Healthcare scheduling in optimization context: a review

Abdalkareem, Zahraa Adnan; Amir, Amiza; Al-Betar, Mohammed Azmi; Ekhan, Phaklen; Hammouri, Abdelaziz I.

doi:10.1007/s12553-021-00547-5

Cited by 59 publications

(23 citation statements)

References 126 publications

(148 reference statements)

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“…( 2) to ( 4) is as follows. Eq (2) states that the best solution is the solution whose fitness score is the lowest one (minimization). Eq.…”

Section: 𝑥 𝑏𝑒𝑠𝑡 = 𝑥 ∈ 𝑋| Min(𝑓(𝑥))mentioning

confidence: 99%

“…Then, they become the objective of any optimization work to find the most efficient way to achieve any goal using limited resources. Due to its characteristics, optimization has become a popular process in many studies in operations research, such as manufacturing [1], health care [2], transportation [3], education [4], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in the vehicle routing problem, vehicles should operate within a specific range of operational time [10], and goods loaded cannot surpass a particular maximum capacity [3]. The other example in the healthcare system is that all patients should be served within the given rooms [2] or nurses [2]. Besides operations research, optimization is also used in other sectors, such as telecommunication [11], energy [12], image processing [13], and so on.…”

Section: Introductionmentioning

confidence: 99%

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https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

2022

IJIES

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This work proposes a new metaheuristic algorithm: a fixed-step average and subtraction-based optimizer (FS-ASBO). This algorithm is the improved version of the average and subtraction-based optimizer (ASBO). There are several improvements related to the original ASBO. First, the proposed algorithm replaces the randomized step size in the guided movement with the fixed step size. Second, the proposed algorithm adds an exploration mechanism after the guided movement in every iteration when the new candidate fails to find a better solution. This proposed algorithm is then implemented into a simulation to evaluate its performance. Through simulation, the proposed algorithm is challenged to solve theoretical optimization problems and real-world optimization problems. The 23 well-known benchmark functions represent the theoretical optimization problem. Meanwhile, the housing optimization problem represents the real-world one. In the simulation, the proposed algorithm is compared with particle swarm optimization (PSO), marine predator algorithm (MPA), Komodo mlipir algorithm (KMA), static Komodo algorithm (SKA), and ASBO. The result shows that this proposed algorithm is competitive to solve theoretical problem and superior to solve real-world problem. The proposed algorithm outperforms all sparing algorithms in solving seven functions. In housing optimization problem, it creates 12%, 10%, 8%, 11%, and 10% better total gross profit than the ASBO, PSO, MPA, KMA, and SKA.

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“…( 2) to ( 4) is as follows. Eq (2) states that the best solution is the solution whose fitness score is the lowest one (minimization). Eq.…”

Section: 𝑥 𝑏𝑒𝑠𝑡 = 𝑥 ∈ 𝑋| Min(𝑓(𝑥))mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

2022

IJIES

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“…A system to adjust the insurance rates in real-time according to the change in character traits of the user is designed in [22]. The cost optimization issue is addressed in [23], that provides a comprehensive review of existing studies and analyzed research studies based on healthcare cost optimization problems. The impact of artificial intelligence on the insurance industry is presented in [24].…”

Section: Related Workmentioning

confidence: 99%

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

Matloob¹,

Khan²,

Hussain³

et al. 2021

Applied Sciences

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The paper presents a novel methodology based on machine learning to optimize medical benefits in healthcare settings, i.e., corporate, private, public or statutory. The optimization is applied to design healthcare insurance packages based on the employee healthcare record. Moreover, with the advancement in the insurance industry, it is rapidly adapting mathematical and machine learning models to enhance insurance services like funds prediction, customer management and get better revenue from their businesses. However, conventional computing insurance packages and premium methods are time-consuming, designation specific, and not cost-effective. During the design of insurance packages, an employee’s needs should be given more importance than his/her designation or position in an organization. The design of insurance packages in healthcare is a non-trivial task due to the employees’ changing healthcare needs; therefore, using the proposed technique employees can be moved from their existing package to another depending upon his/her need. This provides the motivation to propose a methodology in which we applied machine learning concepts for designing need-based health insurance packages rather than professional tagging. By the design of need-based packages, medical benefit optimization which is the core goal of our proposed methodology is effectively achieved. Our proposed methodology derives insurance packages that are need-based and optimal based on our defined criteria. We achieved this by first applying the clustering technique to historical medical records. Subsequently, medical benefit optimization is achieved from these packages by applying a probability distribution model on five years employees’ insurance records. The designed technique is validated on real employees’ insurance records from a large enterprise.The proposed design provides 25% optimization on medical benefit amount compared to current medical benefits amount therefore, gives better healthcare to all the employees.

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“…18 It may also encompass active development of patient-facing chat bots, 19 augmented reality surgical visualization tools, 20 surgical robotics, 21 and hospital scheduling systems. 22 Natural language processing-and ML algorithms are increasingly used to automate CDSS, and their EHR integration is expected to enhance their adoption into clinical workflows. 23 Given the broad potential application of AI in oncology, this review will focus on the technologies and algorithms that directly support the care of patients with cancer.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

Shreve

Khanani

Haddad

2022

American Society of Clinical Oncology Educational Book

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The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field. Cumulative advances across the science are bringing this promise to realization, including refinement of machine learning– and deep learning algorithms; expansion in the depth and variety of databases, including multiomics; and the decreased cost of massively parallelized computational power. Examples of successful clinical applications of AI can be found throughout the cancer continuum and in multidisciplinary practice, with computer vision–assisted image analysis in particular having several U.S. Food and Drug Administration–approved uses. Techniques with emerging clinical utility include whole blood multicancer detection from deep sequencing, virtual biopsies, natural language processing to infer health trajectories from medical notes, and advanced clinical decision support systems that combine genomics and clinomics. Substantial issues have delayed broad adoption, with data transparency and interpretability suffering from AI’s “black box” mechanism, and intrinsic bias against underrepresented persons limiting the reproducibility of AI models and perpetuating health care disparities. Midfuture projections of AI maturation involve increasing a model’s complexity by using multimodal data elements to better approximate an organic system. Far-future positing includes living databases that accumulate all aspects of a person’s health into discrete data elements; this will fuel highly convoluted modeling that can tailor treatment selection, dose determination, surveillance modality and schedule, and more. The field of AI has had a historical dichotomy between its proponents and detractors. The successful development of recent applications, and continued investment in prospective validation that defines their impact on multilevel outcomes, has established a momentum of accelerated progress.

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Healthcare scheduling in optimization context: a review

Cited by 59 publications

References 126 publications

https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

https://inass.org/wp-content/uploads/2022/05/2022083131-2.pdf

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations

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