1. Background and Context Research Question and Proposition
Swiss Federal Institute of Technology
Assessing Health Providers’ ‘Patient Identity’
Matthew J Kerry, Ph.D.
Embrace the cultural diversity and individual differences that characterize patients, populations, and the health care team
-- Interprofessional Education Collaborative (IPEC)
Background and Context
As the health of any labor force has economic effects, conversely, the economy of any health system has clinical consequences.
The shift to value-based compensation systems in care-services delivery compels new provider competencies (IPE, team).  
It also signals a reemphasis on the provider-patient relationship and its importance for:
Clinical efficacy (Kelly et al., 2014; Hughes et al., 2016;), patient safety (Kerry et al., 2017)
Economic sustainability (Okunrintemi et al., 2017)
Drawing on the Common-Ingroup Identity Model from social psych, Patient Identity is a construct similar to empathy, and it has
been defined as “a provider’s perspective for regulating professional competence with patient values, interests, and beliefs”
- Kerry et al. 2016, p.2
The main goal of the following studies was to leverage rich organizational-identity for assessing if PatID may be viably measured via a short, self-report 
Research Question and Proposition
Question  Is there a need for a provider-reported measure that may be useful in the value-compensation model overhaul? If yes, can an adapted identity instrument suffice for baseline?
Proposition  PatID indicates a health provider’s (doctor / nurse) level of identification with their patients, reflecting more personalized medical practices, such as shared-decision making (SDM), teamwork preferences, and stronger patient-safety attitudes.
Study 1
Study 2 cont . . .
Feasibility study of PatID
Assessment via self-report, psychometrics?
Distinguishable from Professional ID (ProID)?
Exploratory
S1 Goals
Medical and nursing students (N=133)
Before-after design, survey-based questionnaire
Measures: ProID, PatID, Interprofessional Readiness (RIPLS),
S2 Protocol
Table 2
Summary Sample-Descriptive Statistics for S1 and S2
Sample (N)
Variable
Mean (S.D.)
American
(N = 119)
Age
28.38 (3.81)
Gender
31% M / 69% F
Profession
59% MD / 41% BSN
German
(N = 133)
25.26 (4.15)
30% M / 70% F
77% MD / 23% BSN
Note. 1 = Male (M), 2 = Female (F). 1 = Medicine (MD), 2 = Nursing (BSN). Mean of nominal variables expressed as %.
Medical and nursing students (N=119)
X-sectional design, survey-based questionnaire
Measures: ProID, PatID, Team Attitudes, Clinic Experiencebiodata.
S1 Protocol
S1 Findings
PatID shows viable preliminary psychometrics
Factorial-discriminant validity between PatID – ProID
Hierarchical-linear regressions PatID’s incremental variance in Team Attitudes
Measurement-invariance supported
Exploratory PatID-ProID discriminant validity extended to confirmatory framework
IRT suggests minimal item-bias and appropriate target-range of the latent-distribution
(low theta= student N)
S2 Findings
Table 1
Summary Descriptive Statistics and Inter-correlation Matrix of S1 Variables
Variables
M (SD)
Experience
22.46 (3.82)
.62
Professional ID
19.79 (3.61)
.04
.87
Patient ID
20.70 (4.16)
-.13
.08
.79
IP Attitudes
71.99 (7.25)
.18*
.27*
.19*
.88
Note. N = 125. ID = Identity, IP = Interprofessional. Cronbach alpha estimates reported in main diagonal. * = p < .05.
Table 3
Summary DIF Statistics of Patient Identity for American and German Samples 
Item
Total X2
d.f. p
Slope X2a
Intercept X2 1
3.30 4
0.52
0.00
0.87
3.20 3
0.36 2
5.50
0.24
0.40
0.53
5.10
0.16
6.90
0.14
0.20
0.67
6.80
0.08
7.10
0.13
1.50
0.22
5.60 5
14.80
0.01
1.20
0.27
13.60
Note. N = 254. Anchor-all procedure.
Table 1.
Root
Raw Eigenv.
50% Eigenv.
95% Eigenv. 1
4.56
.34
.45 2
1.19
.22
.30 3
.11
.15
.21
Future Plans
Table 4
Summary Hypotheses for Planned STUDY-3
H1a: PatID will exhibit factorial-discriminant validity with JeffEmp.
H1b: PatID will correlate positively with SDM, SafClim, and AmbTol.
H2: PatID will add incremental-predictive validity over JeffEmp for SDM, SafClim, and AmbTol
H3a: Practitioners will score higher on PatID compared to students.
H3b: Providers working in general practice will score higher on PatID compared to specialty practices.
Study 2
German-translation / measurement-invariance?
Extend predictive validity to before-after design.
Exploratory  Confirmatory framework (SEM), + IRT benchmarking
S2 Goals