SU+ @ Strathmore University Library Electronic Theses and Dissertations This work is availed for free and open access by Strathmore University Library. It has been accepted for digital distribution by an authorized administrator of SU+ @Strathmore University. For more information, please contact library@strathmore.edu 2024 Effects of payer-provider automation technologies on operational performance of medical insurance providers in Nairobi City County, Kenya. Muiru, Harrison Mugo Strathmore Business School Strathmore University Recommended Citation Muiru, H. M. (2024). Effects of payer-provider automation technologies on operational performance of medical insurance providers in Nairobi City County, Kenya [Strathmore University]. http://hdl.handle.net/11071/15549 Follow this and additional works at: http://hdl.handle.net/11071/15549 https://su-plus.strathmore.edu/ https://su-plus.strathmore.edu/ http://hdl.handle.net/11071/2474 mailto:library@strathmore.edu http://hdl.handle.net/11071/15549 http://hdl.handle.net/11071/15549 EFFECTS OF PAYER-PROVIDER AUTOMATION TECHNOLOGIES ON OPERATIONAL PERFORMANCE OF MEDICAL INSURANCE PROVIDERS IN NAIROBI CITY COUNTY, KENYA HARRISON MUGO MUIRU MBA 82005 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF BUSINESS ADMINISTRATION AT STRATHMORE UNIVERSITY BUSINESS SCHOOL 2024 ii DECLARATION I declare that this work has not been previously submitted and approved for the award of a degree by this or any other University. To the best of my knowledge and belief, the dissertation contains no material previously published or written by another person except where due reference is made in the dissertation itself. © No part of this dissertation may be reproduced without the permission of the author and Strathmore University Name of Candidate: Harrison Muiru APPROVAL The dissertation of Harrison Muiru was approved by the following: Name of Supervisor: Dr. Bernard Shibwabo School/Institution/Faculty: Strathmore University. Dr. Ceaser Mwangi Executive Dean Strathmore University Business School. Dr. Bernard Shibwabo Director, Office of Graduate Studies iii ABSTRACT In the face of rising competition in the health insurance sector, insurers are under increasing pressure to adapt and evolve their payer capabilities. The change towards a patient-first system, spurred by technological improvements and shifting customer expectations in the midst of medical inflation, has necessitated an examination of and transformation of insurers' operational. Health insurance providers are actively embracing payer-provider automated technologies to automate their operations, reduce administrative burdens, minimize errors, reduce risk and achieve significant improvements in operational efficiency. Despite the ongoing transformation in the insurance sector, there is limited empirical evidence on the effect of payer provider automation technologies on medical insurance operational performance. The aim of this dissertation was to bridge the identified empirical gap by investigating the effect of payer provider automation technologies on medical insurance providers operational performance in Kenya. In addressing this, the researcher observed the objectives of determining the effect of electronic data interchange platforms, claims processing automation systems, mobile patient apps and online portals payer provider automation technologies on medical insurance providers operational performance in Kenya. The beneficiaries of such a study span various stakeholders, including; the government, the health insurance sector players, the general public and academia. This study was anchored on three theories; the Diffusion of Innovation Model to highlight the stages via which technology diffuses through organizations and is eventually accepted to assist operational performance. The Technological Acceptance Model is used to explain the acceptance behavior of firms that embrace technology to enhance operational performance. Lastly, the Dynamic Capabilities Theory is instrumental in explaining and expounding on the dynamic nature organizations utilize both the external and internal resources to ensure optimal operational performance. Moreover, the study was based on a positivism research philosophy since it aligns with the core concepts and approaches that the study is anchored on. A correlational research design was used in this study. The study population comprised of forty-five (45) medical insurance providers in Kenya licensed by the Insurance Regulatory Authority as at December, 2022. The unit of observation was the senior management staff from different departments of the medical insurance providers. The departments in question are: claims, information technology, operations, general managers/ heads of departments and managing directors/executive officers. The sample size of the study was 244 senior management staff of the 45 medical insurance providers. As for data analysis and presentation, the data was analyzed using SPSS software. Descriptive and inferential statistics including correlation and multiple regression analysis was used to analyze quantitative data. The results of the regression analysis show that the effects of electronic data interchange, automation of the claims process, mobile patient applications, and online portals are statistically significant. Based on its findings, the study concluded that Kenyan medical insurance providers' operational performance is influenced by their adopted innovations since client preferences and business demands are constantly changing and that innovation uptake helps businesses gain a competitive edge. The results of the regression analysis show that the effects of electronic data interchange, automation of the claims process, mobile patient applications, and online portals are statistically significant. Finally, the study concludes that, payer provider automation technologies have been embraced by medical insurance providers, improving the organization's operational performance and increasing sales income, market share, and customer satisfaction in service delivery. Key words: Electronic Data Interchange, Automation of The Claims Process, Mobile Patient Applications, Online Portals, Medical Insurance Providers, Operational Performance iv TABLE OF CONTENTS DECLARATION ............................................................................................................... ii ABSTRACT ...................................................................................................................... iii TABLE OF CONTENTS ................................................................................................. iv LIST OF TABLES ........................................................................................................... vi LIST OF FIGURES ........................................................................................................ vii LIST OF ABBREVIATIONS ........................................................................................ viii DEFINITION OF TERMS ............................................................................................... x DEDICATION .................................................................................................................. xi CHAPTER ONE: INTRODUCTION ............................................................................. 1 1.1 Introduction ........................................................................................................................ 1 1.2 Background to the Study .................................................................................................... 1 1.3 Statement of the Problem ................................................................................................. 12 1.4 General Objective ............................................................................................................. 14 1.5 Research Questions .......................................................................................................... 14 1.6 Scope of the Study ........................................................................................................... 15 1.7 Significance of the Study ................................................................................................. 15 CHAPTER TWO: LITERATURE REVIEW .............................................................. 18 2.1 Introduction ...................................................................................................................... 18 2.2 Theoretical Review .......................................................................................................... 18 2.3 Empirical Review ............................................................................................................. 23 2.4 Gaps in the Literature ....................................................................................................... 39 2.5 Conceptual Framework .................................................................................................... 42 CHAPTER THREE: RESEARCH METHODOLOGY .............................................. 44 3.1 Research Philosophy ........................................................................................................ 44 3.2 Research Design ............................................................................................................... 44 3.2 Target Population ............................................................................................................. 45 3.3 Sample Design and Sample Size ...................................................................................... 45 3.4 Data Collection Methods .................................................................................................. 47 v 3.5 Data Collection Instruments ............................................................................................. 48 3.6 Reliability and Validity .................................................................................................... 49 3.7 Data Analysis ................................................................................................................... 50 3.8 Ethical Considerations ..................................................................................................... 51 CHAPTER FOUR: PRESENTATION OF RESEARCH FINDINGS ....................... 52 4.1 Introduction ...................................................................................................................... 52 4.2 Response Rate .................................................................................................................. 52 4.3 Background Information .................................................................................................. 53 4.4 Descriptive Statistics ........................................................................................................ 58 4.5 Correlation Analysis ......................................................................................................... 67 4.6 Diagnostic Analysis ......................................................................................................... 68 4.7 Regression Analysis ......................................................................................................... 70 CHAPTER FIVE: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 74 5.1 Introduction ...................................................................................................................... 74 5.2 Summary of Findings ....................................................................................................... 74 5.3 Discussion of Findings ..................................................................................................... 75 5.4 Conclusion ........................................................................................................................ 79 5.4 Recommendations ............................................................................................................ 80 5.5 Limitations of the Study ................................................................................................... 81 5.6 Areas for Further Research .............................................................................................. 81 REFERENCES ................................................................................................................ 83 APPENDICES ................................................................................................................. 95 Appendix A: Participant Information & Consent Form ......................................................... 95 Appendix B: Letter of Introduction ........................................................................................ 96 Appendix C: Questionnaire .................................................................................................... 97 Appendix D: Sample Size Table .......................................................................................... 105 Appendix E: Ethical Approval Confirmation ...................................................................... 106 Appendix F: NACOSTI Research License .......................................................................... 107 vi LIST OF TABLES Table 2.1 Summary of Empirical Literature Gaps ................................................................. 40 Table 2.2 Operationalization of Variables ............................................................................. 43 Table 3.1 Target Population ................................................................................................... 45 Table 3.2 Sample Size ............................................................................................................ 47 Table 4.1 Age of the Respondents ......................................................................................... 54 Table 4.2 Level of Education ................................................................................................. 55 Table 4.3 Position in the Medical Insurance Sector ............................................................... 55 Table 4.4 Number of Years of Service in the Institution ....................................................... 56 Table 4.5 Implementation of Payer Provider Automation Technologies .............................. 57 Table 4.6 Payer Provider Automation Technologies Used .................................................... 57 Table 4.7 The Effect of Electronic Data Interchange (EDI) Platforms on Medical Insurance Providers' Operational Performance ...................................................................................... 59 Table 4.8 The Effect of Claims Processing Automation on the Operational Performance of Medical Insurance Providers .................................................................................................. 60 Table 4.9 The Effect of Mobile Patient Apps on the Operational Performance of Medical Insurance Providers ................................................................................................................ 62 Table 4.10 The effect of online portals on the operational performance of medical insurance providers ................................................................................................................................. 64 Table 4.11 Effect of payer provider automation technologies on operational performance of MIPs .............................................................................................................. 66 Table 4.12 Correlation Analysis ............................................................................................ 68 Table 4.13 Autocorrelation Results ........................................................................................ 69 Table 4.14 Collinearity Results .............................................................................................. 69 Table 4.15 Model Summary ................................................................................................... 71 Table 4.16 ANOVAa .............................................................................................................. 71 Table 4.17 Coefficientsa ......................................................................................................... 72 vii LIST OF FIGURES Figure 2.1 Conceptual Framework ......................................................................................... 42 Figure 4.1 Response Rate ....................................................................................................... 52 Figure 4.2 Gender of Respondents ......................................................................................... 53 Figure 4.3 Normal P-P Plot ..................................................... Error! Bookmark not defined. viii LIST OF ABBREVIATIONS AI: Artificial Intelligence AKI: Association of Kenya Insurers BPA: Business Process Automation DCT: Dynamic Capabilities Theory DOI: Diffusion of Innovation DSR: Design Science Research DFS: Digital Financial Services ECM: Enterprise Content Management EHRs: Electronic Health Record EMR: Electronic Medical Records HIS: Health Information System HMO: Health Maintenance Organization ICT: Information Communication Technology IoT: Internet of Things IRA: Insurance Regulatory Authority LDA: Linear Discriminant Analysis MIPs: Medical Insurance Providers ML: Machine-Learning MRP: Manufacturing Resource Planning NHIF: National Health Insurance Fund NHIS: National Health Insurance System OMP: Operation Management Practices PEOU: Perceived ease of use R&D: Research & Development RAI: Responsible Artificial Intelligence RBV: Resource-Based View ix ROA: Return On Asset ROE: Return On Equity TAM: Technological Acceptance Model UHC: Universal Health Coverage US: United States VBR: Value-Based Recruitment WHO: World Health Organization x DEFINITION OF TERMS Automation Technologies: Comprises all processes and work equipment that enable plants and systems to run automatically. These include machines, apparatus, equipment and other devices. Human intervention is minimal (Lenert, 2020). Health Information Exchange: Involves transmitting health-related data between facilities, payers, providers, and patients electronically (Lenert, 2020). Health Information Technologies: Refers to the electronic systems health care professionals – and increasingly, patients (McSwain, 2020). Insurance: An arrangement by which a company or the state undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for payment of a specified premium (Smith, 2019). Medical inflation: The incremental increase of costs related to medical treatment, trends and developments including costs of advances in treatments and procedures, factors arising from economic inflation and the increased availability of usage around the world (AKI, 2022). Payer: An entity that assumes financial responsibility for healthcare services. Payers can include health insurance companies, government programs, and self-insured employers (Shah, 2022). Payer provider The interaction and space between healthcare facilities providing healthcare service and healthcare service payers such as insurance companies (Brooks & Stiernstedt, 2022). Payment provider automation: Refers to the use of technology and automated processes to manage and facilitate payments between insurance companies, policyholders, and other relevant parties (Vijayalakshmi et al., 2023). Provider: An organization or individual that delivers medical services or care to patients. They include hospitals, physicians, nurses, pharmacists, laboratories, and various healthcare facilities (Singh, & Urolagin, 2021). xi DEDICATION This dissertation is dedicated to my family and work colleagues at Smart Applications International Ltd and my peers across the insurance industry who have supported me with the materials required for the research and moral support when needed. 1 CHAPTER ONE: INTRODUCTION 1.1 Introduction This chapter highlights the evolving landscape of the healthcare industry, emphasizing the importance of medical insurance in Nairobi City County, Kenya. It underscores the role of automation technologies in improving efficiency and quality of care, setting the context for the research. The chapter also discuss the problem statement which identifies the existing challenges in the Kenyan medical insurance sector and the need to investigate the effects of payer provider automation technologies to address these issues. Further, the chapter includes the research objectives outlining the goals of the study and research questions guiding the study. Moreover, the study's scope is clarified, specifying its focus on payer provider automation technologies in the context of medical insurance operational within Nairobi City County, Kenya. Finally, the chapter underscores the importance of the research, noting its significance for policymakers, industry practitioners, and academia. 1.2 Background to the Study Insurance firms' primary function is to relieve its customers of their risks and anxieties by guaranteeing to compensate them for any injuries, illnesses, or losses that could harm their property or lives (Ayoker, 2022). Typically, insurance businesses use middlemen like insurance agents and brokers rather than selling directly to customers. Insurance underwriters and medical insurance providers to act as payer providers (Baker, Logue, & Williams, 2021). The resulting effect is an interplay of medical insurance payer-provider with interconnected roles of healthcare payers and healthcare providers in the healthcare system. Healthcare payers finance healthcare services by collecting premiums or contributions from individuals, employers, or government sources in exchange for insurance coverage (Rawat et al., 2022). On the other hand, healthcare providers deliver medical services or care to insured patients, they submit claims to healthcare payers to request payment for the services provided to patients who are covered by insurance plans or government programs (Mangla, & Aggarwal, 2020). 2 The multiplicity of players in the medical insurance sector fosters competition as each player competes for market share and customers. As a result, medical insurance providers (MIPs) have been forced to find disruptive ways to create value and enhance their operational performance for competitive advantage. One of the trends noted in MIP space is the automation of processes using payer provider automation technologies to achieve higher operational performance (Gilbert et al., 2019; Singh, & Urolagin, 2021). Payer- provider automation technologies ensure effective communication and coordination between healthcare payers and healthcare providers through claims processing, reimbursement, and the provision of high-quality care to patients (Fenny et al., 2021). Accordingly, MIPs, the insurance and scheme administrators, use the payer-provider technology for the smooth operation of the healthcare system. There are numerous payer-provider automation technologies in the medical insurance sector that reflect the evolving landscape of healthcare and insurance, as well as the broader trends in technology and healthcare policy. According to Brooks, and Stiernstedt, (2022) medical insurance payer provider automation technologies include electronic data interchange (EDI) platforms, claims processing automation systems and portals, eligibility and benefits verification platforms, electronic remittance advice (ERA), prior authorization automation systems (Peters, 2022). Further, payer-provider automation technologies include provider credentialing and enrollment systems, provider portals, electronic funds transfer (EFT) and automated clearing house (ACH) payments systems, telehealth and telemedicine integration platforms (Gilbert et al., 2019). Additionally, payer provider automation technologies encompass artificial intelligence (AI) and machine learning platforms, robotic process automation (RPA), mobile apps for patients, predictive analytics, and blockchain for secure data sharing (Rawat et al., 2021). Payer-provider automation technologies have a substantial impact on the operational performance of MIPs. To begin with, payer-provider automation technologies aim to streamline interactions and processes between MIPs and healthcare providers (Wei et al., 2023). According to Sharma et al., (2023), payer provider automation technologies help automate MIP tasks related to claims processing, billing, and communication. On his part, Marović, (2021) asserts that payer provider automation technologies offer MIPs innovative 3 ways of managing medical schemes from a purchaser to a supplier such as a health insurance firm to a health care provider. According to Fenny et al., (2021) payer provider automation technologies offer a range of solutions and tools aimed at automating and streamlining administrative processes between healthcare payers and healthcare providers. Therefore, payer-provider automation technologies have a profound impact on MIPs operational performance by transforming the way they operate and deliver services through positioning them to thrive in a rapidly evolving insurance landscape. 1.2.1 Payer Provider Automation Technologies In the field of medical insurance, payer provider automation technologies are innovative and potent instruments. They are broadly characterized as a mode of payment integrated with all auxiliary systems, including contracting, payment method-related accountability mechanisms, and management information systems (Dash et al., 2019). Therefore, payer provider automation technologies in the context of health systems achieve much more than just transferring payments to cover service expenses (Dubey et al., 2020). The payment systems used by the providers provides incentives. They include, for example, management information systems to facilitate provider payment methods and have a significant impact on the distribution of health care resources and the provision of services (Kommadi, 2021). They have built accountability systems between buyers and providers, which have a significant impact on the distribution of health care resources and the provision of services (Johnson et al., 2021). Payer technologies involve a range of digital tools and platforms used by healthcare payers. The development, implementation and adoption of these technologies is however influenced by several factors including leadership, financial and human capital, scale and size, as well as policies and strategies. Zenger and Folkman (2017) found that effective leadership plays a crucial role in driving the adoption of innovative technologies noting that strong leadership commitment and vision are necessary to champion technological innovation and digital transformation. Nyakei et al. (2022) on the other hand noted that adequate financial resources are essential for investing in technologies as companies need sufficient capital to procure, develop, 4 implement, and maintain technology infrastructure, and digital platforms. Breznik and Lahovnik, (2016) further highlighted that the availability of skilled IT professionals and experts is critical for successful technology adoption. According to Kang'e (2020), the size and scale of insurance companies can influence their approach to technology adoption. Larger companies have greater resources and capabilities to invest in innovative technology solutions, undertake complex digital transformation initiatives, and navigate regulatory compliance requirements. Rawat, Rawat, Kumar, and Sabitha(2021) on the other hand brought out the importance of clear policies, strategies, and governance frameworks for guiding technology adoption and ensuring alignment with organizational goals and regulatory requirements. Payer provider automation technologies are implemented to varying degrees and their scope transcends various activities. Payer-provider automation technologies include; claims processing technologies (Kommadi, 2021), eligibility verification automation, pre- authorization automation (Gottlieb et al., 2018), EDI platforms, RCM technologies, automated EHR systems, communication and collaboration automation as well as analytics and reporting. This study will focus on the most commonly adopted payer provider automation technologies disrupting the Kenyan market; EDI, payer-provider claims processing automation, payer-provider mobile apps and payer-provider online portals (IRA, 2022; AKI, 2022). In the insurance industry, EDI is a vital tool that makes it easier for healthcare providers and insurance payers to share standardized data. According to Brooks and Stiernstedt, (2022) EDI is used to streamline various administrative processes, primarily related to claims processing and healthcare transactions. Ge (2021) reports that, EDI is the automated transfer of data in a specific format following specific data content rules between a health care provider and healthcare payers. Because EDI transactions are computerized, they may be processed more quickly and more affordably by healthcare providers as well as payers. Similarly, Peters (2022) notes that EDI in the insurance sector, particularly in claims processing, significantly reduces manual data entry, improves data accuracy, expedites reimbursement, and enhances overall operational efficiency. Nyakei et al. (2022) state that health financial institutions, medical providers, and patients can share asserts data related 5 to health services in real time through the digital platform Slade 360 by Savannah Informatics in Kenya. This feature significantly reduces account errors, scams incidents, and payment processing times. Nyakei et al. (2022) state that health financial institutions, medical providers, and patients can share asserts data related to health services in real time through the digital platform Slade 360 by Savannah Informatics in Kenya. This feature significantly reduces account errors, scams incidents, and payment processing times. Payer-provider claims processing automation refers to the use of technology and automated processes to handle and streamline the submission, review, adjudication, and payment of healthcare claims between insurance payers and healthcare providers (Vijayalakshmi et al., 2023). This automation aims to make the claims processing workflow more efficient, reduce errors, and expedite reimbursements. Payer-provider claims processing automation not only reduces the administrative burden on healthcare providers but also enhances the efficiency and accuracy of the claims adjudication process (Kommadi, 2021). By automating these tasks, payers and providers can expedite reimbursement, reduce claim denials, and improve overall operational efficiency in the healthcare billing and payment workflow. Payer-provider mobile apps in the insurance sector are mobile applications designed to facilitate communication, transactions, and information exchange between healthcare payers and healthcare providers. Shah, (2022) observes that these apps are typically available on smartphones and tablets and offer various features and functionalities to streamline administrative processes and improve the efficiency of healthcare interactions. Payer-provider apps aim to simplify the administrative aspects of healthcare interactions, making it more convenient for healthcare providers to interact with insurance companies, submit claims, and access important information related to patient coverage and payments (Manywanda, 2021). These apps can improve the efficiency of healthcare operations and enhance the overall experience for both providers and patients. 6 Payer-provider online portals in the insurance sector are web-based platforms that facilitate communication, collaboration, and transactions between healthcare payers and healthcare providers (Singh, & Urolagin, 2021). These portals offer a range of features and functionalities to streamline administrative processes, improve efficiency, and enhance communication between these two parties. Payer-provider online portals aim to simplify the administrative processes involved in healthcare billing and claims processing, ultimately improving the experience for both providers and patients while reducing administrative costs for insurers (Lauffenburger et al., 2021). As at 2022, there were 6 established payer provider technology platforms in use by the medical insurance providers in Kenya as follows: - Medismart by Smart Applications International Ltd: This was the first payer provider system implemented in East Africa as from 2005. It incorporates the use of biometric smart card technologies and interconnected systems between the healthcare facilities and the medical insurance companies. Slade by Savannah Informatics: This was the second payer provider system implemented in Kenya as from 2016. It incorporates the use of a web based interconnected e-claims platform between the healthcare facilities and the medical insurance companies. Another payer provider technology platforms in use in Kenya is Mtiiba by Carepay: This was the third player who came in to the Kenya market in 2018. It incorporates a medical savings wallet in collaboration with Safaricom. This is also a joint venture with AAR Insurance. Medbook by Medbook Ltd: was the fourth player in the Kenya market in 2019, birthed out of a collaboration between Strathmore University, iLab Africa and Medbook Kenya. Livia by Neotech Solutions Ltd: was the fifth player in the Kenya market in 2020, who combines payer provider technologies with the provision of telehealth. LCT by Liaison Insurance Brokers Limited: was the sixth player in the market in 2021, birthed out of a joint venture between Liaison Insurance Brokers and Compulynx Ltd. 1.2.2 Operational Performance of Medical Insurance Providers The efficacy and efficiency with which a company carries out its fundamental operational procedures and produces the intended results is referred to as operational performance. Buer et al., (2021) notes that operational performance encompasses various aspects of an 7 organization's day-to-day operational and can be measured using key performance indicators (KPIs) aligned with the organization's goals and objectives. For Kaydos (2020) measuring and monitoring operational performance involves setting appropriate targets, collecting relevant data, and analyzing performance against established benchmarks or standards. Regular assessment and analysis allow organizations to identify areas for improvement, implement corrective actions, and drive operational excellence (Battesini et al., 2021). Therefore, some key elements of operational performance are; efficiency, quality, timeliness, flexibility, compliance and risk management, customer service, employee engagement and satisfaction, and continuous improvement. Medical insurance providers use various performance indicators to assess their performance. These indicators help insurance companies evaluate their performance, make informed decisions, and enhance their services. Operational performance can be evaluated based on various dimensions, including claims processing efficiency (Battesini et al., 2021), customer service quality, technology integration, financial performance, and compliance with regulatory standards (Buer et al., (2021). Evaluating these aspects provides a comprehensive understanding of how payer-provider automation technologies impact the overall operational performance of medical insurance providers. MIPs’ claims processing efficiency can be assessed based on its Claims Processing Time with Shorter claims processing times indicating efficiency and responsiveness, leading to higher customer satisfaction. Another measure is their Claims Denial Rate with a low denial rate suggesting clear communication with policyholders and healthcare providers and minimized disputes. Further, MIPs’ Customer Satisfaction Ratings are important indicators of their operational performance, with high customer satisfaction indicating a positive overall experience, which can lead to customer retention and referrals (Battesini et al., 2021) Another important measure of MIPs’ operational performance is their member Retention Rate with high retention rates indicating that policyholders are satisfied with their coverage and service. Similarly, MIPs’ Complaint Resolution Time particularly, swift resolution of complaints contributes to customer satisfaction and trust in the insurance provider. Likewise, MIPs’ Network Adequacy, the extent and accessibility of healthcare providers, 8 hospitals, and facilities within the insurance provider's network, enhances their operational performance (Buer et al., (2021). MIPs’ Medical Loss Ratio (MLR) that exhibits the ratio of claims and healthcare expenses paid by the insurer to the premiums collected is an important determination of operational performance. What is more, MIPs’ Fraud Detection and Prevention ability safeguards the insurer's financial stability and helps keep premiums affordable hence a high operational performance. The financial performance of MIPs, typically measured by the company's profit margin is another key indicator of high operational performance. A healthy profit margin ensures the insurer's financial stability and ability to meet its obligations. Likewise, adherence to government regulations and compliance with industry standards helps avoid legal issues and maintains the insurer's reputation. Besides, the ability to meet financial obligations and claims without financial distress protects a company from insolvency issues which can have severe consequences on operational performance. Consequently, MIPs’ operational performance indicators collectively help them assess their performance, meet regulatory requirements, enhance customer satisfaction, and ensure the long-term sustainability of their operations (Buer et al., (2021). 1.2.3 Relationship between Payer Provider Automation Technologies and Medical Insurance Operational Performance Payer provider automation technologies serve as a bridge connecting various stakeholders, including insurance providers, healthcare facilities, and patients, and enables seamless interactions and transactions (Dash et al., 2019). Patients interact with payer technologies through various channels, such as mobile apps, online portals, and customer service centers. These technologies allow patients to access their insurance information, submit claims, check coverage details, and communicate with insurance representatives. Insurance companies on the other hand use payer technologies to manage their operations, interact with patients, and process claims. Payer technologies enable insurers to automate processes, reduce paperwork, and improve efficiency. According to Sternet al., (2022), payer technologies therefore enable data exchange and integration between patients, insurance companies, and other healthcare providers allowing for seamless sharing of 9 information, such as patient records, claims data, and coverage details, which is essential for providing timely and accurate care. The daily operations of MIPs are significantly impacted by payer-provider automation technology. The adoption of automation technologies transforms the way these MIPs operate and deliver services. Automation technologies streamline various tasks, reducing the time and effort required for administrative work, data entry, and paperwork. Therefore, the MIPs process policies, claims, and other transactions more quickly, leading to higher productivity and capacity to handle a larger volume of business. Further, automation minimizes the risk of human errors in data entry and documentation resulting in improved accuracy leading to fewer mistakes in policy issuance, claims processing, and customer interactions. Additionally, automation provides MIPs with real-time access to policy details, claims data, health facility and customer information hence enabling faster response times to customer inquiries and more informed decision-making. Moreover, automated communication tools enable MIPs to interact more efficiently with policyholders and healthcare providers leading to quicker resolution of issues and improved customer service. Likewise, automation technologies analyze large datasets to provide MIPs with valuable insights into customer preferences, market trends, and operational performance which then inform strategic decisions and marketing efforts. What is more, automation helps MIPs stay compliant with industry regulations, ensuring that policies and claims adhere to legal and contractual requirements. Automation also reduces the need for manual labor and paper-based processes, resulting in lower operational costs hence MIPs can allocate resources more efficiently, potentially increasing profitability. Payer-provider automation technologies also improve MIPs operational performance by improving their brand value and competitiveness. According to Sternet al., (2022), automation allows MIPs to offer self-service portals and digital interfaces for policyholders, accordingly, their customers can access information, initiate claims, and make policy changes more easily, leading to a better customer experience. Likewise, automation enables MIPs to adapt to changing market conditions, regulatory updates, and emerging technologies more rapidly thus they can modify their processes and offerings to 10 remain competitive. Similarly, automation incorporate risk assessment and fraud detection algorithms, helping MIPs identify potential issues before they become significant problems. Further, as intermediaries grow their client base, automation technologies allow them to scale their operational without a proportional increase in administrative staff. For those reasons, MIPs that embrace automation differentiate themselves in the market by offering faster, more efficient services and more competitive pricing. A payer provider payment system streamlines healthcare claims processing through automation to increase the speed of payment for services provided. According to TechTarget (2022), payer provider automation technologies remove common bottlenecks and disconnects in the flow of patient claims processing, leading to soaring productivity of medical insurance operational performance. In particular, three fundamental steps improve the operational performance of payer provider payment solutions for MIPs in terms of lower costs: digitizing documents to reduce the need for local paper scanning; automating workflows to index, route, and move documents; and integrating the EHR and Enterprise Content Management (ECM) system with HL7 integration to facilitate seamless data flow and enhanced transparency. According to Kommadi (2021), automated data extraction and indexing assist in doing away with the time-consuming, traditional keyed data entry procedures, therefore lowering the possibility of errors. Improved cash flow is a result of prompt approvals, precise data entry, and insights derived from data analysis. Kuria (2022) further asserts, the payer provider payment system decreases time-to-payment from health providers’ end thus cutting through the challenges of insurance correspondence handling. For instance, the payer provider payment system captures information from multiple document sources, matches the information to the patients’ EHR thus increasing the speed of workflows. As a result, MIPs are able to easily manage healthcare information and patient claims and hence save time and enjoy higher operating margins. Another impact of the payer provider payment system is that it minimizes manual work and potential errors thus increasing employee productivity and the speed by which claims are processed (Mambo & Moturi, 2022). Employees are able to handle authorization more efficiently and accurately, approve procedures faster, complete claim resolution faster and 11 automate report making (Lanfranchi, & Grassi, 2022). Payers are putting increased pressure on providers to shift their business practices to emphasize value—which is defined as the point where cost and quality converge—as the healthcare system continues to develop from more conventional payment methods (Nasiadko, 2021). In the end, value- based methods aim to alter the way provider groups operate in order to reduce healthcare costs and enhance patient care management. Payer provider automation solutions, in accordance with Short (2019), can expedite processes and minimize the likelihood of reprocessing claims, which can lead to even more cost savings. Health insurance companies can therefore save a significant amount of money by utilizing payer provider automation technologies for medical claims processing. Payer provider automation systems, according to Holzman (2018), decrease errors in patient information verification and data matching. Furthermore, automation for processing healthcare claims is kept up to date with the most recent billing guidelines. Thus, payer- provider automation solutions improve overall client satisfaction and experience. 1.2.4 Medical Insurance Providers in Kenya Considering the government's push for the private sector to participate in the delivery medical services in the 1980s and 1990s, the private insurance market first grew in tandem with the establishment of private hospitals (Mwaniki, 2017). The authorities loosened restrictions on private practice, gave medical professionals licenses to open their own clinics, and let them to work in both the public and private sectors. The concurrent introduction of the government's cost-sharing program led to a decline in the quality of care in public facilities, which in turn encouraged patients to seek treatment in private facilities. Employers who offered private health insurance as a benefit to their staff in an effort to draw in better candidates, lower absenteeism, and boost productivity further increased demand for private health insurance (AKI, 2020; IRA, 2022). According to the IRA Kenya Insurance Performance report (2006), medical insurance was acknowledged as a type of insurance in Kenya in 2005. Since then, Kenyan medical insurance providers have registered with the regulating organization, and the sector has grown rapidly in recent years. In 2022, there were five licensed reinsurance businesses and 12 56 licensed insurance companies. Following a progressive increase throughout the years— 33 in 2018, 31 in 2019, 34 in 2020, and 38 in 2021—medical insurance providers (MIPs) climbed to 45 in 2022. In 2022, the number of insurance surveyors decreased from 32 to 31. Additional research revealed that the gross written premium went from KES 47.64 billion in 2021 to KES 54.89 billion in 2022, a 15.22% rise. From KES 34.64 billion in 2021 to KES 42.5 billion in 2022, the net earned premium climbed by 22.84%. Reinsurance surrendered fell by 7.78% in 2022, but net claims incurred and overall costs rose by 25.12% and 22.15%, respectively. Underwriting loss had a sharp increase of 191.98%, from KES 303 million in 2021 to KES 885 million in 2022 (AKI, 2022). Based on the aforementioned data, medical insurance companies have a lot of room to grow in the future, particularly if they have systems in place to guarantee excellent operational performance. In order to significantly improve operational performance, medical insurance providers have recently shifted their attention to disruptive technologies (Shah, 2022; Deloitte, 2021; 2023). 1.3 Statement of the Problem The scope of operational performance refers to the range of activities and metrics that are used to evaluate the effectiveness and efficiency of an organization's operations. It encompasses various aspects of an organization's activities, processes, and systems that contribute to its overall performance. Payer-provider automation technologies play a crucial role in shaping the operational performance of medical insurance providers by driving efficiency, accuracy, compliance, customer experience, data analytics, interoperability, and risk management. Embracing and leveraging these technologies can help insurance providers stay competitive, agile, and resilient in an increasingly complex and dynamic healthcare landscape. The insurance sector has been undergoing digital transformation, with insurers and healthcare providers increasingly adopting automation technologies to streamline operational and improve performance. Indeed payer-provider automation technologies have been linked with reduced administrative costs, improved efficiency, and minimized 13 errors, making them an attractive investment for cost-conscious MIPs. Additionally, stringent regulations, continue to drive the adoption of automation technologies to ensure data security and compliance with healthcare laws hence improving operational performance. In a world that is rapidly becoming customer-centered, technology has made it possible for medical insurance providers to provide services that are pertinent to customers efficiently while improving their operational performance (Yego et al., 2021; Omerikwa, 2022). Notwithstanding efforts made by medical insurance providers to improve their operational performance through payer-provider automation technologies, the uncertainty surrounding return on investment (ROI) is a common adoption challenge. MIPs often weigh the potential benefits against the costs and risks associated with implementing automation. The investment uncertainty challenge is exacerbated by the competing priorities and limited budgets, MIPs face hence deciding to invest in automation may mean deprioritizing other projects, which can impact ROI calculations for those projects. There have also been concerns over the challenging to quantify the specific benefits of automation technologies. According to some stakeholders, while there may be a clear expectation of improved efficiency, reduced errors, and enhanced patient care, assigning precise financial values to these benefits can be uncertain. On the contrary, industry experts’ advice that ROI uncertainty is a challenge, MIPs that carefully plan and execute their automation strategies achieve significant benefits over time through improved operational performance. There are insufficient studies that examine the impact of payer-provider automation technologies on the operational efficiency of medical insurance providers in Kenya, despite conflicting opinions on the matter. For example, the goal of Ntwali et al.'s (2020) study was to evaluate how claims management affected insurance companies' financial performance. In underdeveloped nations like Kenya, Boore et al. (2020) investigated how blockchain technology may be used to enhance the security and interoperability of e-health systems for the benefit of many stakeholders. The study conducted by Kiptoo et al. (2021) investigated the correlation between risk management and the financial performance of insurance companies in Kenya between 2013 and 2020. The goal of Omerikwa's (2022) study was to determine how innovative methods affected the National Hospital Insurance 14 Fund's performance in Kenya. In their work from 2022, Mambo and Moturi investigated the use of data mining to identify fraud in Kenyan health insurance claims. While empirical research on the impact of payer-provider automation technologies in Kenya may be limited, it is possible to address the identified research gap. Therefore, this study sought to establish the effect of payer-provider automation technologies on the operational performance of medical insurance providers in Kenya. Such a study provides valuable insights into the benefits and challenges of automation through payer provider technologies and how they impact the Kenyan medical insurance industry operational performance, ultimately leading to more informed decision-making and policy development. 1.4 General Objective The main purpose of this study was to assess the effect of payer-provider automation technologies on operational performance of medical insurance providers in Kenya. 1.4.1 Specific Objectives i. To establish the effect of electronic data interchange platforms on the operational performance of medical insurance providers in Kenya. ii. To determine the effect of claims processing automation on the operational performance of medical insurance providers in Kenya. iii. To examine the effect of mobile patient apps on the operational performance of medical insurance providers in Kenya iv. To establish the effect of online portals on the operational performance of medical insurance providers in Kenya. 1.5 Research Questions i. What is the effect of electronic data interchange platforms on the operational performance of medical insurance providers in Kenya? ii. To what extent does claims processing automation affect the operational performance of medical insurance providers in Kenya? 15 iii. What is the effect of mobile patient apps on the operational performance of medical insurance providers in Kenya? iv. What is the effect of online portals on the operational performance of medical insurance providers in Kenya? 1.6 Scope of the Study The geographical, substance, and temporal scope of this were presented as follows. Determining the study's boundaries or limit without having a scope that was either too tiny to investigate or too big to manage was the primary focus of the scope. Nairobi-based medical insurance providers were the subject of the investigation. The study period was restricted to October 2023 through December 2023. The period or the time frame was selected as a specific observation window to witness payer provider automation technologies on medical insurance providers’ operational performance in Kenya. 1.7 Significance of the Study The beneficiaries of a study on the effect of payer-provider automation technologies on medical insurance operational performance span various stakeholders, including; the government, the health insurance sector players, healthcare facilities, the general public and academia. 1.7.1 The Government The study will continue to be unique in that it takes a comprehensive approach to analyzing how payer provider automation technologies affect the operational performance of medical insurance, as determined by the government and its agencies. The majority of industries struggle with issues of accountability, efficiency, and transparency—three factors that are essential to the utilization of payer provider technologies. Governments will be encouraged to automate payers and providers for public healthcare programs, such as the proposed universal healthcare coverage scheme, by the study's findings. The study will actually help legislators find and advocate for suitable legislation that will guarantee payer provider automation improves the payment and operational procedures in the health sector, hence fostering the industry's viability. 16 1.7.2 The Health Insurance Sector Players Companies offering automation technologies, software solutions, or insure-tech innovations can benefit from the study's findings. It can validate the effectiveness of their products and services, help identify areas for improvement, and guide future developments to better meet the needs of health insurance providers and healthcare organizations. Insurance companies and health plans are direct beneficiaries of such a study. They can gain insights into the impact of automation technologies on their operational efficiency, cost savings, and customer satisfaction and thus overall operational performance. The study can help insurers identify best practices, assess the ROI of implementing automation, and make informed decisions regarding technology investments. Medical service providers, such as hospitals, clinics, and healthcare organizations, can also benefit from the study's findings. Understanding the effect of payer-provider automation technologies can help healthcare providers align their processes and systems with insurers, streamlining administrative tasks, improving claims processing, and enhancing revenue cycle management. 1.7.3 The General Public The study will be of great significance to the general public to embrace payer provider automation technologies as a key contributor to medical insurance operational performance in Kenya and thus provide cooperation in the use of the same. Moreover, by examining the effect of automation technologies on medical insurance operational performance, insurers can improve their processes, leading to faster claims processing, reduced administrative delays, and enhanced customer service. Insured individuals may experience smoother interactions with their insurers, quicker reimbursement, and improved overall satisfaction. Such forms of demand and impact are essential to not only support the growth of payer provider automation but also to promote medical insurance sustainability and in tandem grow the medical insurance reach and penetration through affordability of medical insurance products which is a direct benefit to the public. 17 1.7.4 Academicians and Future Researchers In terms of academics, this study will not only aid in the conception of payer provider automation technologies on medical insurance operational performance by fulfilling the goals outlined in this chapter. However, it will also improve the body of research on the impact of payer provider automation technologies on the operational performance of medical insurance, adding to the body of current empirical research on the topic's effectiveness. 1.7.5 The World Health Organization The World Health Organization has a clear objective in the promotion of universal health coverage of which one of the key aspects is medical insurance sustainability and reach, be it private or public. UHC involves making sure that no one has financial hardship and can obtain the health services they require at the appropriate time and location (WHO, 2019). Additionally, according to WHO (WHO, 2019), over 100 million people are forced into extreme poverty each year as a result of out-of-pocket medical expenses. For this reason, medical insurance is a crucial mitigating strategy that is ingrained in WHO's work stream on health system governance and financing. This research will therefore serve towards providing data to this WHO work stream on how sustainable medical insurance programs can be developed and replicated across countries through payer provider automation technologies to address the Universal Health Coverage agenda. 18 CHAPTER TWO: LITERATURE REVIEW 2.1 Introduction The chapter provides a thorough analysis of numerous hypotheses that have an impact on medical insurance operational performance and are related to payer provider automation technology. The goal is to identify research gaps, evaluate the contributions made by prior studies, and acknowledge the efforts that have been done through a critical examination of the empirical literature and critique of the review. The concepts that capture the important factors and bind them together are used to explain the conceptual framework, with a focus on the characteristics of medical insurance operational performance and payer provider automation technologies. 2.2 Theoretical Review A theoretical review is a thorough examination of existing theories in light of the objectives of the study. This study was anchored on three theories; the diffusion of innovation model (DOI) to highlight the stages via which technology diffuses through organizations and is eventually accepted to assist operations. The technological acceptance model was used to explain the acceptance behavior of firms that embrace technology to enhance operations. In summary, the dynamic capacities theory played a crucial role in elucidating the way in which providers leverage both internal and external resources to maintain optimal operations. 2.2.1 Diffusion of Innovation Theory Rogers first proposed the Diffusion of Innovation (DOI) hypothesis in 1962, which claims that "diffusion is the process of communicating invention for a period of time to members of a community utilizing certain channels." The many stages that a person or company goes through while adopting technology are described by Rogers (2003). The knowledge phase, the persuading phase, the choice phase, the execution phase, and the confirmation phase are these (Rogers, 2003). The adoption process is influenced by ideas about the technology's properties, including compatibility, relative benefit, complexity, observability, and trialability. An individual discovering the existence of new technology 19 and looking for information about it are characteristics of the knowledge phase (Rogers et al., 2003). At the point of persuasion, the person develops a good or negative attitude toward the new technology, but this attitude does not always translate into acceptance or rejection of the new technology (Brown & Duguid, 2000). The individual decides whether to accept and make full use of the new invention during the decision phase or to reject it. The new technology is put into use during implementation, while the decision-maker seeks confirmation of their choice during the confirmation phase (Oliveira & Martins, 2010). The DOI model considers the person, organizational structure, and external factors of a corporation as significant backdrops to innovation (Valente, 2010). Thus, this model focuses on the factors that influence technology adoption. The DOI model can be criticized for its static nature, as it does not adequately account for the dynamic and iterative nature of innovation adoption (Brown & Duguid, 2000). In today's rapidly evolving technological landscape, innovations may undergo continuous modifications, making the diffusion process more complex than what the DOI model depicts. Some critics argue that the theory portrays diffusion as a static process, overlooking the dynamic interactions, feedback loops, and co-evolutionary dynamics that occur between innovators, adopters, and the broader socio-technical system over time. The theory could benefit from a more dynamic and systemic perspective that considers feedback mechanisms and adaptation processes. The study will therefore enhance the theory by involving integrating concepts from the Dynamic Capabilities Theory, which emphasizes an organization's ability to adapt and respond to changing environments. According to DOI theory, technology's predicted benefits for assisting enterprises are the driving force behind adoption. In accordance with this paradigm, a person's or an organization's decision to accept a technology or not is greatly influenced by their perception of the technology's influence (Rogers, 1962; 2003). People fall into the groups of early adopters, early majority, late majority, and laggards as a result of these perceptions. In this sense, compared to the rest, early adopters are happy with the impact of technology 20 sooner. So, technology is more likely to be accepted if it provides quick fixes (Rogers et al., 2003). This theory is relevant to the current study as it will help understand which payer-provider- automation technologies have been adopted by MIPs. By applying the Diffusion of Innovation Theory to the payer-provider-automation technologies adopted by MIPs, the researcher will gain a deeper understanding of the payer provider automation technologies that have been adopted most and to what levels by the MIPs. The theory suggests that some MIPs may be more willing to adopt some payer-provider-automation technologies early, while a majority of adopters come in the middle and later stages. The theory will come in handy in understanding which payer provider automation technologies and what operational performance aspect motivates these MIPs to adopt. This knowledge can inform strategies for promoting adoption, addressing barriers, and maximizing the benefits of these technologies in integrated medical insurance plans. 2.2.2 Technology Acceptance Model Fred Davis advanced the Technological Acceptance Model (TAM) in 1989. According to the model, opinions of the technologies' utility and usability have a substantial impact on how quickly businesses or organizations embrace new technologies. Perceived utility, according to Davis (1989), is the user's conviction that utilizing the technology will enhance their performance, well-being, or deservingness. Additionally, perceived simplicity of use, according to Davis (1989), relates to the user's perception that utilizing the technology will be simple. The Technology Acceptance Model (TAM) comes to the conclusion that the two definitions of perceived usefulness and ease of use of the technologies describe how users behaved before embracing the technology. A theoretical framework known as the Technology Acceptance Model (TAM) is used to analyze and forecast how people or organizations will accept and embrace new technologies (Davis et al., 1989). It focuses on people' attitudes and beliefs around the adoption of technology. Regarding MIPs' adoption of payer-provider automation technologies, TAM can offer important insights into the variables affecting the choice to implement and utilize these technologies (Venkatesh et al., 2003). Perceived usefulness, as 21 it relates to payer-provider automation systems, is the degree to which these technologies are thought to be advantageous and able to expedite claims processing, lower error rates, enhance communication, and enhance overall operational performance. Perceived ease of use (PEOU) refers to the user's perception of how user-friendly and accessible the technology is. PEOU is crucial because if MIPs professionals find payer- provider automation technologies too complex or time-consuming to use, they may resist adoption (Legris et al., 2003). Attitude toward using payer-provider automation technologies is influenced by perceived usefulness and perceived ease of use. A positive attitude toward using these technologies is more likely to lead to their adoption. Behavioral intention in TAM refers to the user's intention to adopt and use a technology (Venkatesh & Davis, 2000). In the context of payer-provider automation technologies, it implies the likelihood that MIPs or professionals will adopt these technologies in their daily practices. The TAM may be criticized for lack of contextual specificity, as it does not account for variations in adoption behaviors across different contexts, cultures, and organizational settings. This limits its applicability in diverse environments where adoption decisions may be influenced by unique contextual factors (Venkatesh et al., 2003). Additionally, TAM assumes a static relationship between the key constructs and does not explicitly account for the dynamic and contextual nature of technology adoption. Users' perceptions of usefulness and ease of use may change over time as they gain experience with the technology or as external factors (e.g., changes in technology features, market conditions) evolve. The study will address this critique by contextualizing the TAM to specific settings and populations specifically MIPs. As such, TAM offers a useful framework for comprehending the variables influencing the uptake of MIPs (payer-provider automation technology). MIPs are able to make well-informed judgments regarding the deployment of automation technologies by evaluating perceived utility, ease of use, attitudes, intentions, and actual use in achieving operational performance. 22 2.2.3 Dynamic Capabilities Theory (DCT) The Schumpeterian innovation theory (1934), which asserts that a competitive advantage can be achieved by creating disruptions to the existing resources and recombining them to create the most recent functional capabilities within an organization, is where the concept of dynamic capabilities first emerged. These competencies allow companies, via their leadership, to formulate hypotheses regarding changing consumer preferences, industry and business environment issues, technological advancements, and realign resources and activities. Businesses that use dynamic capabilities can outperform rivals that might put efficiency ahead of innovation or who don't pay enough attention to how their customers' requirements are changing. It entails ongoing detecting, grabbing, and changing (Teece, 2017). Thus, this theory aims to clarify the variables that facilitate or hinder an organization's ability to adjust to changes in the external environment, which ultimately results in the maintenance or acquisition of a competitive advantage. Companies constantly find themselves in a state of intense competition in a dynamic environment, despite the long-standing assumption that they will be able to see opportunities, maximize their use, and therefore do the much-needed reconfiguration of resources and capabilities (Breznik & Lahovnik, 2016). Being able to revitalize and recreate the company's resources and use them in a different way is a sign of managerial aptitude. According to Mohamed (2015), marketing aptitude is said to as a long-term requirement for competitive advantage because it entails utilizing pertinent and previously acquired market knowledge to effectively handle potential shifts. Technological competence is the ability of a company to leverage its resource base to adapt and apply IT; Research & Development (R & D) capability is the inclination to use knowledge to spread innovation by looking for opportunities both inside and outside the organization; Competitive advantage is largely dependent on the competencies of human resources (Breznik & Lahovnik, 2016). Some scholars have raised concerns about the tension between DCT's emphasis on dynamic capabilities and its treatment of capabilities as relatively stable and enduring over time. While dynamic capabilities involve the ability to adapt and change in response to environmental shifts, they are also shaped by existing organizational routines, structures, 23 and cultures, which may hinder rapid adaptation and innovation in practice. Moreover, DCT provides limited guidance to managers on how to develop, nurture, and leverage dynamic capabilities in practice. The theory often remains abstract and conceptual, offering few actionable insights or practical recommendations for firms seeking to enhance their competitive advantage through dynamic capabilities. As a result, managers may struggle to translate DCT's theoretical principles into actionable strategies and organizational practices. Despite these critiques, the theory of dynamic capabilities in strategic management centers on the capacity of an organization to adjust, incorporate, and reorganize its internal assets and competencies in reaction to swiftly transforming external circumstances. In the context of Medical Insurance Providers (MIPs) and their operational performance, dynamic capabilities theory can offer insights into how MIPs can effectively respond to industry changes, regulatory shifts, and emerging technologies to improve their performance. It provides a strategic framework for MIPs to enhance their operational performance by being proactive, adaptive, and innovative in the face of dynamic healthcare industry conditions. By applying the principles of dynamic capabilities, the study will be able to understand how MIPs build resilience, improve efficiency, and better serve their policyholders in an evolving healthcare landscape. 2.3 Empirical Review 2.3.1 The Effect of Electronic Data Interchange (EDI) Platforms on Medical Insurance Providers' Operational Performance Chandra et al. (2022) performed a study to better understand the role of digital technology and the instruments of Industry 4.0 in the context of the COVID-19 pandemic, particularly in developing and emerging nations. The exploration configuration utilized was a desk research survey. The example size comprised 14 applicable articles gathered from different information bases. The following main discussion areas emerged from the established findings: Advanced innovations and Industry 4.0 instruments, likely applications and current applications. Corresponding to electronic information exchange (EDI) stages on clinical protection suppliers explicitly, the discoveries revealed that EDI empowers quicker 24 and more straightforward correspondence of clinical information. EDI likewise holds the possibility to diminish costs and further develop administrations, as it considers the fast transmission of clinical approvals and protection claims. Moreover, EDI assists with removing pointless desk work and gives admittance to continuous data, which can work on the precision of information. Teresia looked into Nairobi City County's private health institutions' performance and the electronic data exchange system (2022). In addition to two representatives from the IT departments of the chosen private hospitals, the survey, which was intended to function as a census, included all of Nairobi's registered private hospitals. A questionnaire was used in the collection of the primary data. The findings showed that real-time processing in mobile electronics is made possible by electronic data interchange, which does away with the need for sending, receiving, and entering order data on computers. Additional investigation showed that there is a significant decrease in one-on-one client interaction when electronic data interchange is outsourced. Consequently, it was found that a greater reliance on an outside provider for data centers could lead to security issues and require extra precautions like establishing infrastructure and incurring unexpected costs. In the Attaran (2022) study, the difficulties and prospects of using blockchain technology in the healthcare industry are outlined, along with a summary of the blockchain-related health goods and the major businesses providing solutions for various applications. The study reviewed the literature and looked for studies that mentioned the term "blockchain in healthcare" in their abstracts, titles, or keywords. The outcome showed that blockchain can enhance data integrity, provenance, access control, and interoperability in the healthcare industry. The distributed nature of blockchain technology, coupled with its clear information structure and unchangeable records that are stored across all participating users, can aid in lowering the associated costs. The study came to the conclusion that technology could be utilized to improve patient engagement, help ensure patient information is available, allow direct and secure communication between patients and providers, and promote family health management. It could also be used to safely coordinate and combine information from multiple providers. 25 The current study aims to investigate three characteristics that are essential to an effective payer provider automation technology: scope, degree, and quality of implementation. The paper's focus was focused on quality. A study by Nyangena et al. (2021) aims to assess the degree of inter-HIS communication in Kenya. The HIS Interoperability Maturity Toolkit was developed by MEASURE Evaluation and the Health Data Collaborative Digital Health and Interoperability Working Group to quantitatively evaluate the readiness of interoperability capabilities. The people who are members of the Ministry of Health's Digital Health Technical Working Group made up the study's sample. Most domains were at the lowest two levels of maturity, with the exception of the subdomains of HIS governance structures, defined national enterprise architecture for HIS, defined technical standards for data exchange, nationwide communication network infrastructure, and capacity for hardware operations and maintenance. The highest level of domain maturity was "emerging," and no other levels existed. This study is relevant to the EDI platforms used by medical insurance firms since knowing the interoperability maturity level is essential for a successful deployment. The influence of introducing electronic prior authorization on drug filling in an electronic health record system in a big healthcare system was examined by Lauffenburger et al. in 2021. The authors used generalized estimating equations to compare e-preauthorization prescriptions with non-e-preauthorization prescriptions for the same insurance plan, medication, and site, prior to as well as following e-preauthorization implementation. This allowed them to assess primary retention, or the percentage of prescriptions filled within 30 days, by matching e-preauthorization prescriptions with non-e-preauthorization prescriptions. The study found that implementation issues including insurance heterogeneity and misfiring may have reduced the program's efficacy, which has ramifications for other health informatics initiatives used in outpatient treatment. The study found that although e-preauthorization was becoming more popular as a way to streamline script filling, adoption had no effect on medication adherence. Eckert and Osterrieder (2020) indicate that digital transformation has increased relevance in the business models of insurance companies. Innovation in insurance companies through 26 digital technologies, platforms, and other infrastructures offers a wide range of entrepreneurial activities in the insurance companies. It has, however, led to increased challenges, especially within the IT department and the core enablers and preventers. Eckert and Oster Rieder’s research provides an in-depth analysis of recent developments in digital technologies like AI and cloud computing, and its applications in the insurance industry. In their research, they have reviewed literature from various academic sources, industrial studies and other publications of the supervisory authorities. As a result, based on the research, one points out the reorients of an insurer and interdependencies between the digital technologies. As a result, the study's findings stress the need for comprehensive digital strategies at insurance firms. Saldamli et al. (2020) conducted research to prevent health insurance fraud by developing a system of securities that will monitor all insurance activities by effectively integrating data and all insurance companies. The research design used in this study is a qualitative research approach, including interviews with healthcare experts and surveys from medical insurance providers. The study utilized a sample size that included healthcare providers and insurance companies who provided information on how EDI technology can improve payer-provider relations in medical insurance. The study established that the high need for EDI platforms in medical insurance companies is meant to increase transparency and security of protecting the patient and the payer. The research by Aerts and Bogdan-Martin sought to identify the challenges faced by low- and middle-income countries when attempting to implement digital health solutions. The research was qualitative in nature, with over two hundred papers reviewed and over a hundred significant players interviewed. Based on the findings, digital health systems need to have the following six pillars in place: a national digital health strategy; policy and regulatory frameworks that support innovation while protecting security and privacy; universal access to digital infrastructure; component interoperability; effective partnerships; and sufficient funding. The article focuses on EDI systems used by medical insurance firms and highlights the importance of considering legal and regulatory frameworks to ensure data privacy and security. 27 Level 5 hospitals in Kenya's public health system were the focus of Mbugua and Namada's (2019) investigation of the effects of IT integration on hospital productivity. Using a causal, non-experimental, cross-sectional study design, they empirically explored the relationship between the degree to which hospitals have integrated information technology and their operational performance. To choose their final pool of 164 subjects, they used a method known as "stratified random sampling." The findings revealed that the integration of IT had a major impact on operational performance, accounting for 44.9% of the variation in that parameter. The findings also lent credence to the idea that government contracts could play a mediating role between IT integration and successful operations. Electronic data exchange (EDI) solutions have the ability to contribute to these aims by improving coordination across the many parts of Kenya's health sector and amplifying the effect of IT integration on operational performance. 2.3.2 The Effect of Claims Processing Automation on the Operational Performance of Medical Insurance Providers The role of the Ayushman Bharat Digital Mission (ABDM) in shaping India's public digital health narrative was examined by Sharma et al. in 2023. The study looked into Indian health insurance providers using a cross-sectional research design. The study found that the health claim exchange (HCX) platform developed under the ABDM is successful in addressing the issues facing the health insurance sector and enhancing the patient experience in terms of timely service delivery. In order to facilitate the automation of the health insurance claims processing workflow, it offers an open, machine-readable, auditable, verifiable, explainable, and interoperable standard communication protocol between payer, provider, and beneficiary. It offers an open standard that makes it possible to automate communication between the parties, handle a large number of claims, and make it easier to settle claims quickly and verifiably. At the core of this insurance reset, according to Deloitte (2023), is customer loyalty and retention, both of which are heavily influenced by customer interactions with their insurers. Specifically, the claims experience—which, in the case of medical insurance, is influenced by medical service providers in their interactions with insured members at the point of service—drives these two growth engines. Claims operations, which have historically been 28 viewed as the products of a "reactive back office," will thus need to develop into a potent differentiator that is inventive, uncompromising in its efficiency, endowed with a diverse workforce, and able to provide impressive outcomes. The transformation of the insurance claims process, the adoption of new technologies, a networked partner ecosystem, and a talent model that prioritizes technical claims handling and data science capabilities are the main factors that will likely enable the future of claims. As the number of no-touch insurance claims processes rises, use of new technology should alleviate the strain of an aging workforce. Claims specialists will also require more technical proficiency in order to benefit from the faster and higher volume of information that is now available. Sood et al. (2022) characterized the source's intent as an analysis of blockchain's effect on the non-life insurance sector. To this end, we conducted a comprehensive literature evaluation of blockchain applications in the insurance industry as part of our research design. According to the results of the study, just a fraction of insurance providers are exploring and implementing blockchain solutions. This entails the computerization of claim filing, fraud detection, and cash flow monitoring. In addition to reducing insurance firms' administrative costs, this technology aids in reducing disparities connected to false claims by tracking the customer's history. The importance of this research lies in the fact that it is one of the few to examine the relationship between blockchain and non-life insurance. Kemboi (2022) examined whether claims digitalization has an impact on insurance firms' ability to provide services in Kenya. The study used a descriptive survey approach with a sample size equal to all 56 insurance companies in Kenya to accomplish this. He used Google Forms to send out questionnaires, and then transferred the data to spreadsheet programs like Excel and SPSS for further processing. The results showed a robust beneficial correlation between digitalizing claims and providing services. Among the many aspects of claims digitalization, claims automation was the most consequential. However, the impact of self-service tools and interaction with third-party suppliers was quite minor. According to the findings, it is necessary to fully digitalize the claims process by 29 incorporating self-service capabilities, end-to-end claims automation, and databases of third-party suppliers. Amponsah et al. (2022) looked into whether or not machine learning methods mixed with blockchain technology could aid in identifying and preventing healthcare fraud, namely in the claims processing sector. To make sense of the raw claims data, the researchers used a decision tree categorization technique. Health insurance information was used to calculate a suitable sample size for this investigation. According to the findings, the optimal method had a classification accuracy of 97.96% and a sensitivity of 98.09%. This result revealed the proposed strategy was successful in improving the blockchain smart contract's ability to detect fraud with a 97.96% degree of accuracy. This study's findings also showed that automation using machine learning and blockchain technology has the potential to be an efficient tool for spotting and combating healthcare fraud. Kajwang (2022) conducted this research to learn how the advent of Big Data Analytics will affect the field of insurance fraud management. For this desktop literature study, we used Google Scholar to find the most influential and foundational journal papers and references published in the past decade. The research showed that insurance companies may use Big Data Analytics to cut costs and boost results by acquiring greater insight from larger data sets. Research also suggests that insurers' underwriting processes could benefit greatly from the automation of insurance claim processes and the use of digital insurance control mechanisms. Understanding how Big Data Analytics might be beneficial for the insurance sector, as well as the strategy of structuring better cost-benefit evaluations and scenario planning to address potential unfavorable repercussions, are all areas in which this source makes a distinctive theoretical and practical contribution. The goal of Rawat, Rawat, Kumar, and Sabitha's research from 2021 was to determine how machine learning algorithms could assist insurance businesses in identifying trends in different Insure-tech segments and branches. Two datasets were employed in the investigation, and claim analysis was conducted using several categorization techniques. According to the study's findings, claim analysis can be used to understand the demographics and claiming behaviors of consumers, which can then be used to modify policies and determine more affordable premiums for them. The policies can also be 30 adjusted to track the insurance company's profit/loss ratio by comprehending its acceptance tendencies. The current study focuses on medical insurance businesses, whereas the study by Rawat et al. was more broadly focused on insurance companies. According to Dubey, Bhatnagar, and Bhatia (2020), innovations aimed at expediting the claims process are not solely focused on enhancing client convenience. They are also intended to lower the expenses incurred by insurers in handling those claims. According to their survey of claims managers at top insurers, ineffective procedures and a deficiency in digitalization are common complaints about the claims management process. However, some insurers such as Tata-AIG in India and Suncorp in Australia, are now filing and recording insurance claims using automated digital platforms. Within minutes, insurers have successfully processed claims by using AI to interpret first-notice-of-loss photographs. Applications utilized by payers and providers of care, as well as life sciences businesses, are the focus of Davenport and Kalakota's (2019) proposed study of the effects of AI on healthcare. The research design used was a qualitative systematic review examining AI applications' usage and potential impacts in the healthcare industry. No specific sample size was defined, as the focus was on casting a wide net across the different aspects of AI applications in healthcare. The findings of this research pointed to several impacts that healthcare providers must consider while implementing AI in their operations. The study found that AI can do healthcare duties almost as well as, if not better than, humans. However, operational problems and ethical considerations may delay broad automation of healthcare professional employment for some years. Additionally, the findings indicate that AI applications could positively affect the operational performance of medical insurance providers through automation of their claims processing operations. 2.3.3 The Effect of Mobile Patient Apps on the Operational Performance of Medical Insurance Providers Halima and Yassine's (2022) study aimed to examine how the rise of app-based mobile patients has affected the efficiency and effectiveness of Morocco's health insurance markets. To ensure that all factors associated with the adoption of mobile patient systems 31 are taken into consideration, a mixed methods research design will be used. Specifically, the research design involved a quantitative survey and two focus group interviews to gain an understanding of the effect of mobile patient apps on operational performance. To ensure a representative sample size, the qualitative portion included four participants, and a total of 200 participants took the survey. The study established that mobile patient apps significantly impact the efficiency, effectiveness, and retention of Moroccan health insurers. Consequently, researchers plan to look into the potential effects of the COVID- 19 pandemic, as well as the difficulties and restrictions of mobile patient systems. The research undertaken by Wilson et al. (2022) includes two programmatic case studies that analyze the challenges and successes of adopting Digital Financial Services (DFS) for health with the goal of increasing access to Universal Health Coverage. This research employs a mixed methods process assessment strategy, combining primary and secondary data to address three issues. Primary data came from in-depth interviews with a variety of stakeholders, including program implementers, developers, and users in Rwanda and Kenya. Participants included program administrators, software designers, and end users of the two applications under review. According to these data, mobile patient apps do improve the efficiency of healthcare insurers' daily operations. The participants reached a consensus that DFS enhanced the responsiveness of health systems, enabled programs to adapt digital services to new regulations or features suggested by clients, and enhanced access to high- quality data for better administration and higher service quality. Program managers and some beneficiaries have complimented the convenience of digital functions over paper- based approaches, and both primary and secondary statistics indicate that these implementations helped to expand access to health insurance. According to Kuria (2022), the medical insurance industry is witnessing a significant transition towards digital transformation, driven by the need for value-based treatment, operational excellence, patient experience, and cost reduction. Payer provider automated technologies range from artificial intelligence and machine learning to mobile apps and software that helps doctors' clinical judgments. By creating a thorough picture of a user's entire health, digitization helps reduce risk and costs. The platforms assist in connecting patients with healthcare professionals. Furthermore, securing data dominance in the 32 insurance industry can be achieved by maximizing customer experience and digital tools. For instance, several insurance firms employ the mobile technology platform M-TIBA to improve efficiency and transparency in the healthcare system (Kuria, 2022). A systematic review conducted by Bokolo (2021) assessed the impacts of telemedicine and eHealth as a proactive measure to work on clinical care during the pandemic. Methods included a systematic search for relevant articles followed by a review of the results. The sample comprised different peer-reviewed journal articles, policy documents and reports published between January 2020 and September 2020. The study's conclusions center on the significance of telemedicine and contemporary applications such as mobile applications used during the epidemic, as well as regulations implemented throughout the world to promote its management. It also included the capability of telemedicine and eHealth to provide convenient, safe, successful, and "green" healthcare services. Furthermore, it was recommended that telemedicine and eHealth supportive platforms such as mobile patient apps can be used as convincing instruments for providing quality clinical treatment during times of health emergency. Renner-Micah et al.'s (2020) analysis of the implementation of national health insurance in a developing country provides insight into the knock-on effects on the development and use of digital infrastructure. A qualitative, interpretive case study with institutional theory as the analytic lens. Institutional, normative, and cultural-cognitive influences were studied using a sample from Ghana, a poor nation. Findings identified three institutional facilitators (a health-seeking culture, widespread usage of mobile networks, and appropriate rules and regulations). The research lay the groundwork for understanding the institutional enablers and constraints surrounding digital platform development and use for national health insurance, which is crucial for determining the impact of mobile patient apps on the operational performance of medical insurance providers. Shitanda et al. (2020) set out to find out to what extent blockchain technology affects insurance businesses' bottom lines in Kenya. Researchers employed a stratified random sample technique to select 16 of 52 insurance providers depending on their service region, and then used qualitative and quantitative methods to compile descriptive data. The poll indicated that the inability to communicate with clients, the inability to verify payment 33 data, the misappropriation of clients' premiums, the loss of payment records, and legal reporting systems were among the most significant obstacles to collecting and processing payments from/to consumers. Businesses are aware of blockchain technology, but the results showed that they have not yet implemented it. The benefits of applying block chain technology for insurance firms were also discussed, such as how it may increase transparency, data security and integrity, and digitizing speed; decrease fraud; and speed up claim settlements. The study concluded with a call for the establishment of policy guidelines for the activities of insurance companies. 2.3.4 The effect of online portals on the operational performance of medical insurance providers Yinusa et al. (2023) examined the precarious situation of present healthcare systems and the necessity for pragmatic adjustment to establish balance between demand and capacity. The research team behind this study set out to measure how online payer-provider portals affect the efficiency with which health insurers go about their daily business. To do this, they conducted a literature analysis focusing on healthcare funding models, medical entrepreneurship, and macro-, meso-, and micro-level healthcare aspects. The sample size included 42 studies that provided relevant information for this review, including 17 macro- level studies, 9 meso-level studies, and 16 micro-level studies. The findings indicated the need to develop a healthcare financing model to promote and safeguard the health system. Furthermore, this model should safeguard human welfare and sustain socioeconomic development. The findings also