Predictive Analytics and Computer Visualization Enhances Patient Safety to Prevent Falls
NCT ID: NCT06339125
Last Updated: 2024-04-01
Study Results
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Basic Information
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NOT_YET_RECRUITING
NA
4500 participants
INTERVENTIONAL
2024-05-31
2025-07-31
Brief Summary
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Detailed Description
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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
1. 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?
2. What are the perceptions of nurses related to:
1. The impact of three study technologies implemented to assist with the identification of increased fall risk.
2. The reduction of nurse burden on the assessment of fall risk and the recommendation for additional interventions to prevent falls.
Research aims:
1. 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.
2. 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.
Conditions
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Study Design
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NON_RANDOMIZED
PARALLEL
SUPPORTIVE_CARE
NONE
Study Groups
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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.
Fall prevention algorithm
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
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.
Inspiren camera visualization
The Inspiren computer camera visualization is an additional strategy for nurses to employ when there is a change in a patient's fall risk.
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.
Fall prevention algorithm
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
Inspiren camera visualization
The Inspiren computer camera visualization is an additional strategy for nurses to employ when there is a change in a patient's fall risk.
Unit 4
Control group, no intervention and usual care.
No interventions assigned to this group
Interventions
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Fall prevention algorithm
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
Inspiren camera visualization
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 Intervention Names
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Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Crico
OTHER
Inspiren Inc
UNKNOWN
RGI Informatics LLC
UNKNOWN
Massachusetts General Hospital
OTHER
Responsible Party
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Colleen Snydeman PhD, RN
Executive Director, Quality, Practice, Innovation & Research
Principal Investigators
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Colleen K Snydeman, PhD
Role: PRINCIPAL_INVESTIGATOR
Massachusetts General Hospital
Central Contacts
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References
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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.
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.
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.
Related Links
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American Hospital Association 2022
Center for Disease Control and Prevention (2017). Fact sheet: medications linked to falls.
Institute for Healthcare Improvement (2020). A national action plan to advance patient safety.
United States Census Bureau (2018).
Other Identifiers
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2023p003637
Identifier Type: -
Identifier Source: org_study_id
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