Imagine a future where you go to the doctor, and they have all your medical records right in front of them. They can immediately start providing you with the best possible care because they have all the information they need. This is the future of healthcare, and it is being made possible by Artificial Intelligence (AI).
By harnessing the power of AI, doctors can access data from patients’ electronic health records, insurance claims, and other sources to provide more effective value-based care. This is a win for both patients and doctors, as it results in better care for patients and lower costs for providers. So how is AI enabling value-based care? Let us take a closer look.
The current healthcare system
The current healthcare system in the United States is facing several challenges. One of the biggest issues is moving from fee-for-service care to value-based care. This shift is a challenge for many healthcare providers, who are used to being paid for each procedure or service they provide. Increasing care costs represent another serious pain point.
The healthcare system is also grappling with the growing prevalence of chronic diseases, such as diabetes and heart disease. These are difficult problems with no simple solutions. Regardless, the current healthcare system is not sustainable in its current form.
According to 2021 census data, around 30 million people in the United States lack healthcare insurance, making it difficult even to purchase medicines. In 2020, approximately 9% of patients received delayed or no care due to financial constraints.
Adopting a proactive approach
The healthcare industry is shifting towards a more proactive approach. The focus now lies in preventing illness rather than simply waiting for people to get sick, primarily due to rising costs, an aging population, and the additional burden of chronic disease.
The preventive model is based on the principle that it’s better to prevent illness than treat a condition. Healthcare providers have the power to significantly improve health outcomes and cut costs by identifying risks early and eliminating them before they become problems for patients. This trend has already begun to impact the American healthcare system, and we’re expecting it to continue into the next decade.
Concept of value-based healthcare
The health industry has increasingly focused on value-based care in recent years. The basic idea behind value-based care is that healthcare providers should get paid based on the quality of care they provide rather than the number of services they perform. For example, a doctor who successfully treats a patient with a complex illness would receive higher reimbursement than a doctor who provides a routine checkup.
Value-based care is designed to promote better health outcomes by incentivizing healthcare providers to focus on quality over quantity. This should lead to better health for patients and lower overall healthcare costs. While the implementation of value-based care is still in its early stages, it promises to improve the quality of health care while containing costs.
Application of artificial intelligence in delivering value-based care
Technological advancements have revolutionized predictive analytics, population health management, automation, and digitalization in the recent decade. Payers and providers must transform their present operating models by becoming more digitally enabled and clinically integrated to improve patients’ health and achieve greater value for their money. This is where AI can come into play. The influence of AI on the entire healthcare ecosystem is significant.
Hospitals: Decreased workload of medical staff by automating the daily processes leads to lower burnout rates for providers, who can invest time and energy in the more important and higher-value tasks. According to a Medscape National Physician Burnout and Suicide Report 2020, 42% of physicians reported that they are burned out.
Payers: Enhanced automation can reduce costs, improve patients’ experience, and promote a higher Net Promoter Score (NPS) for payers that may maximize member retention and growth.
Patients: Patients enjoy better outcomes, empowerment through additional education about their health conditions and AI-enabled apps for care management.
AI can analyze the copious amount of data from different sources – EMR and EHR, payer claims, demographic factors, and patient-generated health data. This wealth of data can detect unsee patterns, helping physicians make well-informed decisions. As a result, health care is becoming distributed, spanning both hospitals and homes – interconnected platform infrastructure is essential to manage patient data across settings.
AI has the potential to detect disease and define the risk of someone getting a disease using sophisticated models. At HealthEM.AI, we are developing a chronic disease prediction and risk assessment model based on EMR and payer claim data to enable providers to intervene before the situation worsens, educate patients about their problems, and limit the needless use of healthcare resources.
While AI is on a trajectory to achieve the objective of value-based healthcare, many executives are uncertain about how fast the transformation from traditional systems should occur.
Future of value-based care
The greater acceptance of outcome-based models highlights a need for an alignment of prices between patients, providers, and payers. Healthcare executives must consider the research in generating novel treatments that patients and payers are willing to pay for. New technology and automation will create opportunities for greater efficiencies. In addition, it will enhance the customer experience by allowing modern health care to reach rural and distant locations via telemedicine.
Despite many uncertainties, transitioning health care to the value-based model will help us achieve accessible and high-quality care for patients across the globe. With HealthEM.AI, we are providing the necessary tools so that your organization can make this shift with ease and efficiency. We believe in value-based care and our platform is designed to support this approach.