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. Author manuscript; available in PMC: 2026 May 5.
Published in final edited form as: Pain Manag Nurs. 2025 May 5;26(5):e434–e440. doi: 10.1016/j.pmn.2025.04.001

Assessing and Managing Pain in Dementias: Insights From Interdisciplinary Text Message Communications

Ashley Woods *,1, Matthew Farmer *, Mihail Popescu †, Kimberly Powell *
PMCID: PMC12374243  NIHMSID: NIHMS2079913  PMID: 40328576

Abstract

Purpose:

This study aimed to explore the difference between the representation of pain assessment and management among older adults with and without Alzheimer’s Disease and Related Dementias (ADRD) residing in nursing homes (NH) using interdisciplinary text messages (TMs) exchanged prior to hospital transfer.

Design:

A retrospective cohort study and secondary data analysis of (n = 21,000) TMs exchanged among interdisciplinary healthcare team members and survey data about NH residents who transferred to the hospital from 2016 to 2020.

Methods:

The study analyzed TMs using natural language processing tools to identify mentions of pain assessment, management, and pain medications. We used a two-sample independent t-test and a negative binomial generalized linear model to assess the association between pain mentions and various resident characteristics, including ADRD status.

Results:

The analysis included 694 transfer events involving 414 residents, 39% of whom had ADRD. Significant differences were found in pain mentions, with ADRD residents having fewer mentions (t = 2.38, p =.02) than those without ADRD. The generalized linear model identified statistically significant predictors (age, race, and specific comorbidities) of the number of TMs exchanged with pain mentions about NH residents. Dementia stage and avoidable transfers were not significant.

Conclusions:

Our findings indicate pain is underrepresented in communications about Black residents and those with ADRD, potentially contributing to disparities in pain assessment and management in these populations.

Clinical Implications:

This study indicates a need for more equitable pain practices with improved pain assessment tools and pain assessment and management, particularly with TM communication in healthcare settings.

Keywords: Pain, Alzheimer’s disease, Dementia, Geriatric nursing, Natural language processing

In the general adult population, a healthcare worker can typically rely on a patient using a numeric pain scale, giving nonverbal cues of pain, or requesting pain medication appropriately. However, some older adults have Alzheimer’s Disease and Related Dementias (ADRD) impacting their ability to communicate effectively (Achterberg et al., 2019; Chai & Horton, 2010; Wang et al., 2022). These communication difficulties make typical adult pain assessment tools and cues challenging to use, as they rely heavily on the patient’s ability to adequately describe their pain (Helvik et al., 2023; Smith et al., 2022; Vitou et al., 2022). There are many adapted pain assessment tools for detecting pain in older adults with ADRD (Achterberg et al., 2019; Husebo et al., 2016). However, these adaptations have questionable reliability (Husebo et al., 2016; Jordan et al., 2012). For example, a systematic review found that multiple pain assessment tools designed for use in the dementia population cannot distinguish between behaviors of dementia and behaviors of pain. Further, these tools cannot effectively evaluate responsiveness to pain interventions, which is an essential aspect of pain management (Husebo et al., 2016). In particular, the reliability of these tools in nursing homes (NH) is not adequate (Felton et al., 2021a; Husebo et al., 2016). Behavior pain assessment tools can also be used, but these tools pick up on behaviors, not necessarily the presence or severity of pain (Felton et al., 2021a; Husebo et al., 2016; Jordan et al., 2012). Therefore, pain management in older adults, especially those within NH, presents significant challenges (Felton et al., 2021a; Helvik et al., 2023; Smith et al., 2022).

Misinterpretation of pain symptoms as worsening behavioral symptoms is problematic as it can lead to inadequate pain treatment (Nguyen et al., 2023). This undertreatment can result in negative consequences such as worsening underlying conditions and increased frustration and agitation (Cole et al., 2023; Nguyen et al., 2023). Barriers to effective pain management include: (1) staff lacking knowledge or training in pain assessment methods, (2) lack of knowledge of nonpharmacological interventions, and (3) reluctance to prescribe analgesics due to concerns over side effects such as delirium (Fong et al., 2009; Oh et al., 2017; Smith et al., 2022). Unfortunately, untreated pain can also cause delirium (Daoust et al., 2020).

Effective assessment and management of pain in older adults with cognitive impairment could be improved using modern communication tools including text messages (TMs). Healthcare team communication using TMs has become an acceptable and convenient tool for sharing information about residents in real-time, enhancing interdisciplinary collaboration (Powell et al., 2021; Rantz et al., 2017; Small et al., 2023). TMs have been studied in healthcare settings, such as hospitals, among surgical teams, and rural clinics, and have been found to have positive outcomes including improved response times, reduced treatment delays, and enhanced collaborative decision-making (Astarcioglu et al., 2015; Gulacti et al., 2016; Johnston et al., 2015). In long-term care settings, like NH, TMs have been found to facilitate earlier detection of changes in resident conditions and effective interdisciplinary collaboration (Powell et al., 2021; Rantz et al., 2017). No studies, to our knowledge, have focused on assessment of pain using TMs in NH residents with ADRD, a unique clinical challenge that requires careful observation, comprehensive assessment tools, and consideration of resident-specific factors (Felton et al., 2021b; Lichtner et al., 2014). It is unclear whether TMs— despite their convenience and efficiency—provide sufficient context to support appropriate decisions about pain assessment and management (Leonard, 2004). Analysis of TMs may provide a rich understanding of the real-time interdisciplinary decision-making process that is not available in electronic health record retrospective chart reviews.

The purpose of this study was to evaluate the differences between the representation of pain assessment and management in TMs exchanged by an interdisciplinary healthcare team and the frequency and nature of hospital transfers among NH residents with ADRD. By analyzing the content of TMs related to pain assessment and management, this study aimed to identify any difference between the lack of, or the presence of, pain assessment and management in texts and the likelihood of avoidable hospital transfers in this vulnerable population. Because pain symptoms are often confused with symptoms of ADRD, we hypothesized that there would be fewer instances of representation of pain assessment and management in TMs about residents with ADRD compared to those without ADRD.

Methods

Study Population and Data Sources

All study procedures and methods were approved by the University of Missouri Institutional Review Board (2092039). Using a retrospective cohort study design, we leveraged secondary data from a Centers for Medicare and Medicaid Services demonstration project, The Missouri Quality Initiative (Rantz et al., 2017). These data include approximately 21,000 TMs sent by the interdisciplinary healthcare team regarding residents’ NH-to-hospital transfers from 2016 to 2020. The interdisciplinary healthcare team roles include: (1) physicians; (2) advanced practice registered nurses (APRNs); (3) registered nurses; (4) licensed practical nurses; (5) administrative and management staff; (6) social workers; and (7) physical therapists.

We used data from two sources: (1) TMs from a HIPAA (Health Insurance Portability and Accountability Act) compliant communication and clinical collaboration tool and (2) survey data containing details of each resident transfer. The TM data include the message content, date and time stamps, sender, and recipients. The TM application is accessible by health workers via a desktop or mobile device. Features include read receipts, average response time tracking, adding and removing users to a message, and multi-user accounts. The second data source was a survey developed using the Interventions to Reduce Acute Care Transfers (INTERACT) tool (Ouslander et al., 2014). The survey was completed by APRNs every time a resident transfer occurred during the study period. This survey data contained multiple questions about each acute care transfer of NH residents in open-ended and close-ended question formats and additional data on resident demographics, including race. Details about the APRNs and RNs involved with the transfer and how they decided to transfer were also documented in the survey. The question about whether a transfer was avoidable was close-ended in a yes/no format. Whether a transfer was avoidable or not was determined through retrospective chart review and nurse interviews to identify the root cause of the transfer. Ultimately, this investigation of the transfer by the APRN and the Missouri Quality Initiative RN Supervisor determined the yes or no response. This approach has been validated as a robust quality improvement initiative (Ouslander et al., 2014).

Measures and Variable Selection

To better understand the relationship between pain assessment and management messages in NH residents with ADRD and related hospital transfers, we selected the following independent variables: (1) resident ADRD status; (2) pain assessment and management words represented in TMs; (3) pain medication names representing pain management in TMs; (4) resident demographics (race, age, sex); (5) other diagnoses, (6) pain/behavior related changes in condition prior to transfer; (7) documented code status of do not resuscitate or full code; and (8) the clinician determination if the transfer could have been avoided, displayed in Table 1. The dependent variable included extracted data of the count of pain words and pain medications found in the TMs.

Table 1.

Variable Selection.

Variable Data Type Source Notes
Pain assessment, management, and medication mentions Numeric, count Text messages Dependent variable
Sex Nominal, binary INTERACT
Age Numeric INTERACT
Race Nominal, binary INTERACT Includes: White, Black/African American, Asian, American Indian/Alaskan Native, Hispanic or Latino, Other/Multiracial, Not reported
Presence of ADRD Nominal, binary INTERACT
ADRD stage Ordinal INTERACT Early, middle, late, not reported
Additional diagnoses Nominal, binary INTERACT Yes/no diagnosis of anemia, anxiety, bipolar, CVA, COPD, CKD, chronic pain, heart failure, depression, diabetes, dysphagia, ESRD, GERD, HTN, hyperlipidemia, hypothyroidism, neurogenic bladder, paralysis, paraplegia, schizophrenia, traumatic brain injury
Change in condition (prior to transfer) Nominal, binary INTERACT New or worsening conditions: abdominal pain, behavior, general pain
Code status Nominal, binary INTERACT Yes/no: Do not resuscitate
Avoidable transfer Nominal, binary INTERACT Clinician determination if the hospital transfer was potentially avoidable or not
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ADRD = Alzheimer’s Disease and Related Dementias; CVA = cerebrovascular accident; COPD = chronic obstructive pulmonary disease; CKD = chronic kidney disease; ESRD = end-stage renal disease; HD = hemodialysis; GERD = gastroesophageal reflux disease; TBI = traumatic brain injury.

Analysis Strategy

Data analysis was completed within Python (version 3.10.10) Jupyter notebooks for data extraction, processing, analysis, and visualizations. Data were first grouped by transfer events and filtered to the variables listed in Table 1. Pain assessment and management words and medication mentions in TMs were extracted using coded terms developed by the research team and a natural language processing package called Drug Named Entity Recognition (DNER) (Wood, T. A., 2024). After data extraction, we continued analysis with descriptive statistics, hypothesis testing, and generalized linear model (GLM) analysis.

Data Extraction

Mentions of pain assessment and management were extracted from the TMs using a Python dictionary that was created by the research team. This dictionary allows the extraction process to iterate through each message and check for the presence of specific words related to pain assessment and management. This coding included natural language words or phrases related to pain symptoms, headache, back pain, joint pain, muscle pain, nerve pain, cramps, requests for pain management, and absence of pain. For example, when a TM includes the phrase “c/o pain,” which is an abbreviated version of “complains of pain,” the dictionary is referenced, and the phrase is categorized as “pain symptoms.” Due to the informal and unstructured language in TMs, this dictionary included 49 words or phrases that could represent pain assessment and management, which can be seen in Supplementary S1.

Mentions of pain medication names were extracted using the Python drug name entity recognition package DNER. This open-source package identifies drug names in English text and recognizes generic and brand names and possible synonyms of these medications. To extract specific pain medication names, the research team created a Python dictionary that includes the identified drug name and assigns the associated drug class of the pain medication. The classes of interest for this study were over-the-counter analgesia medications of acetaminophen, nonsteroidal anti-inflammatories, and Salicylates, as well as Opioids, Antide-pressants, and topical analgesics. Although Anxiolytics and Steroids can have analgesic effects, these classes of medications are less likely to be prescribed for the primary effects of analgesia and were, therefore, not included in the dictionary.

Statistical Analysis

We utilized a two-sample independent t-test to test our hypothesis. Results were reported with the t-test result, p value, and mean values between the groups. A Poisson GLM was fit to the data to understand pain words that represent pain assessment and management and pain medication names in TMs about residents by avoidable transfers and resident characteristics, including ADRD status. A negative binomial Poisson GLM (alpha 1.0) was fit to the data using a log link function, with a scale parameter fixed at 1.0. The negative binomial distribution naturally accounts for overdispersion through its variance function. The model’s handling of dispersion is supported by the Pearson chi-square to degrees of freedom ratio of 0.92 (610/662). Results were reported for model fit, variable coefficients, 95% confidence intervals, and p values.

Results

Descriptive Analysis

The data included 414 individual residents involved in 694 transfer events. 39% (n = 164) of the residents were reported to have ADRD. The average age of all residents was 73.6 years, with residents with ADRD averaging 79.2 years and residents with other diagnoses averaging 69.9 years. Residents were predominantly female at 62.1% (n = 257). Race classification was majority White at 76% of residents followed by 23% Black/African American. 43% (n = 299) of the transfers were reported as avoidable. Overall, 60% had no Dementia. Of those with ADRD, 69.7% had no stage reported, 15.0% had early stage, 11.2% had middle stage, and 4.0% had late/final stage ADRD. After natural language processing of the TMs, from the 49 words in the dictionary, 402 words or phrases were identified as pain words, and 146 were identified as pain medication names from the medication name dictionary.

Hypothesis Testing

The two-sample independent t-test was run to test the hypothesis. Pain mentions were defined as pain words in the Named Entity Recognition (NER) dictionary, representing pain assessment and management in the text. Pain medication mentions were defined as medication names found by the Python DNER package. Both pain word mentions, and pain medication mentions were analyzed between residents with and without ADRD. The results showed a significant difference in pain mentions and pain medication mentions. This was calculated with mean mentions, which was calculated by dividing the sum of the count of mentions by the total number of TMs observed for each group. The comparison of pain word mentions resulted in t = 2.38 (p =.02), with the mean count of mentions being 0.63 in residents without ADRD and 0.46 in residents with ADRD. For pain medication mentions, residents without ADRD also had significantly higher amounts of mentions with t = 2.30 (p =.02). The mean pain medication count for residents without ADRD was 0.24 and 0.14 for residents with ADRD, meaning one out of every four TMs contained a mention of pain medications for residents without ADRD and one in every seven contained pain medication mentions in residents with ADRD (see Fig. 1).

Figure 1.

Figure 1.

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Mean Pain Mentions and Pain Medication Mentions in ADRD vs. Non-ADRD.

Generalized Linear Model

The negative binomial GLM was fit to the data using a log link function and alpha level = 1.0. The model converged after nine iterations. Overall model results included −2LL = −800.68, Deviance = 579.80, Pearson Chi2 = 610 (662 residual DF), and pseudo R2 = 0.14. Results are presented in Table 2.

Table 2.

Generalized Linear Model Results.

Variable Coefficient Std. error z 95% CI IRR p Value
Lower Upper
Const 0.66 0.44 1.52 −0.19 1.51 1.94 .13
Sex (male) −0.21 0.13 −1.57 −0.47 0.05 0.81 .12
Age* −0.02 0.01 −2.81 −0.03 −0.01 0.98 <.01
Race (White)* 0.49 0.17 2.91 0.16 0.82 1.63 <.01
Dementia stage −0.01 0.07 −0.13 −0.14 0.12 0.99 .90
Anemia −0.02 0.17 −0.10 −0.36 0.32 0.98 .92
Anxiety −0.30 0.19 −1.61 −0.67 0.07 0.74 .11
Bipolar −0.19 0.31 −0.60 −0.80 0.43 0.83 .55
CVA −0.28 0.21 −1.35 −0.68 0.13 0.76 .18
COPD 0.13 0.15 0.85 −0.17 0.42 1.13 .40
CKD 0.11 0.15 0.74 −0.18 0.40 1.12 .46
Chronic pain* 0.52 0.22 2.36 0.09 0.95 1.68 .02
Heart failure −0.03 0.14 −0.24 −0.31 0.24 0.97 .81
Depression −0.18 0.17 −1.06 −0.51 0.15 0.84 .29
Diabetes −0.14 0.13 −1.08 −0.41 0.12 0.87 .28
Dysphagia −0.19 0.21 −0.87 −0.60 0.23 0.83 .39
ESRD −0.23 0.47 −0.49 −1.16 0.69 0.79 .63
ESRD on HD 0.03 0.32 0.11 −0.58 0.65 1.03 .92
GERD −0.34 0.17 −2.04 −0.67 −0.01 0.71 .04
Hypertension* 0.30 0.13 2.31 0.05 0.56 1.36 .02
Hyperlipidemia −0.19 0.17 −1.11 −0.52 0.14 0.83 .27
Hypothyroidism* −0.82 0.25 −3.34 −1.31 −0.34 0.44 <.01
Neurogenic bladder* 0.56 0.26 2.21 0.06 1.06 1.76 .03
Paralysis −0.61 0.35 −1.73 −1.30 0.08 0.54 .08
Paraplegia 0.20 0.40 0.51 −0.58 0.99 1.23 .61
Schizophrenia −0.01 0.23 −0.06 −0.47 0.44 0.99 .96
TBI 0.43 0.46 0.92 −0.48 1.33 1.53 .36
New or worsening abd. pain 0.35 0.23 1.52 −0.10 0.81 1.42 .13
New or worsening behavior −0.63 0.39 −1.64 −1.39 0.13 0.53 .10
New or worsening pain 0.29 0.26 1.11 −0.23 0.81 1.34 .27
Do not resuscitate (DNR) 0.06 0.14 0.42 −0.21 0.32 1.06 .67
Avoidable transfer −0.11 0.13 −0.84 −0.37 0.15 0.90 .40
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CVA = cerebrovascular accident; COPD = chronic obstructive pulmonary disease; CKD = chronic kidney disease; ESRD = end-stage renal disease; HD = hemodialysis; GERD = gastroesophageal reflux disease; TBI = traumatic brain injury.

Bold values are Significant p Values.

*

Significant coefficient, 95% CI does not cross zero.

Significant variables include age, race, diagnosis of hypertension, chronic pain, neurogenic bladder, GERD, and hypothyroidism, as noted in Figure 2. Noted variables that lacked significance include dementia stage, code status, avoidable transfer, biological sex at birth, and any new or worsening conditions.

Figure 2.

Figure 2.

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Significant Predictors of Pain Mentions.

Discussion

This study highlights the underrepresentation of pain assessment and management in TMs concerning older adults with ADRD. Our findings reveal a significant disparity in pain-related text communication between residents with ADRD and those without. Specifically, residents with ADRD were mentioned less frequently in discussions of pain assessment and management (mean mention count: ADRD = 0.46, non-ADRD = 0.63; p =.02) and pain medications (mean mention count: ADRD = 0.14, non-ADRD = 0.24; p =.02) than those without ADRD. These results emphasize the critical issue of pain under-recognition in older adults with ADRD. The lower frequency of pain mentions in residents with ADRD likely reflects the challenges healthcare providers face in assessing and managing pain in this population, where symptoms of pain may be mistaken for behavioral symptoms of dementia (Felton et al., 2021a). This also aligns with the documented challenges and variability in pain assessment tools used in NH, particularly for residents with cognitive impairments (Van Kooten et al., 2016, 2017). The potential under-recognition of pain contributes to inadequate pain assessment and management and can lead to poor resident outcomes. However, our study adds a unique perspective by analyzing real-time text communications between interdisciplinary teams, thereby offering insights into the practicalities of pain assessment and management in everyday clinical settings using modern technology.

The GLM identified several variables that were significantly associated with the frequency of representations of pain assessment and management mentions. Higher age was significant for fewer messages about pain. White residents had significantly more messages about pain compared to Black residents. The lower frequency of pain mentions for Black residents aligns with prior research on deep-seated inequities (Letzen et al., 2022). In particular, previous research has found that older adults from racial and ethnic minority backgrounds, particularly Black and Hispanic individuals, are less likely to receive adequate pain assessment and management compared to their White peers (Anderson et al., 2009; Lee et al., 2019; Morales & Yong, 2021). This finding aligns with prior research reporting healthcare providers may be less likely to recognize and appropriately treat pain in patients from minoritized groups compared to their White counterparts. This discrepancy could be the results of implicit biases; however, further research is needed (FitzGerald & Hurst, 2017; Hall et al., 2015).

Several co-morbidities, such as hypertension, chronic pain, and neurogenic bladder, were significantly associated with increased frequency of pain mentions. This finding aligns with prior research showing that specific and excess co-morbidities may contribute to pain experiences and the likelihood of chronic pain (Desai et al., 2020; Ferguson et al., 2020; Foley et al., 2021). However, other variables were not significant in our model. Dementia stage, avoidable transfers, biological sex at birth, and cardiopulmonary resuscitation status, were not statistically significantly different when comparing those with and without ADRD. The lack of significance for dementia stage in our study was contrary to other studies showing severe or late dementia stage was associated with more observation of pain, and early-stage dementia was associated with more reported pain (Van Kooten et al., 2017). However, this could be explained by our evaluation of TMs, rather than documentation extracted from the electronic health record.

Limitations

One limitation of this study is the use of NER. For example, language in TMs can be very informal. A healthcare worker could mention acronyms or slang for pain that the NER may miss. That said, they could also text something like “a painful decision” that has nothing to do with pain concerning the resident. However, the NER could capture this wording as a representation of pain assessment or management. This is, in part, the technology and the fact that the dictionary created for pain words and pain medications is not exhaustive and is flawed by the sheer number of possibilities of words for pain and the lack of a gold standard dictionary available. NER relies heavily on predefined dictionaries and rule-based patterns, which limits its ability to understand the nuances of TMs or include inferring meaning when language is ambiguous or informal. A large language model might be better suited for this work. However, it is challenging and expensive to access HIPAA-compliant large language model software. It is important also to mention that these data comes from a sample with minimal racial, ethnic, and geographic diversity as it was collected from NH in one Midwest state, who are predominantly White.

Lastly, there is potential for unaccounted-for confounding variables. As these data are from different facilities, one concern may be the variability in the staff training to assess and manage pain. Different pain assessment tools or procedures may be required at various facilities. It is unclear if the providers have specific training to treat pain in older adults, in particular among residents with ADRD. This may have led to an incomplete capture of pain assessment and management or differences in the perception of pain not evident in the TMs.

Interpretation and Clinical Implications

Our findings highlight the potential under-recognition of pain in older adults with ADRD, as found in previous studies. This underrepresentation could contribute to inadequate pain assessment and management and, therefore, worse outcomes in this population. Findings also highlight disparities in pain assessment and management in minoritized older adults. Nurses and other healthcare staff should receive targeted training to recognize pain more effectively in residents with ADRD, mainly when using informal communication methods like TMs. NH should consider adopting or refining standardized pain assessment tools specifically designed for use among older adults with ADRD and using structured formats (for example, Situation, Background, Assessment, and Recommendation or otherwise known as SBAR) for staff to communicate findings (Leonard, 2004). Finally, NH staff should be aware of potential biases and strive to make pain assessment practices equal across all racial groups to avoid contributing to disparities in care (Booker & Herr, 2015; Hall et al., 2015; Rogger et al., 2023).

Conclusion

Pain assessment and management are significantly underrepresented in communications concerning NH residents with ADRD, Black residents, increased age, and specific co-morbidities. This underrepresentation could contribute to disparities in care and highlights the need for more equitable and effective pain assessment and management practices. The findings suggest improving communication and adopting standardized pain assessment tools could improve outcomes for these vulnerable populations.

Supplementary Material

1

Funding

Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG078281. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

CRediT authorship contribution statement

Ashley Woods: Writing – review & editing, Writing – original draft, Methodology, Conceptualization. Matthew Farmer: Writing – review & editing, Visualization, Formal analysis, Data curation. Mihail Popescu: Writing – review & editing, Methodology, Formal analysis, Data curation, Conceptualization. Kimberly Powell: Writing – review & editing, Methodology, Funding acquisition, Conceptualization.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.pmn.2025.04.001.

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