Impact of claim settlement procedure of health insurance companies on customer satisfaction during the pandemic: A case of third-party administrators

  • Received July 25, 2022;
    Accepted October 6, 2022;
    Published November 14, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ins.13(1).2022.06
  • Article Info
    Volume 13 2022, Issue #1, pp. 66-80
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The claim settlement process is one of the most critical aspects of health insurance. Many policyholder grievances often surface during claim settlement, which will likely shape the insurer’s reputation. Hence, this study aims to evaluate the relationships between hypothesized factors concerning the third-party administrator’s claim settlement process as perceived by policyholders. The paper used the data of policyholders from Delhi/NCR, India, who have availed the cashless claims in the last three years. In the process, a total of 790 questionnaires were sought to be. The methodology used was the extractive factor analysis comprising the KMO test, reliability assessment with Cronbach’s Alpha, and correlation assessment. The study attempted to evaluate all the contributing factors that shape the third-party administrator’s behavior during the claim settlement. Therefore, different factors were identified (themes A, B, C, and D). The study reported a significant relationship between insurance company perceptions (0.162), network hospital perceptions (0.182), product design (0.180), insurance agent (0.332), communications (0.114), disclosure (0.122), internal practices (0.143), and TPA claim settlement prospects across the Indian perspective. The contextual impacts on individual and group decision-making must be monitored and accommodated across effective public policy management concerning settlement of health insurance claims. The study findings could help insurers create business models leading to better customer satisfaction and congruence between agents, policyholders, TPAs, and health insurance companies.

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    • Figure 1. Proposed conceptual model
    • Figure 2. Aggregate effect modeling: SEM
    • Figure 3. SEM model option-1
    • Table 1. Mapping the nature of determinants with dimensions
    • Table 2. Inclusion and exclusion criteria set
    • Table 3. Variance examination
    • Table 4. Causal path relationship: Aggregate
    • Table 5. Regression weights
    • Table 6. Model-fit indices
    • Table 7. Summary of regression outcomes
    • Table 8. Literary support for model
    • Conceptualization
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Data curation
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Formal Analysis
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Investigation
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Methodology
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Project administration
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Software
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Supervision
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Validation
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Visualization
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Writing – original draft
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar
    • Writing – review & editing
      Sunil Kadyan, Narinder Bhasin, Vikas Madhukar