Predictive Analytics and Computer Visualization Enhances Patient Safety to Prevent Falls

NCT06339125 | Completed

• 1 other identifier: 2023p003637
• Study Type: interventional
• Target: 5,350
• Locations: 1 country
• Sites: 1

Brief Summary

Annually, in the United States there are 700,000 - 1,000,000 inpatient falls reported, and one-third of patients sustain an injury. The average estimated cost per fall is $6,694, resulting in over $1.4 -1.9 billion dollars in losses each year (AHRQ, 2017). This study aims to compare the impact of different fall prevention strategies on the rate of occurrence of falls and falls with injury in an academic medical center on three adult medical units. While maintaining the usual standard of care for fall prevention, each unit will add one of the following: (1) use of a fall risk alert to nurses using an algorithm based on electronic health record data or (2) computerized camera visualization or (3) a combination of both.

Conditions

Fall Patients; Fall Injury

Outcome Measures

Primary Outcomes (2)

• Fall patient

  Rate of patient falls per 1000 patient days, National Database Nurse Sensitive Indicators

  Measured monthly/quarterly over one year

• Fall injury

  Rate of falls with injury per 1000 patient days, National Database Nurse Sensitive Indicators

  Measured monthly/quarterly over one year

Secondary Outcomes (2)

• Nurse perceptions

  three, six, and 12 months

• Nurse perceptions

  three, six, and twelve months

Study Arms (4)

Unit 1

EXPERIMENTAL

Usual care and live streaming electronic health record driven Algorithm alerts nurses to possible increase in fall risk for review of interventions in place.

Other: Fall prevention algorithm

Unit 2

EXPERIMENTAL

Usual care and computer camera visualization detects and anticipates patient movement for patients at risk for falls and alerts nurses with fall risk potential.

Other: Inspiren camera visualization

Unit 3

EXPERIMENTAL

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.

Other: Fall prevention algorithm; Other: Inspiren camera visualization

Unit 4

NO INTERVENTION

Control group, no intervention and usual care.

Interventions

Fall prevention algorithm OTHER

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

Also known as: RGI fall prevention algorithm

Unit 1; Unit 3

Inspiren camera visualization OTHER

The Inspiren computer camera visualization is an additional strategy for nurses to employ when there is a change in a patient's fall risk.

Also known as: Computerized camera visualization

Unit 2; Unit 3

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Adult medical patients admitted to the study units
• All nurses working on the study units

You may not qualify if:

• None

Sponsors & Collaborators

• Massachusetts General Hospital: lead
• Crico: collaborator

Study Sites (1)

Massachusetts General Hospital

Boston, Massachusetts, 02114, United States

Location

Related Publications (10)

• 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.

  PMID: 21045097 BACKGROUND

• 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.

  BACKGROUND

• 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.

  PMID: 34847057 BACKGROUND

• 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.

  PMID: 32826513 BACKGROUND

• 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.

  PMID: 33201236 BACKGROUND

• 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.

  PMID: 33190509 BACKGROUND

• 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.

  PMID: 24305561 BACKGROUND

• 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.

  PMID: 19092477 BACKGROUND

• 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.

  PMID: 30633063 BACKGROUND

• 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.

  PMID: 34460098 RESULT

Related Links

• 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).

Study Officials

• Colleen K Snydeman, PhD

  Massachusetts General Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Executive Director, Quality, Practice, Innovation & Research

Model Details: 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 employ streaming analytics and the MGH algorithm only, Unit 2 will employee Inspiren's AUGI computer visualization only and Unit 3 will employ 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.

Study Record Dates

• First Submitted: March 25, 2024
• First Posted: April 1, 2024
• Study Start: September 24, 2024
• Primary Completion: September 23, 2025
• Study Completion: September 24, 2025
• Last Updated: May 4, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)