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Video monitoring: a room with a view, or a window to challenges in falls prevention research?

Approximately 1 million falls occur annually in acute care settings in the United States, with incidence of 2.3-7 falls per 1,000 patient days (Gray-Micelli & Quigley, 2012; Oliver, Healey, & Haines, 2010). Wu, Keeler, Rubenstein, Maglione, and Shekelle (2010) reported estimated additional costs of $3,500 for a fall without injury, with costs increasing to $16,500-$27,500 for additional falls without injury or when serious injury occurs. In 2008, the Centers for Medicare & Medicaid Services designated falls as a hospital-acquired condition (HAC) and challenged hospital reimbursement for HACs deemed preventable (Quigley & White, 2013). Although the National Database of Nursing Quality Indicators reported falls and trauma decreased 14.7% from 2010 to 2013 in select hospital engagement networks, wide variation remains across hospital settings.

Ongoing research aimed at advancing methods to detect and prevent falls is important. Although general antecedents to falls in hospitalized patients have been reported (Oliver et al., 2010), their relevance in the context of emergent falls prevention technologies (e.g., video monitoring [VM]) is not well understood. Understanding antecedents to patient falls may facilitate the design of reliable, valid tools for concurrent use with VM interventions in hospital settings. Maximizing the validity and reliability of a VM intervention will enable further testing regarding the intervention's ability to reduce falls and improve outcomes.

Purpose

The purpose of this study was to explore antecedents to falls in the acute care setting from the lived experiences of a diverse sample of expert health professionals, patient care assistants (1:1 safety sitters), VM technicians, nurses, and fall prevention experts. The following research questions guided the study: (a) How are the antecedents to a preventable fall in patient rooms on a medical unit described? (b) How do the identified antecedents appear on the screen to a VM technician monitoring patients on a medical unit? (c) What are the environmental conditions and patient behaviors that should precipitate fall prevention action during VM?

Review of Literature

A review of the relevant literature was conducted using CINAHL, PubMed, Scopus, and Google Scholar databases. Keywords included falls, fall prevention, hospital, video monitoring, and nursing research. Falls have been reported to occur during times of position change and ambulation, and are often unwitnessed (Deandrea et al., 2013; Hitcho et al., 2004; Oliver et al., 2010). Therefore, emerging interventions using assistive technology for fall prevention (e.g., electronic sensors, infrared sensors, fall detectors, bed alerts, microphones, pressure sensors, floor sensors, cameras, VM) are gaining attention quickly. They provide a window through which falls can be examined in a new way, with previously obscured information now available regarding hospital-based falls and their antecedents.

As one of the most recent technologic advancements, VM uses in-room cameras installed at vantage locations to provide real-time viewing of patient activity through computer visualization, which differs from other forms of assistive technologies used to prevent falls. VM staff members are trained to view the screen, detect an impending fall event, and intervene. In a hospital-based study, Hardin, Dienemann, Rudisill, and Mills (2013) evaluated the impact of webcams with central monitoring and alarmed virtual bed rails as surveillance devices. They found a significant difference (p<0.05) in the fall rate per 1,000 admissions. More recently, Potter and co-authors (2016) analyzed video-monitored falls in hospitalized patients and found weakness affecting ability to move, gait problems, and occluded pathways were contributors to falls. In addition, the time between the patient beginning to leave the bed and occurrence of the fall was under 2 minutes. Dupree, Fritz-Campiz, and Musheno (2014) noted VM should be used as part of a targeted solution to reduce falls in hospitalized patients who are unwilling or unable to use the call light. An additional study by Burtson and Vento (2015) included VM as part of a fall prevention program. Improvements in quality outcomes and cost were demonstrated over 2 years. The current study will begin to fill a gap regarding antecedents to falls seen in VM, and may support development of a VM intervention for further testing.

Sample and Setting

Participants were recruited using fliers posted on the hospital unit, and through announcements on the hospital website and at staff meetings. Interested persons contacted primary investigators (PIs) who were not affiliated with the hospital. Hospital-affiliated personnel were invited to participate in the study if they were at least age 18; could speak English; had experience as a patient care assistant (1:1 safety sitter), VM technician, registered nurse (RN) from the VM unit; or were fall prevention experts. The sample consisted of 34 participants in four homogenous groups: fall prevention experts (expert nurses, geriatricians [n=9]), RNs from the VM unit (m=10), VM technicians (n=6), and patient care assistants throughout the hospital (n=9).

The study setting was an academic medical center in southeast Michigan where VM had been implemented in five rooms to enable concurrent VM of eight patients. Patients were screened for acceptance into a monitored bed by a group consisting of the administrative manager, director of specialty programs, and charge nurse. Admission criteria for VM ([greater than or equal to] age 65 with acute illness putting the patient at risk for/exhibiting delirium, or high risk for fall) were developed in collaboration with unit geriatricians and nurse leaders.

Focus groups were conducted in a classroom at the hospital. Each focus group lasted approximately 60-90 minutes and followed a semi-structured format. Groups were audio-recorded and conducted by the PIs, who had no hospital affiliation; field notes also were written. After a brief introduction, PIs used several questions related to the study's purpose (e.g., what behaviors predict or precede a fall, how would those behaviors appear to a VM technician, what specific behaviors would alert you to take action to prevent a fall?).

This study was approved by Wayne State University and the study site's Human Investigation Committee and Institutional Review Board. Measures to assure confidentiality of all shared content were explained through verbatim reading of the informed consent followed by participant consent before the focus groups.

Methods and Design

Focus group methodology was consistent with Morgan and Krueger's (1998) inclusive approach. Authors defined the focus group as a "research technique that collects data through group interaction on a topic determined by the researcher" in which the "researcher's interest provides the focus, whereas the data themselves come from the group interaction" (p. 7).

Findings

Antecedents to a Preventable Fall

Antecedents to a preventable fall in the patients' rooms had a clear demarcation. They were represented by the major themes of environmental factors and behavioral representations.

Challenging Hazards within the Room

Hazards in the room were environmental factors represented by structural and process issues. Structural issues included characteristics of the room itself and items in proximity to patients. Process issues were related to events experienced by patients.

Structural issues within the room. Participants agreed patient rooms were often crowded. As one patient-care assistant noted, "... the walker, the computers, ... the table, and the IV poles and the beds and it's all stuffed into one room and there's like this much room (holding arms up demonstrating width) to walk to the bathroom ..." Extreme room temperatures also were reported as an issue. One fall prevention expert noted, "... people are hot or cold, that's when they start pushing the blankets, IV line ... see that a lot." Unit noises can be foreign to patients, occurring at variable times and levels. As one RN described, "It's confusing for the VM patients for that voice (audio) to be in that room."

Concerns emerged regarding lines attached or close to the patient, who could become entangled in or trip on them in getting out of bed or ambulating (e.g., feeding tube, suctioning equipment, indwelling urinary catheter bag, monitoring line, intravenous line, call lights, phone line). One patient-care assistant explained, "Tubing and the IV pole ... you know, you see someone starting to get up and you know they're hooked up to monitors and that's like, oh, no!" In addition to lines, patients may try to free themselves of soiled bedding or clothing, as one VM technician said, "On midnights, they used to tell us (in staff education on fall prevention) when the patient is wet, they start trying to get out of bed."

The bedside table provides the patient easy, routine access to personal items (e.g., cell phone, water). Inaccessible personal items can prompt patients to reach unsafely. One patient-care assistant voiced, "If that's (bedside table) not near them ... they'll reach and then roll right out ..."

Vulnerable times for a fall reported by participants included the day of admission, day of discharge, in the morning at awakening, meal times, shift changes, and staff breaks. Night was a particular concern, as one patient-care assistant described, "... when it gets dark, in the evening ... people that are completely fine during the day just start getting a little confused and that's when they'll get up ... they'll be more likely to fall."

Process issues within the room. People entering or leaving a patient's room were reported to increase vulnerability to a fall. One RN described asking the VM technician to monitor patients more closely for about 5 minutes after she left the rooms because patients may reconsider their needs and try to get out of bed unattended without calling for help. Further validation was provided by another RN, who remarked: "How many times have you heard a nurse say, 'I was just in that room'?"

Behavioral Phenomena Exhibited by Patients

Patient behaviors described as antecedents to falls were differentiated by two sub-themes: physical behaviors and psychological/emotional phenomena.

Physical behaviors exhibited by patients. A behavior frequently described by participants was "restlessness." One fall prevention expert described "... a lot of non-purposeful behavior like picking, fidgeting; picking at blankets, picking at themselves, picking at the lines, picking at the bed, picking at whatever may be around them before they decide to climb out of the bed." Participants reported, "The patient may sit up abruptly" and provide visual signals such as "looking around the room" or a "dead stare into the camera" as if to say, "Okay, who's going to see me here, can I get away?" Reaching was perceived to increase risk for falls, with patients described as misjudging the distance to an object. As one RN explained, "A patient was sitting in a chair and went to reach for something she dropped on the floor, and just over-reached and just slid right out, right under the table ..."

Participants reported a pattern and order of movements progressing in magnitude/required strength as the patient prepared to get out of bed. Initial fine movements (e.g., restlessness, picking, looking around) often were followed by more gross movement (e.g., sitting up suddenly and straight, scooting to the edge of the bed with the hips, rolling over). Finally, as described by one participant, the patient may show a "whole body and gracious roll" of unprecedented strength as he or she moves out of bed.

Issues associated with ambulation were reported to be antecedent to falls, such as improper use of unfamiliar assistive devices, excessive speed in walking, foot dragging, unsteadiness, syncope, and verbalizations of dizziness or lightheadedness. Several patient-care assistants indicated chair sitting also can cause problems. When patients "... fall asleep or rest their arm over the arm rest and all it takes is a slip of the arm and you get off balance and fall forward, or push the table away from in front of them..", they increase their risk for fall due to reduced protection from the bedside table and/or chair arm rests.

Psychological and emotional behaviors exhibited by patients. One RN described the challenge when cognitive changes occur suddenly and unexpectedly (as in delirium): "... you have some elderly person coming in with a UTI (urinary tract infection); they get that infection and they're very impaired ... you get the infection cleared up and then they're fine again." Agitated or confused patients often needed fears allayed, as one VM technician voiced "... he's crying he wants to get up ... he wants to go home ... he just wanted somebody to listen. He wanted somebody to understand his story and once you did that, he laid back down."

Antecedents Appearance During Video Monitoring

Participants generally agreed VM can provide a distorted view of reality: "It's not as clear ... you can't really see what they're doing exactly." However, they agreed VM provided a view otherwise unavailable unless in the room. VM distortions described by participants included the room appearing "far way," partial visualization of the room, patient movements appearing as "slow motion," lack of clarity despite infrared capability in the dark, and patient behaviors appearing accentuated. These distortions led staff to respond to observed patient behavior (movement) by using audio communication to the room or sending staff to the room. Participants indicated audio communications actually precipitated behaviors that could lead to a fall. For example, a sleeping patient is awakened with confusion and fear of the "intercom voice."

Environmental Conditions and Patient Behaviors Prompting Action

Participants identified VM antecedents that warranted increased awareness or further action to avoid a fall. Structural environmental factors included changes in the ambient environment (sound and light levels, room temperature), and changes in the local environment (loose or running cords, wet floors, personal care objects out of reach, overcrowding, room changes). Process-based environmental factors included changes in patterns of activity (shift change, visitors, mealtimes, being awakened from sleep). Patient-based factors prompting action fell into two categories: physically based (being soiled, in pain, with sensory deficits, physiologically compromised, delirium) or psychologically/emotionally based (dementia, anxiety, presence of psychological disorders, patient unwilling to follow safety parameters, patient misperception of capabilities).

Analysis

Oral recordings and field notes were transcribed manually after each focus group. Two PIs not affiliated with the hospital conducted separate data analyses starting with a first and second reading of the transcripts. Initial coding involved compressing the text based on themes (e.g., behavioral and environmental conditions determined to be antecedents to falls). After the initial coding scheme, a second level of coding included an inductive approach based on new knowledge gained from the interviews.

Trustworthiness

Lincoln and Guba's (1985) four criteria to ensure trustworthiness in qualitative investigations were used in this study. To assure credibility, non-hospital researchers were assured by confirming the reliability of the findings through comparing the consistency of the codes and their thematic grouping between the non-hospital affiliated PIs. Additional peer debriefing also was obtained through a review of the collective codes/themes by the hospital PI and consultant clinical nurse specialist of the research team as a means to obtain further insights regarding the final thematic coding schema. Triangulation of the data also was accomplished by comparing the audio recordings of the focus groups with the field notes. Transferability of the findings was supported through use of purposive sampling and the rich participant descriptions provided in the findings. Confirmability of the results as supported by the data was enhanced with a cross-comparison of the decision trails used by the PIs to collapse the thematic coding schema. Dependability of the methodological procedures was enhanced through study design consultation with an external expert in qualitative methodology.

Limitations

The sample was restricted to self-selected groups of individuals. Participants also represented one hospital setting. The study lacked a focus group of patients who had experienced a hospital fall.

Discussion

Video-monitoring technology provides an intervention not previously available to healthcare providers. This study was a beginning elucidation of the appearance on a VM screen of many of the known antecedents to falls in patient rooms. Focus group participants in this study identified antecedents to hospital falls generally consistent with patient-specific factors and environmental risk factors as previously identified in the literature; this expanded prior work on knowledge of antecedents (Deandrea et al., 2013; Oliver et al., 2010) and confirmed its relevancy in a VM inpatient environment.

Nursing Implications

This study's findings may inform nurses who are planning to implement a VM intervention in their hospitals. Although the Morse Fall Scale was demonstrated as effective in predicting falls when used in a VM inpatient setting (Hardin et al., 2013), neither that scale nor the STRATIFY falls risk assessment tool was designed as a decisional aid for determining the best strategy to prevent falls for at-risk individuals.

Moreover, neither of these assessment tools includes an indicator (e.g., specific diagnosis of delirium or dementia) that may guide the decision to use a VM intervention versus a different modality (e.g., 1:1 safety sitter). Nurses may believe these diagnoses are appropriate for a VM intervention, but this study suggests nurses must remain cautious when initiating audio communications from the VM central station to patients who are prone to difficulty with dual-tasking (e.g., cognitive aging or impairment). Further investigations are needed on the characteristics of patients who may be best served by VM.

Conclusion

The availability of VM in inpatient units provides new opportunities to understand how known antecedents are viewed and prompt action in the context of a VM intervention. The findings from this study add to existing knowledge, which can be applied to developing and refining VM interventions to prevent falls. Further investigations conducted with real-time VM designed to describe and validate reported antecedents to falls are needed, despite the inherent methodological difficulties in designing and conducting VM research studies (Hardin et al., 2013). Additionally, further investigation of what constitutes an appropriate, safe VM intervention response is warranted in light of the finding that certain VM intervention responses (audio speaker to the patient) may trigger a fall in some patients. Until then, nurses will need to consider carefully personal characteristics of patients when admitting patients to a VM unit and responding to potential antecedents to falls detected during a VM intervention.

Acknowledgments: The authors thank Therese Swann, RN, and Kelly Marie Ellsworth, who provided assistance with literature searches during their graduate programs at Wayne State University (Detroit, MI).

REFERENCES

Burtson, P.L., & Vento, L. (2015). Sitter reduction through mobile video monitoring: A nurse-driven sitter protocol and administrative oversight. Journal of Nursing Administration, 45(7/8), 363-369.

Deandrea, S., Bravi, F., Turati, F., Lucenteforte, E., La Vecchia, C., & Negri, E. (2013). Risk factors for falls in older people in nursing homes and hospitals. A systematic review and meta-analysis. Archives of Gerontology and Geriatrics, 56(3), 407-415.

DuPree, E., Fritz-Campiz, A., & Musheno, D. (2014). A new approach to preventing falls with injuries. Journal of Nursing Care Quality, 29(2), 99-102.

Gray-Micelli, D., & Quigley, PA. (2012). Falls prevention: Assessment, diagnosis, and intervention strategies. New York, NY: Hartford Institute for Geriatric Nursing.

Hardin, S., Dienemann, J., Rudisill, P., & Mills, K.K. (2013). Inpatient fall prevention: Use of in-room webcams. Journal of Patient Safety, 9(1), 29-35.

Hitcho, E.B., Krauss, M.J., Birge, S., Dunagan, W.C., Fischer, I., Johnson, S.,... Fraser, V.J. (2004). Characteristics and circumstances of falls in a hospital setting: A prospective analysis. Journal of General Internal Medicine, 19(7), 732-739. doi: 10.1111/1.1525-1497.2004.30387.x

Lincoln, Y.S., & Guba, E.G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage Publications.

Morgan, D.L., & Krueger, R.A. (1998). The focus group kit. Thousand Oaks, CA: Sage.

Oliver D., Healey, F., & Haines, T.P (2010). Preventing falls and fall-related injuries in hospitals. Clinics in Geriatric Medicine, 26(4), 645-692. doi:10.1016/j.cger.2010. 06.005

Potter, P, Allen, K., Costantinou, E., Klinkenberg, D., Malen, J., Norris, T.,... & Tymkew, H.H. (2016). Anatomy of inpatient falls: Examining fall events captured by depth-sensor technology. The Joint Commission Journal on Quality and Patient Safety, 42(5), 225-232.

Quigley, P, & White, S., (2013). Hospital-based fall program measurement and improvement in high reliability organizations. Online Journal of Issues in Nursing, 18(2), Manuscript 5. doi:10. 3912/OJIN.Voll 8No02Man05

Wu, S., Keeler, E., Rubenstein, L., Maglione, M.A., & Shekelle, PG. (2010). A cost-effectiveness analysis of a proposed national falls prevention program. Clinical Geriatric Medicine, 26(4), 751-766. doi:10.1016/j.cger.2010.07.005

Kay Klymko, PhD, ARNP, FNP-BC, is Nurse Practitioner, Florida Health Care Plans, Daytona Beach, FL.

LuAnn Etcher, PhD, GNP-BC, is Associate Professor, Spring Arbor University School of Human Services, Arbor, MI.

Joan Munchiando, BSN, RN-BC, CRRN, CMSRN, NE-BC, CDP, is Director of Specialty Programs, Nursing Administration, NICHE Co-Coordinator, Beaumont Health System, Royal Oak, MI.

Mary Royse, MSN, RN, CMSRN, CDP, is Clinical Nurse Specialist for Medical & Acute Care of the Elderly Unit, Beaumont Health System, Royal Oak, MI.
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Title Annotation:Research for Practice
Author:Klymko, Kay; Etcher, LuAnn; Munchiando, Joan; Royse, Mary
Publication:MedSurg Nursing
Article Type:Report
Date:Sep 1, 2016
Words:3403
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