Background
Decision-making is crucial for safe and effective healthcare practice. It is required to promote the best clinical and public outcomes—cures and optimum healing that are efficient, cost effective, and patient-centered. Inappropriate or uncertain decisions result in low-quality care and delay best-evidence implementation. However, decision-making uncertainty is not always caused by lack of evidence; it is increasingly caused by challenges posed by multiple options with similar comparability and efficiency as tremendous advancements in medicine have increased the number of options and their complexity. The value of these options varies in different contexts, among different patients or populations, and according to decision-makers’ abilities and values.
In the last few decades, evidence-based medicine (EBM) has facilitated decisions based on the integrating of research evidence, personal experience, and patients’ preferences. Nevertheless, EBM has been challenged for being misappropriated by vested interests and being less patient-centered, having a large information load, less clarity on options’ clinical significance, and guidelines that are difficult to tailor to complex multi-morbidities. 1 For EBM to overcome these challenges, more efforts must be exerted to form a junction between knowledge and practice. Since an EBM question has a singular answer to the question about an option’s efficiency/effectiveness, applying it in practice necessitates determining the value of the option, which may require many questions being answered in multiple domains to make a decision. Therefore, decision-making is the junction and must be studied and improved for better outcomes.
Unlike in other disciplines, the process of choosing from among multiple decision alternatives has not been well studied in healthcare literature. Kelly et al. 2 highlighted the need to make values explicit, explore them systematically, and integrate them into decision-making, since values are integral to the practice of EBM. According to them, the science of EBM focuses primarily on methods for reducing bias in the evidence, while the role of values in different aspects of the process has been almost completely ignored. Thus, this review proposes a meta-decision framework as an attempt to contribute to this field. The main aim of this framework is planning and determining how to make decisions through a hierarchical structure formation comprising three distinct steps suggested by Simon. 3Simon 4 stated that “when the problem is simple or when the situation is static, the approaches available to rational decision-makers are acceptable, but the same cannot be said when the situations are dynamic, complex, and involving uncertainties.”
In this adapted framework of meta-decision in healthcare, it is suggested that “value” is based on the well-described and identified criteria of the important outcomes in healthcare—the triple aim. It was hypothesized as an approach to facilitate the planning of interventions and ensuring cost effectiveness and patient-centered care. However, achievement of one aim should not come at the expense of the remaining two aims. Berwick et al. 5 argued for addressing these dimensions simultaneously to deliver the desired outcomes. Nevertheless, despite many reports of successful Triple Aim implementations, commonly used measures often differ and fail to capture all its domains. 6–8.
With data increasingly being generated through the wide use of electronic health records in healthcare at the patient care and healthcare management levels, and for financing and quality improvement with instant processing at point-of-care. It is anticipated that the study of meta-decisions in healthcare will be of assistance in this field through the identification of gaps and suggestion of solutions9. Additionally, this approach will allow for generating knowledge on managing artificial intelligence data. Here, the initiation starts with a need for meta-decision frameworks that when applied can result in efficient processing and better outcomes, avoiding blind datamining and unstructured data management. This approach can be automated in steps following the meta-decision processes with interruptions and cycles as needed.
Finally, it is necessary to conduct studies that help in understanding the dynamic aspects of decision-makers’ behavior during the decision-making process. 10 Many meta-decision case studies in healthcare reporting their success and failure can help promote learning and knowledge transfer for practice and facilitate use of the best evidence.
This review discusses the conceptualization relevance and application of meta-decisions in healthcare, with emphasis on the prerequisites of the decision-maker, growing demand for detailed measurements, and the context of meta-decisions’ implementation, along with examples.