The complexity of healthcare financial management (HFM) for sustainable development (SD) requires the effective use of Artificial Intelligence (AI) tools. The WHO 2030 agenda is for 100% universal health coverage (UHC), while currently, it is around 66%. The UN's 2030 Agenda has 17 SD Goals (SDGs). SDG-3 focuses on healthcare with 169 healthcare targets, including UHC, while other SDG’s such as 4, 6, 8, and 9 complements it. These SDGs are achieved through a full range of strategic healthcare measures such as preventive care, promotive care, curative (chronic and acute) care, rehabilitative care, palliative care, and emergency care. Assuring efficient delivery of these healthcare requires intelligent HFM by all the stakeholders viz., scholars, citizens, patients, private and public institutions, primary healthcare centers (PHC) at the community level, besides states and countries. The HFM includes managing costs, budgets, expenditures, revenues, taxes, insurance, reimbursements, effective financial intermediation, and efficient financial markets.
WHO's Global Health Security (GHS) index is currently 40.2 out of 100, implying that almost every country has critical healthcare gaps to address, whether it is with prevention, detection & reporting, rapid response, the capacity of the healthcare system, conforming to global norms and risk situation for SD. The problem is that healthcare services usage is directly related to out-of-pocket health spending funded by a household’s income (including transfers), savings, or loans. Such spending primes service delivery only if the household pays, becomes a source of socioeconomic inequality in accessing healthcare. In reality, out-of-pocket health spending is directly proportional to the severity of the health condition (sicker people spend more) and is subjected solely on the household’s ability to pay), it can lead to financial hardship. Thus achieving SDG becomes difficult. This Research Topic addresses the complexity of HFM prudently by including households, government, and third-party payers like a health insurance fund or a private insurance company (PIC) in the joint decision making for SD through adopting AI tools/techniques to minimize the cost for all stakeholders and optimal use of resources both physical and infrastructure.
This Research Topic welcomes articles on:
1. PHC and Community-based data models, for health data management, sharing, and reuse.
2. Rational quality healthcare pricing structure including spender mix, healthcare service mix, capital needs, impact of government payment deficits, and community benefits/costs.
3. Financial Intermediation in Healthcare: rising debt and non-bank credit intermediation through efficient financial markets and financial intermediaries.
4. Post-covid-19 crisis policy frameworks for financial intermediation and risks.
5. Optimal combination of macro-prudential policies and related tools in non-bank credit intermediation to address health vulnerabilities.
In these topics, AI tools, specifically, process mining techniques are employed to optimize improved financial intermediation and healthcare workflows; pattern recognition in organization operation and healthcare processes; XAI are employed to aid stakeholders' decision, household experience, and outcomes; Healthcare analytics to ensure accuracy of insurance programs and find patterns of fraudulent activities through automating multiple reports for health insurance claims.
The complexity of healthcare financial management (HFM) for sustainable development (SD) requires the effective use of Artificial Intelligence (AI) tools. The WHO 2030 agenda is for 100% universal health coverage (UHC), while currently, it is around 66%. The UN's 2030 Agenda has 17 SD Goals (SDGs). SDG-3 focuses on healthcare with 169 healthcare targets, including UHC, while other SDG’s such as 4, 6, 8, and 9 complements it. These SDGs are achieved through a full range of strategic healthcare measures such as preventive care, promotive care, curative (chronic and acute) care, rehabilitative care, palliative care, and emergency care. Assuring efficient delivery of these healthcare requires intelligent HFM by all the stakeholders viz., scholars, citizens, patients, private and public institutions, primary healthcare centers (PHC) at the community level, besides states and countries. The HFM includes managing costs, budgets, expenditures, revenues, taxes, insurance, reimbursements, effective financial intermediation, and efficient financial markets.
WHO's Global Health Security (GHS) index is currently 40.2 out of 100, implying that almost every country has critical healthcare gaps to address, whether it is with prevention, detection & reporting, rapid response, the capacity of the healthcare system, conforming to global norms and risk situation for SD. The problem is that healthcare services usage is directly related to out-of-pocket health spending funded by a household’s income (including transfers), savings, or loans. Such spending primes service delivery only if the household pays, becomes a source of socioeconomic inequality in accessing healthcare. In reality, out-of-pocket health spending is directly proportional to the severity of the health condition (sicker people spend more) and is subjected solely on the household’s ability to pay), it can lead to financial hardship. Thus achieving SDG becomes difficult. This Research Topic addresses the complexity of HFM prudently by including households, government, and third-party payers like a health insurance fund or a private insurance company (PIC) in the joint decision making for SD through adopting AI tools/techniques to minimize the cost for all stakeholders and optimal use of resources both physical and infrastructure.
This Research Topic welcomes articles on:
1. PHC and Community-based data models, for health data management, sharing, and reuse.
2. Rational quality healthcare pricing structure including spender mix, healthcare service mix, capital needs, impact of government payment deficits, and community benefits/costs.
3. Financial Intermediation in Healthcare: rising debt and non-bank credit intermediation through efficient financial markets and financial intermediaries.
4. Post-covid-19 crisis policy frameworks for financial intermediation and risks.
5. Optimal combination of macro-prudential policies and related tools in non-bank credit intermediation to address health vulnerabilities.
In these topics, AI tools, specifically, process mining techniques are employed to optimize improved financial intermediation and healthcare workflows; pattern recognition in organization operation and healthcare processes; XAI are employed to aid stakeholders' decision, household experience, and outcomes; Healthcare analytics to ensure accuracy of insurance programs and find patterns of fraudulent activities through automating multiple reports for health insurance claims.