Predictive Analytics to Support Clinical Decision Making: Opportunities and Directions

Predictive Analytics to Support Clinical Decision Making: Opportunities and Directions

Nilmini Wickramasinghe (Swinburne University of Technology, Australia & Epworth HealthCare, Australia)
DOI: 10.4018/978-1-7998-1371-2.ch020

Abstract

A key activity in healthcare is clinical decision making. This decision making typically has to be made rapidly and often without complete information. Moreover, the consequences of these decisions could be far reaching including the difference between life or death. Today analytics can assist in clinical decision making as the following chapter highlights. However, to gain the most from any type of analytics, it is first necessary to fully understand the dynamics around the clinical decision making process.
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Cdm Process

According to Hardy & Smith (2008), many psychologists have come up with numerous interpretations to define clinical decision making. However, Gillepse (2010) defines CDM as the reasoning practice of selecting another course of action in offering patient care concerning both clinical judgment and reasoning. This comprises of managing a range of data from diverse sources to come up with critical judgment. In CDM, physicians most precisely evaluate and realize deviancies from a standard medical depiction of healthiness or disease and make a choice in accordance with the information accessible (ibid).

With changes in the delivery of care-services, augmented patient insight, and improved responsibility in physicians’ choices, it is crucial to comprehend in what ways caregivers come up with medical choices and the key aspects that impact them (Hardy and Smith, 2008). Individuals within acute care settings have worse conditions that require more experienced clinical experts who will offer the utmost degrees of value-based care (ibid). This is predominantly accurate in acute precaution (ibid). In this environmental setting, choices are made with urgency and more frequently (Smith et al, 2008). An interruption in the decision making practice with such a setting can be a problem of survival or death (ibid). According to Stinson (2017), there are several key aspects of clinical decision making which influence the entire decision-making process. These include the experience, environment, education, and intuitions (ibid). Moreover, the experience aspect is vital when considering a CDM practice (ibid). It continues to be a critical provider even when determining medical choices (ibid).

Clinical decision making is an intricate practice that obliges nurses to be experienced and to have access to suitable data sources and work in a supportive environmental setting (ibid). As the health practitioner’s occupational role expands, they turn out to be accountable for a wider array of medical decisions (Kozlowski et al, 2017). Clinical decisions necessitate that physicians ought to be experienced and well informed in pertinent facets of nursing (ibid). Consequently, clinical decision making becomes less complicated and more manageable (ibid). Physicians have to decide what the issue is, what data to collect, how to handle the concern, if the therapeutic is efficient or whether the patient requires to have a clinical review for further examination and cure (Stinson, 2017).

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