Noticing Acute Changes in Health in Long-Term Care Residents : Rehabilitation Nursing Journal

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Noticing Acute Changes in Health in Long-Term Care Residents

Bowen, Mary Elizabeth PhD1,2; Rowe, Meredeth PhD, RN, FGSA3; Flynn, Aidan MS4; Basehore, Heather PhD5; Shin, Ju Young PhD, APRN, ANP-C6

Author Information
Rehabilitation Nursing 48(2):p 47-55, 3/4 2023. | DOI: 10.1097/RNJ.0000000000000405



Falls and acute conditions such as respiratory (e.g., influenza and pneumonia) illnesses and urinary tract infections (UTIs) are associated with hospitalizations, loss of independence in activities of daily living (ADLs), and reduced quality of life among older American adults (age 65+ years) in long-term care facilities (Gruneir et al., 2010). Falls are common in long-term care with up to 50% of older adults in these facilities falling every year (Vlaeyen et al., 2021). The risk factors for falls include biological, behavioral, environmental, and other factors. In long-term care, older adults are at an increased risk because of increasing age, physical and cognitive decline, gait and balance deficits, vision loss, medications, comorbidity, and sedentary behavior (Harris-Kojetin et al., 2019).

Older adults with multiple chronic illnesses, frailty, and immunosenescence are predisposed to respiratory illnesses and are more likely to report complications, increased hospitalization, and mortality from the infection. For example, the incidence rate of pneumonia per 1,000 resident days is 0.72, with a 30-day mortality of 28.7% (Falcone et al., 2018; Zimmerman et al., 2020). Second only to pneumonia, the UTI diagnosis rate ranges from 7.96 to 18.37 per 10,000 resident days, with prevalence rates ranging from 0.6% to 21.8% (Salem-Schatz et al., 2020). Facility-level factors such as grouped-living status and close contact with healthcare staff are also associated with increased risk for respiratory illness and other infections (Li et al., 2020).

Despite extensive scientific and clinical explorations designed to prevent falls and provide for the earlier recognition and treatment of acute illnesses in this population, acute event rates continue to be high. In addition, little is known about how staff identify and act on health status changes. To address this gap, the aim of this study was to examine how long-term care staff identify and act to prevent falls and acute conditions in long-term care residents. This study used information collected from six multidisciplinary focus groups across two long-term care settings, with a total of 26 healthcare staff.


Study Design and Participants

This substudy is part of a larger study examining how intra-individual changes in ambulation characteristics, assessed by continuously worn real-time locating sensor technology, may be used to predict acute events in long-term care. In this substudy, the multidisciplinary research team used a narrative approach (Padgett, 2012) to conduct six semistructured focus group interviews with 26 healthcare staff from two Department of Veterans Affairs (VA) long-term care facilities in eastern Pennsylvania. Eligibility criteria included being a staff member on a long-term care unit with at least 1 year of experience of providing care to residents. A list of eligible participants was provided to study staff by unit supervisors. Potential participants were e-mailed to determine interest in participating in a virtual semistructured focus group interview on identifying acute health changes in long-term care residents. If interested, participants chose a virtual call date and time and were subsequently provided with a virtual Zoom meeting link with audio and video capabilities. This substudy received institutional review board approval (1599121) from the Research Office at the VA and was funded by a VA Rehabilitation Research and Development Merit Award (I01RX002413). Participant verbal consent was attained prior to the meeting start.

All focus groups were conducted remotely during times when staff were available (e.g., lunch, between or after shifts), and each group was interviewed once and recorded. The first 10 minutes of the session was dedicated to the discussion of study procedures, introduction to topic areas of interest (e.g., ways in which residents may exhibit the onset of an acute health condition), and clinical follow-up (e.g., charting, clinical assessment, and reporting). Using the same interview schedule, participants were prompted to discuss social, behavioral, medical, and other factors that may affect how healthcare staff identify and respond to perceived changes in acute changes in health (see Supplementary Appendix A, Questions were derived from interests in the parent study and earlier work showing little/no standardized tools or processes providing for frequent and tailored health assessment in long-term care populations (Bowen et al., 2015).

Each of the six focus groups was composed of two to seven participants and was scheduled for 60 minutes. Participants consisted of two registered nurses, three rehabilitation nurses, three licensed practical nurses, three certified nursing assistants, one restorative aide, one nursing aide, five recreational therapists, one occupational therapist, one kinesiotherapist, five nurse unit managers, and one nurse administrator. Consistent with the standards of reporting in qualitative research (O'Brien et al., 2014), the author (M. E. B.) and some members of the research team (A. F. and H. B. only) were engaged with some of the participants in this substudy for up to 2 years prior. This prolonged engagement included spending time at each research site to understand the setting and observe workflows and processes and to develop rapport with healthcare staff as part of the larger parent study. Other authors (M. R.), and an independent scientist reviewer (J. S.), had no prior experience with the study participants or research sites. The mean number of years healthcare staff worked in long-term care was 12.4 years. Focus group interviews were transcribed verbatim and checked for accuracy prior to thematic coding. Participant information was de-identified.

Thematic Analysis

Data were analyzed using thematic content analysis (Colorafi & Evans, 2016). Three investigators of the research team independently read and coded one focus group transcript (different for each member) to identify major ideas/topics discussed by the focus group participants. Each investigator identified relevant meaning units from phrases, sentences, or paragraphs in the transcripts and organized these into categories, which tracked to the main themes in the interview schedule. Investigators shared and discussed categories for consistency. Investigators repeated this process with an additional three transcripts, which produced six categories. Then, two investigators coded a novel, uncoded transcript using the six categories. Investigators further refined the categories into subcategories and recoded all transcripts using the agreed-upon categories and subcategories, and an acceptable interrater agreement (using percentage agreement across categories and subcategories) of >80% was achieved. Two investigators compared and discussed the findings from all transcripts until a consensus was reached regarding categories and subcategories. A third independent investigator reviewed all transcripts, categories, and meaning units to confirm this work.


Six categories were derived from thematic analysis of meaning units; categories largely reflected interview questions (see Supplementary Appendix A, Each category and associated meaning units are detailed below.

Describing and Explaining “Normal” or Expected Behavior

Health care staff discussed what they would consider to be “normal” resident behaviors—those behaviors that may be different but would not necessarily be associated with an acute change in health status and would not be cause for alarm (see Table 1). For example, in three focus group discussions, wandering behaviors (repetitive ambulatory patterns/movement), which are associated with a dementia diagnosis, were not concerning because “if it’s repetitive we see it repeat, I see that as their baseline behavior” (Focus Group 3, Participant 10) and “it’s just a pattern, a routine” (Focus Group 3, Participant 8). These discussions revolved around the healthcare staff’s perception of the resident’s norm, which included the resident’s medical history and diagnoses, and was used to assess a potential change in the health status of the resident. Even though some behaviors were abnormal, staff used their perception of the resident’s norm to determine whether the abnormal behaviors represented something of concern. For example, two focus groups discussed how wandering behaviors would not be cause for alarm, “unless they were going into someone else’s room” (Focus Group 1, Participant 2). In these instances, healthcare staff developed individualized strategies based on their knowledge of the resident to curb this behavior. For example, one resident who spent time “downstairs wandering around” could be given a ham sandwich and a coke, and he would be “good as gold” (Focus Group 2, Participant 7). Notably, these behaviors did not trigger a clinical assessment or concern as participants did not associate wandering, repetitive patterns, or routine behaviors with an acute health change.

Table 1 - Results From Six Focus Group Interviews (N = 26)
Categories Meaning Units
1. Describing and explaining “normal” or expected behavior unrelated to an acute change in health status 1a. Reasoning why residents exhibit this normal behavior
2. Utilizing phenotypes to determine changes in a resident 2a. Description of initial observation of changes (not connected to specific cause)
2b. Noticing behavioral change
2c. Noticing cognitive/mental change
2d. Noticing emotional change
3. Determining the significance of the change
4. Hypothesizing reasons for an observed change 4a. Connecting a behavioral, physical, mental, or emotional change with a clinical explanation
4b. Connecting a change to a change at the institutional level (e.g., staffing, change in care delivery)
5. Response (i.e., intervention) to an observed change 5a. No clinical intervention necessary
5b. Make clinical documentation of the event/change
5c. Use of a clinical assessment to assess change
5d. Report the change to supervisors/other staff
5e. Wait and discuss behavior with others
5f. How an intervention is decided and acted on
6. Resolution of the clinical change/concern 6a. Observed effectiveness of a possible intervention
6b. Documentation of the intervention/resolution

Given the primary aim of the parent study, healthcare staff were specifically asked whether they assessed changes in ambulation or ambulatory patterns reflecting health changes in residents. Staff provided examples when an ambulation assessment was used. Some residents may be confused in general, “one was always looking for the bathroom, didn’t know where he was” (Focus Group 2, Participant 7), or because of a change in their routine or other environmental change. For example, “we notice that a lot after room changes, a lot of residents will go back to the other area” (Focus Group 3, Participant 9). It may also be the time of the day; for example, a resident may be more confused “first thing in the morning—he doesn’t know what he wants to do. Later in the day doesn’t do it as much” (Focus Group 3, Participant 8). Beyond confusion, other explanations for what healthcare staff would consider a “normal” ambulatory pattern exhibited by a resident could be attributed to walking to “sleep better at night” or “restlessness” (Focus Group 3, Participant 10).

Utilizing Phenotypes to Determine Changes in a Resident

Staff members each developed a complex profile (individualized phenotype) of the residents based on their knowledge of the resident. They used these individualized phenotypes to determine what is or not a notable change with regard to a resident’s behavioral, cognitive/mental, or emotional presentation. For example, eight staff across four focus groups discussed how a noticeable change “depends on the residents themselves, their normal pattern” (Focus Group 5, Participant 17). One healthcare staff member stated, “working in the [unit], you get used to their routines, you know who is normally awake for coffee, any deviation from normal you would notice” (Focus Group 1, Participant 2). It could be “someone who is usually up in the morning but declines to get up” (Focus Group 1, Participant 5), or “if they are usually very social and stop coming out of their room” (Focus Group 2, Participant 6), or “someone doesn’t want to participate in something they normally would do, [that] would be something different for them” (Focus Group 4, Participant 13). In general, the initial noticing of a change in the resident is “all patient-specific, you have to know who you are talking about to figure out what is going on” (Focus Group 6, Participant 21). Some participants noted gait and balance changes; “if they were slowing down, or had an unsteady gait or something like that” and “if they look like they’re falling but they are able to catch themselves because they’re holding onto the rail” (Focus Group 1, Participant 3). The participants’ typology of each resident’s norm included a wide range of physical, mental, and behavioral patterns, including sleeping changes, fatigue, appetite, changes in vital signs during rounds, pain, a new diagnosis, depression, anxiety, anger, hostility, sundowning, and increased falls. There may be a general decline in the ability to perform everyday tasks, such as “someone who might be able to get dressed or transfer is not participating at all” (Focus Group 4, Participant 12).

These discussions were more of a list of potential changes in residents that would be noticed on the unit rather than a discussion of how these were associated with an underlying disease progression or related to a new disease, condition, or warning sign of an imminent acute event. Notably, when behavioral, emotional, and mental status changes were linked to some underlying health change, participants referred to a specific experience they had with a resident on the unit. For example, one healthcare staff member noted “a lot of ambulating. We had one particular gentleman—when he would get a UTI, we would see a lot of scissoring, leaning to one side, off balance, and sure enough, within a week he had a UTI” (Focus Group 6, Participant 24). Another participant discussed, “one guy, when he gets anxious, he walks around lap after lap” (Focus Group 3, Participant 11), and one healthcare staff member explained pacing (walking back and forth between the same two points) in relation to anxiety. The importance of noticing behavior changes in residents in general was a consistent theme across focus groups as behavior changes indicate “something going on inside that they can’t exactly tell us” (Focus Group 4, Participant 13). Another healthcare staff member stated, “there is more expressed with behaviors, the majority of them anyways” (Focus Group 4, Participant 12). Other behavioral changes attributed to an emotional cause (e.g., stress, crying, depressed, and mourning the loss of a loved one) included having difficulty getting out of bed, not participating in regular activities, or not being as active as before. Behavioral changes associated with a mental status change included “forgetting someone’s name they would already know” (memory loss; Focus Group 1, Participant 2), “going from room to room, then the yelling starts” (delirium; Focus Group 2, Participant 7), and confusing day and night, “not sure when to get up” (Focus Group 3, Participant 8).

Determining the Significance of the Change

After participants discussed how they would identify an acute status change in residents and which behavioral, mental, physical, and/or social changes warranted cause for concern, participants discussed how they would explore the significance of the identified change (see Table 1). These conversations continued to center on the “normal” phenotype of the resident as a reference (“we are around them so often when there are changes like that it’s easy to see” [Focus Group 3, Participant 9]) but stressed that the length of the noted change is also important. For example, one participant noted that “if it were multiple days, or visible that something is wrong, I would get immediate help” (Focus Group 1, Participant 2). Another said, “it also depends on how often they miss that routine; if it’s multiple times, it’s a concern” (Focus Group 3, Participant 10). Two healthcare staff in two focus groups similarly voiced that “one time is not a concern” (Focus Group 3, Participant 9) and “if they did it once, sometimes it doesn’t mean anything, slept all day, had a bad dream. But if it continued, you would wonder what’s going on” (Focus Group 4, Participant 12). One healthcare staff member indicated that any significant change would be picked up in the medical record: “If they’re the same level every week and then they decrease they will capture that in the MDS (minimum data set)” (Focus Group 3, Participant 11).

Hypothesizing Reasons for an Observed Change

Participants discussed the complexity of their participants’ medical conditions and how difficult it is to determine the source of the noted change. For example, one healthcare staff member noted, “it could be other than medical, too. It could be a stressor on the unit, or they didn’t get enough sleep, or their mental status is off, could be other issues, too” (Focus Group 1, Participant 26). The entire medical and other history of the residents were considered when a potential change in health status was identified. “Are there things that are coming up, even anniversaries? Some of these guys, they go through really hard stuff, and if it is that month, or that week, when their buddy was blown up right beside them, they start going through it” (Focus Group 2, Participant 7). Or it could be a more recent event that was upsetting. For example, the resident “got a phone call, someone said something they didn’t understand, upset them, got them off their normal routine” (Focus Group 4, Participant 13). Another healthcare staff member talked about how they would ask other staff on the unit if there was a significant event in the resident’s life. “They might say, yes, he just found out his brother died and is upset and can’t sleep or has dementia and thinks his wife is leaving him and is upset” (Focus Group 6, Participant 21). These conversations demonstrate how complicated it can be to determine the significance of an observed change as participants consider not only comorbid mental and physical health conditions that affect behaviors but also social, environmental, and other factors. For example, one healthcare staff member said, “You can tell it’s different than normal. Something’s going on—you kind of know because their behaviors have changed, or they’re not sleeping, maybe it’s nothing, maybe a full moon, but maybe something’s going on” (Focus Group 4, Participant 12). Participants shared the thought processes or steps they would take to begin to identify and determine the significance of health status changes. For example, if the resident is “up at 4 in the morning, something is going on, and we would follow our steps. We would look at everything—if they’re napping all afternoon, we’re not surprised they are up early. Did they get a new medication? Did they eat the day before, are they hungry or thirsty?” (Focus Group 4, Participant 12). Some of the health status changes were attributed to facility or staff-level factors—such as changes in care delivery, the facility schedule, and care team preference or schedule. For example, one healthcare staff member related, “we see a lot of falls happen at shift change” (Focus Group 3, Participant 8).

Response to an Observed Change

There are various ways in which participants would respond to an identified significant change in status including additional clinical observation; chart reviews; documentation; checking vitals; talking with the resident; and reporting observations to a supervisor for their review, monitoring, or further assessment (see Table 1). Three focus groups discussed documenting the change in the medical record. Two of these focus groups additionally discussed functional decline in detail (requiring a wheelchair, altered speech, and decreasing blood pressure) and how this would prompt further chart review including assessing vital signs. Three focus groups suggested that such a noticeable change would prompt further monitoring, more frequent rounding, and face checks to ensure resident well-being. A participant from one of these focus groups said: “I would see what is going on mentally with the patient. If their normal way of communicating or their normal state of orientation seemed to be somewhat off, I would start looking at what are their labs, their vital signs, what medications were started/ stopped…exploring physical reasons for it” (Focus Group 2, Participant 6).

Participants across three focus groups said they would talk with the resident to see if the resident had insight into how they were feeling or if they were behaving differently. Two focus groups mentioned formal assessment of vital signs, blood work, or other biological tests to assess for anything aberrant. Health care staff across four groups mentioned that they would communicate any clinical change or cause for concern to someone else to monitor or assess. Participants in three groups mentioned that they would discuss this with other staff to see if they noticed the change. Two of these groups mentioned they would contact a physician or specialized care (e.g., physical or occupational therapist) for further assessment. Two groups also highlighted the role of restorative staff in increasing the activity levels of residents who need to move more. Two focus groups discussed how they would wait until the morning huddle, when an interdisciplinary group of healthcare staff would be able to discuss the situation to ascertain what is going on with the resident.

In terms of deciding what course of action would be most appropriate to take (clinical observation versus assessment vs. reporting), three focus groups discussed the reliance on other specialized clinical staff to make this decision. A healthcare staff member from one of these groups used the “5 Why’s” strategy to determine the root cause of the change: “We have an interdisciplinary team here in our huddle, PT, OT, restorative, as well as a pharmacist, Sometimes after the pharmacist review it turns out that med changes can lead to dizziness or lead to falls. We do root cause to see if we can ask the ‘5 whys’ and see if we can get to the bottom of it” (Focus Group 5, Participant 14). “Five whys” is a problem-solving technique to understand the cause of the problem by asking five “why” questions to determine the root cause of a problem (Pojasek, 2020).

Resolution of the Clinical Change/Concern

Across the six focus groups, there was little or no discussion on how resident changes in acute health status were resolved. Though unclear, this may be because the event was reported to a supervisor or referred to physical or occupational therapy for follow-up, and the participant was not informed of the results of any of these other interventions. Participants did not report how they themselves followed up on the noted changes in the resident. One healthcare staff member stated, “I know we reported it [a behavior change] to staff as well as put it in the chart. I guess people monitored and were aware. I am not sure what happened, but I am sure everyone started to monitor” (Focus Group 1, Participant 3). One focus group discussed how they were unaware of how the resident care was updated, but that the proper course of action would be to document and then monitor the resident.


This qualitative study used information from six focus groups to examine how healthcare staff identify and act on changes in health status of residents in long-term care. In this study, healthcare staff described the complexity of the physical and mental conditions of long-term care residents and highlighted how difficult it was, for even experienced healthcare staff, to identify and determine the significance of health status changes in residents. To identify potential health status changes in this population, healthcare staff used a complex array of medical, social, behavioral, and other information about the resident to create an individualized phenotype to serve as a baseline for each resident. This phenotype was used throughout the activities of the day to determine whether the resident was behaving “normally.” Finally, this study found that any deviation from the resident’s individualized phenotype did not necessarily prompt action from staff. There were little or no protocols for such observed changes in residents in this setting, and many of these changes are attributed to day-to-day deviations in the medical and other conditions these residents have.

Many long-term care residents are unable to communicate symptoms, and these residents can have atypical presentations. For example, long-term care residents may be more likely to present with subtle behavior changes such as agitation, anxiety, increased or decreased ambulation, fatigue, and dizziness (Sabih & Leslie, 2022). As a result, precursors to a fall or an acute condition can be missed. An estimated 33% of UTIs in this population are missed, misdiagnosed, or improperly treated (Sabih & Leslie, 2022). One of the ways of noticing health status changes in residents may be through changes in behaviors—including changes in ambulation activity and patterns. Although several statements were made from healthcare staff about noticing and evaluating such changes in our focus groups, healthcare staff did not acknowledge the importance in using ambulation as an important assessment element. Gait and balance assessments are used in long-term care to gauge ambulation ability and assistive device needs, though these are conducted infrequently (at admission or after an acute event), and notably most frail older adults have deficits in these areas—so these assessments alone may not provide enough information to determine the source or nature of changes in ambulation activity and/or patterns (Borowicz et al., 2016).

Health care staff also lacked standardized protocols to manage a concern for a resident’s changing health status. For instance, when someone mentioned a follow-up next step, it was to report the observation to another, usually more experienced, staff member. There was little discussion on using established protocols or mechanisms for how and when to follow up. Overall, healthcare staff lacked formal processes for noticing and reporting a suspected change in health status and the time to discuss with other healthcare staff about the change. Health care staff also lacked timely formal handoff procedures of information. Taken together, these gaps in process and protocol suggest healthcare staff in long-term care may benefit from additional objective data to help support clinical decision-making.

It is notable that the majority of the hands-on care and resident assessment that occurs daily in long-term care facilities is conducted by nursing assistants (Zysberg et al., 2019). Nursing assistants receive the least amount of training of all healthcare staff in a long-term care facility but interact with and understand the most about the residents and their changing needs, providing assistance with everyday ADLs, including bathing, dressing, and eating (Page & Rowles, 2016). Although these aides are positioned to best conceptualize the resident’s individualized phenotype (the characteristic “norm” of the resident), they lack the training associated with integrating complex medical, physical, mental, social, and other (e.g., environmental) resident factors. Formal assessment and reporting structures would be an excellent adjunct strategy for this level of staff, but these did not seem to be in place.

Even professionally licensed staff did not detail formal mechanisms for ongoing assessment of atypical symptoms and changes from a resident’s individualized phenotype. Nor was there discussion on how the noted changes would be addressed and reevaluated. Previous work shows that these staff receive training largely focused on dementia-related behavioral management or psychiatric conditions with little/no training on the clinical skills required to identify what are often very subtle signs and symptoms (Gilster et al., 2018; Husebo et al., 2016). A novel finding in this study was how reliant staff were on the individual phenotype to determine a change in health status. This strategy seems to be learned on-the-job with some healthcare staff likely using it more proficiently than others because there is little training in this particular strategy. Incorporating this into educational offerings for long-term staff and formalizing procedures to report and follow up on changes could enhance the benefit of this commonly used method in all levels of long-term care (Lamppu et al., 2021; Lamppu & Pitkala, 2021; Spector et al., 2016).

Few long-term care facilities have the healthcare staff and resources to assess ADLs or cognitive status more frequently than the Resident Assessment Instrument Minimum Data Set; currently, this is required on admission, discharge, and when a change in health status occurs (or every 90 days); (Centers for Medicare and Medicaid Services, 2019). Research has noted the lack of objective information in assessing acute events like falls (Robinovitch et al., 2013) and how this lack of assessment/objective point of comparison may impede the timely recognition of conditions that contribute to worse functioning (e.g., UTI). In addition, participants in this study referenced the ability of the MDS assessment to detect acute changes in the health status of residents; however, the MDS assessment is not meant to predict changes but to document health status change over time. These practices stand in contrast to other medical settings with vulnerable populations (e.g., during acute hospitalization) that regularly incorporate physical assessments to test gait, balance, and ADL functioning to inform clinical decision-making in an active and dynamic manner (Gervais et al., 2014).

There is increasing interest in and use of various health information technologies to provide objective and continuous data to improve safety, quality of care, staff productivity, and patient monitoring in long-term care and other healthcare settings. For example, research suggests objective and continuous data from sensor devices can improve resident outcomes, including maintenance of ADLs (Bowen & Rowe, 2019), reduce adverse drug events (Resnick et al., 2009), and increase identification of medication errors (Handler et al., 2011; McKibbon et al., 2012) while also maintaining the privacy required for residential environments. Emergent work in this area also shows that geolocation and tracking data from wrist-worn sensor devices can provide behavioral change and ambulation activity and pattern information associated with changes in physiological vulnerability and acute health events (Bowen & Cacchione, 2021; Ramazi et al., 2022) at competitive costs—especially as the plethora of commercially available technologies increase. Thus, the formalized use of automated continuous and objective health information technologies and/or more frequent objective functional and other clinical assessments in long-term care settings may lead to more timely identification of acute changes in health status, provide for tailored interventions, and result in improved clinical outcomes.

In summary, an increasing risk for falls and the onset of acute conditions are difficult to recognize in this population, even for skilled nursing staff. When changes in health status associated with acute events are noticed, there are no standardized policies or procedures for reporting in the study settings. Thus, future work in this area focusing on staff training in atypical presentations of health status changes and standardizing processes for assessment may improve health outcomes for vulnerable residents.

Study Limitations

There are several limitations in this study to consider while interpreting results. First, study findings may not represent all long-term care staff members’ processes and procedures when an acute change in health status is suspected. Also, this study’s findings reflect two long-term care facilities in the United States and their procedures and protocols for clinical assessment and decision-making. Finally, all study participants were volunteers, which may affect study results.


In summary, the long-term care staff in this study integrated complex medical, physical, mental, and social knowledge of specific residents, which demonstrates the level of clinical skill, attention, and time spent with the patient population. This also provides healthcare staff with the flexibility to determine when the resident deviates from their phenotype or normally occurring behavior and to conduct additional assessments and provide for early intervention. However, these phenotypes are based on subjective information of resident characteristics, and the patient population has complex medical conditions that are constantly in flux—in addition to the expected age and medical-related progressive decline. Thus, it is difficult for healthcare staff to determine whether the resident’s change is “real” and requires intervention or if they should wait 24–48 hours and continue to observe. This inability to move forward immediately when a change is initially detected likely delays care.

This study’s findings suggest a multimodal approach to aid healthcare staff in identifying and acting on suspected acute health status changes in long-term care is needed. First, more frequent assessments are needed in long-term care settings. These assessments would provide ongoing and objective data that could be used to support clinical decision-making and earlier intervention and treatment. Given that it is difficult to conduct frequent assessments in these settings, focusing on developing and testing brief assessments is warranted. Relatedly, another solution that is increasingly being used in these and other healthcare settings is an objective and continuous sensor system, which provides additional behavioral, ambulation activity and pattern and other information, including acute event prediction, which could be used to supplement clinical decision-making (Bowen & Cacchione, 2021; Bowen & Rowe, 2016, 2019; Ramazi et al., 2022). Second, assessments should capture multiple health domains given the complexity of the patient population. For example, assessments capturing changes across physical (e.g., gait, balance, and ADLs) and cognitive health domains (memory and executive function) may aid in differentiating between acute health status changes and complications because of chronic conditions (Bowen et al., 2017). Finally, study findings suggest some healthcare staff are unclear on the processes and procedures for reporting suspected changes in acute health status and to whom to report these changes. Thus, it is critical that long-term care facilities develop standardized formal policies and procedures for reporting.

Key Practice Points

  • Changes in health status may present atypically in complex and chronically ill long-term care residents.
  • Formal facility-level processes and procedures for healthcare staff to identify, understand, and act on suspected changes in vulnerable long-term care residents may prevent poor health outcomes.
  • Standardized assessment timelines and procedures for suspected changes in health status in long-term care may lead to the earlier recognition of increased fall risk and acute conditions in this population.

Conflict of Interest

The authors declare there are no conflicts of interest.


This work was supported by a Department of Veterans Affairs (VA) Merit award from the Rehabilitation Research and Development Service (I01RX002413). The information here does not necessarily represent the views of the VA. The VA did not have any involvement in the study design, collection, analysis, and interpretation of data, nor did they have any role in the writing of this report.


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Behavioral symptoms; dementia; frail older adults; skilled nursing facilities

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