Going Outside the Hospital for Help: How Readmissions Have Been Reduced at El Camino | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

Going Outside the Hospital for Help: How Readmissions Have Been Reduced at El Camino

August 7, 2013
by Rajiv Leventhal
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Through technology, statistical analysis, and specialized support post-discharge, the California hospital has seen a significant reduction in readmissions
Greg Walton

Studies estimate that nearly two million Medicare beneficiaries are readmitted to hospitals within 30 days of discharge annually, costing Medicare $17.5 billion for the additional care. The national average readmission rate has remained steady at around 19 percent for several years, even as many hospitals have worked harder to lower theirs.

Last year, Medicare began levying financial penalties against hospitals with avoidable readmissions under a mandate from the Affordable Care Act. The maximum penalty this federal fiscal year (FFY) is 1 percent of regular payments, with that percentage growing to 2 percent in FFY 2014 and 3 percent in FFY 2015.

For this reason, as well as the “clinical mission to continue to provide care to patients after they leave the hospital,” officials at the 443-bed El Camino Hospital in Mountain View, Calif. decided that, rather than stand by and wait for these penalties to come down, action needed to be taken and the bar needed to be set high, says Greg Walton, CIO at El Camino.

A recent case study by the Ann Arbor, Mich.-based College of Healthcare Information Management Executives (CHIME) details how El Camino is using information technology to influence readmission reductions—far below the national average—while taking patient care to the next level. As part of the solution, El Camino has focused on enhanced communication and care coordination with sub-acute providers through the use of telepresence, which is a sophisticated, lifelike, two-way videoconferencing solution, according to hospital officials. This has helped the hospital see a 25 percent decrease in hospital readmissions already.


When patients are admitted to a hospital and then transitioned into a long-term care facility, such as a skilled nursing facility (SNF), hospital personnel and staff at the long-term care facility engage in regular telepresence sessions to exchange patient information and allow continuing hospital involvement in post-discharge care. This better enables El Camino Hospital’s transition team to collaborate with the nurses, administrators, and social workers of the long-term care facilities that care for the hospital’s recently discharged patients.

“Nurses who are caring for patients who have been transitioned to the nursing home now feel like they are better connected to their patient and also to the prior caregiver,” says Walton. “They realize that someone is paying attention to the patient’s status post-discharge, and the communication level is vastly improved.”

El Camino Hospital’s telepresence program began last July, and additionally, via its own health information exchange, connects to 28 SNFs, 500 physicians, and four home health agencies that care for recently discharged patients.

In a few weeks, Walton says an SNF will put data on the network for the first time, as it’s mostly been a one-way exchange to this point. “Now [SNFs] are starting to contribute when their capabilities come online. Many of these places don’t have the capability, so we’ve had to make it easier for them to get our data, and now, we’re trying to make it easier for them to put data on the network to be shared.”

When designing the program, El Camino chose to evaluate readmissions after seven days in order to more accurately measure the impact of the interventions. The results have shown that the hospital’s efforts are paying off. In the first three months of 2013, the hospital saw a 4.7 percent seven-day readmission rate among the 584 patients discharged to SNFs. By contrast, over the same three months in 2012, the hospital’s readmission rate had been 6.2 percent among 526 patients discharged to SNFs, according to the case study.

Walton stresses these results can actually be accomplished without technology, though. “The technology just facilitates communication—it’s not the magic bullet,” he says. “More impact is coming from this specialized team and the increase in communication throughout. You can do all this without technology, but it would be less effective and timely without it.”


Aside from telepresence, El Camino has also been able to use statistical analysis of its data to detect patients at high risk for readmissions. Its research has identified demographic information that is highly accurate in pinpointing high-risk patients early in their hospitalization—typically immediately after admission—and allows the institution to alert the healthcare team who is providing care for the admitted patient.

El Camino Hospital took the data from 10,000 patients hospitalized in 2010 and did a regression analysis of 25 characteristics about patients that the hospital would know the day after admission. It developed a formula to predict readmissions, and in 2011, it applied the formula to a validation group of 2,500 patients--it didn’t differentiate its care or discharge planning for those 2,500 patients--and then examined readmission rates.

“You’re looking for something that could contribute to an event,” explains Walton.  “This could include diagnoses such as congested heart failure, pneumonia, stroke, or sepsis. Other factors include whether the patient’s primary care physician (PCP) was identified in the patient’s active medical record. Each of these factors leads to another ‘point.’ So you’re looking at various factors, and adding up these ‘points.’ Then you’re saying [person X’s] readmission probability is higher than [person Y’s], and therefore when [X] comes in, we are focused on what will that mean when we discharge him or her. Basically, it’s a statistical predictor based on what that patient has experienced and how other patients with similar conditions would show up statistically.”


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