Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

Abstract

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.

The application of artificial intelligence in health financing: a scoping review

“…AI methodologies can be applied in need-based and optimal insurance packages based on definite criteria. It will not only allow employers and insurance companies to design suitable insurance schemes for the provision of healthcare benefits but will also prevent financial losses in the long run [ 75 ]. Using clustering techniques in this field will also provide opportunities and solutions for decision-makers to monitor insurance coverage based on socioeconomic, geospatial, and demographic variables in general and health insurance in particular [ 76 – 78 ].…”

K-means clustering of outpatient prescription claims for health insured in Iran

“…The clustering method can be used in a variety of ways to identify health insurance fraud (19), from grouping patients based on their electronic medical records (20) and health insurance package that is optimized (21) as well as other cases. In the same context a speci c interest may be in focusing attention on groups or groups for which drug costs are rising rapidly.…”

“…Features of all data for all insureds in each cluster for a four-year period can be referred to demographic information, gender (male or female) and age (classi ed as [1][2][3][4][5][6][7][8][9][10], [10][11][12][13][14][15][16][17][18][19][20], [20][21][22][23][24][25][26][27][28][29][30], [30][31][32][33][34][35][36][37][38][39][40], [40][41][42][43][44][45][46][47][48][49][50], [50-60], and ≥ 60), main ( being householder or member of family), the total average Insurance pa...…”

Utilizing Machine Learning to Determine the Cost of Medical Insurance

“…"A Conceptual Review Paper on Health Insurance in India" -The paper reviews existing literature on health insurance in India to understand the growth and potential benefits of health insurance for the population. [5]. "Need-based and Optimized Health Insurance Package Using Clustering Algorithm" -This research proposes the use of clustering algorithms to design health insurance packages based on the specific needs of employees, aiming to provide optimized coverage.…”

“…It offers coverage against a range of risks, including fire accidents, earthquakes, floods, thefts, storms, travel accidents, and legal liabilities. Amongst these, health insurance holds particular importance as it ensures a secure and stable life by safeguarding against unexpected medical expenses that can disrupt financial stability and long-term goals [5]. Given the complexities of modern health challenges, planning for healthcare has become a necessity, leading to the availability of insurance plans for individuals and families.…”