Advances in AI and Technology for Efficient Value-Based Care 2024

5 Key Trends Shaping Value-Based Care in 2024

Value-based care in 2024: As 2024 approaches, healthcare is undergoing unprecedented innovation and digital transformation. Emerging technologies and capabilities are empowering organizations, including payers, providers, and self-funded employers, to confidently embrace risk within value-based care programs.

The American Academy of Family Physicians reports that 49% of practices participate in some form of value-based payment, and the Centers for Medicare and Medicaid Services (CMS) is still pursuing the goal of having all Medicare beneficiaries and most Medicaid beneficiaries in an accountable care relationship by 2030.

The VBC model incentivizes providers and payers to deliver care to achieve higher quality and optimize patient outcomes while reducing costs. VBC has gained ground in recent years and its momentum will only increase. Healthcare delivery organizations that are ready for these changes will prosper in a future focused on value-based care in 2024.

5 pivotal trends are positioned to influence the future landscape of Value-Based Care in 2024:

  1. Emphasizing the Efficiency of Administrative Processes:

A significant portion of a healthcare team’s time is spent on administrative tasks, causing burnout and leading to significant inefficiencies. The increasing reporting and compliance requirements of VBC contracts have only added to the administrative challenges of recent years.

But there is hope ahead. Advances in technology can reshape administrative processes, reducing manual and time-consuming tasks for everyone from physicians and clinical care teams to administrators and payers.  , the employer’s human resources department, and their consulting partners. Digital solutions automate repetitive processes, streamline reporting, and generate actionable insights that give healthcare providers more time to spend with patients. Another key to improving efficiency is unleashing the power of data. Healthcare organizations have access to a wealth of information from medical claims history, EHR data, social determinants of health, and more. But for many organizations, separate and distinct data management systems make it difficult, if not impossible, to access all of this valuable information and use it to improve care. squirrel.

An end-to-end technology platform that can bring disparate data together, then clean, standardize, and link that data to each member or patient, making it available to providers, and payers, and give employers the tools to generate actionable insights they previously only dreamed of.  Seamless communication across healthcare systems improves the efficiency and engagement of all teams involved in patient care. More interconnected data systems will help redefine how we share data, improving the quality of patient care.

  1. The advancement of AI in healthcare:

The significant potential of artificial intelligence (AI) software has risen to the forefront of the technology world. This is seen as an opportunity to radically restructure almost every industry, and healthcare is no exception. AI tools have played a key role in automating and streamlining time-consuming tasks and improving care, including:

Automating billing, medical coding, and processing claims processing to expedite the reimbursement process and eliminate errors that contribute to increased costs and administrative waste.

Review millions of de-identified records in databases to detect high-level trends, alerting providers, payers, and employers to risks and opportunities for potential association. Assess the risk that patients or members of the public will develop chronic diseases or see their conditions worsen and develop awareness strategies to minimize these risks.

Predict behaviors that may lead to poor health, such as the likelihood that a person will not receive a flu vaccine. This information helps healthcare stakeholders target their efforts to reach those who need it most.

Evidence-based clinical guidelines reference and cross-reference existing patient data to identify and recommend the next steps in the care pathway. These AI tools will not replace the knowledge and human touch of doctors. However, they will be important partners in helping providers make optimal decisions about their patient care.

By leveraging AI capabilities, physicians can achieve new levels of operational efficiency to redirect valuable time and resources toward better patient care. However, as more and more physicians and healthcare executives exploit the many uses of AI, they are also realizing what this emerging technology means for privacy, security, and data use. Is there any morality?

  1. Establishing ethical AI practices:

As healthcare professionals integrate AI into their daily processes, greater emphasis needs to be placed on the responsible use of AI. Deloitte’s upcoming 2024 Healthcare Outlook survey identifies a clear need for further research into AI in general, with 60% of healthcare executives calling for further assessment of the impact and its potential risks.

An important aspect of value-based care is reducing inequities that undermine care delivery. As tech companies continue to innovate in generative AI, they must do so with appropriate safeguards to eliminate bias and errors or perpetuate problems like inconsistent accessibility.  Consistent with quality care about race, gender, or socioeconomic status.

A machine learning model trained with a small, incomplete, or inaccurate data set may provide inaccurate information about the appropriate next steps for patient care. We have seen the harmful impacts of AI tools that deny coverage or prevent patients from getting necessary care. With the stakes so high in health care, there is no room for unethical use or errors that further exacerbate inequity.

  1. Rethink the pre-approval process:

The pre-approval process has long been a source of frustration for clinical teams. A recent survey by the American Medical Association found that these procedures waste a lot of time and resources. Clinical teams reported spending an average of about 14 hours per week on this, equivalent to more than 700 hours per year in previous approvals.

These hours can be spent caring for patients, and the costs are evident, not only in  the level of burnout among physicians and their teams, but also in  access to patient care. core. Nearly 90% of doctors reported delays, and some patients had to wait days for important medical decisions to be made.

Despite these challenges, payers believe prior authorization is an important tool for minimizing unnecessary (and potentially costly) treatments. , AI-based solutions could be the key to improving this broken system by automating and streamlining processes. Machine learning algorithms can improve the efficiency of prior authorization decisions by speeding up the approval process and ensuring that patients receive appropriate and timely care, consistent with the principles of Value-based care.

Prior authorization recently made headlines when two major health insurers faced class action lawsuits for using AI in a manner that denied coverage to Medicare Advantage beneficiaries. As mentioned above, AI is a tool that helps optimize inefficient and time-consuming processes. However, this will not replace the knowledge and ideas of doctors and healthcare professionals.

  1. Entering a new era of patient care and efficiency:

In 2024, the healthcare landscape will benefit from an increased focus on value-based care. Technological advances promise high efficiency, and artificial intelligence-based solutions have the potential to revolutionize outdated and frustrating processes. As the healthcare industry faces these opportunities and challenges, we can all work towards an effective, ethical and patient-centered VBC healthcare system.

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