JMIR MEDICAL INFORMATICS

Wang et al

Implementation Report

Design and Implementation of an Inpatient Fall Risk Management
Information System
Ying Wang1,2, MSM; Mengyao Jiang2, MSN; Mei He2, MSN; Meijie Du2, MSN
1School of Management, Wuhan University of Technology, Wuhan, China

2

Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Corresponding Author:
Ying Wang, MSM
School of Management
Wuhan University of Technology
122 Luoshi Road
Hongshan District
Wuhan, 430070
China
Phone: 86 027 83662317
Email: wangying_tjh@hotmail.com

Abstract
Background: Falls had been identified as one of the nursing-sensitive indicators for nursing care in hospitals. With technologicalprogress, health information systems make it possible for health care professionals to manage patient care better. However, there
is a dearth of research on health information systems used to manage inpatient falls.
Objective: This study aimed to design and implement a novel hospital-based fall risk management information system (FRMIS)
to prevent inpatient falls and improve nursing quality.
Methods: This implementation was conducted at a large academic medical center in central China. We established a nurse-led
multidisciplinary fall prevention team in January 2016. The hospital’s fall risk management problems were summarized byinterviewing fall-related stakeholders, observing fall prevention workflow and post–fall care process, and investigating patients'
satisfaction. The FRMIS was developed using an iterative design process, involving collaboration among health care professionals,
software developers, and system architects. We used process indicators and outcome indicators to evaluate the implementation
effect.
Results: The FRMIS includes a fall risk assessment platform, a fall risk warning platform, a fall preventive strategies platform,
fall incident reporting, and a tracking improvement platform. Since the implementation of the FRMIS, the inpatient fall rate wassignificantly lower than that before implementation (P<.05). In addition, the percentage of major fall-related injuries was
significantly lower than that before implementation. The implementation rate of fall-related process indicators and the reporting
rate of high risk of falls were significantly different before and after system implementation (P<.05).
Conclusions: The FRMIS provides support to nursing staff in preventing falls among hospitalized patients while facilitatingprocess control for nursing managers.
(JMIR Med Inform 2024;12:e46501) doi: 10.2196/46501
KEYWORDS
fall; hospital information system; patient safety; quality improvement; management; implementation

Introduction

reported in hospitals—have been identified as a nursing-sensitive
quality indicator of patient care.

Context

Given the growing technological progress, health IT may help
enhance the quality and safety of provided care, facilitating theeffectiveness and efficiency of the clinical workflow, andsupporting the provision of integrated multidisciplinary care
[4-11]. The hospital information system (HIS) is a promising

Falls are one of the nursing-sensitive indicators for nursing care[1], which are a leading cause of fatal and nonfatal health loss
globally [2,3]. Reducing and preventing falls has become aninternational health priority. Falls—common adverse events
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approach to improve care quality and safety in the complex
hospital environment. Despite extensive literature on fall risk
factors and preventive strategies [12-18], few studies havefocused on health information systems for managing inpatient
falls.

Problem Statement
To address these issues, we formed a nurse-led multidisciplinary
fall prevention team in January 2016, including the hospitaladministrative staff, quality management specialists, physicians,
nurses, pharmacists, and informatics staff. This teamretrospectively analyzed 19 inpatient fall cases that occurred in2015 (fall rate 0.015‰), ranking first among all in-hospitalnursing adverse events. Among the fall cases, 30%-40% of
patients had grade ≥3 injuries, which significantly exceeded the
3.978% proportion seen in similar hospitals during this period.
Falls caused severe harm and financial burden to inpatients,
with 3 patients experiencing severe head injuries and 2 having
hip fractures. The longest hospital stay resulting from falls
reached 36 days.
The hospital’s fall risk management problems were summarized
by interviewing fall-related stakeholders, observing fall
prevention workflow and postfall care process, and investigating
patients' satisfaction; these included (1) nonachievement of
real-time fall risk assessment, real-time uploading, andinformation sharing; (2) absence of fall risk warning
management; (3) complicated fall risk management workflow;(4) absence of process control in fall prevention (such as processcontrol for different fall risk levels, process control for different
time nodes, etc); and (5) lack of standardized pathways for
inpatient fall incident reporting and improvement tracking.

Similar Interventions
Several studies have highlighted the benefits of using healthinformation systems for patient fall management. For example,

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Giles et al [19] reported that data collected from nursing
information systems can be used to identify high-fall-riskpatients. Mei et al [20] designed an electronic patient fallreporting system in a US long-term residential care facility,
which could improve the fall reporting process and subsequentquality improvement efforts. Katsulis et al [21] combined theFall TIPS (Tailoring Interventions for Patient Safety) [22] witha clinical decision support system, which increased its ease ofuse over the paper version. Jacobsohn et al [23] developed an
automated clinical decision support system for identifying andreferring older adult emergency department patients at the risk
of future falls. Mlaver et al [24] at the Brigham and Women’s
hospital developed a valuable electronic healthrecord–embedded dashboard that collected inpatient fall risk
data. However, the abovementioned fall information system
only focused on a specific domain of fall management. So far,there is still no report about an HIS for overall fall risk
management.

Aims and Objectives
This implementation aims to design and implement a fall risk
management information system (FRMIS) to reduce falls among
inpatients and improve nursing quality. Our goal is to create aculture of safety and reduce the incidence of falls hospital-wide,
ensuring the well-being and security of all patients.

Methods
This study adhered to the iCHECK-DH (Guidelines andChecklist for the Reporting on Digital Health Implementations)checklist [25].

Blueprint Summary
This FRMIS consists of 4 major functional platforms to facilitate
comprehensive fall prevention pathway management as shownin Textbox 1.

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Textbox 1. The 4 major functional platforms of the fall risk management information system.
A fall risk assessment platform (Multimedia Appendix 1):
The assigned nurse uses a personal digital assistant to conduct fall risk assessments within 4 hours of patient admission. Upon completion, the personal
digital assistant automatically compiles the Morse Fall Risk Score [26,27] and risk level, marking it in the electronic nursing record. All patients'
Morse Fall Risk Scores are collected and shared in real time through the information platform. Simultaneously, nurses receive nursing guidelines
specific to different fall risk levels. They implement corresponding fall prevention measures such as hanging “Fall Prevention” warning signs nearhigh-risk patients' beds, distributing “Fall Prevention Information Sheets” to guide patients and their families on preventive measures, and documenting
and passing on relevant information during shift changes. The head nurse conducts daily inspections and guidance on the accuracy of Morse fall
assessments and the implementation of fall prevention measures, upon completion of departmental reviews.
A fall risk warning platform (Multimedia Appendix 1):
Patients at different fall risk levels are color-coded for easy identification: high-risk (Morse Fall Risk Score≥45), red; moderate (score approximately
25-44), yellow; and low (score approximately 0-24), green. The fall risk warning module comprehensively displays the daily number of high-riskfalls, department distribution and ranking, percentage of the population at the risk of falls, specific bed locations, medical diagnoses, Morse Fall Risk
Scores, and assessment times through charts and color-coded indicators. This provides nursing managers real-time insights into the key populations,departments, and information related to fall risk management, enabling proactive fall risk prevention and providing precise information support foreffective fall prevention process control.
A fall preventive strategies platform (see Multimedia Appendix 1):
Evidence-based fall prevention strategies are developed, incorporating fall event analysis and expert discussions to extract key process monitoring
indicators. An electronic fall prevention bundle strategies quality tracking checklist was established for accurate assessment of fall risk, increased
awareness of preventive measures, enhanced handover process for high-risk patients, environmental safety, implementation of fall prevention knowledge
training, and guidance on proper use of assistive devices. Nursing department and ward-level managers can use mobile devices (iPads) to conduct
targeted goal management and quality inspections of fall prevention strategies. Real-time monitoring is conducted on key fall process indicators suchas accuracy of Morse fall risk assessments, implementation of health education, adherence to handover procedures, and compliance with environmental
safety measures.
A fall incident reporting and tracking platform (see Multimedia Appendix 1):
The platform regulates the reporting process for inpatient fall events. After a fall incident occurs, the ward head nurse promptly logs into the fall
incident reporting platform to proactively report the incident. They provide details such as the time and location of the fall, the sequence of events,
whether the patient was injured, the extent of the injury, and the emergency treatment process. Once the information platform receives the ward's
report, it immediately sends text messages to the chief nurse and members of the nursing department's safety management team. On the platform,
safety management team members can quickly trace the Morse Fall Risk Score, risk level, appropriateness of fall prevention interventions, timelinessof assessments, and any dynamic evaluations associated with that patient. After gaining a comprehensive understanding of the patient's relevant
information, they visit the ward in a timely manner to conduct on-site inspections and tracking. They provide guidance to the department by applying
root cause analysis to thoroughly analyze the fall event, identify the underlying causes, and propose areas of improvement directly on the web-based
platform. Ward head nurses and the chief nurse can access expert guidance instantly on the platform and make necessary improvements based on theadvice provided.

Technical Design
The FRMIS was developed using an iterative design process,involving collaboration among health care professionals,
software developers, and system architects. The design aimed
to create a user-friendly interface, incorporate data integration
capabilities, and enable real-time reporting functionalities. In
order to meet the usage needs of both PC and mobile devices,the development language selected for this system includes C#,
jQuery, and Java; the development tools used were Visual Studio(Microsoft Corp) and Eclipse (The Eclipse Foundation), andthe development platforms used were Windows and Android.

regulations. The data collection scope follows the principle of“minimum necessary” and adopts measures such as data
desensitization, data encryption, and link encryption to prevent
data leakage during the data collection process.

Interoperability
To maximize the effectiveness of the FRMIS, standardizationof data elements and the development of interface systems toallow seamless data exchange between our HISs were necessary.
The FRMIS used Health Level Seven Fast Healthcare
Interoperability Resources (HL7FHIR) to enable seamless dataexchange and streamline workflows.

TargetParticipating Entities

The FRMIS was designed to assist nursing staff in preventing
inpatient falls through IT, facilitating process control for nursing
managers and ensuring patient safety.

The FRMIS project has obtained the approval and support of
hospital management, who have provided strong guarantees interms of personnel, resources, funding, and working hours
required for the implementation of the research plan. Our
hospital is an advanced information management hospital withstate-of-the-art scientific technologies. The computer center has
rich experience in developing information management
platforms; they have independently developed and implemented
19 hospital operational management systems. The FRMIS’s
development was initiated by the nursing department, with the

Data
Our hospital has a dedicated computer center, which serves as
the technical support department for network security. It is
responsible for the construction and operation of hospital
network security protection measures. The collection of various
data in the FRMIS complies with relevant national laws andhttps://medinform.jmir.org/2024/1/e46501

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assistance of the computer center to fulfill the corresponding
requirements.

Budget Planning
The FRMIS development process lasted about 4 months, andthe total development cost was approximately 500,000 Renminbi(approximately US $68,300). The subsequent maintenance costs
were estimated to be 8% of the total development cost annually.
Funding for the FRMIS’s development and maintenance wasprovided by our hospital. The ownership of the FRMIS belongs
to Tongji Hospital.

Sustainability
The FRMIS’s implementation was carried out through theissuance of relevant policy documents by the nursing
department, ensuring its clinical adoption. All risk assessment
and incident reports concerning the inpatient falls wereconducted through this information system thus far, replacing
the previous paper-based forms. Over the past few years ofusing this system, our hospital's computer center staff has been
maintaining and fixing occasional bugs that occur during clinicalimplementation of this system. The computer center staff also
made necessary modifications and improvements to certain
details as needed to enhance system functionality, optimize
workflows, and adapt to evolving health care practices.

Statistical Analysis
Statistical comparisons were made on the fall incidence rate
among inpatients and the reporting rate of high-fall-risk patients
before and after FRMIS implementation. Data entry andstatistical analysis were performed using SPSS (version 17.0;
IBM Corp). The chi-square test was used to compare thedifferences in the fall incidence rate among inpatients, the rateof high-fall-risk patients, and the implementation rate ofpreventive fall quality bundle strategy indicators before andafter FRMIS implementation. A value of P<.05 was considered
statistically significant.

Ethics Approval
The study was approved by the institutional review board of
Tongji hospital (protocol TJ-IRB20191209).

Implementation (Results)
Coverage
Our hospital is a large academic medical center in central China.
In 2016, the hospital had a total of 4000 open beds, 106 nursing
wards, and 53 specialized nursing units. The average daily
admission rate ranges from 4500 to 5000 patients, with a totalof 193,709 admitted patients throughout the year. The
cumulative number of bed-days reached 1,756,946, of which
277,365 (15.79%) were for critical patients.

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Outcomes
We carried out the process and outcome evaluation with regard
to the FRMIS’s implementation. The process evaluation
indicators include (1) the accuracy rate of the Morse fall risk
assessment: number of accurate Morse fall risk assessments /total number of Morse fall risk assessments inspected; (2)implementation rate of fall prevention health education: numberof implemented health education check items / number ofpatients inspected × total number of fall prevention healtheducation check items; (3) implementation rate of shift handoff:number of implemented shift handoff check items / number ofpatients inspected × total number of fall prevention shift handoff
check items; (4) implementation rate of environment safety:
number of implemented environment safety check items / thenumber of patients inspected × the total number of environment
safety check items.
The staff of the quality control office in the nursing department
reviewed the FRMIS on a daily basis to identify the clinicaldepartments where high-fall-risk patients were distributed acrossthe hospital. For departments with more than 5 high-fall-riskpatients and a proportion exceeding 20% of the total patients,
we assigned 2 supervisory staff from the quality control team.
They used the electronic form “Fall Prevention Bundle Strategy
Quality Tracking Form” (see Multimedia Appendix 1) on an
iPad to conduct quality inspections on the nursing units for thehigh-fall-risk patient population, randomly checking theimplementation rate of fall prevention bundle strategy indicators(fall risk assessment, fall-related health education, fall-related
shift handoff, and environment safety). Before implementing
the FRMIS, a total of 1250 patients were randomly sampled forinspection. After implementing FRMIS, a total of 1806 patientswere randomly sampled for inspection. Additionally, acomparative analysis was performed on the hospitalization
period between February and October 2017 (after FRMIS
implementation, the total bed days occupied by inpatients was
1,323,667) and between February and October 2015 (before
FRMIS implementation, the total bed days occupied byinpatients was 1,303,094) to evaluate the hospital-wide reporting
rate of high-fall-risk cases, incidence rate of patient falls, andseverity of fall-related injuries.
The results showed that since the FRMIS’s implementation, theinpatient falls rate was significantly lower than that before
implementation (P<.001), as shown in Table 1. In addition, thepercentage of major fall-related injuries was significantly lower
than that before implementation, as shown in Table 2. The
implementation rate of fall-related process indicators and thereporting rate of high risk of falls were significantly different
before and after system implementation (P<.001), as shown in
Table 3.

Table 1. Comparison of fall-related outcome indicators.
Before implementation (total bed
days=1,303,094), n (%)

After implementation (total bed
days=1,323,667), n (%)

Chi-square (df)

P value

High-fall-risk patients’ reports

1036 (0.8)

3007 (2.3)

931.7 (1)<.001

Fall incident reports

23 (0.02)

11 (0.01)

4.4 (1)<.001

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Table 2. Results of fall-related injuries.
Cases of fall-related injury, n
No injury

Minor

Moderate

Major

Before implementation

15

28

12

2

After implementation

20

13

9

0

Table 3. Comparison of fall-related process indicators.
Before implementation (n=1250), n (%) After implementation (n=1806), n (%) Chi-square (df)

P value

Fall risk assessment

1056 (84.48)

1709 (95.73)

88 (1)<.001

Fall-related health education

1107 (88.56)

1769 (97.95)

117.5 (1)<.001

Fall-related shift handoff

1114 (89.12)

1767 (97.84)

104 (1)<.001

Environment safety

1127 (90.16)

1796 (99.45)

153 (1)<.001

Lessons Learned
The FRMIS’s development and implementation followed astructured process, starting with needs assessment andculminating in ongoing monitoring and improvement. With thismultidisciplinary team and comprehensive approach, we wereable to provide a more robust and effective fall risk management
system for the entire hospital. The FRMIS addressed theshortcomings of paper-based reporting, such as untimely fall
assessments, delayed reporting, information transmission delays,
loss of assessment forms, and incomplete tracking information.
The FRMIS achieved a holistic fall prevention strategy thatspanned from risk assessment to postfall intervention, whichbrought several benefits to both patients and health careproviders. The FRMIS alerted nursing staff about high-riskpatients, enabling timely interventions and reducing falloccurrences. It also standardized the reporting process for fall
events, allowing for efficient tracking and analysis of incidents.

Discussion
Principal Findings
This study has designed and implemented an FMRIS at thehospital level. The novel system provided a simple, intuitive,
and highly operational prevention management model,
encompassing fall risk assessment, high fall risk screening,
forecasting, and monitoring. It significantly improved theprocedural and standardized levels of fall management for
hospitalized patients, having prompted nurses to proactively
implement fall preventive interventions, conducted timely fallrisk assessments, reduced underreporting of high-fall-riskpatients, and increased the forecast rate of high-fall-risk patients.
Unlike previous studies that focus on a specific stage of fall
management (such as risk identification [19] or fall incident
reporting [28]) or patients in a specific department [23], oursystem catered to the entire process of fall risk management for
all inpatients. The FRMIS showed promise in enhancing patient
safety, reducing fall incidents, and improving overall carequality.
To facilitate the successful implementation of the FRMIS inclinical practice, we first developed the Standardized
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Management Guidelines for Preventing Inpatient Falls at thehospital-wide level. This policy document comprehensively
revises and improves clinical fall prevention efforts, which
include patient fall risk assessment, health education, fall
preventive interventions, fall management workflow, fall
incident reporting, and system record-keeping. The policy
document was distributed in hard copy by the nursing
department to all departments and also uploaded electronicallyon the hospital's Office Automation platform. It mandated eachclinical department to conduct fall prevention training based on
the guidelines, requiring all nurses’ participation andproficiency. This document served as a supporting tool,
providing nurses with guidance on how to use the FRMIS
effectively in their clinical practice to prevent inpatient falls.
In addition, we conducted standardized nurse training througha web-based platform. Three main implementation strategies
were used. First, we conducted diverse forms of training,
including ward-, department-, and hospital-level fall prevention
training, as well as case-based warning education, bedsidesimulation assessment, experience sharing sessions, and special
lectures, to comprehensively implement the content of theStandardized Management Guidelines for Preventing Falls.
Second, we performed objective evaluation. We incorporated
simulated case examinations for patient fall prevention into theclinical skills evaluation of nurses, head nurse position
evaluation, and their performance appraisal to comprehensively
assess the level of knowledge of fall management guidelines
and the emergency handling capabilities for patient fall
incidents. Third, we achieved full participation among all nurses.
The training rate and assessment results of nurses in the wards
were included in the performance management projects of ward
head nurses, achieving the participation of all nurses andcomprehensive evaluation of standardized fall prevention
training. Based on the strategies mentioned above, the FRMIS’s
implementation in clinical practice has been relatively
successful.

Limitations
This study still has certain limitations that should be
acknowledged. First, the FRMIS was specifically designed andimplemented by our hospital's computer center. It is currently
applicable to 3 different hospital campuses within our institution
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but has not been widely disseminated to other hospitals or
integrated with diverse HISs. Therefore, its applicability andeffectiveness in different hospital contexts remains uncertain.
Second, the FRMIS heavily relied on the voluntary reportingby clinical nurses. The accuracy of these fall risk reports needed
to be individually verified by staff members in the quality
control office of the nursing department. This process iscurrently manual and lacks automation, which may introduce
delays and potential inconsistencies. In the future, further
improvements could be made by integrating artificial
intelligence (AI) technologies. By automatically extracting fallrisk factors from patients' electronic medical records, the system
could achieve automated risk stratification and reduce
dependence on manual reporting.
Despite these limitations, it is important to note that this study
represents a significant step toward enhancing inpatient fall risk

Wang et al
management through the FRMIS implementation. Future
research and development efforts could focus on expanding thesystem's applicability to other hospitals, integrating AI
capabilities for automated risk assessment, and improving data
accuracy and automation processes. These advancements would
contribute to more comprehensive and intelligent fall risk
management practices for inpatients.

Conclusions
The design and implementation of an FRMIS significantly
contributed to the prevention and management of falls among
inpatients. The FRMIS enhanced patient safety through IT,
providing comprehensive support for fall prevention andensuring efficient management of fall events in health caresettings.

Acknowledgments
We sincerely thank the nursing management and the participating nurses of the Tongji hospital for their support and participationin this study. This study was partly funded by the Huazhong University of Science and Technology Independent Innovation Fund(2013YQ008, 2018KFYYXJJ016), Chinese Nursing Association Research Project (ZHKY202204), and China Nursing Management
Research Fund (CNM-2020-03).

Data Availability
The data sets used or analyzed during this study available from the corresponding author on reasonable request.

Authors' Contributions
WY designed the study. JMY and HM collected the data. HM and DMJ analyzed the data. JMY wrote the original draft of themanuscript. WY and HM reviewed and edited the manuscript. WY applied for funding. All authors have read and agreed to theversion of the manuscript intended for publication.

Conflicts of Interest
None declared.

Multimedia Appendix 1
The presentation of the fall risk management information system.
[DOCX File , 766 KB-Multimedia Appendix 1]

References
1.

2.

3.
4.
5.
6.
7.

Oner B, Zengul FD, Oner N, Ivankova NV, Karadag A, Patrician PA. Nursing-sensitive indicators for nursing care: A
systematic review (1997-2017). Nurs Open. 2021 May;8(3):1005-1022 [FREE Full text] [doi: 10.1002/nop2.654] [Medline:
34482649]
James S, Lucchesi L, Bisignano C, Castle C, Dingels Z, Fox J, et al. The global burden of falls: global, regional and national
estimates of morbidity and mortality from the Global Burden of Disease Study 2017. Inj Prev. 2020 Oct;26(Supp 1):i3-i11[FREE Full text] [doi: 10.1136/injuryprev-2019-043286] [Medline: 31941758]
Burns ER, Stevens JA, Lee R. The direct costs of fatal and non-fatal falls among older adults - United States. J Safety Res.
2016 Sep;58:99-103 [FREE Full text] [doi: 10.1016/j.jsr.2016.05.001] [Medline: 27620939]
Eslami Andargoli A, Scheepers H, Rajendran D, Sohal A. Health information systems evaluation frameworks: a systematic
review. Int J Med Inform. 2017 Jan;97:195-209 [doi: 10.1016/j.ijmedinf.2016.10.008] [Medline: 27919378]
Gaspar AGM, Lapão LV. eHealth for addressing balance disorders in the elderly: systematic review. J Med Internet Res.
2021 Apr 28;23(4):e22215 [FREE Full text] [doi: 10.2196/22215] [Medline: 33908890]
Field M, Fong K, Shade C. Use of electronic visibility boards to improve patient care quality, safety, and flow on inpatient
pediatric acute care units. J Pediatr Nurs. 2018 Jul;41:69-76 [doi: 10.1016/j.pedn.2018.01.015] [Medline: 29395791]
Woodward M, De Pennington N, Grandidge C, McCulloch P, Morgan L. Development and evaluation of an electronic
hospital referral system: a human factors approach. Ergonomics. 2020 Jun;63(6):710-723 [doi:10.1080/00140139.2020.1748232] [Medline: 32220218]

https://medinform.jmir.org/2024/1/e46501

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e46501 | p. 6
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
8.

9.

10.

11.

12.

13.

14.
15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.
27.

Chow CB, Leung M, Lai A, Chow YH, Chung J, Tong KM, et al. Development of an electronic emergency department-based
geo-information injury surveillance system in Hong Kong. Injury. 2012 Jun;43(6):739-748 [doi: 10.1016/j.injury.2011.08.008][Medline: 21924722]
Carrillo I, Mira JJ, Vicente MA, Fernandez C, Guilabert M, Ferrús L, et al. Design and testing of BACRA, a web-based
tool for middle managers at health care facilities to lead the search for solutions to patient safety incidents. J Med Internet
Res. 2016 Sep 27;18(9):e257 [FREE Full text] [doi: 10.2196/jmir.5942] [Medline: 27678308]
Balaguera HU, Wise D, Ng CY, Tso H, Chiang W, Hutchinson AM, et al. Using a medical intranet of things system toprevent bed falls in an acute care hospital: a pilot study. J Med Internet Res. 2017 May 04;19(5):e150 [FREE Full text][doi: 10.2196/jmir.7131] [Medline: 28473306]
Dalal AK, Fuller T, Garabedian P, Ergai A, Balint C, Bates DW, et al. Systems engineering and human factors support ofa system of novel EHR-integrated tools to prevent harm in the hospital. J Am Med Inform Assoc. 2019 Jun 01;26(6):553-560[FREE Full text] [doi: 10.1093/jamia/ocz002] [Medline: 30903660]
Stockwell-Smith G, Adeleye A, Chaboyer W, Cooke M, Phelan M, Todd J, et al. Interventions to prevent in-hospital falls
in older people with cognitive impairment for further research: a mixed studies review. J Clin Nurs. 2020
Sep;29(17-18):3445-3460 [doi: 10.1111/jocn.15383] [Medline: 32578913]
Tricco AC, Thomas SM, Veroniki AA, Hamid JS, Cogo E, Strifler L, et al. Quality improvement strategies to prevent falls
in older adults: a systematic review and network meta-analysis. Age Ageing. 2019 May 01;48(3):337-346 [FREE Full text][doi: 10.1093/ageing/afy219] [Medline: 30721919]
LeLaurin JH, Shorr RI. Preventing falls in hospitalized patients: state of the science. Clin Geriatr Med. 2019
May;35(2):273-283 [FREE Full text] [doi: 10.1016/j.cger.2019.01.007] [Medline: 30929888]
Cameron ID, Dyer SM, Panagoda CE, Murray GR, Hill KD, Cumming RG, et al. Interventions for preventing falls in older
people in care facilities and hospitals. Cochrane Database Syst Rev. 2018 Sep 07;9(9):CD005465 [FREE Full text] [doi:10.1002/14651858.CD005465.pub4] [Medline: 30191554]
Ambrens M, Tiedemann A, Delbaere K, Alley S, Vandelanotte C. The effect of eHealth-based falls prevention programmes
on balance in people aged 65 years and over living in the community: protocol for a systematic review of randomised
controlled trials. BMJ Open. 2020 Jan 15;10(1):e031200 [FREE Full text] [doi: 10.1136/bmjopen-2019-031200] [Medline:
31948985]
Turner K, Staggs V, Potter C, Cramer E, Shorr R, Mion LC. Fall prevention implementation strategies in use at 60 UnitedStates hospitals: a descriptive study. BMJ Qual Saf. 2020 Dec;29(12):1000-1007 [FREE Full text] [doi:10.1136/bmjqs-2019-010642] [Medline: 32188712]
Morgan L, Flynn L, Robertson E, New S, Forde-Johnston C, McCulloch P. Intentional Rounding: a staff-led quality
improvement intervention in the prevention of patient falls. J Clin Nurs. 2017 Jan;26(1-2):115-124 [doi: 10.1111/jocn.13401][Medline: 27219073]
Giles LC, Whitehead CH, Jeffers L, McErlean B, Thompson D, Crotty M. Falls in hospitalized patients: can nursing
information systems data predict falls? Comput Inform Nurs. 2006;24(3):167-172 [doi: 10.1097/00024665-200605000-00014][Medline: 16707948]
Mei YY, Marquard J, Jacelon C, DeFeo AL. Designing and evaluating an electronic patient falls reporting system: perspectives
for the implementation of health information technology in long-term residential care facilities. Int J Med Inform. 2013
Nov;82(11):e294-e306 [doi: 10.1016/j.ijmedinf.2011.03.008] [Medline: 21482183]
Katsulis Z, Ergai A, Leung WY, Schenkel L, Rai A, Adelman J, et al. Iterative user centered design for development of apatient-centered fall prevention toolkit. Appl Ergon. 2016 Sep;56:117-126 [doi: 10.1016/j.apergo.2016.03.011] [Medline:
27184319]
Dykes PC, Duckworth M, Cunningham S, Dubois S, Driscoll M, Feliciano Z, et al. Pilot Testing Fall TIPS (Tailoring
Interventions for Patient Safety): a Patient-Centered Fall Prevention Toolkit. Jt Comm J Qual Patient Saf. 2017
Aug;43(8):403-413 [doi: 10.1016/j.jcjq.2017.05.002] [Medline: 28738986]
Jacobsohn GC, Leaf M, Liao F, Maru AP, Engstrom CJ, Salwei ME, et al. Collaborative design and implementation of aclinical decision support system for automated fall-risk identification and referrals in emergency departments. Healthc(Amst). 2022 Mar;10(1):100598 [FREE Full text] [doi: 10.1016/j.hjdsi.2021.100598] [Medline: 34923354]
Mlaver E, Schnipper JL, Boxer RB, Breuer DJ, Gershanik EF, Dykes PC, et al. User-centered collaborative design anddevelopment of an inpatient safety dashboard. Jt Comm J Qual Patient Saf. 2017 Dec;43(12):676-685 [doi:10.1016/j.jcjq.2017.05.010] [Medline: 29173289]
Perrin Franck C, Babington-Ashaye A, Dietrich D, Bediang G, Veltsos P, Gupta PP, et al. iCHECK-DH: guidelines andchecklist for the reporting on digital health implementations. J Med Internet Res. 2023 May 10;25:e46694 [FREE Full text][doi: 10.2196/46694] [Medline: 37163336]
Morse J. Preventing Patient Falls. Thousand Oaks, CA. Sage Publications; 1997.
Schwendimann R, De Geest S, Milisen K. Evaluation of the Morse Fall Scale in hospitalised patients. Age Ageing. 2006
May;35(3):311-313 [doi: 10.1093/ageing/afj066] [Medline: 16527829]

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Wang et al

Gardner LA, Bray PJ, Finley E, Sterner C, Ignudo TL, Stauffer CL, et al. Standardizing falls reporting: using data fromadverse event reporting to drive quality improvement. J Patient Saf. 2019 Jun;15(2):135-142 [doi:10.1097/PTS.0000000000000204] [Medline: 26332598]

Abbreviations
AI: artificial intelligence
FRMIS: fall risk management information system
HIS: hospital information system
HL7FHIR: Health Level Seven Fast Healthcare Interoperability Resources
iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations
TIPS: Tailoring Interventions for Patient Safety

Edited by C Perrin; submitted 14.02.23; peer-reviewed by M Binandeh, R Hu, A Krupp; comments to author 06.04.23; revised version
received 15.08.23; accepted 29.11.23; published 02.01.24
Please cite as:
Wang Y, Jiang M, He M, Du M
Design and Implementation of an Inpatient Fall Risk Management Information System
JMIR Med Inform 2024;12:e46501
URL: https://medinform.jmir.org/2024/1/e46501
doi: 10.2196/46501
PMID:©Ying Wang, Mengyao Jiang, Mei He, Meijie Du. Originally published in JMIR Medical Informatics (https://medinform.jmir.org),
02.01.2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information,a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

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