How AI Can Automate Complex Manual Workflows in Healthcare
Sep 30, 2024
Gradient Team
The Bottleneck
In healthcare, complex workflows involve multiple stakeholders, approvals, and intricate data exchanges. These processes span various aspects of healthcare, such as patient management, insurance claims processing, and regulatory compliance. When handled manually, they slow down healthcare delivery, increase costs, and introduce opportunities for human error.
For example, consider clinical notation: the process involves multiple parties—doctors, patients, and administrative staff—must ensure that the documentation process for patient interactions are accurately recorded and up-to-date before being passed over to the medical billing and coding team. Similarly, claims processing requires extensive data validation, cross-checking, and approvals from multiple departments before insurance reimbursements are finalized. In compliance, healthcare organizations face an ever-changing regulatory landscape, where employees must continually review and update practices to meet evolving standards, consuming valuable time and resources.
These examples highlight how manual execution of healthcare workflows can be time-consuming, prone to errors, and highly inefficient. As healthcare organizations scale, the need for more accurate, timely, and automated workflows becomes critical to improving both care quality and operational effectiveness.
How AI is Transforming Healthcare Workflows
AI is revolutionizing how healthcare organizations manage complex workflows, particularly those that are data-heavy and require high levels of oversight. The key to this transformation lies in data—specifically, how healthcare providers and health tech companies process, interpret, and utilize it to improve outcomes.
Data is the backbone for every healthcare workflow, and it’s growing more complex with the proliferation of electronic health records (EHRs), medical imaging, lab results, and patient-generated health data. As this data accumulates, healthcare providers often struggle to extract meaningful insights and act on them quickly. This is particularly true for complex data workflows, which are largely composed of unstructured data and represent a significant portion of all healthcare information. What's even more critical is that much of this data represents institutional knowledge essential to patient care, and it must be preserved and shared efficiently.
With the introduction of large language models (LLMs), it is now possible to process vast amounts of healthcare data at unprecedented speeds, automating intricate workflows while reducing the operational burden on healthcare staff. However, the challenge lies not just in having the data, but in unlocking its full potential to improve healthcare delivery. Here are just a few areas in which AI can help transform critical workflows in healthcare:
Patient Care Coordination: AI can automate the scheduling, referral, and follow-up processes across multiple healthcare providers, ensuring timely and coordinated care without manual intervention.
Quality Assurance: AI can enhance quality assurance in healthcare by automatically analyzing clinical data, identifying patterns, and detecting anomalies to ensure accuracy, consistency, and adherence to clinical guidelines.
Claims Processing: AI can handle the validation and approval of thousands of insurance claims, cross-referencing data and detecting anomalies to prevent errors and fraud.
Regulatory Compliance: AI can monitor regulatory changes in real-time and ensure healthcare organizations remain compliant by automatically updating internal policies and procedures.
Medical Documentation: AI-powered tools can automate the transcription and organization of medical notes, freeing healthcare professionals from administrative burdens and allowing more time for patient care.
The Challenge of Unlocking Value from Complex Healthcare Data
Healthcare data is often complex by nature. But what exactly makes healthcare data "complex"? It's more than just its volume. Complex data can be unstructured, such as physician notes, medical images, or patient feedback. It can involve multiple layers of relationships—between patient records, treatment plans, medications, and lab results. The sheer number of factors to consider when analyzing patient data or making clinical decisions makes it challenging to process manually.
Imagine trying to make sense of health data from various departments—each using different systems, formats, and protocols. Manually interpreting this data takes significant time and effort, potentially slowing down decision-making and increasing the risk of errors that could impact patient outcomes.
Healthcare organizations often deal with this complexity through manual interventions—employing large teams of administrators and analysts to reconcile data across systems. Alternatively, they may invest significant time in building machine learning models to automate basic tasks, but these models often require constant updates and still struggle to fully capture the complexities of healthcare data.
How AI is Changing Healthcare Data Processing
AI is fundamentally transforming the way healthcare organizations process complex data. Unlike traditional methods that require humans to map data relationships or define structures, large language models (LLMs) can dynamically reason about healthcare data. But what does it mean for AI to "reason about data"? Just as clinicians identify patterns in patient symptoms or treatment outcomes, AI can uncover connections between data points that may be missed by humans. Achieving this level of reasoning today using simple algorithms is nearly impossible, and even if feasible, it would come at a high cost. Ultimately, this enables healthcare providers to automate decision-making processes with far greater speed and accuracy using less resources.
In healthcare settings, where workflows often depend on complex, interconnected data, AI’s ability to reason about data is crucial. AI doesn’t just process healthcare information; it interprets it, deriving insights and making decisions that previously required human expertise. This opens the door to automating a wide range of complex healthcare workflows, from diagnosis to administrative tasks, in ways that were never possible before.
Introducing Gradient’s Data Reasoning Platform
Gradient’s Data Reasoning Platform leverages AI to extract, reconcile, and reason about complex, unstructured data, while its proprietary Control System incorporates guardrails and human feedback to ensure high accuracy. Given the stringent regulations in industries like healthcare, Gradient adheres to the highest security and compliance standards, including SOC 2 Type 2, HIPAA, and GDPR, to ensure full regulatory alignment.
From quality assurance and clinical notation to claims processing and regulatory compliance, Gradient’s platform empowers healthcare organizations to tackle the most challenging aspect—automating complex data workflows. By leveraging the full potential and utilization from healthcare data, healthcare providers and health tech companies can leverage their institutional knowledge to improve both operational efficiency and patient care.
For instance, in the realm of compliance monitoring, Gradient’s platform can automatically review regulatory updates, cross-reference them with existing healthcare policies, and flag areas that require attention. In patient care coordination, it can automate the flow of information between providers, ensuring that patients receive the right care at the right time.
Take a look at some healthcare use cases in detail that healthcare providers and health tech companies are using Gradient for today.
Unlocking untapped data to automate healthcare processes is challenging, but when done right, it leads to better patient outcomes, reduced administrative burden, and a more efficient healthcare system. As data workflows grow more complex, healthcare organizations face inefficiencies and errors from manual intervention. AI is transforming this by automating data-driven processes, enabling better care with less overhead. Gradient’s Data Reasoning Platform simplifies even the most complex data, streamlining workflows and paving the way for a more automated future. As the industry evolves, automating these processes will be essential for improving both efficiency and patient care.