1. Clinical Decision Making for Collaborative Practice
Michelle Parker-Tomlin1
Shirley Morrissey1
Mark Boschen1
Ian Glendon1
School of Applied Psychology, Griffith University

2. Collaborate for Better Health

3. Overview Research format Objective Background Methods Results
Discussion
Question's

4. Objectives
This study aimed to evaluate conceptual models of health practitioners’ preferred decision making styles

5. Background
Interprofessional health care is not a new concept we still have things to learn
Literature indicates adverse events still occur when teams are not effectively communicating and collaborating
Many national and international ‘think tanks’, organisations, and initiatives ultimately aiming to improve collaborative practice and increase the quality of patient care.
Australian Interprofessional Practice and Education Network (AIPPEN)
The National Centre for Interprofessional Practice and Education (NEXUS)
The ability to make effective clinical decisions is one of the most important skills required for health professionals and interprofessional health teams
Understanding individual health practitioner’s clinical decision making (CDM)
IP IPL IPE IPP not new terms focus on interprofessional rather than multidisciplinary teams
Multidisciplinary - Multidisciplinary health professionals represent different health and social care professions - they may work closely with one another, but may not necessarily interact, collaborate or communicate effectively
Interprofessional - members of the health service delivery team participate in the team's activities and rely on one another to accomplish common goals and improve health care delivery
Literature indicates adverse events - Communicating Collaborating – Use Orchestra analogy
One of the main focused of improving collaboration and communication in health teams
Think tank –
CDM important aspect of improving interprofessional team practices being able to explain, justify, and work collaboratively within interprofessional settings around clinical decisions is paramount for successful intervention and modern health care planning, and patient centered care.
Start by understanding health practitioner's preferred decision making styles, and factors that influence this, is key to examining future methods of enhancing clinical decision making and IPP processes

6. Background – Influencing Factors
Personality – Big 5 personality factors
Experience – Clinical and Interprofessional Practice
Education – Clinical and Interprofessional
Age
Interpersonal Motivation
Task Structure – High or low
Cognitive, Thinking and Decision Making Style
Personality - Big 5 personality dimensions (Costa & McCrae, 1976)
Openness
Conscientiousness
Neuroticism
Extraversion
Agreeableness
Experience – Increase clinical judgment
Level of Education – Increase clinical Judgment
Age – Increase clinical judgment
Interpersonal Motivation - Fear negative evaluation, desirable responding
Task structure – high or low
Much of the decision making literature (e.g., McLaughlin et al., 2014) supports the evidence that there are differences in the cognitive processes and decision making styles between novice and experts, with the general consensus that naturally experts use more intuitive styles of decision making and novices more analytic.

7. Background – Conceptual Model
H1: IPE, IPP, and discipline-related education will partially mediate the relationship between age and preferred decision-making style. The greater age, higher discipline related education, greater IPE, and greater IPP experience, will align an individual’s preferred decision-making style towards the intuitive end of the spectrum.
H2: Clinical experience will partially mediate the relationship between age and preferred decision-making style. More clinical experience and greater age will align an individual’s preferred decision-making style towards the intuitive end of the spectrum.
H3: IPP, IPE, and discipline related education, will partially mediate the relationship between clinical experience and preferred decision-making style. More IPE, IPP, and discipline-related education and greater clinical experience will align an individual’s preferred decision-making style towards the intuitive end of the spectrum.
H4: The Big-Five personality traits will directly predict decision-making styles. Conscientiousness and openness will be associated with analytical decision- making styles, while being strongly inversely related to neuroticism. Extraversion and agreeableness will be associated with the intuitive decision- making style.
H5: Amount of clinical experience will directly predict decision-making style. Those with greater clinical experience (between: novice, intermediate, expert status) will demonstrate an intuitively aligned decision-making style.
H6: Interpersonal motivation will moderate the relationship between personality and preferred decision-making style. Consistent with the literature, high scores will reflect participants’ attempts to answer in a manner perceived to be a socially pleasing and potentially indicating a response bias.
H7: When considering a low-structured clinical task, compared with relative experts, novices will be more likely to naturally align their decision-making style towards the analytical end of the continuum. Experts will gravitate towards the intuitive end of the continuum.
H8: When considering a high-structured clinical task, compared with relative experts, novices will be more likely to naturally align their decision-making style towards the analytical end of the continuum. Experts will gravitate towards the intuitive end of the continuum.

8. Method – Participants
N = 229: health professionals and health students
Age: range years (86% aged years).
Clinical experience: 32% novice, 37% intermediate, 31% expert.
Gender: Females were 82% of the sample.
Education: Completed tertiary education in their field 89%.
IPE: No IPE experience 37% (e.g., IPE workshops) – 44.1% limited; 18 % multiple experiences.
IPP: IPP work experience (e.g., inter-professional team work) – none 16%, novice 25%, intermediate 34%, expert 25%.
Sample - The study examined a convenience sample of health practitioners and students from a variety of disciplines (medicine, nursing, psychology, allied health), with a range of clinical experience. Participants were either Australian-based health professionals currently working within their professional field, or health students currently studying within their chosen discipline (N = 229), of whom 79% were currently practicing within Queensland. Other demographic characteristics of the sample:
Age range years (86% aged years).
Clinical experience: 32% novice, 37% intermediate, 31%expert.
Females were 82% of the sample.
Completed tertiary education in their field 89%.
No IPE experience 37% (e.g., IPE workshops) – 44.1% limited; 18 % multiple experiences.
IPP work experience (e.g., inter-professional team work) – none 16%, novice 25%, intermediate 34%, expert 25%.

9. Method - Design Professional’s – Organisations Students
Online self-report survey
Professionals were recruited from organisations offering health services (e.g., Gold Coast Health), and from professional membership groups or bodies (e.g., Australian Psychological Society).
Students were recruited from health disciplines offered at Griffith University.
Participants were invited (via ) to complete an online survey

10. Method – Assessment Measures
Demographic Questions – IPP, IPE, Age, Clinical Experience…
The decision-making instrument – short form: (DMI-SF; Lauri & Salanterä, 2002)
Rational-experiential Inventory (REI; Pincini & Epstien, 1999)
Big Five-10 (BF-10; Rammstedt & John, 2007)
Balanced Inventory of Desirable Responding 6 Short Form (BIDR 6-SF; Bobbio & Manganelli, 2011).
Fear of Negative Evaluation – Brief (BFNE; Leary, 1983).

11. Method – Statistical Analysis
Path analysis: IBM SPSS AMOS v22.0
Model Fit – eg., chi square goodness-of-fit
Explaining goodness-of-fit – eg., Comparative Fit Index (CFI)
IBM SPSS AMOS v22.0 was used to conduct path analysis to test the ability of the conceptual model (Figure 1) to predict the hypothesised relationships of factors predicting decision-making style. Model fit was assessed by a number of standard parameters
Model Fit -

12. Results 7 x path analytic models were run to test the 8 hypothesis
H1: IPE, IPP, and discipline-related education will partially mediate the relationship between age and preferred decision-making style. The greater age, higher discipline related education, greater IPE, and greater IPP experience, will align an individual’s preferred decision-making style towards the intuitive end of the spectrum.
H2: Clinical experience will partially mediate the relationship between age and preferred decision-making style. More clinical experience and greater age will align an individual’s preferred decision-making style towards the intuitive end of the spectrum.
H3: IPP, IPE, and discipline related education, will partially mediate the relationship between clinical experience and preferred decision-making style. More IPE, IPP, and discipline-related education and greater clinical experience will align an individual’s preferred decision-making style towards the intuitive end of the spectrum.
H4: The Big-Five personality traits will directly predict decision-making styles. Conscientiousness and openness will be associated with analytical decision-making styles, while being strongly inversely related to neuroticism. Extraversion and agreeableness will be associated with the intuitive decision-making style.
H5: Amount of clinical experience will directly predict decision-making style. Those with greater clinical experience (between: novice, intermediate, expert status) will demonstrate an intuitively aligned decision-making style.
H6: Interpersonal motivation will moderate the relationship between personality and preferred decision-making style. Consistent with the literature, high scores will reflect participants’ attempts to answer in a manner perceived to be a socially pleasing and potentially indicating a response bias.
H7: When considering a low-structured clinical task, compared with relative experts, novices will be more likely to naturally align their decision-making style towards the analytical end of the continuum. Experts will gravitate towards the intuitive end of the continuum.
H8: When considering a high-structured clinical task, compared with relative experts, novices will be more likely to naturally align their decision-making style towards the analytical end of the continuum. Experts will gravitate towards the intuitive end of the continuum.

13. Results – Example Path Analysis Model
Hypothesis X2 p
CFI
NFI
RMSEA
CI 90%
L H H1
1.607
.205
.998
.996
.052
• p value. The probability that a model differs significantly from the null model is assessed by the chi-square statistic. Non-significant p values (>.05) indicate a good fit for the data hypothesised.
• Normed chi square (X2). Is a standard goodness-of-fit measure. This aims to “accept” the null hypothesis, and not reject it.
• Comparative Fit Index (CFI). A good fit is deemed >.95, and an acceptable fit >.90
• Normed Fit Index (NFI). A value between .90 and .95 is acceptable and > .95 is good
• Root Mean Square Error of Approximation (RMSEA). Values less than .05 indicate a good fit, values up to .08 represent a reasonable fit, values between .08 and .10 indicate a mediocre fit, while values exceeding .10 indicate a poor fit
• Confidence Intervals (CI). The narrower the CI range, the greater the confidence that the true value of the RMSEA is close to the estimated value.

14. Results – Table
Summary of resutls

15. Results – Hypothesis H1- Partially supported H2 – Partially supported
H3 – Unsupported
H4 – Partially supported
H5 – Unsupported
H6 – Unsupported uncovered interesting finding
H7 & H8- Unsupported
When considering this population of health practitioners, unsurprisingly, age was noted to have a significant direct effect on the amount of discipline education, discipline experience, IPP and IPE experiences the participants have received (higher ages indicated greater amount of experiences with regards to the other factors). However these factors did not influence individuals preferred decision making styles in any clear direction (intuitive or analytic). Personality factors continue to prove their complexity during examination. However, this research supports the literature in that conscientiousness and openness personality traits were associated with rational/analytical styles of decision making (Pacini & Epstein, 1999). The hypotheses were not supported with regards to other personality traits (agreeableness, neuroticism and extraversion). Interestingly, those scoring highly on neuroticism significantly indicated a rational/analytic cognitive thinking style which is contradictory to H4. Unequivocally the data relating to this sample of health practitioners did not support the hypothesis that interpersonal motivation moderates the relationship between personality and preferred decision making style. However, those scoring highly on interpersonal motivation demonstrated experiential/intuitive decision making (cognitive/thinking) styles.

16. Discussion
What could this mean for transitioning into clinical practice?
Yes factors do influence preferred decision making styles.
Picture is not clear. Not all results are consistent with the literature.
What we do know is that factors that influence CDM styles can bring natural biases and errors along with them.
Unsurprisingly this provides evidence of a complex picture and highlights the need for a CDM orientation framework to counteract biases and complexity.
Optimisation of CDM.
Increase effectiveness of CDM communication within Interprofessional teams.
Increase effectiveness of CDM collaboration of interprofessional teams.
The research examined expert and novice medical, nursing, and allied health professional CDM styles resulting in an unclear picture; probably due to the complex social and individual variables involved. Results supported the need for a CDM orientation strategy to help combat complexities, decision errors and biases. The development and feasibility of an interprofessional educational workshop in CCT, aimed at improving individual and team understanding of decision-making processes, and from such workshops, improve CDM communication and collaboration within interprofessional teams

17. Discussion Still to come …
Exploring Clinical decision making orientation framework.
Cognitive Continuum Theory (CCT: Standing, 2010)

18. Discussion

19. Complete Research Format
Study 1: National online multiple health professional survey exploring factors that influence health practitioners natural CDM participants
Study 2: Professional ‘Interprofessional Clinical Decision Making (CDM) for Collaborative Practice’ professional development workshops. 50+ participants
Study 3: Student Randomised Control Trial Workshops. Total 50+ participants
Exploring Decision Making Style (Experimental group)
Multidisciplinary Mental Health (Control group)

20. Questions?

21. References
Bobbio, A., & Manganelli, A. M. (2011). Measuring social desirability responding. A short version of Paulhus' BIDR 6. TPM. Testing, Psychometrics, Methodology, in Applied Psychology, 18,
Lauri, S., & Salanterä, S. (2002). Developing an instrument to measure and describe clinical decision making in different nursing fields. Journal of Professional Nursing, 18,
Leary, M. R. (1983). A brief version of the Fear of Negative Evaluation Scale. Personality and Social Psychology Bulletin, 9,
Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information processing styles to personality, basic belief, and the ratio-bias phenomenon. Journal of Personality and Social Psychology,76,
Rammstedt, B. & John, O. P. (2007).  Measuring personality in one minute or less: A 10 item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203‐212.
Standing, M. (2010). Clinical Judgement and Decision-Making in Nursing and Inter-professional Healthcare. New York: Open University Press.