The landscape for hospitals and health systems is rapidly changing, as hospitals race to meet expanded regulatory requirements and quality metrics, like patient safety and readmissions rates, which are increasingly tied to financial penalties. At the same time, profit margins are shrinking and many hospitals are dramatically scaling back operating expenses. It’s now become financially difficult to hire additional clinicians to help meet these important metrics and provide quality care to an ever-growing roster of patients.
And still, despite the challenges, hospitals need to think ahead to the strategies and new, innovative technologies that can help them create best practices and standardize care within their organizations. Hospitals and health systems have already invested millions in EHRs, which have helped to streamline processes and deliver quality patient information to clinicians at the touch of a button.
However, more is needed. EMRs have been optimized to capture transactional information, like medication allergies and family history, and some are able to receive feeds from patient monitors. But because EMRs don’t receive this information in real time – and because they have not been optimized for immediate reporting to clinicians – they can only take us part of the way to our goal toward what’s critically needed to transform healthcare: providing the right clinical information to care teams, before the team even knows the information is needed.
| Manu Varma, VP Marketing and Strategy, Philips Hospital to Home |
This is the power of clinical decision support, and it’s not just a pipedream. Innovations in healthcare are exploding, and each new technology is helping the industry deliver the best quality, real-time information to physicians. Patient monitors are doing a better job of understanding and reporting shifts in patient vital signs. Cloud technology is moving data out of silos so that it can be shared between systems, EHRs and even hospitals to provide a big-picture view of patient conditions, while data analytics have become extraordinarily powerful to pinpoint relevant signs and identify trends. These technologies are making it possible for a limited clinical staff to provide quality care and help clinicians catch patient deterioration early, before it’s too late to intervene.
Predictive decision support for sepsis
Sepsis is one of the hugely pressing issues that hospitals are facing and is a critical example of how clinical decision support can be used to help improve patient outcomes. More than 750,000 patients per year are treated for sepsis1, and the cost of treating these patients is more than $20 billion in hospital costs alone.2
There have been many attempts to try to improve patient outcomes after they go into septic shock. Researchers are working to develop more effective treatments, like drugs, dialysis and surgery, but even today, if a patient goes into septic shock, the outlook is grim. Mortality rates remain high, reaching up to 29 percent3. Instead of treating or responding to an urgent situation, caregivers instead need to tackle the difficult task of predicting which patients are headed toward sepsis so that they can intervene beforehand.
But at nearly every hospital, the bedside care team is operating at full speed. Since patient changes can happen rapidly and unexpectedly, clinical staff must remain alert while they change bandages, administer medication, record patient data in the EHR and interact with patients. These are critical, invaluable tasks that cannot be overlooked. Because the bedside care team is so busy and focused on immediate, short-term needs, they may miss subtle signs of deterioration that would enable them to identify trends and predict which patients are heading into septic shock. For that, they need a spare cognitive capacity to help spot trends and predict patient deterioration.
Clinical decision support tools are useful here, but they become even more powerful when combined with a carefully selected team of clinicians who can foster supportive relationships with bedside teams to provide real-time clinical decision support via telehealth. This “command center” team (usually comprised of one intensivist and several highly trained nurses) is not encumbered by the transactional, immediate needs of the patients. Because of this, they can serve in the trend-spotting role to help the bedside team. Since this command center team is not at the patient bedside, they rely on advanced telehealth technologies to give them insight into patient status, taking in the full view of patient EHRs, and leveraging real-time video monitoring at the bedside and patient monitoring data.
Here’s how it works: The command center identifies the patients within the hospital who are at high risk for developing severe sepsis. Once those patients are identified, the team filters vital signs, lab values and other physiologic parameters into algorithms that help the command center team spot trends and potential patient deterioration early. If they determine that a patient is showing signs of progressing into sepsis, they alert the bedside team and coordinate a care plan development.
Driving staff efficiencies
By creating a command-center care model, clinicians can receive the real-time, clinical decision support needed to not just respond to patient changes, but to predict them so that clinicians can intervene sooner. This not only improves patient outcomes, but also makes bedside teams more efficient and less stressed. The strain felt by care teams responding to a patient entering septic shock is far greater than if they are simply moving forward with a coordinated care plan earlier in the process and greatly impacts the time and attention they can devote across their patient population.
In addition, this model also gives the bedside team the ability to call someone immediately. While hospitals are staffed with capable care teams, the intensivists who oversee care and have the clinical knowledge to notice subtle, but significant, changes in a patient’s condition are often on call and not always available at the bedside. Bedside care teams may be reluctant to call during off hours if they’re unsure about how urgent a situation is, and it’s more difficult to give an accurate assessment of a subtle shift in a patient’s condition by telephone. This command model gives the bedside team 24/7 support so that they can get a consultation immediately.
Getting the formula right
As important as it is to provide clinical decision support, it’s more important for hospitals and health systems to get the formula right – and right for their organization. Just as every patient is different, with different response to the same treatment plans, each hospital and health system has different needs. The same technologies or strategies won’t work for everyone. Instead, executive leadership needs to think carefully about the needs of their organization and how critical decision support can be most useful to them. If something isn’t working, clinician leaders need to be empowered to make changes.
By offering relevant, real-time information to physicians and the bedside team, clinical decision support can help us transform the way care is delivered, so that we can move our goal from just reacting to adverse events to actually identifying symptoms typically associated with adverse events early on so that clinicians can intervene before an event happens.
References:
1http://www.nigms.nih.gov/Education/Pages/factsheet_sepsis.aspx
2http://www.sepsisalliance.org/news/2013/sepsis_most_expensive_condition/
