–associated infections, and surgical site infections, hospital CEOs are going to pay more attention to infections they may not have watched as closely before.

“I think in light of the CMS zero tolerance of these infections going forward, we are in a position where I think they now understand they are losing money from their bottom line,” Hebden says. “They have always been losing money from their bottom line potentially, but now it is much more evident. These infections historically resulted in reimbursement from the payment system, so I don’t think leadership truly understood their impact. Now we’re in a position where they’re eliminating reimbursement for potentially preventable adverse outcomes of care.”

These infections are now tied to a monetary value. For example, the cost of a central line–associated bloodstream infection could be as much as $34,000, Hebden says. Purchasing a data-mining system may be more appealing if there is proof that its value would be realized after eliminating just a few infections.

And although The Joint Commission (formerly JCAHO) doesn’t require an automated system, having one will certainly help comply with the National Patient Safety Goals and other IC standards that focus on conducting a risk assessment, which is easily mapped out with surveillance technology, Greene says. Additionally, the presence of an automated system shows an accrediting agency that the IP has a broad overview of IC in the facility.

“With an electronic surveillance system, you can prove to regulatory agencies that you really have your [finger on the] pulse of the whole organization,” Greene says. “It may not even be something you are following intentionally like bloodstream infections in the intensive care unit, but you have a handle on it.”

Purchasing a system

There are several data-mining vendors on the market, each with different accessories, but there are a few things to look for when purchasing a system, according to the previously mentioned APIC position paper. It’s important that a data-mining system be able to do the following:

  • Obtain essential clinical information for individual patients from sources throughout the facility
  • Retrieve data from various clinical systems such as the laboratory, pharmacy, and radiology departments and send alerts
  • Provide real-time updates
  • Send standard electronic messages and documents to public health departments, such as the CDC’s National Healthcare Safety Network

APIC’s position paper also suggests steps that are useful in choosing an appropriate system:

  • Make a list of must-have and nice-to-have features
  • Write down standard scenarios that you would like the system to respond to and ask vendors to demonstrate their system’s functionality in those situations
  • Talk to other users of the system
  • Assess the system’s ability to adapt to change
  • Evaluate the system’s security

Hebden says it’s important not to downplay the importance of staff members such as programmers or data analysts, who can help enhance the productivity of the system. For example, the University of Maryland Medical Center has a central repository from which analysts can quickly pull data with flexible degrees of specificity.

“I could just call the data analyst up and say, ‘I want you to go back three years and tell me how many positive bloods we’ve had for a certain organism,’ and they can do that,” Hebden says.