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.