Improving Decision-Making for Nurses with Visualizing Taken Decisions

Introduction

In hospitals, where every second is crucial, making the right decision can be a matter of life and death. From emergency departments to intensive care units, healthcare professionals face a constant stream of complex decisions. The stakes are high, and the pressure is immense. However, a new approach leveraging decision log data could help refine and enhance decision-making processes in healthcare.

The Challenge of Modern Healthcare

Today’s hospitals are loaded with information. Clinicians deal with data from patient records, monitoring devices, and clinical decision support systems (CDSSs). While these systems are designed to assist, they often add to the complexity, leading to alarm fatigue—a condition where the sheer number of alarms desensitises healthcare providers, reducing their response rates and potentially compromising patient safety.

Alarm fatigue is just one symptom of a larger issue. Despite advances in technology, inaccuracies in decision-making remain a significant challenge. The root of the problem lies in the integration and utilisation of data. Adding more systems or alarms hasn’t provided the desired improvements. Instead, there’s a growing need to optimise existing systems and make better use of the data already available.

A New Approach: Mining Decisions from Data

Rather than complicate things further, we are exploring a different path: mining existing decision log data. This approach involves retrospectively analysing the data stored in hospital systems to uncover patterns and insights that can improve decision-making.

At the heart of this innovative solution is the fuzzy classifier algorithm, designed to discover and visualise decisions from CDSS logs. By employing the Decision Model and Notation (DMN) standard, the algorithm can identify key decisions made by healthcare professionals and represent them in a clear, understandable (business standard) format.

Enhancing Decision-Making with Visual Feedback

One of the most noteworthy aspects of this approach is the use of the DMN standard for visualising decisions. DMN provides a common notation that makes complex decision processes easy to understand for both technical and non-technical stakeholders.

Consider a decision log from an emergency department. It captures data like patient ID, symptoms, vital signs, and the resulting treatment. The fuzzy classifier algorithm sifts through this information, discovering that certain combinations of symptoms and vitals consistently lead to specific treatments. These insights are then visualised using DMN, creating decision tables and decision requirements diagrams (DRDs).

For example, a decision to administer nitroglycerin to a patient with chest pain and high blood pressure can be illustrated in a decision tree, outlining the logical steps leading to that treatment. This visualisation helps healthcare professionals understand the rationale behind decisions, aids in training new staff, and supports the periodic evaluation of clinical protocols.

The Feedback Loop: Continuous Improvement for Nurses

The visualisation of decision logs using DMN not only clarifies the decision-making process but also establishes a feedback loop for continuous improvement. Nurses and other healthcare professionals can review these visual models to understand how past decisions were made, identify any deviations from standard protocols, and refine their decision-making processes.

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Improving Decision-Making for Nurses with Visualizing Taken Decisions