Decision-Making in a Mental Health Environment

Decision-Making in a Mental Health Environment

Problem Statement

Contemporary Problem

Decision-making in a mental health environment is a complex and crucial process that greatly enhances patient care. Practitioners working in mental health are often confronted with dilemmas whereby they have to make decisions based on clinical procedures, patient choice, ethical elements, and resource availability. Mental health advancements in the field, such as telehealth and the use of AI in developing treatment plans, are among the new cognitive ideas. For example, today’s trends, such as telehealth, require clinicians to make diagnoses and treatment plans with limited or no ability to assess nonverbal behaviours. Likewise, AI can help suggest treatment regimens based on the identified patterns, but can also provide clinicians with too much information to filter out the most relevant ones.

Selected Area

The selected area of cognitive psychology for this proposal is decision-making. Foundational theories in this area include dual-process theory and bounded rationality. The dual-process theory proposed by Bellini-Leite (2022) suggests that human cognition operates through two systems: System 1, which is fast, automatic, and intuitive, and System 2, which is slow, deliberate, and analytical. This theory is relevant as mental health professionals often rely on intuitive and analytical decision-making processes. Bounded rationality, introduced by Simon (1957), posits that humans are limited in processing information, leading to satisficing rather than optimal decision-making. This is particularly applicable in mental health settings where decisions must be made under conditions of uncertainty and limited information.

Performance Issues and Limitations

In the institution of mental health, the human cognitive system can suffer from several factors that impair decision-making performance. One is cognitive overload, which happens when a clinician receives too much information, causing them to make less-than-stellar decisions. Further, other heuristics like the confirmation bias, where the individual always seeks information to support the decisions he has already taken, and the availability bias, where the individual overemphasizes data that they can easily retrieve in mind (Ee et al., 2020). All these cognitive shortcomings are further fueled by the stressful context of mental health care, where choices must frequently be made with time pressure and where decision-making implications tend to have critical consequences on the patient’s condition.

Potential Improvements

A key research question to address potential improvements in decision-making in mental health settings is: “How effective can decision support systems be in the telehealth and clinical practice and traditional mental health services?” This question examines how decision support systems can support and complement the biological ability of human beings rather than impose on them, thus hindering their ability to arrive at good decisions.

Contemporary Relevance

Utility of Theories

Decision-making is a process that is filled with difficulties for every professional, including choosing a proper behavioral strategy in a specific situation and estimating possible outcomes of a decision; for mental health professionals, decision-making is even more complicated because it requires dealing with complex and often contradictory thoughts and emotions These concepts of dual-process theory and bounded rationality can explain why mental health professionals may be prone to decisional conflict and why decisional self-efficacy can play important (Featherston et al., 2020).

Cognition – Dual-process theory works well to explain why decision-making requires both the recognition and reflection modes of processing, and, therefore, supporting these modes of thinking requires decision-support systems. For instance, if a decision is based on instincts, AI can help by providing the means to sort and highlight relevant information. However, if it is based on data analysis, other tools that can enhance the presentation of figures and statistics will be useful. Bounded rationality implies that decision support systems should be developed in a way so that they would not exert too much complexity on the clinician’s cognitive abilities and or capacity but instead try to fit within the clinician’s rationality by helping them to make the best decision within the available rationality.

Apply

Among the four perspectives, bounded rationality holds the most applicability for organizational practitioners in tackling issues in mental health decision-making. This theory focuses on bounded rationality, which also means that ways ought to be developed to assist clinicians in decision-making within complex and information-intensive contexts. By acknowledging these constraints, they can be worked around to optimize the flow of information while providing support in specific areas, hence fortifying decision-making and boosting patient care (McCutcheon et al., 2023).

Interpretation of Research Findings

Question

The research question, therefore, pertains to how decision support systems are applied within a mental health context. These objectives are supported by several studies that point to the possibility of improving decision-making in such systems. For instance, Morozova et al. (2022) explained how Intensive AI-informed decision support frameworks assist in identifying mental health disorders efficiently and provide crucial information to practitioners. In another study by McCutcheon et al. (2023), the use of telehealth applications, which included decision support, led to enhanced treatment compliance and benefits, especially in patients diagnosed with depression.

McCutcheon et al. (2023) found that decision-making biases, such as confirmation bias and availability bias, can significantly impact clinical judgment. Decision support systems can mitigate these biases by providing balanced and comprehensive information, thus promoting more accurate and objective decision-making. Featherston et al. (2020) highlight the importance of decision self-efficacy in mental health professionals. Notably, by enhancing clinicians’ confidence in their decisions, decision support systems can improve both the decision-making process and patient outcomes. Additionally, Li et al. (2020) discussed the dual-process theory, which emphasizes the interplay between intuitive and analytical thinking in decision-making. Decision support systems can support both cognitive processes by offering quick access to data for intuitive decisions and detailed analysis for more deliberate decisions. Further, Jamieson et al. (2023) introduce the concept of bounded rationality, which recognizes the limitations of human cognitive capacity. Decision support systems can complement these limitations by processing large amounts of information and presenting the most relevant data to the clinician.

Support

The key strength of such research findings is that they illustrate the effectiveness of decision support systems in improving clinical decisions. However, such constraints include possible over-dependence on technology and clinicians having to think through their interventions. Also, there are some limitations to the studies: lack of information about the effect of such systems on the clinician and patient outcomes in the long term and the requirement for focusing on integrating such systems into practice to not interfere with the delivery of care.

Methodological Principles

Strategies and Techniques

To address the problem identified above, the use of decision support systems integrated with AI capabilities for mental health settings is proposed. These systems should not overshadow human decision-making; instead, they should present clinicians with sufficient tailored information that underpins intuitive and rational decision-making. Here are nine appropriate, socially responsible strategies and techniques for improving human cognitive processes that apply to an applied setting:

1. Implementing AI-Driven Decision Support Systems: These systems can analyze large datasets to provide clinicians with real-time, evidence-based recommendations, enhancing their decision-making processes.
2. Providing Comprehensive Training Programs: Ensuring mental health professionals receive thorough training on the effective use of decision support systems can improve their ability to integrate these tools into their practice.
3. Encouraging Collaborative Decision-Making: Promoting teamwork and interdisciplinary collaboration among healthcare providers can lead to more holistic and well-rounded decision-making, benefiting from diverse perspectives.
4. Utilizing Cognitive Behavioral Techniques: Incorporating techniques from cognitive behavioral therapy (CBT) into training programs can help clinicians manage cognitive biases and improve their critical thinking skills.
5. Regularly Updating Decision Support Systems: Keeping these systems up-to-date with the latest research and clinical guidelines ensures that they provide the most current and accurate information to support clinical decisions.
6. Implementing Feedback Mechanisms: Establishing systems for regular feedback from clinicians on the effectiveness and usability of decision support tools can help continuously improve these systems and better meet the needs of healthcare providers.
7. Promoting Ethical Use of AI: Ensuring that AI-driven decision support systems are used ethically, respecting patient privacy and autonomy, and avoiding potential algorithm biases.
8. Supporting Continuous Education: Encouraging ongoing professional development and education for mental health professionals can help them stay current with advancements in cognitive psychology and decision-support technologies.
9. Enhancing Patient-Clinician Communication: Using decision support systems to facilitate better communication between patients and clinicians can lead to more informed and shared decision-making, improving patient outcomes.

Notably, these strategies can assist in triaging patients by acuity level, recommending proper treatments based on condition, and indicating possible complications from patients’ medical history and current conditions. The achievement of these strategies would have several ramifications. Firstly, educating clinicians on how to use the new tools would be important as a follow-up to assimilate the tools into the practice. It is also possible that the workers resist changes. Therefore, appropriate communications, training, and encouragement must be provided to show that the implemented systems are for their benefit. The effect on patients is also profound, as better decision-making equals improving the final results. However, a question arises: How can we avoid losing humanity in healthcare?

Conclusion

AI decision support systems in mental health can improve decision-making and patient care by offloading cognitive burdens, managing biases, and providing decision-makers with valuable information. These systems enhance both heuristic and systematic processing and improve the credibility and speed of clinical choices. Still, it is imperative to develop these tools to augment human decision-making and provide ongoing updates based on the current guidelines. Effective integration and training programs to note ethical issues like patient privacy or algorithm bias are critical.

Further studies should assess the long-term perspectives and effectiveness of these systems in different mental health contexts for clinicians and patients. At the same time, expanding the potential of these approaches to other fields of healthcare may uncover other advantages. In general, AI decision support systems represent significant potential for enhancing mental health practice as long as technology is implemented with and not instead of the human element.

References

Bellini-Leite, S. C. (2022). Dual process theory: Embodied and predictive; symbolic and classical. Frontiers in Psychology, 13(13). https://doi.org/10.3389/fpsyg.2022.805386

Ee, C., Lake, J., Firth, J., Hargraves, F., Manincor, M. de, Meade, T., Marx, W., & Sarris, J. (2020). An integrative, collaborative care model for people with mental illness and physical comorbidities. International Journal of Mental Health Systems, 14(1). https://doi.org/10.1186/s13033-020-00410-6

Featherston, R., Downie, L. E., Vogel, A. P., & Galvin, K. L. (2020). Decision-making biases in the allied health professions: A systematic scoping review. PLOS ONE, 15(10), 1–15. https://doi.org/10.1371/journal.pone.0240716

Jamieson, M. K., Govaart, G. H., & Pownall, M. (2023). Reflexivity in quantitative research: A rationale and beginner’s guide. Social and Personality Psychology Compass, 17(4), 1–15. https://doi.org/10.1111/spc3.12735

Li, P., Cheng, Z. yan, & Liu, G. lin. (2020). Availability bias causes misdiagnoses by physicians: Direct evidence from a randomized controlled trial. Internal Medicine, 59(24), 3141–3146. https://doi.org/10.2169/internalmedicine.4664-20

McCutcheon, R. A., Keefe, R. S. E., & McGuire, P. K. (2023). Cognitive impairment in schizophrenia: Aetiology, pathophysiology, and treatment. Molecular Psychiatry, 28(5), 1–17. https://doi.org/10.1038/s41380-023-01949-9

Morozova, A., Zorkina, Y., Abramova, O., Pavlova, O., Pavlov, K., Soloveva, K., Volkova, M., Alekseeva, P., Andryshchenko, A., Kostyuk, G., Gurina, O., & Chekhonin, V. (2022). Neurobiological highlights of cognitive impairment in psychiatric disorders. International Journal of Molecular Sciences, 23(3), 1217. https://doi.org/10.3390/ijms23031217

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We’ll write everything from scratch

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For your rough draft, you will submit a complete proposal that includes all the required elements of the final proposal and incorporates any relevant instructor feedback you received on Milestones One and Two.

In your rough draft, be sure to address all the following critical elements:

1. Problem Statement

1. Describe the contemporary problem that is the focus of your proposal with full details with respect to your selectedapplied setting. Here, consider how new developments orchanges in your applied setting are creating newcognition-related challenges. For instance, you might notethat increased use of online education is presenting newchallenges to students with ADHD.

2. Identify your selected area of cognitive psychology(attention, learning, memory, language, decision-making) and appropriate foundational theories that apply to your selected problem. What are the foundational aspects of these theories, and how do they relate to the selected problem? Carrying through with the previous exayou would indicate that your area of focus is to identify related theories that can shed further the contemporary problem of attention deficit hyperactivity ADHD.

3. Describe performance issues in your applied setting based on limitations of human cognitive systems. What are some of the specific issuesyour contemporarycontemporary problem, the applied setting, and the limits of the human cognitive system? Here, you will further break down your contemporary problem and explain how the problem relates to the applied setting, what we know about our cognition, and how this impacts performance.

4. Create a research question that addresses potential improvements in practices in the applied setting based on the strengths of human cognitive systems. Remember that your research question should address your contemporary problem. For instance, in keeping withthe previous examplee, you might ask, “How can changes online learninging platforms better support increased attentto courseurse materials for students with ADHD?”

2. Contemporary Relevance

1. Evaluate the utility of the theories you identify when describing your perception of the strengths and limitations. Here, revisit the theories you noted in critical element I, part b. How do the theories you identified further explain the problems and performance issues you identified? What are the strengths and limitations of each theory in helping to understand your identified problem?
2. Which particular theory offers the greatest utility forpractitioners to apply in addressing real-world issuesspecific to the contemporary problem you selected?

Defend your selection.
3. Interpretation of Research Findings: Explain how each primary or secondary resource you selected supports your research question. This is where you will apply sound methodological
principles (by following the prompts below a b) to qualify the research results and statistical findings.
1. How do the research results and statistical findings apply to your research question and your proposed improvements?
2. Explain the strengths and limitations of the research results and findings in supporting the research question. This is where you will explain how the research results and findings you have reviewed support your research question
and identify specific gaps in the research. In other words, in reviewing your sources, is there sufficient support for this research question? This is also where you will identify what research does not yet exist that is necessary to support the application of your research question.
4. Methodological Principles: This is where you will look at your research question (critical element I, part d) and determine what
types of strategies or techniques you would use if you were to hypothesize improving upon the problem in your selected applied setting. Here, you might propose an experiment, a new program or initiative, or adoption of new tools/technologies. Remember, you are not limited to a controlled experiment.
1. What socially responsible strategies and techniques could be used for improving upon human cognitive processes
specific to your applied setting? Here, consider how you could implement your proposed solution in a way that does not further aggravate the problem or put participating parties at risk of new problems or performance issues.
2. What are the implications for using these strategies and techniques? Consider who and what the applied
setting would be impacted by this proposed solution? What would change, and how might these changes be received?

5. Conclusion
1. What potential future direction do you see from the implementation of your research specific to addressing the
contemporary problem you cited in critical element I, part a? Here, consider how the implementation of your proposed solution or improvement can add to the existing body of research on your topic. How might your proposed improvements and any follow-up research prove interesting to other applied settings?

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