The Promise of Large Language Models (LLMs) in Clinical Documentation

Clinical documentation plays a multi-faceted role in the world of value-based healthcare, which by default means it’s a mission-critical aspect for providers. For instance, accurate documentation is critically vital to support full and fair reimbursement. For patients, on the other hand, documentation is key to recording their medical history, current diagnosis, and ongoing treatment plan. And like any mission-critical system, it can create a heavy burden for clinicians and medical staff. According to a report by JAMA Internal Medicine, physicians spend nearly two hours each day after office hours to complete clinical documentation.

The issue of burnout has been a major talking point in the last decade when it comes to clinical documentation. While there have been several technological advances, especially in medical transcription, that have helped alleviate some of that burden, none have held the promise that large language models (LLMs) bring to the table.

The Prospect of LLMs in Clinical Documentation

LLMs were launched into an exponential trajectory of popularity with the launch of OpenAI’s ChatGPT, which brought the foundational models to the forefront of commercial application. These models excel at processing and manipulating natural language, which makes them great at generating, summarizing, classifying and extracting content in text form. By virtue of these capabilities, LLMs have emerged as the single most transformative technology in the field of clinical documentation.

Content synthesis and summarization

LLMs are capable of synthesizing data from numerous sources to generate contextual drafts for speedier collaboration between parties (for instance, between payers and hospitals). For instance, LLMs can streamline the creation of clinical documents by distilling ambient transcripts into a clearly distinguished Subjective Objective Assessment Plan (SOAP) format, converting large volumes of unstructured data into a universal, structured, systematic format.

On the other hand, using LLMs to optimize medical dialogue summarization can provide both physicians and patients with concise points that are easy to understand and retain. Similarly, LLMs can be employed to respond efficiently to patient queries, offering quick and accurate answers to questions regarding diagnoses, treatments, or medications, thereby saving time for healthcare providers while keeping patients well-informed.

Predictive health analytics

When exploring the possibility of LLMs in clinical documentation, predictive analysis of patient data emerges as a high-impact area. By effectively analyzing patient data, LLMs can highlight patients who stand a higher risk of certain conditions, allowing providers to plan more efficient treatment plans.

Improved value-based outcome

Their ability to extract and process unstructured data allows LLMs to provide unprecedented support to clinical decision-making tools and predictive models, based on nothing but natural language inputs. This includes providing data-driven insights into the potential risks and benefits of different treatment paths and informing balanced clinical decisions.

From an administrative perspective, they can automate the coding of medical procedures and services with superhuman accuracy, identifying and correcting coding errors and facilitating improved efficiency in billing processes. This would, in turn, lead to a significant reduction in denied claims and financial penalties.

The Risks and Road Ahead

For as long as LLMs have enjoyed the limelight, there has been a growing concern in the shadow: reliability of models. LLMs are pre-trained on a vast pool of data. However, the purpose of this training data is to decode human language, not use it as a knowledge repository.

The lack of reliability of LLMs takes many forms, but the one that is talked about is hallucination. This happens when an LLM that is faced with a question it does not know the answer to start generating an “educated” guess that may include false information or made-up facts.

There are a few other reliability issues with LLMs like:

  • Sycophancy: The LLM generates responses that conform to the user’s beliefs rather than the factually correct information
  • Potentially risky behavior: The LLM tries to manipulate corresponding systems and human users through its responses (read how GPT-4 convinced a human to fill a CAPTCHA for it)
  • Overconfidence: The LLM repeatedly generates incorrect responses due to overdependence on training data.

Proper guardrails can, however, ensure the model’s accuracy, which includes undertaking traditional validation exercises, developing a process to flag high-risk errors in a clinical setting, and always keeping a “human in the loop”. Moreover, while LLMs are more or less reliable when it comes to reductive tasks like analyzing data, summarization, categorization, and extraction), the risks are much higher for generative tasks.

Despite their shortcomings, LLMs have a disruptive potential that can not only transform medical documentation but also elevate the quality-of-care outcomes with greater efficiency. However, the application of this technology calls for thorough testing before it is implemented by care providers. Altysys, a technology expert partner, helps care providers rapidly prototype and implement LLMs across healthcare use cases to mitigate rising clinician burnout and derive the greatest possible value. Get in touch with us today and start reaping the benefits of LLMs in your medical documentation workflow.


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

Automating Medical Transcription Workflow

About the company

A California-based, leading healthcare business process outsourcing firm specializing in medical transcription, billing, claim processing, and revenue cycle management services for providers, clinicians, and QMEs/IMEs.


Business Need

After two decades of serving global doctors and hospitals, the company aimed to further update the ERP system with novel technologies, offering clients and users seamless interaction. Their goal was to automate their document processing phase, which was
traditionally an essential but time-consuming process. The company wanted to optimize the workflow, enabling multiple individuals to collaborate effectively on ongoing tasks.

Therefore, the company enlisted the expertise of Altysys to –

  • Develop cutting-edge products capable of executing tasks, such as word processing, editing, and system design
  • Design an intricate workflow structure to facilitate simultaneous collaboration among numerous medical transcribers
  • Effectively increase the volume of work delivered

Solution

After assessing the customer’s IT systems in depth, Altysys decided to re-engineer the application leveraging advanced technologies. The solutioning team –

  • Opted for cloud native development with microservices architecture
  • Redefined the UI/UX of the medical transcription application
  • Automated the uploading and downloading of transcription files to and from the different cloud drives or file systems
  • Automated the reading of files using OCR
  • Deployed a transcription solution for audio files that converted speech to text
  • Interpreted the medical information and categorized the data in notes, operative reports, patient records, consultations, and discharge summaries

Business Impact

  • Improved time management and productivity of transcribers and QCs by 30%
  • Reduced time spent in reading and interpreting medical transcriptions by physicians, resulting in better patient retention and increased patient volume

About Altysys:
Altysys, founded by executives with several years of experience in technology consulting and services, is a provider of healthcare technology consulting and solutions. Headquartered in Bengaluru – India’s Silicon Valley, Altysys is a data and cloud-first company with deep expertise in health clouds, data interoperability, data analytics, GenAI and AI/ML enabled technology solutions, serving Payers, Providers, Health Techs, and Pharma.

Clinical Decisioning Systems Analytics

About the Company

A reputed healthcare technology company providing critical drug and medical device databases to healthcare providers, payers, clinicians, technology developers, and pharmaceutical retailers.


Business Need

Medication alerting is crucial for ensuring patient outcomes. However, it has also caused concern to patients, hospitals and physicians. A research study found that patients and physicians negate 90% of medication alerts and consider over half of the notifications irrelevant. Due to over-alerting, timing mistakes, and limited information, physicians suffer from alert fatigue, causing substantial risk to patient safety.

As a result, hospitals have to spend significant time and money to pull out medication alerting data from electronic health records (EHRs), understand their underlying cause, and analyze their impact on patient care and hospital operations.

Therefore, the client reached out to Altysys to develop an analytics system that would –

  • Decrease alert noise
  • Highlight patients at greater risk of harm
  • Provide targeted medical warnings
  • Provide pharmacogenomics and Best Practice Advisories (BPA)

Solution

The Altysys team comprehensively assessed the client’s requirements and formulated a solution roadmap. The team then put the plan into action –

  • Developed a Tableau-based analytics solution for clinical decisioning to identify the top ten medication alerts activated at the healthcare provider’s end
  • Designed an intuitive dashboard to see the impact of medication alert changes
  • Enabled analysis of the reasons behind each medication alert
  • Utilized AWS tech stack to integrate the solution with the hospital’s Epic system that would pull medication alert statistics and patient data and analyze the data
  • Integrated the analytics solution with an alerting system to close the loop and optimize the alert settings based on the solution’s recommendations or findings
  • Deployed the solution with BPA service

Business Impact

  • Streamlined medical alerting system with targeted medical warning for conditions such as QT Prolongation and Opioid Risk, along with BPA and drug-drug interaction alerts
  • Enhanced alerting content with pharmacogenomics and alternative therapies
  • Identified and resolved CDS malfunction with actionable guidance

About Altysys: Altysys, founded by executives with several years of experience in technology consulting and services, is a provider of healthcare technology consulting and solutions. Headquartered in Bengaluru – India’s Silicon Valley, Altysys is a data and cloud-first company with deep expertise in health clouds, data interoperability, data analytics, GenAI and AI/ML enabled technology solutions, serving Payers, Providers, Health Techs, and Pharma.

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