Patient Engagement III
Technology to Empower Patients

Reducing Inpatient Falls by Integrating New Technology with Workflow Redesign

Case Study · July 10, 2017

By integrating a novel patient-observer technology into a redesigned clinical workflow, the neuroscience unit at Mission Hospital (Asheville, North Carolina) achieved zero unassisted inpatient falls and zero injuries during a 3-month pilot period in 2015. Original neuroscience-unit fall rates during a 12-month baseline period from August 2014 to July 2015 were 5.74 total falls, 4.77 unassisted falls, and 0.91 injuries per 1,000 patient-days of care.

Key Takeaways

  1. A busy neuroscience unit can successfully redesign its processes to adopt innovative fall-reduction technology.

  2. Review your institution’s multicomponent fall-prevention program and monthly falls data to identify opportunities for improvement.

  3. Use a multidisciplinary team to come up with creative ways to reduce inpatient falls; involve that team in the implementation phase.

  4. Ensure that technology solutions and redesigned clinical workflows complement each other.

  5. Identify metrics of success, track them continuously, and analyze them at regular intervals.

The Challenge

Of the hundreds of thousands of patients who fall in U.S. hospitals each year, 30% to 50% are injured in the fall.  Such falls frequently prolong or complicate hospital stays, adding 6.3 days to the typical stay in one study. A single patient fall with injury costs an average of nearly $14,000.  A systemic review of the inpatient fall-prevention literature concluded that inpatient multicomponent programs reduce falls effectively, but with no clear evidence about which program components work best.

At Mission Health, we have long adhered to standard fall-prevention interventions: bed locked in low position with rails up; call light, call bell, assistive devices, and personal items within the patient’s reach; nonslip patient footwear; clutter-free room; appropriate use of sensory aids; dry floor and adequate lighting; hourly clinician rounds; and patient and family education. We have also used the patient’s individual fall risk to tailor interventions, such as bed and chair alarms, yellow identification armbands, and alert signage outside the patient’s room. Despite our multicomponent fall-prevention program, compassionate care staff, and strong culture of safety, falls on the neuroscience unit still occurred at an overall rate of 5.74 per 1,000 patient-days of care. We believed this rate could be significantly reduced.

The Goal

Mission Health collaborated with Cerner Corporation to design, develop, and trial-test a scalable strategy for reducing falls on the hospital’s 34-bed neuroscience unit. The approach, jointly funded by Mission Health and Cerner, emphasized new technology and redesigned workflows as an addition to the standard fall-prevention program. The goal was to conduct a 3-month pilot and evaluate the impact of a “virtual sitter” system (described below) as an alternative to using qualified staff sitters in patients’ rooms. Impact was measured by comparing the two approaches in terms of fall rates, injury rates, and patient-to-sitter ratio. Both groups received the standard fall-prevention interventions listed above.

The Execution

“Virtual sitter” technology. In October 2014, Cerner Corporation first introduced the Mission Health Center for Innovation to Microsoft® Kinect® technology, which uses a depth sensor to visualize full-body 3-D movement within a 20-foot range. The system’s infrared camera, acting as a virtual sitter, can monitor a patient’s movements under any ambient light. Thanks to open-software programming, a unique application can be created to define and draw virtual zones, trip wires, and other trigger points around a patient within a field of view. A continuously live video feed is sent across a secure wireless network to a high-definition monitor. Additional features include two-way audio, voice recognition, patient-privacy modes, and customizable alerting. When clinical leaders and bedside staff were shown a demonstration with mock patients, the nursing unit manager for neurosciences immediately recognized that this system might reduce the fall rate for high-risk patients. He volunteered to be the clinical sponsor for a pilot project.

Nursing workflow. The neuroscience unit had the highest historical fall rate at Mission Hospital (excluding behavioral health units). Clinical leaders, supported by the neuroscience unit director and nursing manager, assembled a multidisciplinary project team focused on adopting the “virtual sitter” technology and redesigning processes to reduce fall rates. Almost all patients admitted to the neuroscience unit are at risk for falls, according to the Morse Fall Scale (MFS).

Given that we had only six “virtual sitter” cameras for the pilot, a team of registered nurses from the neuroscience unit used the MFS criteria and clinical expertise to develop an algorithm to assist nursing staff in identifying the highest-risk patients for participation in the pilot. If a patient met the algorithm’s criteria for high risk, the nurse would educate that patient and/or family member about the virtual sitter system, enter a nursing order, and register the patient with the monitor technician (stationed at a central location on the unit). A wireless camera was placed on a wall mount and plugged into an electrical outlet in the patient’s room. Within 1 minute, the monitor technician had a full view of the room, could introduce himself or herself via two-way audio, and then briefly explain the “virtual sitter” monitoring system to the patient and family. The algorithm’s effectiveness in identifying patients at highest risk for falls has not been fully validated.

Inpatient Falls: Virtual Sitter Patient Fall Risk Algorithm

  Click To Enlarge.

Technician workflow. A select few certified nursing assistants (CNAs), serving as monitor technicians, were trained on the system’s software to ensure that monitored patients’ rooms were properly configured. The monitor technician remotely draws virtual zones and virtual trip wires around the patient’s bed or chair, and visual and auditory alerts are set to detect when a patient moves across the virtual boundaries (see video). First, a red box outlines the patient’s video display window. Then an easily interpretable audible alert delivers the appropriate message, such as “patient is getting out of bed.” The highly customizable, user-friendly interface ensures that the Kinect® sensor detects risky patient behaviors while minimizing alert fatigue.

Clinical pilot phase. After nursing-staff champions and monitor technicians on the neuroscience unit became comfortable with the new workflows and technology, a 3-month, six-camera “virtual sitter” pilot was launched in August 2015. The aforementioned algorithm identified patients at highest risk for falls, the ideal candidates for the new system. Patients at risk for suicide, homicide, drug overdose, or under legal restriction required an in-person (not a virtual) sitter, as per hospital policy. In all, 15% of patients in the neuroscience unit were enrolled in the 3-month pilot.

When a patient’s movements generated a virtual sitter alert, the monitor technician intervened using the following escalation pathway:

  1. Use the two-way audio interface to interact with the patient. Either direct the patient to remain in the bed or chair, or assess the need for further intervention.
  2. Contact the patient’s registered nurse or CNA depending on the patient’s need.
  3. Contact the unit supervisor for emergency situations.

Monitor technicians used VoIP (Voice over Internet Protocol) phones to communicate with the care team when their involvement was required. Each technician intervention was recorded on a log sheet to track their number and effectiveness. The pilot project team met weekly to review outcomes and identify opportunities for improvement.

The Metrics

The 98 patients monitored with the virtual sitter (during 348 patient-days of care) experienced zero unassisted falls and zero injuries during the 3-month period from August through October 2015. All other patients on the neuroscience unit had an unassisted fall rate (per 1,000 patient-days of care) of 4.06 and an injury rate of 2.45 during the same period. Analysis showed that verbal redirection alone (accounting for 50% of all virtual sitter interventions during the pilot) was highly effective in keeping the patient in his or her bed or chair. This discovery was significant because the verbal redirection not only averted patient falls, but it also prevented nurses from being interrupted from other tasks.

By the end of the pilot, there were zero falls and zero injuries for the patients monitored with the “virtual sitter.” These highly encouraging results prompted the hospital’s leaders to support the continued use and expansion of the “virtual sitter” technology and redesigned workflows. Indeed, the 3-month pilot (in patients at highest fall risk) was allowed to continue after the formal pilot ended. The figure below compares rates of falls and injuries for the entire neuroscience unit during the 12 months before and after the “virtual sitter” program was instituted. Although only about 15% of patients were enrolled in the “virtual sitter” program during the second 12 months, the overall annual rate of falls and injuries for the unit declined significantly — in effect, an intervention for a small subset of patients was so powerful that it had a clear impact on the unit’s overall performance.

Inpatient Falls: Unassisted Fall and Injury Rates Before and After Virtual Sitter Implementation

  Click To Enlarge.

Given that Mission Health’s “virtual sitter” project is in its early stages (and not yet at full scale), we have estimated our expected financial returns. Before the virtual sitter pilot, the annual number of hours for non-mandatory bedside staff sitters (1 sitter per patient) was approximately 47,000 (cost, ~$680,000). Extrapolating from our experience with virtual sitters thus far, we expect to reduce annual bedside-sitter hours by 42% — to roughly 27,300 — during the next 2 years (a minimum savings of $286,230). If we grow the patient-to-virtual sitter ratio to 12:1, the average cost is expected to decline from $350 (using 1 in-room sitter for each patient) to $29 per patient per inpatient day (using virtual sitters). Given the estimated non-reimbursable cost of $14,000 per fall with significant injury, Mission Hospital has the potential to reduce its fall-related costs by more than $250,000.

Lessons Learned

The technology for motion detection and alerting functioned as designed and as expected during the pilot period. Nonetheless, we see opportunity for improvement.

For example, although monitor technicians successfully used VoIP phones to contact the care team, a faster, scalable, more reliable, less labor-intensive alternative is desirable. We will now explore using a platform that automatically communicates rapidly and reliably with the right care team member at the right time for the right intervention.

We also would like to develop capabilities that monitor patients in multiple units, on multiple floors, across multiple hospitals from a single location. The pilot used a 6:1 patient-to-monitor technician ratio, but the technology’s alerting capabilities and our experience suggests that ratio can increase substantially, perhaps to 12:1.

Finally, we are exploring opportunities to use the virtual sitter system in other units at the hospital and, subsequently, across all Mission Health hospitals, with an eye toward its becoming a standard component of all fall-prevention programs. Potential additional uses include patient elopement, virtual visits, nurse documentation through voice recognition, and others.

 

Acknowledgments: We thank Josh Lewis, RN; Tony Guidone, RN; Ellen Blackmon, RN; Jessica Martin, RN; Noelle Shepherd, CNA; Stephanie Burnette, RN; Rhonda Robinson, MSN; Chad LaRowe; Michael Kusens; the entire Mission Neuroscience unit care team and clinical support staff; and Cerner.

This case study originally appeared in NEJM Catalyst on January 19, 2017.

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