FormalPara Key Summary Points

Why carry out this study?

Alzheimer’s disease (AD) is a progressively disabling neurodegenerative disease, and the patient journey from early signs and symptoms to late-stage AD dementia may be burdensome, complex, and fraught with barriers.

We speculated that social media data from AD stakeholders (patients, caregivers, and clinicians) reflect out-of-clinic experiences that could help identify potential barriers in the patient journey, and that insights gained may help devise strategies to remove these barriers, and ultimately improve the patient journey.

What was learned from the study?

We found that identified barriers fell into two broad categories: issues with help-seeking behavior (i.e., unwillingness to seek care, possibly related to stigma, inadequate motivation, and issues in interpersonal interactions/communication) predominated, followed by structural barriers (e.g., financial limitations, affordability, geography/transportation); among stakeholder-specified posts in which pharmacological agents/classes for the symptomatic management of AD were named, 17% mentioned treatment-related concerns.

The insights gained from this descriptive analysis of valuable real-life experiences reflected in social media posts by AD stakeholders highlight the need to improve the patient journey in AD and ease patient and caregiver burden.

Introduction

Alzheimer’s disease (AD) is the leading cause of dementia, and affects over 30 million people globally [1]. The patient journey of this progressively disabling neurodegenerative disease, from early signs and symptoms to late-stage AD dementia, may be burdensome, complex, and fraught with barriers to timely/appropriate diagnosis and management [2,3,4]. Better understanding of the issues and challenges patients face may help remove barriers and improve the patient journey through the AD continuum.

Stigma and lack of awareness have been identified as important barriers to diagnosis in a report by Alzheimer’s Disease International (ADI) [5]. In the ADI report, additional critical healthcare challenges identified by patients and caregivers included lack of access to trained clinicians, fear of diagnosis, and cost; in comparison, clinicians identified lack of access to specialized diagnostic tests, lack of knowledge, and a belief that nothing could be done (sense of futility) [5]. Social exchange is essential to timely identification and management of AD, whereby a person communicates their problem in exchange for knowledge or a means by which their problem can be solved or lessened. Interpersonal openness is key to social exchange, thus a lack of willingness to disclose a problem or seek help can delay needed care.

Social media can provide insight into highly stigmatized disease such as AD [6, 7]. Many social media platforms allow anonymity and a means to network with supportive online communities [8,9,10]. Social media plays an increasing role in health research investigations and can complement routine office visits, standard clinical assessments, and traditional surveys/interviews which typically are more structured (i.e., not in the context of real-life experiences) [6, 11, 12]. In a previous study, we used the Real Life Sciences (RLS) Reveal platform, a software-based data analytics platform built on customized natural language processing (NLP) technologies to examine spontaneous online narratives or posts by AD stakeholders (patients, caregivers, and clinicians) across a wide range of public websites [6]. The repository of AD stakeholder posts from that original study serves as the data source for our current investigation.

The objective of the current study was to use social media data from AD stakeholders to identify issues/challenges and key barriers along the patient journey in AD. We speculated that the insights gained from the analysis may help devise strategies to address/remove these barriers, and ultimately improve the patient journey.

Methods

Data Sources

In this descriptive social media study, we sought to examine the patient journey in AD using stakeholder narratives from an existing repository of 2,466,709 spontaneous/unprompted posts generated by 146,423 potential AD stakeholders. The posts were originally extracted [6] between January 1998 and December 2021 from 112 publicly available English-language social media sites indexed by major search engines (i.e., Google, Bing) (Supplementary Material [ESM] Table S1). These social media posts were all “qualified” for the analysis sample by text-mining algorithms which confirmed that the stakeholder post specifically referred to a patient with AD (i.e., terms such as Alzheimer’s, Alz, AD were qualifying, whereas the term dementia without mention of AD was disqualifying).

This research was conducted in compliance with ethical guidelines and did not require institutional review board approval or informed consent since no recruitment was conducted, all stakeholder data were pre-existing on social media sites publicly indexed on common search engines, and no personally identifiable data (names, residential addresses, or phone numbers) were collected. To further ensure anonymity, self-reported quasi-identifiers, such as age, gender, and country-level geographic location, were aggregated before dataset inclusion. We strictly followed each website’s robots.txt terms, such as crawl delay directives, user-agents, disallow directives, and sitemaps; no password-protected sites (e.g., Facebook) were accessed.

Data Extraction and Analysis

The data-analytics software platform (RLS Reveal™; Real Life Sciences, LLC, New York, NY, USA) used for this research is based on taxonomy-based and semantic text-mining NLP algorithms that extract medical, clinical, and functional terms from unstructured data. These NLP algorithms and quality control (QC) measures have been described in detail previously [6]. Initial NLP outputs were further curated/refined by trained data analysts as part of the QC process (ESM Appendix). In the current analysis, NLP text-mining algorithms used patient journey-specific taxonomies (ESM Table S2) to create three patient journey groups, corresponding to the “early signs and experiences” phase, the “screening, assessment, and diagnosis” phase, and the “treatment and management” phase; in subsequent text, these phases will be referred to using shortened names: “Early Signs,” “Screening,” and “Treatment,” respectively. Posts could be classified into more than one phase of the patient journey. Within each patient journey phase, posts were subsequently classified by AD stakeholder type (patient, caregiver, clinician) and/or the stage of AD (early, middle, late) [13], if such information could be identified.

Subgroup Taxonomies

Within each of the patient journey phases, further subgroup taxonomies (key terms) were utilized to extract additional information of interest (Fig. 1; detailed taxonomies in ESM Table S2). In the Early Signs phase, terms from an existing AD identification framework (ADIF) were used to identify the most common patient-experienced issues/challenges. The ADIF is a hierarchical classification system consisting of five broad domains (social and relational well-being, physical well-being, psychological well-being, cognition, and functional independence) and has been described previously [6] (ESM Table S3). In the subsequent patient journey phases, behavioral and psychiatric issues were identified using terms from the psychological well-being domain of the ADIF (ESM Table S4). We pursued this particular ADIF domain since social media may be especially well suited to evaluation of stakeholders’ spontaneous expressions of behavioral/psychiatric issues compared to a structured clinical setting where cognitive evaluation may be prioritized. In addition, access-related terms (ESM Table S2) were used to identify potential barriers to health care.

Fig. 1
figure 1

Overview of study sample identification. aFull Alzheimer disease (AD) patient journey and subgroup taxonomies available in the Electronic Supplementary Material. Posts could be classified into multiple patient journey phases if more than one phase was identified within the post. bAmong posts that specified stakeholder type, the post could only be classified into 1 stakeholder type based on who authored the post (i.e., patient, caregiver, or clinician); however, among posts that specified an AD disease stage, posts could be classified into multiple disease stages if more than 1 stage was identified within the post. cPatient journey phase-related terms were not identified in 2,466,709 posts from the initial repository, and these posts were excluded from the current report. dStakeholder-type was not specified in 27,350 posts from the collective patient journey sample (19,383 early signs posts, 11,667 screening posts, and 19,448 treatment posts were excluded due to no specification of stakeholder-type). eDisease stage was not specified in 217,295 posts from the collective patient journey sample (45,370 early signs posts, 36,679 screening posts, and 189,631 treatment posts were excluded due to no specification of disease stage). ADIF AD identification framework, MMSE mini-mental state examination, NLP natural language processing

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Illustrative Social Media Posts

Illustrative stakeholder social media posts for each patient journey phase were selected for qualitative review. Selection was based on the presence of ‘tags’ assigned to a post (example tags: “early signs and symptoms”; “patient”; “clinician”; “late stage”; “psychiatric issue”). As there could be many posts with a given set of tags to select from, posts were rank ordered based on length (i.e., shortest posts first); posts with all required tags were shared and selected after group review. Excerpts from representative social media posts are included in the Results section. These sample posts have been paraphrased to remove potential identifiers; however, misspellings, unconventional abbreviations, and/or typos have been left uncorrected.

Results

Study Sample

A total of 225,977 AD patient journey-related social media posts containing AD patient journey-related taxonomies were identified using NLP and formed the broad analysis sample (Fig. 1). The majority (> 80%) of social media posts were derived from disease-specific forums (ESM Table S1). After excluding 27,350 posts in which the AD stakeholder was not clearly specified, 198,627 social media posts from 12,919 AD stakeholders formed our stakeholder-specified analysis sample (Fig. 1). Most stakeholders in our sample were caregivers (n = 9824), followed by clinicians (n = 1795), and then patients (n = 1300); these groups were responsible for approximately 65%, < 2%, and 34% of posts in the stakeholder-specified sample, respectively.

Most (85%; 192,595/225,977) posts from the broad analysis sample could be classified into the Treatment phase of the patient journey; of these, 173,147 could be classified by a stakeholder type and 2964 by disease stage (early, middle, late). Of the 49,259 posts that could be classified into the Early Signs phase, 29,876 and 3889 could be classified by a stakeholder type and disease stage, respectively. Of the 40,849 posts that could be classified into the Screening phase, 29,182 and 4170 could be classified by a stakeholder type and disease stage, respectively.

Issues/Challenges (Early Signs Phase)

All Early Signs phase posts (n = 49,259) were qualified by the presence of at least one ADIF issue/challenge (ESM Tables S2, S3). Among Early Signs posts mentioning an ADIF issue/challenge that could be classified by stakeholder (n = 29,876), a wide range of issues was identified, including anxiety, depression, fatigue, emotional distress, fall, crying, agitation, fear, anger, stigma, aggression, feeling abnormal, and insomnia. However, most individual issues were mentioned by < 10% of stakeholders. The most commonly mentioned issues/challenges across all stakeholder posts were anxiety (14–17%), malaise (13–14%), confusional state (9–18%), and depression (6–11%) (Fig. 2a). A subset of the Early Signs phase posts could be classified by disease stage (n = 3,889) since they represented retrospective stakeholder recollections of signs/experiences that had occurred prior to diagnosis; most of these posts (81%) were classified as early-stage AD. Mentions of cognitive disorder and amnesia were more common in early- and middle-stage posts (15–22%) than in late-stage posts (7–10%) (Fig. 2b).

Fig. 2
figure 2figure 2

The 50 most commonly patient-experienced ADIF issues/challenges identified during the Early Signs phase of the patient journey in Alzheimer's disease (AD). Distribution of posts by (a) stakeholder type (n = 29,876) and (b) stage of AD (n = 3889).a aThe “n values” represent the total number of posts mentioning patient-experienced issue/challenges. A single post could be classified into multiple disease stages. ADIF AD identification framework

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Behavioral/Psychiatric Issues (Screening and Treatment Phases)

Behavioral/psychiatric issues from the psychological well-being domain of the ADIF were identified in 41% (16,752/40,849) of all Screening phase posts and 56% (108,753/192,595) of all Treatment phase posts. Among Screening posts that mentioned a behavioral/psychiatric issue and could be classified by stakeholder (n = 11,674), anxiety was the predominant patient-experienced issue and more commonly identified in patient and caregiver posts (39–40%) than clinician posts (27%) (Fig. 3a). Among behavioral/psychiatric-related Screening posts that could be classified by disease stage (n = 1220), most were early stage-posts (Fig. 3b); mentions of agitation, aggression, and emotional distress appeared to be more common in late-stage posts than early-stage posts. Findings regarding behavioral/psychiatric issues in the Treatment phase were generally comparable to those in the Screening phase (ESM Fig. S1A and S1B).

Fig. 3
figure 3figure 3

Patient-experienced behavioral/psychiatric issues in the screening phase by (a) stakeholder type (n = 11, 674) and (b) stage of AD (n = 1220).a aThe “n values” represent the total number of posts mentioning patient-experienced behavioral/psychiatric issues. A single post could be classified into multiple disease stages

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Access/Barriers to Health Care

Access-related terms were examined in the later patient journey phases and identified in approximately 14% (5742/40,849) and 17% (32,223/192,595) of all posts in the Screening and Treatment phases, respectively (16% of all posts in both phases combined). Among posts with access-related terms that could be classified by stakeholder type (Screening phase, n = 3900; Treatment phase, n = 21,791), unwillingness/resistance to seeking care was a predominantly identified barrier to care across stakeholder posts in both these patient journey phases (75–82%) (Fig. 4a, b). Qualitative review of posts suggested that factors such as stigma, fear, and inadequate knowledge about AD may underlie some of the unwillingness to seek care. Affordability and out-of-pocket cost-related were the next most identified access themes, especially in patient and caregiver posts, accounting for 69%, 66%, and 52% of patient, caregiver, and clinician posts, respectively, in the Screening phase and for 82%, 72%, and 52% of posts, respectively, in the Treatment phase. Access-to-care issues related to geography/transportation/distance were also identified in slightly more patient and caregiver posts (21–24%) than clinician posts (13–15%) in both these patient journey phases. In the Screening phase, reimbursement and insurance-coverage-related access issues were identified in 8–9% of patient and caregiver posts, but in no clinician posts; similarly, in the Treatment phase, such issues were identified in 5–6% of patient and caregiver posts, but in < 1% of clinician posts. Approximately 6% of clinician posts and < 3% of patient and caregiver posts mentioned lack of support and accessing professionals/appointments/treatment in the Screening phase; these access themes were each identified in 3–5% of Treatment phase posts across all stakeholders.

Fig. 4
figure 4

Access/barriers to health care in the AD patient journey by stakeholder post in the (a) Screening phase (n = 3900) and (b) Treatment phase (n = 21,791).a aThe “n values” represent the total number of posts mentioning an issue related to access/barriers to health care

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No clear patterns regarding access-themes were discerned among Screening posts that could be classified by disease stage (n = 482); however, there were only 35 middle- and 62 late-stage posts (ESM Fig. S2A). Among Treatment posts that could be classified by stage (n = 1522), the issue of affordability was more commonly associated with later stages: mentioned in approximately half (50–52%) of middle- and late-stage posts and in 41% of early-stage posts (ESM Fig. S2B).

Screening, Assessment, and Diagnosis

In the overall Screening phase sample (n = 40,849), 53% of posts contained general terms such as “assessment.” In the subgroup of 11,358 posts that named the assessment method/tool and could be classified by a stakeholder type (Fig. 5a), magnetic resonance imaging (MRI) was the most common imaging modality in posts by patients and caregivers (17–20%) relative to posts by clinicians (7%), whereas computed tomography (CT) was more commonly mentioned in clinician posts (19%) than in posts by patients and caregivers (14–15%); the broader term “brain scan” was mentioned in 12–14% of patient and caregiver posts, but no clinician posts. AD-related biomarkers (amyloid, plaque or tau) were more commonly mentioned in patient posts (12%), followed by caregivers (8%) and clinicians (6%). Among standardized clinical instruments, the mini mental state examination (MMSE) was mentioned in 10–11% of posts by patients and caregivers, but < 1% of clinician posts, whereas activities of daily living/instrumental activities of daily living (ADL/IADL) were mentioned in 29% of clinician posts and only 1–3% of patient and caregiver posts.

Fig. 5
figure 5

Screening, assessment, and diagnosis in the AD patient journey. a Posts that specifically named a type of assessment by stakeholder type (n = 11,358)a,b, b posts that specifically named a type of assessment by disease stage (n = 2974)a,c. aThe figure shows assessment types specifically named in ≥ 1% of posts in any of the groups. bOf 29,182 Screening posts that could be classified by an AD stakeholder type, 11,358 specifically named the assessment tool(s). cOf 4170 Screening posts that could be classified by disease stage, 2974 specifically named the assessment tool(s). A single post could be classified into multiple disease stages. ADL/IADL Activities of daily living/instrumental activities of daily living, ADASCOG Alzheimer’s Disease Assessment Scale-Cognitive Subscale, APOE4 apolipoprotein-E4, CSF cerebrospinal fluid, CT computed tomography, MOCA Montreal cognitive assessment, MRI magnetic resonance imaging, PET positron emission tomography

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Among posts that could be classified by disease stage (n = 2,974), most (84%) were early-stage posts (Fig. 5b). AD biomarkers were mentioned in 15–18% of posts across all stages. MRI was mentioned slightly more commonly in early-stage posts (20%) than in middle- and late-stage posts (15–17%); similarly, mention of genetic testing was more common in early-stage posts (5%) than in middle- and late-stage posts (1–2%). Mention of MMSE was more common in middle-stage posts (15%) than early- or late-stage posts (8%).

Treatment and Management

In the overall Treatment phase sample (n = 192,595), 80% of posts included broad terms such as “treatment” or “meds.” A subgroup of 9498 posts (approx. 5% of Treatment phase posts) specifically named a drug/drug class for symptomatic management of AD and could be classified by stakeholder (ESM Fig. S3A). Among symptomatic treatments indicated for AD, cholinesterase inhibitors were the most commonly mentioned (30%, 24%, and 8% of patient, caregiver, and clinician posts, respectively), followed by N-methyl-d-aspartate (NMDA) receptor antagonists (7–9% across all stakeholder posts). Selective serotonin reuptake inhibitors (SSRIs) were the most commonly mentioned psychiatric medications (17% of patient and caregiver posts, 5% of clinician posts), followed by antianxiety medications, such as benzodiazepines (4%, 7%, and 13% of patient, caregiver, and clinician posts, respectively), and antipsychotic agents (5–8% across all stakeholder posts). Treatment-related issues were identified in 17% (1610/9498) of posts that named a pharmacologic drug/class for the symptomatic management of AD; ineffectiveness was identified by 52–58% of patients and caregivers and 33% of clinicians; tolerability/safety was identified by 48–52% of patients and caregivers and 70% of clinicians. In the subgroup of 638 of these posts that could be classified by disease stage (ESM Fig. S3B), cholinesterase inhibitors were mentioned more commonly in middle-stage posts (38%), followed by early- (30%) and late- (25%) stage posts; NMDA receptor antagonists were mentioned more in middle- and late- stage posts (15–18%) than in early-stage posts (9%). SSRIs were more commonly identified in early-stage posts (16%) than in middle- and late-stage posts (11%), whereas antipsychotic agents were more common in late-stage posts (11%) than in early- and middle-stage posts (3–6%).

Illustrative Social Media Posts

Illustrative Social Media Excerpts from the Early Signs phase

Paraphrased excerpts from a representative patient post and caregiver post describe the Early Signs phase of the patient journey with AD. Example 1 is from a patient who is experiencing uncertainty and disruption/displacement, having resigned from a new job due to cognitive issues and having moved to a new town to live with family members who have young children; this patient seeks advice on making social contacts. Example 2 is a caregiver post that retrospectively describes the early phase of their family member’s patient journey. The caregiver notes that early symptoms, such as difficulty recalling words, were attributed to “normal” aging and that early behavioral changes, such as shorter phone calls, altered reading habits, and less elaborate cooking, were not recognized as being related to a disease pathology, at the time.

Example 1—Patient Post

“… I recently moved to a new town to live with my family. I had to resign from my job…due to cognitive issues, and have just been diagnosed with Alzheimer's disease. I am 64 years old…not sure what to do from this point…don't have social contacts yet, ..not sure how to go about it now. I have difficulty with going out by myself but know I need to try....I would appreciate thoughts, ideas, etc. about what to try from here...”

Example 2—Caregiver Post

“The first thing was her speech. She was unable to find the right word for so many things…and we all laughed and said she was getting old! We did not think much about it for the longest time. When I look farther back from the speech issues. She stopped reading the books she loved and became a magazine reader. She stopped cooking anything that was more complicated than sandwiches, etc. Telephone conversations were much shorter…. I think [other family member] covered for her for a long time. and I simply did not recognize what was happening..... small things easily missed or explained away...”

Illustrative Social Media Excerpts from the Screening phase

Excerpts from three selected social media posts illustrate issues from the perspectives of each stakeholder type during the Screening phase of the patient journey in AD. In Example 1, an early-stage patient describes emotional and work-related impacts and expresses hope that they will have insurance coverage for an upcoming MRI. In Example 2, a caregiver describes their concerns about affordability and out-of-pocket costs related to diagnostic imaging for their family member’s upcoming PET scan. Example 3 is a clinician post advising a patient to avoid genetic testing because of the potential for negative repercussions on life insurance/long-term care coverage; this clinician post also conveys a sense of futility regarding management options.

Example 1—Patient Post

“…I'm trying to wrap my head around all this and not get overwhelmed as best I can. Thinking in practical terms of how to set things up with work … to cover for me if needed seems to be helping me distance myself from fear and emotions. Hopefully, I'll be pre-certified for insurance coverage for the MRI within the next couple days.”

Example 2—Caregiver Post

“He is scheduled for this particular scan next Friday! They told me it will be expensive and there is no insurance coverage yet! FDA did approve the Amyvid PET scan last April. I understand how valuable this will be but...if I didn't want to know if he has AD or not I would wait. Given our circumstances we must know!!!! So we did schedule. Does anyone have more information?”

Example 3—Clinician Post

“Don't have it done. If you are found to have a genetic predisposition to any dementia, you have cut yourself out of any life insurance coverage or long term care coverage for the rest of your life. Also there is no telling how secure your information is or how that information will be used in the future. As long as there is no treatment, no cure and no "vaccine," it does you no good to know either way. Just prepare your financial future as if you may get it.”

Illustrative Social Media Excepts from the Treatment Phase

The three selected social media posts from the Treatment phase illustrate issues from perspectives of each stakeholder type. Example 1 is a patient post noting that they stopped their AD-related treatment due to a gap in insurance coverage; although this particular patient did not notice a deterioration of symptoms, the post illustrates how such insurance issues disrupt patient care. Example 2 is a caregiver post from a family member of a patient who needs 24/7 care and is potentially a danger to himself, but in denial about his condition, refusing medication and unwilling to accept nursing home placement—the heart wrenching nature of such situations and the toll on close family members can be inferred. Example 3 is a clinician post expressing concerns about a hospitalized patient who may not have nearby family/caregivers to help make key decisions—highlighting the negative impacts of geographic barriers and lack of caregiving support.

Example 1—Patient post

“I recently stopped taking [drug name] (as my insurance coverage from work has run out and state disability still in process) and I am surprised to report that there has not been much of a change in my processes, Or perhaps it is my [family member’s] prayers to slow the symptoms from progression.”

Example 2—Caregiver post

“He is incapable of taking care of himself…He refuses to take his medication… is in complete denial about his condition…but he needs 24/7 care. However, he absolutely refuses to go to a nursing home.…[family member] has Power of Attorney over him. But as long as he is able to vocally refuse to go to a nursing home, he cannot be forced into a nursing home against his will… only way…at this point would be to go to court to have him declared mentally incompetent and a danger to himself, not an easy task. This would entail paying a geriatric psychiatrist and at least two of his doctors to testify against him not to mention the attorney fees...”

Example 3—Clinician post

“…Since there seems to be nobody nearby to make all the critical decisions that are necessary prior to her discharge from the hospital she could end up back home by herself in the same situation she was in before. I sure hope not. I really hope she at least gets some time in a rehab facility before they leave her on her own”

Discussion

This NLP-based analysis of social media data examined 225,977 posts in order to identify potential issues/challenges as well as barriers to health care experienced during the patient journey in AD. We believe that a key insight is that identified barriers to health care fell into two broad categories: help-seeking behavior (possibly related to stigma, inadequate motivation, and issues in interpersonal interactions/communication) and structural barriers (e.g., financial limitations related to affordability/cost or insurance coverage; geography/transportation).

Unwillingness/resistance to seeking care was the predominantly identified barrier to care across all stakeholders. Stigma associated with AD is well established [5, 14,15,16], and likely was an underlying contributor to unwillingness/resistance to seeking care, although the term “stigma” may not always have been used explicitly in social media posts. A systematic literature review of 48 studies examining “help seeking” in dementia found that inadequate knowledge and stigmatic beliefs were the main barriers to help seeking [16]. Denial or inadequate understanding/knowledge regarding AD may also underlie unwillingness/resistance to seeking care [16,17,18]. Additional factors that may hinder help seeking for cognitive problems specifically include normalization of cognitive symptoms as part of natural aging, misattribution to other psychiatric conditions, and not believing there will be a benefit to seeking help [19]. Improving help-seeking behavior for AD-related issues can be challenging in practice due to patient/caregiver factors, such as stigma and fear related to the diagnosis, as well as healthcare factors, such as inadequate resources for care and lack of treatment options. The negative impacts of an unwillingness to seek help may include delayed diagnosis, and therefore delayed treatment and the potential for poor outcomes [20]. In patients with AD, there could be worsening cognitive/functional impairment, reduced social engagement, and greater financial burden due to disease progression over time. Analysis of social media data may help to better understand stakeholder perspectives, especially if they are expressed more freely, without fear of stigmatization and prejudice. This may assist in the development of appropriate educational materials and awareness initiatives in AD.

Structural barriers, including affordability and out-of-pocket cost, followed by issues related to geography/transportation/distance, and reimbursements/coverage were also commonly discussed in social media posts, particularly by patients and caregivers. Such structural barriers to health care in AD/dementia have been documented in the literature [21,22,23]. In our prior social media research, we observed that clinician posts typically reflected targeted engagement with patients/caregivers, such as providing clinically oriented advice in on-line discussions [6]; therefore, it could be that patients/caregivers are not engaging with clinicians regarding access-related issues. In addition, clinicians may not prioritize affordability/costs or reimbursement/coverage in AD, especially with respect to current pharmacological treatments for management of AD symptoms, since most are available generically; this may change as new AD treatments are approved.

Based on observations in clinical practice, we had expected that “lack of support” would be more commonly identified as a barrier to health care (HW Querfurth, personal communication October 12, 2021). It is possible that while direct use of the wording “lack of support” was not common, this concept may have been implicit within other challenges to accessing care. For example, in the clinician post that described a patient who did not have family nearby to help with decision making, NLP identified the “geography” as the barrier to care, but this could also be considered lack of support. In addition, patients turning to social media to describe barriers to care may inherently reflect a lack of support in their personal lives.

Across all patient journey phases, anxiety, depression, and crying (sadness) were among the most commonly mentioned issues/challenges. In the Early Signs phase of the patient journey, we found that a wide variety of patient-experienced issues/challenges were mentioned, but few issues were highlighted in > 10% of stakeholder posts, consistent with the understanding of heterogeneity in AD [24, 25]. Of note, in our prior research we found that patients identified substantially greater burden of issues/challenges within their posts than caregivers [6]; however, we did not observe marked differences in the distribution of burden between patient and caregiver posts in the Early Signs phase. Investigations of the psychological well-being domain of the ADIF in the Screening and Treatment patient journey phases found that anxiety was the most commonly mentioned issue/challenge, consistent with our prior report [6]. Among posts that could be classified by disease stage, we found mention of agitation and aggression to be more common in late- than early-stage posts. Of interest, clinical studies have had mixed findings regarding whether behavioral/psychiatric symptoms differ by disease stage [26,27,28,29]. An observational study of > 1000 patients reported that neuropsychiatric symptoms were prevalent across all stages of AD with large intraindividual heterogeneity and little relation to clinical severity [26]; other investigations have suggested that such symptoms can differ (e.g., in terms of type, frequency, and/or intensity) based on AD stage [27,28,29].

Our finding that imaging methods such as MRI and CT were the most commonly specified assessment types in the Screening phase of the patient journey is consistent with the current care pathway for AD in which an MRI is commonly used for differential diagnosis; CT may be used if findings based on MRI are contraindicated and to confirm findings in clinical practice [30, 31]. AD biomarkers, such as amyloid, were more commonly mentioned in patient posts relative to other AD stakeholders, possibly reflecting the greater interest in and value placed on such assessments by patients. Although biomarkers can help identify early AD and predict risk and clinical deterioration in AD, they are not yet routinely used in clinical practice [32, 33]; this, as well as the absence of national guidelines regarding biomarkers [34] and lack of insurance coverage, could be reasons for the low mention in clinician posts.

Most social media posts in our analysis sample were qualified into the Treatment phase of the patient journey—we speculate that when a patient requires treatment for their AD symptoms, it may drive more conversation on social media. While discussion of treatments predominated, much of the terminology used on-line was broad/non-specific (most posts did not clearly indicate whether the treatment was pharmacologic or nonpharmacologic). Among posts that named pharmacological medications/classes used for the symptomatic treatment of AD, we found that treatment-related issues were prevalent (identified in 17% of these posts), with ineffectiveness mentioned in over half of patient/caregiver posts and one-third of clinician posts; tolerability/safety was mentioned in half of patient/caregiver posts and almost three-fourths of clinician posts. The observed differences between stakeholder types may reflect differing perspectives or priorities regarding treatment. Collectively, these findings underscore the shortcomings of current symptomatic treatments for AD.

The illustrative social media posts demonstrated that a single narrative could yield a high volume of information. Social media narratives can have “story telling” quality, describing a single patient’s journey with AD over many years; for example, a caregiver post could discuss a patient with late-stage AD, but also provide details about the timing of the patient’s early signs and describe experiences with screening and diagnosis.

Given the complex path that patients face from the time they develop early signs of AD to the screening/diagnosis and treatment phases of their journey with AD, it is of paramount importance that patient and caregiver burden, unmet needs in daily living/various life domains (e.g., professional career, social, economic), gaps in care/treatment and service delivery, as well as general stakeholder wellbeing be addressed to help improve the patient journey. We recommend leveraging social media platforms for patient/caregiver support and out-of-clinic engagement as well as integrating social media insights into clinical management.

Other investigators have sought to address gaps in AD care. A global panel of clinicians and cognitive neuroscientists recommended that adequate infrastructure, equipment, and resources be integrated into the primary care setting to optimize the patient journey in AD and accommodate widespread cognitive evaluation [35]. It has also been suggested that the “lexicon” of AD needs to change from “inevitable, incurable, and poorly manageable” to “preventable, curable, and manageable” by addressing knowledge gaps and developing treatment options that have a greater impact on symptoms or progression—and, once available, assuring these treatments are used at the appropriate disease stage [3]. Of interest, a recent multi-criteria decision analysis survey that included AD caregivers and neurologists found that, in determining whether to offer a new anti-amyloid therapy to society, there was stakeholder consensus that the need for new therapy and treatment efficacy were among key considerations, whereas cost was not prioritized [36]. Given the emergence of AD treatments that target disease-associated pathophysiology and require early detection/timely diagnosis, the need for developing and implementing a “next-generation AD clinical care pathway” is recognized [37]. Consideration of patient and care partner perspectives is essential to the success of such efforts [37].

This study builds on our prior social media-based research in AD [6], and is the first of several planned follow-up investigations. Future investigations will include longitudinal analysis of AD stakeholder needs, demands, and subjective outcomes along the patient journey as patients progress through disease stages. Another area of interest is whether insights derived from general social media platforms differ from those derived from health-related social media/blogs/websites.

Limitations

Natural language processing of social media data is associated with inherent limitations that have been described previously [6, 38]. Information in social media is often unstructured and can be difficult to analyze and interpret; however, we used a previously described conceptual framework (the ADIF) [6] which enhanced our ability to capture a broad spectrum of real-world insights regarding patient burden. Our iterative data processing with data visualization enabled us to identify patterns and trends in the data. Another key limitation is that the accuracy of “self-reported” information in social media posts cannot be verified. For example, even though most posts were from AD disease-related forums, diagnoses of AD and/or mild cognitive impairment (MCI) could not be confirmed clinically. NLP may miss key contextual clues or nuance of language; therefore, despite the use of exhaustive taxonomies and QC processes, some of the patient journey classifications, disease staging, issues/challenges, access-related issues, and other items that we examined were possibly mentioned in a theoretical or anticipatory context rather than directly experienced. In posts that were classified in more than one patient journey phase, it is possible some issues/challenges and barriers were not attributed to the correct phase; however, NLP algorithms utilized proximity of terms to help minimize this. Although we used QC measures to identify and remove duplicate posts/stakeholders, it is likely that some duplicates were missed. Another notable limitation for our current analysis is that social media users commonly described experiences with broad non-specific terms such as “assessment,” “doctor,” “office,” and “treatment.” This may, in part, simply be due to the casual nature of “conversations” on social media. In some cases, patients/caregivers may not have known the precise or formal names a test/method. For example, for cognitive screening, simple approaches, such as the clock drawing test or 3-item recall, may be used in clinical practice rather than more involved instruments [39]. In the past 20 years, many clinical trials of new treatments in AD have failed [40], and there have been no major updates in clinical guidelines. Likewise, clinical practice has not changed—this may lead to a lack of hope among stakeholders that impacts topic engagement related to specific AD treatments in social media posts. This lack of hope is exemplified in the illustrative clinician post that recommended against genetic testing noting, “…As long as there is no treatment, no cure and no "vaccine," it does you no good to know”. Finally, although social media listening research such as ours captures first-hand experiences with AD, these platforms can be biased towards certain demographics; thus, the perspectives expressed in social media may not be representative of all AD stakeholders.

Conclusion

This descriptive analysis of out-of-clinic dialogs from social media posts by AD stakeholders sought to gain insights regarding real-life patient experiences and possible barriers to care in different phases of the complex patient journey in AD. Unwillingness/resistance to seeking care was identified as a key barrier, followed by affordability and cost. Another gap in the care of AD is that existing treatments are limited. The insights gained from the lived experiences of AD stakeholders in our study highlight the need to improve the patient journey in AD and ease patient and caregiver burden.