Endocrinology
January 1, 2022
Baidu, Inc.
January 1, 2022Heuristic Evaluation
Clinical decision support implies giving clinicians knowledge and patient-related information intelligently filtered and presented at adequate times to improve patient care outcomes. This is widely used but often ineffective due to poor usability.
The use of a good search algorithm is often a critical factor in the performance of an intelligent system. As with most areas of AI, there has been steady progress in the heuristic field research over the years. There have been advancements in finding effective solutions to larger problems, giving quality decisions to fixed-size problems, evaluating more complex domains that include dynamic fields with incomplete and uncertain information, being able to analyze and predict the performance of heuristic search algorithms, and the increasing deployment of real-world applications, especially in the biomedical and healthcare domain.(1)
Clinical decision support implies giving clinicians knowledge and patient-related information intelligently filtered and presented at adequate times to improve patient care outcomes. This is widely used but often ineffective due to poor usability.
The use of a good search algorithm is often a critical factor in the performance of an intelligent system. As with most areas of AI, there has been steady progress in the heuristic field research over the years.
There have been advancements in finding effective solutions to larger problems, giving quality decisions to fixed-size problems, evaluating more complex domains that include dynamic fields with incomplete and uncertain information, being able to analyze and predict the performance of heuristic search algorithms, and the increasing deployment of real-world applications, especially in the biomedical and healthcare domain.(1)
Heuristics in Artificial Intelligence
The area of computer science still has quite a few open and unsolved problems, one of them being the use of heuristics to solve optimization problems. A heuristic in artificial intelligence can be defined as any device, be it a program, rule, data structure, or piece of knowledge which one is not entirely confident will provide a practical solution, but has a reason to believe it will serve the purpose. Therefore it is added to a problem-solving system with the hope that, on average, the performance will ameliorate.
A Heuristic Algorithm aims to find a certain result of X that maximizes “f” (or the profit) utilizing a heuristic function. The result that maximizes “f” will be the adequate solution to the optimization problem. A heuristic makes one or more modifications to a given solution or result to obtain a different alternative, which is either superior or leads to a superior one. A Heuristic Function estimates (or measures) the cost of the solution at a particular state in the search process.
The area of computer science still has quite a few open and unsolved problems, one of them being the use of heuristics to solve optimization problems. A heuristic in artificial intelligence can be defined as any device, be it a program, rule, data structure, or piece of knowledge which one is not entirely confident will provide a practical solution, but has a reason to believe it will serve the purpose. Therefore it is added to a problem-solving system with the hope that, on average, the performance will ameliorate.
A Heuristic Algorithm aims to find a certain result of X that maximizes “f” (or the profit) utilizing a heuristic function. The result that maximizes “f” will be the adequate solution to the optimization problem. A heuristic makes one or more modifications to a given solution or result to obtain a different alternative, which is either superior or leads to a superior one. A Heuristic Function estimates (or measures) the cost of the solution at a particular state in the search process.
Heuristic Evaluation
In systems engineering, heuristics are defined as systematically designed procedures that do not guarantee an optimal solution but provide near-optimal solutions. They represent broad rules of thumb to achieve an optimal design.
Heuristic evaluation is frequently used in human-computer interaction studies to analyze the usability of information systems.(2)
The approach is an inspection method of evaluation, where experts or evaluators use defined criteria to evaluate the interface and provide feedback on potential problems (apply a set of usability heuristics to an item, identify the heuristic violations, and assess the severity of each one). It is frequently employed as a method to evaluate usability due to its low cost and simplicity in approach. It can assign a severity rating from no problem through to catastrophic issues.
In systems engineering, heuristics are defined as systematically designed procedures that do not guarantee an optimal solution but provide near-optimal solutions. They represent broad rules of thumb to achieve an optimal design. Heuristic evaluation is frequently used in human-computer interaction studies to analyze the usability of information systems.(2)
The approach is an inspection method of evaluation, where experts or evaluators use defined criteria to evaluate the interface and provide feedback on potential problems (apply a set of usability heuristics to an item, identify the heuristic violations, and assess the severity of each one). It is frequently employed as a method to evaluate usability due to its low cost and simplicity in approach. It can assign a severity rating from no problem through to catastrophic issues.
Nielsen Principles
Jakob Nielsen elaborated ten general principles for human-computer interaction design. These principles are recognized in the world of machine modeling and design, as well as by healthcare researchers as the gold standard of heuristic evaluation.
Future Perspectives
It is agreed that the ten heuristic principles identified by Nielsen are adequate for evaluating the usability of a general system and user interface issues. Nevertheless, their appropriateness has been questioned when it comes to systems that use visualizations. For graphic user interfaces (charts, clinical data, biological graphs), the implementation of heuristics in a model that helps clinical decision support remains a challenge.
Kientz’s heuristics reveal the need to consider the user’s emotional perspective when evaluating the product’s ability to change user behavior.
As we can see, there are many pitfalls regarding the use of heuristics. This means that research has a long road ahead to develop methods that will accurately guide researchers’ and clinicians’ decision-making.
It is agreed that the ten heuristic principles identified by Nielsen are adequate for evaluating the usability of a general system and user interface issues. Nevertheless, their appropriateness has been questioned when it comes to systems that use visualizations. For graphic user interfaces (charts, clinical data, biological graphs), the implementation of heuristics in a model that helps clinical decision support remains a challenge.
Kientz’s heuristics reveal the need to consider the user’s emotional perspective when evaluating the product’s ability to change user behavior. As we can see, there are many pitfalls regarding the use of heuristics. This means that research has a long road ahead to develop methods that will accurately guide researchers’ and clinicians’ decision-making.