- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06339125
Predictive Analytics and Computer Visualization Enhances Patient Safety to Prevent Falls
Predictive Analytics Combined With Computer Visualization Enhances Patient Safety and Eases Nurse Burden for Preventing Falls
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
To decrease falls in the hospital setting, and building on previous nursing fall research, as well as the MFS and the Fall TIPS program, MGH developed a decision support algorithm to identify changes in clinical factors as they occur to alert nurses to the need to adjust fall prevention interventions. MGH Nursing, through a collaboration with RGI Informatics, then deployed the MGH algorithm on one clinical general care unit. The RGI software uses the MGH algorithm live streaming EHR data from Epic to identify patients whose risk of falling may have increased and provide clinical decision support to nurses through an alert on their hospital issued cell phones. Preliminary results demonstrated feasibility and a statistically significant reduction (p <0.01) in falls with injury over an 11-month period.
Mutually exclusive preliminary work, on a second inpatient general care unit, involving a computerized patient visualization system also yielded reduction in falls. Combined usage of the two technologies may yield a synergistic effect thereby further reducing the incidence of falls in the acute care setting. To date, there is no evidence derived from evaluation of patient outcomes from simultaneous testing of the two technologies. Thus, the purpose of this study is to determine the impact of three different fall prevention interventions (RGI/MGH Algorithm only, Inspiren only and combined RGI/MGH Algorithm and Inspiren) on patients at risk for falls and falls with injury on three adult general care units in a large academic medical center.
Our proposed solution is the only known strategy that extracts and synthesizes physiologic and physical data from multiple sources, to create a dimensional view of a patient's safety profile related to fall risk. Timely alerts will inform nurses of patient's fall risk, reason for risk and their clinical decisions regarding fall prevention strategies. This initial proposal focuses on patients at risk for falls and we are confident that this innovative approach is adaptable to address other critical safety issues for example, pressure injuries and catheter associated urinary tract infections. Detailed information about RGI Analytics and Inspiren is provided below.
Methodology: An observational cohort, mixed-methods study design will be conducted to determine the impact and effectiveness of usual care and three different fall prevention strategies that exceed the standard of care on three inpatient units at MGH over one year. Unit 1 will employee streaming analytics and the MGH algorithm only, Unit 2 will employee Inspiren's AUGI computer visualization only and Unit 3 will employee the combined streaming analytic/MGH algorithm and Inspiren's AUGI device. Unit 4, the control unit, will serve as an internal comparison group from the same institution. In addition to the study interventions all four units will continue to maintain usual MGH evidence-based practice, standards of care for fall prevention.Patient, unit, and nurse demographic data collected for the study currently can be accessed from or calculated from existing sources. Sources include the ADT, PCS financial, acuity, and quality data stored in the PCS Datawarehouse. Unit patient demographic data in the aggregate will include age, gender, and race. Nurse demographic data will include the number of fulltime equivalents, years of experience as a nurse, years of experience at MGH, and highest level of education. Unit data will include counts of patient admissions, patient days, length of stay, nursing acuity, patient type by gender, age, race, ethnicity, number of unit falls and unit falls with injuries, and nurse staffing indicators. Nurse perceptions of the three interventions units will be measured in association with the intervention using real time feedback from cell phone alerts (helpful/not helpful), nurse feedback, and quarterly surveys. The Fall Prevention Efficiency Scale (Dykes, et al., 2021) is a peer reviewed 13-item tool that focuses on four key areas: saves time, does not waste time, is worth the time and is helpful in preventing falls. The survey questions will be adapted to meet the needs of this study and will be administered via REDCap, a Harvard Catalyst secure, web application for managing on-line survey tools.
Research questions
- In the acute care, inpatient hospital setting, is there a difference in rate of occurrence of falls and injurious falls, comparing three distinct methods of alerting nurses at the point of care to a change in a patients risk of falling while maintaining all other current standards of care for fall prevention and adding these new standards during the study: (1) use of streaming analytics and a fall risk algorithm that alerts nurses to a change in fall risk, (2) computer visualization and artificial intelligence interpretation of patient movement and (3) a combination of both technologies?
What are the perceptions of nurses related to:
- The impact of three study technologies implemented to assist with the identification of increased fall risk.
- The reduction of nurse burden on the assessment of fall risk and the recommendation for additional interventions to prevent falls.
Research aims:
- Compare the impact of the three fall prevention innovations, within and between units and to one control unit (all four units using same usual standard of care) on falls and falls with injury.
- Determine the perceived effectiveness of fall prevention innovations and alerts on clinical decision support and nurse burden using nurse surveys, responses to alerts and focus groups.
Study Type
Enrollment (Estimated)
Phase
- Not Applicable
Contacts and Locations
Study Contact
- Name: Colleen Snydeman, PhD
- Phone Number: 16176430435
- Email: csnydeman@mgb.org
Study Contact Backup
- Name: Hiyam M Nadel, MBA
- Phone Number: 6176430064
- Email: hnadel@mgb.org
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
Adult medical patients admitted to the study units. All nurses working on the study units.
Exclusion Criteria:
- None
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Supportive Care
- Allocation: Non-Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
Experimental: Unit 1
Usual care and live streaming electronic health record driven Algorithm alerts nurses to possible increase in fall risk for review of interventions in place.
|
Algorithm generates fall prevention alerts to nurses in real time, using evidenced based electronic health record information regarding changes in care that may suggest the need for additional fall prevention strategies
Other Names:
|
Experimental: Unit 2
Usual care and computer camera visualization detects and anticipates patient movement for patients at risk for falls and alerts nurses with fall risk potential.
|
The Inspiren computer camera visualization is an additional strategy for nurses to employ when there is a change in a patient's fall risk.
Other Names:
|
Experimental: Unit 3
Usual care and live streaming electronic health record driven Algorithm alerts nurses to possible increase in fall risk for review of interventions in place.
AND Computer camera visualization detects and anticipates patient movement for patients at risk for falls and alerts nurses with fall risk potential.
|
Algorithm generates fall prevention alerts to nurses in real time, using evidenced based electronic health record information regarding changes in care that may suggest the need for additional fall prevention strategies
Other Names:
The Inspiren computer camera visualization is an additional strategy for nurses to employ when there is a change in a patient's fall risk.
Other Names:
|
No Intervention: Unit 4
Control group, no intervention and usual care.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Falls
Time Frame: Measured monthly/quarterly over one year
|
Rate of patient falls per 1000 patient days
|
Measured monthly/quarterly over one year
|
Falls with injury
Time Frame: Measured monthly/quarterly over one year
|
Rate of falls with injury per 1000 patient days
|
Measured monthly/quarterly over one year
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Nurse perceptions
Time Frame: three, six, and 12 months
|
Survey of Nurse perceptions of fall prevention strategies
|
three, six, and 12 months
|
Nurse perceptions
Time Frame: three, six, nine and twelve months
|
Focus groups of nurse perceptions
|
three, six, nine and twelve months
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Colleen K Snydeman, PhD, Massachusetts General Hospital
Publications and helpful links
General Publications
- Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Furstenau D, Biessmann F, Wolf-Ostermann K. Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review. J Med Internet Res. 2021 Nov 29;23(11):e26522. doi: 10.2196/26522.
- Dykes PC, Carroll DL, Hurley A, Lipsitz S, Benoit A, Chang F, Meltzer S, Tsurikova R, Zuyov L, Middleton B. Fall prevention in acute care hospitals: a randomized trial. JAMA. 2010 Nov 3;304(17):1912-8. doi: 10.1001/jama.2010.1567.
- Morse, JM, Morse R.M., Tylko, S.J. (1989). Development of a scale to identify the fall-prone patient. Can J Aging, 8:366-7.
- Fehlberg EA, Cook CL, Bjarnadottir RI, McDaniel AM, Shorr RI, Lucero RJ. Fall Prevention Decision Making of Acute Care Registered Nurses. J Nurs Adm. 2020 Sep;50(9):442-448. doi: 10.1097/NNA.0000000000000914.
- Dykes PC, Burns Z, Adelman J, Benneyan J, Bogaisky M, Carter E, Ergai A, Lindros ME, Lipsitz SR, Scanlan M, Shaykevich S, Bates DW. Evaluation of a Patient-Centered Fall-Prevention Tool Kit to Reduce Falls and Injuries: A Nonrandomized Controlled Trial. JAMA Netw Open. 2020 Nov 2;3(11):e2025889. doi: 10.1001/jamanetworkopen.2020.25889.
- Costantinou E, Spencer JA. Analysis of Inpatient Hospital Falls with Serious Injury. Clin Nurs Res. 2021 May;30(4):482-493. doi: 10.1177/1054773820973406. Epub 2020 Nov 16.
- Pierce JR Jr, Shirley M, Johnson EF, Kang H. Narcotic administration and fall-related injury in the hospital: implications for patient safety programs and providers. Int J Risk Saf Med. 2013;25(4):229-34. doi: 10.3233/JRS-130603.
- Quigley PA, Hahm B, Collazo S, Gibson W, Janzen S, Powell-Cope G, Rice F, Sarduy I, Tyndall K, White SV. Reducing serious injury from falls in two veterans' hospital medical-surgical units. J Nurs Care Qual. 2009 Jan-Mar;24(1):33-41. doi: 10.1097/NCQ.0b013e31818f528e.
- Zhao YL, Bott M, He J, Kim H, Park SH, Dunton N. Evidence on Fall and Injurious Fall Prevention Interventions in Acute Care Hospitals. J Nurs Adm. 2019 Feb;49(2):86-92. doi: 10.1097/NNA.0000000000000715.
- Dykes PC, Khasnabish S, Adkison LE, Bates DW, Bogaisky M, Burns Z, Carroll DL, Carter E, Hurley AC, Jackson E, Kurian SS, Lindros ME, Ryan V, Scanlan M, Spivack L, Walsh MA, Adelman J. Use of a perceived efficacy tool to evaluate the FallTIPS program. J Am Geriatr Soc. 2021 Dec;69(12):3595-3601. doi: 10.1111/jgs.17436. Epub 2021 Aug 30.
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- 2023p003637
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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