Value-based care needs a specific IT infrastructure – and yes, AI is part of it
As the movement toward value-based care and alternative payment models continues to gain momentum, additional investment in associated business processes and overhaul of technology infrastructure are critical, many experts say.
To achieve sustainable success in value-based care, healthcare provider organizations need digitized data and analytics at the level of the individual patient journey, they say.
Artificial intelligence technologiescoupled with machine learning algorithms in a robust data engineering framework that enables integration between systems with this digitized data are needed to make this transition a reality, says Lynn Carroll, chief operating officer at HSBlox, a technology and services company that helps healthcare organizations with value-based care.
Healthcare IT news sat down with Carroll to discuss all these issues and more, especially as artificial intelligence has exploded in healthcare.
Q. Why do you believe that an overhaul of the technology infrastructure is imperative to accommodate value-based care?
A. As the movement toward value-based care and alternative payment models gains momentum, it is increasingly important that payers and providers have comprehensive, accurate patient data – along with the ability to analyze and share that data – to support these new care models. This will be necessary more investments in associated business processes and overhaul of the infrastructure.
A strong data engineering framework is essential for a longitudinal patient record. Integrating digitized data with structured and external data sets can provide a holistic view of the patient that enables actionable insights, helping payers and providers reduce costs and improve quality of care under value-based care contracts.
Such a data engineering framework should be able to handle both standard and non-standard data sets, external data sets such as those recommended by the CDC and other major public health organizations, and unstructured data such as images, graphs, physician notes and free text. The ability to extract unstructured data is critical because more than 70% of digital health data is in unstructured form.
In addition to providing interoperability for exchanging clinical information, a data engineering framework must support a multi-stakeholder “network of networks” in a way that includes payment options.
Q. What role do artificial intelligence technologies and a powerful data engineering framework play in your vision for the future of healthcare IT and value-based care?
A. To find sustainable success in value-based care, we need digitized data and analytics at the level of the individual patient journey. Patient datasets are typically fragmented across systems, requiring them to be digitized and combined with structured and external datasets to create a 360-degree view of a patient’s health, necessary for clinically sound decision-making in value-based arrangements.
Artificial intelligence coupled with machine learning algorithms in a robust data engineering framework enables integration between systems with this digitized data. AI and machine learning can be used to better automate tasks and decision-making processes, providing scalability.
More specifically, AI-based technologies, such as natural language processing and computer vision, can drive the digitization of data by transforming unstructured information from notes and images into structured data that can support care delivery and coordination.
Machine learning algorithms can also help reduce denied claims by improving error detection in billing and coding. And by accelerating the processing of large data sets, AI and machine learning help inform accurate and comprehensive value-based risk predictions and provide recommended actions to improve patient outcomes.
Q. You say that moving to a fully digitalized journey from offline to online is critical. How can this open new opportunities in the delivery of healthcare services, while removing barriers to treatment and improving patient outcomes?
A. Interoperability and access to data are key to the success of value-based care and other alternative payment models.
Digitized patient data, supported by a cloud-based data engineering framework that powers a “network of networks,” can provide a 360 degree view of the patient. This allows healthcare organizations to identify and proactively address healthcare equity issues.
Digitization of data and Interoperability also facilitates care coordination and the delivery of healthcare services in non-traditional locations such as retail environments and the home. And when healthcare organizations widely deploy data that can be digitized and queried, they can practice evidence-based medicine, reduce or eliminate disparities, optimize care pathways, streamline workflows across entities, and proactively identify and address gaps in patient care. .
On the administrative side, a data engineering framework must support rreimbursement and other processes necessary for the many-to-many stakeholder relationships among providers, payers, and community organizations in a value-based care model.
Community organizations in particular may need help introducing and integrating their systems and data into the network of networks. It is no exaggeration to say that without this kind of values-based governance, values-based care cannot succeed.
Q. When will all this happen? You mentioned that we will see an increased focus on construction/acquisition investments in the technology infrastructure and human capital to make value-based healthcare programs successful.
A. It’s happening now. Healthcare organizations are slowly but definitively migrating to alternative payment models and care delivery that require a cloud-based data engineering framework that can run a network of networks.
Barriers certainly remain: healthcare organizations have historically been slow to adopt technology, while data standards are compliant Privacy and security standards for health data are evolving. However, the pandemic served as a warning to healthcare organizations that the traditional fee-based reimbursement model is both risky and not built to optimize patient outcomes or efficiently allocate resources.
So as we head into 2024, I expect a greater commitment to value-based reimbursement models and technologies that support non-traditional care scenarios as healthcare organizations seek to improve patient outcomes, reduce health disparities, and control costs.
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