1. Introduction & The Harsh Reality of the Emergency
Department
For medical students, transitioning to clinical clerkships
reveals that the Emergency Department (ED) is a chaotic, high-stakes
environment where encounters are short, fragmented, and interrupted [36, 37].
For patients, an ED visit is a sudden, anxiety-provoking disruption to their
routine, evoking intense vulnerability and fear [4]. They are thrust into a
sterile, unfamiliar ecosystem, often in physical pain, surrounded by rushing
staff and beeping monitors.
For clinicians, the ED demands balancing “biochemical
care” (resuscitation, diagnostics, and stabilization) with
“psychosocial care” (calming and listening) [36]. Under intense
cognitive overload, time is severely constrained. Studies show emergency
clinicians spend over two-thirds of patient-encounter time in
“intrapersonal communication” (clinical reasoning, reviewing labs,
and documenting), leaving only a tiny minority for true, patient-centered
communication that addresses emotional and psychological needs [7].
This mismatch between patient expectations and clinical
reality creates a profound communication barrier [6]. In modern healthcare,
this subjective experience has become more than just a matter of “bedside
manner.” Under pay-for-performance health plans, patient satisfaction is
systematically monitored and utilized as a key metric for determining hospital
financial incentives and clinician reimbursement [4].
Patient satisfaction is directly linked to clinical
outcomes: effective communication drives patient cooperation, collaboration,
and adherence to medical recommendations [4]. Conversely, poor communication in
the ED acts as a major bottleneck, breeding frustration [6]. Strikingly, one-third
of all formal complaints regarding ED visits are rooted in poor
communication between patients and clinicians, persisting regardless of age,
gender, education, or status [6].
Failing to communicate creates a gap in quality of care
associated with a loss of patient control, poor health literacy, and
abandonment [6]. This can lead to clinical consequences, including increased
risk of post-discharge adverse events, longer hospitalizations, and higher
readmission rates [6]. To achieve personalized medicine, we must move beyond
generic platitudes and understand the precise mechanics of clinical
communication at the bedside [3].
2. Literature Review: Traditional Communication Paradigms
and Their Bedside Failures
On clinical rotations, we are frequently evaluated on our
“interpersonal and communication skills.” Our feedback forms are
filled with abstract check-boxes: Demonstrates empathy, Communicates
effectively, or Provides patient-centered care. Yet, as
students, these evaluations often feel frustrating because they lack
specificity. What does it actually mean to “demonstrate empathy” in a
room where a patient is screaming in pain, their family member is demanding
updates, and a nurse is asking you to sign an order?
Historically, medical literature has identified six broad
domains strongly associated with patient satisfaction:
- Interpersonal
Skills: Clinician expressive quality, responsiveness, and
availability [8]. - Bedside
Manner: Friendliness, courtesy, respect, and compassion [8]. - Engagement: Encouraging
questions and avoiding jargon [8]. - Empathy: Aligning
with the patient’s state of distress [8]. - Information
Provision: Details regarding diagnostics, therapeutics, and
prognosis [8]. - Role
Clarity: Understanding each clinician’s role and responsibility
[8].
Despite this rich body of literature identifying what matters
in a broad sense, the translation of these concepts into actual clinical
practice in the ED has been notoriously challenging and largely unsuccessful
[8]. This failure is not due to a lack of effort on the part of clinicians, but
rather to a fundamental flaw in the way communication research has historically
been structured.
Most traditional studies on clinical communication rely on
retrospective, post-discharge surveys, such as HCAHPS [4]. These surveys are
plagued by severe limitations, including significant recall bias [9]. A patient
asked to rate their emergency department experience weeks after their discharge
is likely to have a hazy, generalized memory of their interactions, heavily
colored by their final clinical outcome or their total wait time [9].
Furthermore, there is a distinct lack of research focusing
on specific, concrete communication messages that clinicians
can deploy at the bedside [8]. We have plenty of data telling us that
“information delivery” is important, but almost no data telling us
the precise phrasing that maximizes a patient’s feeling of being informed [8].
This study by Gabay et al. (2022) was designed to address this exact gap [1,
2]. The researchers moved away from broad, abstract domains and set out to test
the communication power of specific, real-world messages [8]. Their core
research question was: “Which communication messages drive patient
satisfaction with the communication of clinicians in the ED?” [8].
3. Methodology: Conjoint Analysis, Experimental Design,
and the Study Instrument
As medical students trained in traditional clinical trial
designs, we rarely encounter psychophysics methodologies. This study, however,
utilizes a highly specialized, patented methodology known as conjoint-based
experimental design (often referred to as Mind Genomics) [3, 12, 50].
Conjoint analysis was originally developed in mathematical
psychology and marketing to understand how people make complex decisions when
exposed to multi-attribute stimuli [38, 39]. In real life, we do not experience
clinical communication in isolated variables. Instead, we experience a stream
of communication where bedside manner, information delivery, and clinical
actions are blended together. Conjoint-based experimental design mimics this
reality by presenting respondents with “vignettes”—unique combinations
of messages—and asking them to evaluate the combination as a single, unified
experience [12, 14].
3.1. Study Setting and Demographics
The researchers recruited a sample of 112 American
respondents from the New York greater area [9]. This sample size is
consistent with established psychophysical and conjoint analysis standards,
where the analytical focus is on the stability of regression coefficients at
the individual level rather than the stability of population means and standard
deviations [9, 38, 40].
Eligible participants were aged 18 or older and had visited
a tertiary hospital’s ED within the past year [9]. This 12-month limit ensured
memories remained fresh and vivid while allowing the analysis of variations by
visit frequency [9].
The 112 participants represented a diverse cross-section of
typical ED patients: 52% female, 48% male, with a broad age distribution (19%
aged 18-29, 18% aged 30-39, 17% aged 40-49, 16% aged 50-59, 23% aged 60-69, 7%
over 70) [10]. Structurally, 81% identified as White, 9% Black, 5% Hispanic,
and 4% Asian [10]. In terms of marital status, 47% were married, 21% divorced,
and 27% never married [10]. Emergency department utilization showed that 49%
had 1-2 visits in the past year, 27% had 3-4 visits, 14% had 5-6 visits, and
10% had over 6 visits [11].
Participants were recruited through Luc.id, Inc., a
professional panel provider, and signed an informed consent form [9]. To
protect patient privacy, the collection of personal identifying information was
waived, leading to an IRB waiver [39]. The study achieved an exceptionally high
response rate of 82% [21].
3.2. The Experimental Design and Conjoint Engine
The study used a within-subjects, repeated-measures
experimental design where all respondents evaluated multiple
communication scenarios, minimizing individual-level confounders [12]. The
researchers tested a library of 36 distinct communication messages across six
categories of patient-centered care: Empathy (A), Provide Information
(B), Interpersonal Skills (C), Comfort (D), Encouragement, Jargon & Role
Clarity (E), and Bedside Manner (F) [13].
Using an isomorphic permuted experimental design,
the conjoint engine constructed 48 unique vignettes for each
respondent, with exactly one message from each category, ensuring statistical
independence [12, 13, 14, 45]. Across the 112 respondents, 4,032 models were
evaluated [12, 18]. On each screen, respondents were shown a vignette with the
rating question: “To what extent does the following combination of
messages drive your satisfaction with clinicians’ communication in the
ED?” on a scale from 1 to 9 [14]. This forced
them to evaluate the combination as a unity, bypassing standard survey bias
[14].
4. Data Analysis & The Total Panel Illusion: Why the
“Average Patient” Does Not Exist
In medical training, we are conditioned to rely on the
statistical “mean” or “average” of a study population.
However, this study demonstrates how easily aggregate data can mask critical
clinical realities—a phenomenon we call the “Total Panel Illusion”
[24].
4.1. The Mathematical Deconstruction of Responses
To analyze the ratings, the researchers executed a
multi-step regression workflow. Because different individuals utilize Likert
scales differently, the researchers binarized the 1–9 scale to eliminate
calibration bias:
- Ratings
of 7, 8, and 9 (the top third of the scale) were transformed
to 100, indicating that the combination of messages represents
a powerful, positive driver of satisfaction [22]. - Ratings
of 1 through 6 (the bottom two-thirds) were transformed to 0,
indicating that the combination was a weak, neutral, or negative
driver [22].
The researchers then ran Ordinary Least Squares
(OLS) regression at the individual level for each of the 112
respondents, generating 112 distinct linear models [18]. The OLS model was
written as:
$$hat{Y} = beta_0 + beta_1 X_1 + beta_2 X_2 + dots +
beta_p X_p$$
Where $hat{Y}$ is the predicted probability of high
satisfaction, $X_p$ are dummy variables representing the presence or absence of
each message, $beta_0$ is the additive constant (representing
baseline predisposition to be satisfied), and $beta_p$ are the regression
coefficients representing the conditional probability that a specific message
adds to (or subtracts from) the baseline satisfaction [18, 22, 23]. Given the
standard error of approximately 4.0 for the coefficients, a coefficient
of +6 or higher is statistically significant (p < 0.05),
representing a heavy-hitting driver [19, 30].
4.2. The Total Panel Results: An Absolute Flatline
Running the OLS regression on the entire undivided sample of
112 respondents yielded a counterintuitive result. The additive constant was
extremely high (85), showing that patients enter the ED with a powerful, innate
predisposition to value clinical communication in general [24, 25]. However,
not a single individual message was statistically significant [24]. The
coefficients flatlined: the highest-performing message was only +2.5
(“Experienced clinicians so you are comfortable”) [25]. Shockingly,
several conventional communication phrases yielded negative coefficients,
showing they were neutral or counterproductive in the aggregate (e.g.,
“Clinicians are attentive even in cases of long waiting times”
(-3.2)) [27].
4.3. The Failure of Demographic Segmentation
Faced with this flatline, the traditional research approach
would be to split the sample by basic demographics—analyzing gender, age, or
visit frequency [9, 24].
But when the researchers ran OLS regressions for these
subgroups, they found absolutely no statistically significant
variability by gender, age, or the number of annual ED visits [24].
This is a massive clinical revelation. It proves that who
a patient is demographically does not predict how they think or communicate in
a crisis [24, 28]. The “average patient” is a statistical
myth [24]. If we communicate using a single, standardized, aggregate-derived
script, we are guaranteed to miss the mark for a large portion of our patients.
To find the true drivers of satisfaction, we must segment patients based on
their internal, cognitive frameworks: their mindsets [3, 28].
5. Results: Unveiling the Three Communication Mindsets
Discarding the aggregate “total sample” and
applying mathematical segmentation to the data revealed three highly
distinct, statistically significant mindsets regarding clinical
communication preferences in the ED [3, 28]. One-way Analysis of Variance
(ANOVA) coupled with Tukey post-hoc tests confirmed that the differences in
response patterns between these three clusters were highly significant, demonstrating
that they represent fundamentally different cognitive models of patient
satisfaction [20, 28].
These mindsets are almost equally distributed across the
population and completely transcend age, gender, and the frequency of
ED visits [28, 31]. Each mindset possesses a unique baseline
satisfaction (additive constant) and is driven by an entirely different set of
specific communication messages [28, 29].
5.1. Mindset 1 (MS1): “Pay Attention to Me and Make
Me Feel Comfortable”
- Segment
Size: 38 respondents (34% of the sample) [29] - Additive
Constant (AC): 44 [29]
Patients belonging to Mindset 1 view their Emergency
Department visit as a deeply personal, emotional, and psychological crisis
[28]. They are highly vulnerable and feel a profound sense of uncertainty [28].
For these patients, satisfaction is driven by explicit acknowledgment
of their crisis and a genuine human-to-human connection [28, 33].
Their baseline predisposition to rate communication highly is relatively low
(AC = 44), meaning that generic, silent care will result in low satisfaction
[29].
For MS1, there are two primary, statistically significant
communication drivers:
- “Clinicians
carefully listen… show interest in me as a person” (Coefficient =
+8): This is the single strongest message for this group [29,
30]. They need the clinician to sit down, make eye contact, and actively
listen without interruption. They need to feel that they are being treated
as a whole human being [5]. - “Clinicians
allow family and friends to sit with me” (Coefficient = +6): Because
these patients cope with their crisis through social connection, having
their immediate support system at their bedside is an absolute
“make-or-break” factor for their satisfaction [29, 30].
5.2. Mindset 2 (MS2): “They Know What They Are
Doing, and Are Professional About It”
- Segment
Size: 38 respondents (34% of the sample) [29] - Additive
Constant (AC): 56 [29]
Patients in Mindset 2 are highly structured, rational, and
logical copers. While MS1 patients cope through emotional connection, MS2
patients cope through information and professional transparency [30,
33]. They have a moderate baseline satisfaction (AC = 56), which is easily
elevated when their need for clinical order and professional clarity is met
[29].
For MS2, satisfaction is driven by five distinct,
significant messages:
- “Even
from the start… I always know the role of the clinician in my room”
(Coefficient = +9): This is the single most powerful message
tested in the entire study [30]. MS2 patients are highly sensitive to the
revolving door of clinicians in the ED. They want to know exactly who you
are and what your specific responsibility is in their care [8, 30]. - “Clinicians
explain things to me” (Coefficient = +6): They want
structured, step-by-step explanations of their diagnostic workup [30]. - “Clinicians
are compassionate” (Coefficient = +6): Crucially, they view
compassion not as touchy-feely emotionality, but as professional
commitment and dedicated service [30]. - “Clinicians
keep me informed” (Coefficient = +6): They want constant
updates on their clinical trajectory [30]. - “Clinicians
are concerned about my comfort” (Coefficient = +6): Verbal
inquiries regarding their physical comfort are highly valued as signs of
professional thoroughness [30].
5.3. Mindset 3 (MS3): “They Control My Pain and
Respect My Privacy”
- Segment
Size: 36 respondents (32% of the sample) [29] - Additive
Constant (AC): 29 [29]
Mindset 3 represents the most challenging group. With
an additive constant of only 29, these patients enter the ED with
deep baseline skepticism [29]. Without targeted communication, they will leave
dissatisfied [19]. MS3 patients are pragmatic, focused on physical survival and
dignity, and do not want excessive hand-holding or family involvement. Their
satisfaction boils down to two concrete drivers:
- “Clinicians
carefully attend to pain control” (Coefficient = +8): For
MS3, physical suffering is the primary barrier to psychological safety.
They need to hear—and see—that we are actively managing their physical
pain [29, 30]. - “Clinicians
are discreet… respect my privacy” (Coefficient = +7): They
are highly sensitive to physical exposure. They want curtains pulled
tightly, low voices when discussing history, and absolute discretion
regarding their personal clinical details [29, 30].
5.4. The Clinical Danger of a
“One-Size-Fits-All” Script
The true beauty of this mindset segmentation lies in
the clash between patient preferences. Messages which are
“lifelines” for one mindset are actively irrelevant or even negative
for another [29, 30].
Consider the message: “Even from the start… I
always know the role of the clinician in my room.”
- For Mindset
2, this is a massive driver of satisfaction (+9) [30]. - For Mindset
1, this message yields a highly negative coefficient (-7) [29,
30].
To an MS1 patient, who is seeking a warm, personal human
connection, a clinician who immediately starts establishing formal professional
roles can feel cold, bureaucratic, and highly distancing. It makes them feel
like a transaction [5].
Conversely, consider the message: “Clinicians
allow family and friends to sit with me.”
- For Mindset
1, this is a powerful positive driver (+6) [29]. - For Mindset
2, this yields a highly negative coefficient (-7) [29].
An MS2 patient views family presence as a chaotic
distraction that interferes with their ability to receive clear
information. If we use a standardized, hospital-mandated communication
script, we are guaranteed to trigger dissatisfaction in a significant portion
of our patients. To provide truly personalized medicine, we must
diagnose our patient’s communication mindset before we open our mouths [3, 31].
6. Practical Bedside Translation: The Personal Viewpoint
Identifier (PVI)
The Emergency Department is simply too fast and too chaotic
for elaborate diagnostic workflows that do not directly address acute pathology
[37]. To bridge this gap, the authors developed a predictive bedside tool
called the Personal Viewpoint Identifier (PVI) [20, 36].
6.1. The Mechanics of the PVI
The PVI was developed using a Monte-Carlo simulation designed
to select the six most statistically distinguishing messages from the original
set of 36 [20]. These six messages were then converted into simple, binary
(agree/disagree) statements that a patient can quickly rate upon arrival [20].
There are 216 possible patterns of responses to this specific
set of six messages [20]. Through mathematical modeling, each response pattern
is mapped to one of the three communication mindsets [20].
This tool can be hosted as a web-based application on a
tablet or bedside monitor [20, 35]. In a busy clinical workflow, the PVI can be
completed in less than a minute and integrated into the triage
process, completed on a tablet by the patient or administered by a nurse [36].
The algorithm instantly assigns the patient to a mindset [20], which is flagged
in the EHR as a “communication vital sign” to immediately notify
clinicians of their specific communication needs [31, 36].
6.2. The Shift from Situational to Patient-Tailored
Communication
Traditionally, medical communication courses advocate
“situational shifting” where clinicians adjust their voice based on
what they are doing [34]. The PVI represents a paradigm shift
from situation-dependent to patient-tailored communication [34]. Instead of
shifting our voice based on our clinical tasks, we align our voice with who the
patient is cognitively, establishing a stable, mindset-tailored channel [31,
36].
6.3. Bedside Implementation Framework
For medical students, the beauty of the PVI strategy is its
incredible efficiency. It does not demand that we spend more time in the room.
Instead, it demands that we spend our time using different words [3,
31].
- Mindset
1 (MS1) Bedside Actions: Pull up a chair, sit down, maintain
direct eye contact, and listen actively without interruption [29, 32]. Use
pivot phrases like: “I want to make sure I completely
understand what you are experiencing. Please, tell me what happened in
your own words. And if you’d like, we can absolutely bring your family in
here to sit with you.” [29] This acknowledges their
emotional crisis and establishes an immediate human connection [28, 33]. - Mindset
2 (MS2) Bedside Actions: Walk in with absolute professional
posture. Introduce yourself and clearly state your role [8, 30]. Use pivot
phrases like: “Hello, Mr. Smith. I am student-doctor Chen,
and I am working with the attending emergency physician, Dr. Davis, to
manage your care today. Here is our plan: we are going to run an ECG, draw
blood, and order a chest X-ray. I will return to explain the results of
each test as they come back, and keep you updated on any delays.” [16,
17, 30] This provides structural clarity and returns a sense of cognitive
control to the patient [30, 33]. - Mindset
3 (MS3) Bedside Actions: Pull the curtains completely closed,
speak in a low, respectful, confidential tone [29, 30]. Use pivot phrases
like: “Mr. Jones, I see from your triage note that you are in
significant pain. My first priority is to get that pain under control. I
am going to order some IV medication right now. While that is being
prepared, I want to pull these curtains shut so we have absolute privacy
to discuss your history.” [29, 30] This directly targets
their two primary expectations—analgesia and physical dignity [29].
7. Limitations, Ethical Considerations, and Future
Research Directions
As critical consumers of medical literature, we must
thoroughly evaluate its limitations, ethical guardrails, and generalizability
[31, 36].
7.1. Study Limitations and Methodological Vulnerabilities
- Self-Selection
Panel Bias: The study sample was recruited through Luc.id, Inc.,
an online panel provider [9]. Because participation required individuals
to actively engage with a digital study interface, the sample naturally
carries a self-selection bias [36]. This process may exclude vulnerable
populations who lack digital literacy, reliable internet access, or
English proficiency [6]. - Geographical
and Cultural Isolation: The entire cohort of 112 respondents was
recruited from the New York greater area [9]. The baseline communication
expectations and concepts of “bedside manner” in a major
northeastern American metropolis may differ vastly from those in rural
settings or other countries entirely [36]. - The
Confounding Noise of Real-World Visits: While the conjoint design
is mathematically elegant, a patient’s retrospective rating can be heavily
modified by the stark realities of a real ED visit [12, 36]. Severe
clinical outcomes, delayed analgesia, and physical discomfort of waiting
room chairs represent significant confounding variables [6, 36]. - Clinician
Re-education: Implementing a mindset-tailored communication model
requires a profound cultural shift in how healthcare providers are
trained. Clinicians will require dedicated, continuous education to learn
how to rapidly translate a PVI designation into authentic bedside
exchanges [35].
7.2. Ethical Considerations & Regulatory Safeguards
Ethically, since the online tool was designed to evaluate
subjective responses to simulated scenarios, no personal, clinical, or
identifying information was collected [39]. Consequently, IRB review
and formal approval were waived, and consent was obtained digitally [9, 39].
The study was supported by the János Bolyai Research Scholarship and the
Hungarian National Research, Development, and Innovation Office, with no
conflicts of interest declared [39, 40]. The research was published
under a Creative Commons Attribution (CC BY 4.0) license, enabling
free adaptation of the PVI framework [1, 2].
7.3. Clinical Warnings: Avoiding Cognitive Stereotyping
As future physicians, we must maintain a critical ethical
guard against “cognitive stereotyping.” A predictive algorithm like
the PVI is a clinical aid, not a clinical replacement. It is designed to
provide us with a high-probability “starting channel” for our
communication [20]. We must never allow a PVI designation to blind us to the
dynamic, shifting state of the patient in front of us. The PVI is a compass,
not a track. Our clinical intuition, sensory observation, and authentic human
empathy must always remain the ultimate arbiters of how we interact with our
patients [31, 36].
8. Conclusion: A Personal Reflection on Communication as
Precision Medicine
In medical school, we learn that the future of medicine is
highly personalized, moving from broad population-level treatments to targeted
interventions. Yet, when it comes to clinical communication—arguably our most
frequently utilized tool—we are still taught to use generic,
“one-size-fits-all” scripts for every patient [3, 8]. We are told to
“be empathetic” or “be clear” without recognizing that
patients have distinct, deeply ingrained cognitive architectures that process
our words in fundamentally different ways [8, 28].
This study by Gabay et al. (2022) is a profound wake-up call
[1, 2]. It demonstrates that clinical communication is, and should be
treated as, a branch of precision medicine [3]. Just as we would never
prescribe the same generic antihypertensive drug to every patient with elevated
blood pressure without checking their comorbidities and labs, we should never
use the same generic communication script for every patient in the ED without
diagnosing their communication mindset [3, 31].
To an MS1 patient, our formal, role-clarifying statements
feel cold and transactional [29]. To an MS2 patient, our open-ended, emotional
check-ins feel like an inefficient distraction from their need for clinical
information [29, 30]. To an MS3 patient, our explanations are completely white
noise until we aggressively manage their physical pain and secure their
physical curtains [29, 30].
As a medical student, reading this paper has completely
transformed how I visualize my future shifts on the wards and in the Emergency
Department. It has taken communication out of the realm of “soft,
untestable art” and placed it firmly into the realm of “quantitative,
evidence-based clinical science” [12, 19]. It shows us that we do not need
to choose between the speed and efficiency of biochemical stabilization and the
compassion of psychosocial support [36, 37]. By using rapid diagnostic tools
like the Personal Viewpoint Identifier (PVI), we can instantly align our verbal
delivery with the patient’s internal psychological frequency, maximizing their
satisfaction, trust, and cooperation in a matter of seconds [3, 36].
When I walk into my next patient’s room, I will no longer
just see a list of symptoms to be diagnosed and managed. I will see a complex,
unique human being with an unspoken communication mindset waiting to be
decoded. And by choosing my words with the same precision and deliberation that
I use when writing a prescription, I hope to bridge the bedside chasm, turning
what could have been a chaotic, transactional encounter into a deeply
therapeutic, trust-building clinical alliance [36, 37].
9. Comprehensive Summary of the 36 Communication Messages
and Segment Coefficients
To provide a complete academic reference for clinical
rounds, the table below compiles the mathematical results of the study,
contrasting the aggregate “Total Panel” coefficients with the three
distinct mindsets identified through k-means clustering [25, 29].
|
Category |
Total |
Mindset |
Mindset |
Mindset |
|
Category |
||||
|
A1: |
+0.9 |
0 |
0 |
0 |
|
A2: |
+1.3 |
+4 |
+6 |
-6 |
|
A3: |
+2.3 |
0 |
0 |
0 |
|
A4: |
-0.8 |
0 |
0 |
0 |
|
A5: |
N/A |
0 |
0 |
0 |
|
A6: |
+0.6 |
0 |
0 |
0 |
|
Category |
||||
|
B1: |
-0.5 |
+2 |
+6 |
-4 |
|
B2: |
+0.9 |
0 |
0 |
0 |
|
B3: |
+2.1 |
+2 |
+6 |
-3 |
|
B4: |
-0.7 |
0 |
0 |
0 |
|
B5: |
+0.5 |
0 |
0 |
0 |
|
B6: |
-1.6 |
0 |
0 |
0 |
|
Category |
||||
|
C1: |
+0.4 |
+8 |
0 |
+5 |
|
C2: |
-0.5 |
0 |
0 |
0 |
|
C3: |
-1.5 |
-1 |
-2 |
+7 |
|
C4: |
N/A |
0 |
0 |
0 |
|
C5: |
-1.1 |
0 |
0 |
+8 |
|
C6: |
-2.7 |
0 |
0 |
0 |
|
Category |
||||
|
D1: |
+0.6 |
0 |
0 |
0 |
|
D2: |
+1.1 |
+6 |
-7 |
-1 |
|
D3: |
N/A |
0 |
0 |
0 |
|
D4: |
+1.0 |
0 |
0 |
0 |
|
D5: |
+1.1 |
0 |
0 |
0 |
|
D6: |
+2.2 |
0 |
0 |
0 |
|
Category |
||||
|
E1: |
+2.3 |
0 |
0 |
0 |
|
E2: |
+0.1 |
0 |
0 |
0 |
|
E3: I |
-1.1 |
-7 |
+9 |
0 |
|
E4: |
+1.9 |
0 |
0 |
0 |
|
E5: |
+2.5 |
0 |
0 |
0 |
|
E6: |
-0.3 |
0 |
0 |
0 |
|
Category |
||||
|
F1: |
-1.5 |
0 |
0 |
0 |
|
F2: |
-3.2 |
0 |
0 |
0 |
|
F3: |
-0.4 |
0 |
0 |
0 |
|
F4: |
+2.0 |
0 |
0 |
0 |
|
F5: |
+1.1 |
+1 |
+6 |
-4 |
|
F6: |
-1.4 |
0 |
0 |
0 |
|
Additive |
85 |
44 |
56 |
29 |
Note: Bold numbers represent highly significant, positive
coefficients (p < 0.05) that define the specific communication drivers for
that mindset. [30]
Reported by: Dr. Mohammed Abdellah Himedah
Academic Reference: Grounded in Gabay, G., Gere,
A., Zemel, G., & Moskowitz, H. (2022). Personalized Communication
with Patients at the Emergency Department—An Experimental Design Study.
Journal of Personalized Medicine, 12(10), 1542. [1, 2
