Dublin Business School Automated Insurance Claims and Health Report

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Automated Insurance Claims and Health and Wellbeing of Patients with Chronic Illnesses. Student’s Name Supervisor’s Name Date May 2021 Declaration I declare that this research is my original work and that it has never been presented to any institution or university for the award of Degree. In addition, I have referenced correctly and all literature and sources used in this work and this work is fully compliant with the Dublin Business School’s academic honesty policy. Signature: Date: Acknowledgements I would like to express my deepest gratitude to my supervisor, for his expertise, guidance and encouragement throughout this dissertation. Dublin Business School Automated Insurance Claims and Health Report. His continuous feedback, patience and support assisted me throughout this experience and I am very grateful for his effort in supervising me. I would like to thank my family and friends for their support during the process of this thesis and my course for the past two years. The completion of my MBA and thesis would not have been possible without their ongoing support. I would also like to express my gratitude to my classmates and lecturers throughout this course as they were all incredibly kind and helpful, whom I learned a lot from. 3 Contents Introduction …………………………………………………………………………………………………………………… 6 Background …………………………………………………………………………………………………………………………….. 6 Effects of Health Insurance on Health ………………………………………………………………………………………… 9 Aims and Objectives…………………………………………………………………………………………………………………. 9 Significance of the Proposed System Dublin Business School Automated Insurance Claims and Health Report ………………………………………………………………………………………… 11 Advantages and Disadvantages of the System …………………………………………………………………………… 11 Issues Arising in Automated Health Insurance …………………………………………………………………………… 12 Methodology………………………………………………………………………………………………………………… 12 Overall Research Approach……………………………………………………………………………………………………… 13 Chronic Disease Care and Outcomes ………………………………………………………………………………………… 13 Healthcare Outcomes …………………………………………………………………………………………………………….. 14 Automated Insurance Claim Management System …………………………………………………………………….. 14 Tools and techniques used for collecting data …………………………………………………………………………… 15 Demographics ……………………………………………………………………………………………………………………….. 15 Automated Insurance Claim Management System …………………………………………………………………….. 15 Market Validation ………………………………………………………………………………………………………………….. 16 Increasing Administrative Efficiency …………………………………………………………………………………………. 16 Steps to ensure the validity …………………………………………………………………………………………………….. 16 Data Analysis …………………………………………………………………………………………………………………………. 17 Discussion…………………………………………………………………………………………………………………….. 18 Theme one: Background of the Study ……………………………………………………………………………… 19 Theme Two: Risk and Insurance ……………………………………………………………………………………… 20 Theoretical Foundations …………………………………………………………………………………………………………. 21 Expected Utility Theory ……………………………………………………………………………………………………….. 21 Neo-Classical Welfare Economic Theory ……………………………………………………………………………….. 21 Medical Insurance Underwriting ………………………………………………………………………………………….. 22 Theme three: Promotion of Maximum Healthcare Outcomes ……………………………………………. 24 Theme four: Risk Mitigation and Technical Capabilities……………………………………………………… 27 Proposed Automated Technology Solution ………………………………………………………………………. 29 Results …………………………………………………………………………………………………………………………. 32 4 Conclusion Dublin Business School Automated Insurance Claims and Health Report ……………………………………………………………………………………………………………………. 33 Recommendation………………………………………………………………………………………………………….. 34 Ethical Considerations……………………………………………………………………………………………………. 34 Informed Consent …………………………………………………………………………………………………………. 34 Privacy …………………………………………………………………………………………………………………………. 35 The anonymity of Questionnaire Data………………………………………………………………………………………. 35 De-identifying Interview Data Dublin Business School Automated Insurance Claims and Health Report ………………………………………………………………………………………………….. 35 Conflicts of Interest ……………………………………………………………………………………………………….. 35 References …………………………………………………………………………………………………………………… 36 Appendix ……………………………………………………………………………………………………………………… 38 5 Introduction Background Leaders in the healthcare sector have been facing unprecedented series of increasingly critical issues across revenue, quality, and cost. As a result, they embrace artificial intelligence with the hope it will deliver a faster turnaround with minimal margin error. Indeed, as a megatrend, artificial intelligence has played a robust role across various sectors over the past few decades. The complexity and rise of data in healthcare prove that artificial intelligence will increasingly be instituted within this field (Davenport & Kalakota, 2019). The fundamental categories of application involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities (Davenport & Kalakota, 2019). Therefore, artificial intelligence can transform patient care and administrative processes within the provider and payer (Davenport & Kalakota, 2019). Various studies suggest that artificial intelligence can perform better than humans, such as in diagnosis. For example, today, algorithms are outperforming radiologists in identifying malignant tumours (Davenport & Kalakota, 2019). At health insurers, artificial intelligence has the potential to strengthen claims management by systematically identifying and correcting errors by curbing ineffective interventions. The need for better and effective management of chronic diseases is urgent. This is because, as of 2010, approximately 141 million individuals in the United States were living with one or more chronic diseases (Mattke, Mengistu, Klautzer, Sloss, & Brook, 2015). Additionally, the number is projected to increase to 171 million by 2030. Therefore, unless these chronic diseases are addressed effectively and efficiently, the implication of these numbers for morbidity and mortality, workplace, productivity, and health care costs in the coming decades will be overwhelming. For example, it is projected that the number of diabetic persons will rise to approximately 42 million by 2034 (Mattke, Mengistu, Klautzer, Sloss, & Brook, 2015). Additionally, related healthcare spending will increase to $336 billion (Mattke, Mengistu, Klautzer, Sloss, & Brook, 2015). Similarly, the American Heart Association projects that by 2030, 40 per cent of the U.S. population will have some form of cardiovascular disease (Mattke, Mengistu, Klautzer, Sloss, & Brook, 2015). As a result, the related health care costs will triple from the current $273 billion to $818 billion (Mattke, Mengistu, Klautzer, Sloss, & Brook, 2015). Moreover, productivity losses attributed to chronic conditions are projected to increases to $3.4 trillion (Mattke, Mengistu, Klautzer, Sloss, & Brook, 2015). To handle this challenge, there is a need for health care facilities to roll out innovative approaches to improve care for patients with chronic diseases. One of these approaches is by instituting fast processing of payments for insurance claims and the mitigation of potential security risks. Indeed, the automation of insurance claims is inevitable. This is because it ensures 6 streamlining of healthcare payments. It has the capability to promote interaction between machines and healthcare professionals. Timely processing and disbursement of insurance claims promote better healthcare outcomes for the patients. Delivery of quality care depends on the efficiency of insurance frameworks within hospital settings. Barefoot (2020) determined that automation of insurance disbursement frameworks promotes fast decision-making and reduces potential errors. Therefore, the integration of automated healthcare systems ensures that patients receive their insurance disbursements faster and more securely. There are also concerns of security breaches during the disbursement and processing of insurance healthcare claims. Fraudulent activities make disbursement processes unreliable and delay the overall discharge duration. Thus, the adoption of technology is crucial since it is likely to facilitate real-time monitoring of disbursement processes (Wójcik , 2020). Insurance packages are also becoming more complex in the contemporary world; therefore, automation is required to ensure that insurance companies can disseminate information on the best packages. Thus, insurance firms can ensure the provision of packages that meet client requirements. Employee responsibilities become synchronized within the system so that there is fast support desk assistance. Claim assessors, agents, and brokers can foster teamwork to ensure the fast procession of customer claims. The claim support environment becomes characterized by interactive bots used to respond to customers’ claims effectively. Visual analysis is also possible through AI-based insurance systems; thus, promoting the usage of videos and other multimedia to foster quality customer support. Automated insurance claim systems will also enable the adoption of the internet of things (IoT), such as positing geographical systems (GPS) to enable effective service delivery (Wójcik , 2020). The tracking tools will also enable the underwriting of algorithms to ensure that appropriate premium allocation is specific to patients. The phenomenon ensures predictive analysis that is used to determine the best solutions that should be adopted to ensure the delivery of quality healthcare to the patients. Additionally, healthcare organizations face challenges of streamlined informational flow due to less automation of insurance claim systems. The information environment is everchanging in healthcare organizations that affect organizational efficiency. AI-based technologies ensure the integration of unsynchronized frameworks and other healthcare productive channels to leverage communication capacities for hospitals. Healthcare organizations also experience handling big data relating to insurance claims. Most data within these organizations are clusters and scattered, preventing effective utilization of data. Healthcare organizations position themselves nowadays to leverage their information capabilities to create strategic plans (Silvello & Procaccini, 2019). As a result, information can flow effectively within healthcare departments and with other external stakeholders. Dublin Business School Automated Insurance Claims and Health Report. Automated insurance claim systems promote the management of healthcare tasks so that end-to-end data is devoid of errors. 7 The existing claim support systems in healthcare organizations are characterized by excessive human intervention. AI-based systems are touch-less and less immersive. Therefore, human intervention is obsolete when reporting claims, capturing healthcare interventions, updating patients’ records, and fostering digital communication (Davenport & Kalakota, 2019). The system enables healthcare management to harness its financial potentialities regarding filling claims, detecting fraudulent activities, and verifying policy details. Fraud detection algorithms ensure that patients’ information undergoes scrutiny before facilitating payments between the payers and hospitals’ banks. Bots within the system review claim to ensure that there are devoid of potential security risks. Payments become instantaneous due to the elimination of human engagement. Cost-management will become more efficient with the system’s adoption since it will foster cost-management healthcare organizations will not have to incur expenses related to human mistakes and inaccuracies. Furthermore, healthcare professionals experience challenges related to lengthy documents, complex insurance policies, and daunting instructions that require addressing to achieve competitive advantages. There is a negative perception among patients that insurance claim processes are often complex and take a lot of time. In this regard, customer satisfaction diminishes with time. Misleading perspectives taint the image of the entire framework, which aims to promote the healthcare outcomes of the patients. Automation-based claim systems present intelligent bots that offer virtual assistance to healthcare personnel. Virtual assistance is possible through the presentation of data and visual analytics that aid in decision-making. The AI-powered bots comprehend claim queries and offer semantics regarding the best tools (Wójcik , 2020). For instance, healthcare personnel can use their voices to acquire information regarding specific patients’ claim needs. Besides, patients can have an app extension that helps them monitor their insurance coverage. Dublin Business School Automated Insurance Claims and Health Report. Thus, they will have personalized information that will enable them to determine the capacities of their current coverage to cover their health or treatment conditions. Notably, patients can query about their policy issues from the customer support framework. In doing so, they can determine coverage areas that they need to adjust to avoid future issues. Integration of the system into social media tools such as Facebook, Slack, and Twitter ensures better engagement between patients and their insurance payers. All in all, there will explore positive relationships between customers and claim payers; therefore, demystifying certain perceptions about insurance claim processes. Indeed, the applicability of an opportunity on the insurers’ side is fantastic. The biggest breakthrough concerning the use of automated insurance claims for the well-being of patients with chronic conditions is in more sophisticated machine learning. This is majorly attributed to their ability to analyze data and leverage it to drive algorithms and being more predictive. Such opportunities extend beyond the field of insurance claims discussed above. Indeed, the potential spectrum of use cases for artificial intelligence is broad and varied. Therefore, artificial 8 intelligence is going to play a significant role in future health management. This justifies the need for health insurers and healthcare providers to take the opportunity of this position and position themselves at the crest of the wave. This will help them manoeuvre into a good position to handle the mounting challenges in the healthcare sector. Effects of Health Insurance on Health This chapter summarizes the Committee’s analysis of research on the effect of health benefits on multiple health-related outcomes. It investigates studies on the relationship between healthcare coverage (or lack thereof), utilization of preventive treatment, and health consequences for particular diseases and modes of facilities, as well as general health status and attrition. A body of research using various data sources and analytic methods has shown a clear, optimistic association between health insurance benefits and health-related results. According to the latest data, private insurance is correlated with more effective use of health care resources and improved health conditions for adults. The findings covered in this section are those which the Committee deemed to be methodologically reliable and the most insightful about the impact of health care on health-related results. Dublin Business School Automated Insurance Claims and Health Report. The majority of studies find a connection between health insurance benefits and positive results. This segment, however, presents and discusses all experiments with negative outcomes that contradict the Committee’s findings. These results have significant consequences for clinical conditions, as seen in the chapters on cancer and chronic illnesses that follow. For preventive and screening programs, health insurance encourages both the receipt of services and a continued care relationship or regular provider of care, which enhances the chance of providing adequate care. Preventive and monitoring programs are less likely to be covered by insurance than doctor appointments for acute treatment or medical testing for symptomatic diseases. Preventive and diagnostic programs, on the other hand, have seen an increase in coverage over time. In 1998, about threequarters of people with employer-based health plans had a coverage package that included adult physical examinations; two years later, in 2000, the percentage had increased to 90%. Particularly if health insurance coverage benefits do not provide prevention or screening programs, people with health insurance are much more likely to access these prescribed services since they have a reliable source of care, and providing a consistent source of care is generally correlated with accessing recommended services. Aims and Objectives One of the research questions is identifying how do automated insurance claim systems help promote healthcare outcomes and the well-being of patients with chronic illnesses. Also we will seek to understand what are the capabilities of automated insurance claim systems to reduce fraud, costs and ensure timely delivery of healthcare services. Thus, in the project aims to 9 examine the efficacies of automated insurance claim systems in promoting health outcomes for patients with chronic disorders (cancer and other emergencies). Achievement of the aim will entail a survey of healthcare organizations to determine how the adoption or non-adoption of automated insurance c…
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