From Spotify Wrapped to whatever Apple Music’s version of that is to actual wrapping paper, it’s clear that 2023 is steadily winding down.
In the spirit of reflection, we decided to look back on the last year of resources here at IMO – including blogs, webinars, eBooks, whitepapers, and more – to round up our most listened to songs engaging content. Just as music is categorized by genres, we’ve organized our top pieces of the year by topic, spanning: AI in healthcare, NLP and unstructured EHR data, and data quality in healthcare.
AI in healthcare
Natural language processing (NLP), large language models (LLMs), machine learning (ML). This year has spurred many across healthcare to become more familiar with these artificial intelligence (AI)-related acronyms. The content below will help get you up to speed.
Blog | A primer on the language of artificial intelligence
Looking for a place to get started? This primer helps explain NLP in healthcare and how different types of AI interconnect.
Insight brief | How to train your LLM: And why semantic interoperability is the key to success
This brief provides an overview of LLMs and their significance (and limitations) in processing and deciphering complex clinical narratives.
Blog | Generative AI and ChatGPT: Healthcare’s new wave
Experimenting with NLP in healthcare, specifically generative AI tools, has its fair share of risks and rewards. This blog and corresponding white paper help explain.
On-demand webinar | ChatGPT and Generative AI: Paradigm shift in healthcare and biopharma innovations
Explore ChatGPT’s rapid ascent, its role in extracting insights from unstructured healthcare data, and its applications in areas such as real-world evidence and clinical trials.
On-demand webinar | The power of LLMs in healthcare: How they could propel the industry forward
In this webinar, IMO subject matter expert Amol Bhalla, MD, unpacks the complexities of achieving semantic interoperability and explains why clinically trained LLMs, which are then used for NLP applications, are critical to derive accurate healthcare insights.
NLP and unstructured EHR data
With nearly 80% of clinical data in unstructured formats, a wealth of information is largely inaccessible – making it highly difficult and inefficient to use. That’s because transforming information – such as clinical free text – into structured, standardized, usable data is both costly and time-consuming. Many organizations are pursuing NLP solutions to help extract, standardize, and codify unstructured free text from sources like clinical electronic health record (EHR) notes. The below resources explain further.
Insight brief | Reading like a human: The key to successful natural language processing in healthcare
Clinicians use the EHR “notes” section for patient information, but free-form text can lead to unstandardized data. Healthcare organizations can address this with NLP solutions designed to scan and interpret the text.
Blog | Unstructured EHR data: From scattered to streamlined
Explore IMO’s normalization solution – now with NLP – that extracts, standardizes, and enriches structured and free text clinical data and maps it to standard codes.
On-demand webinar | Harnessing AI: How NLP and terminologies can shape the future of rare disease care
Understand how NLP and AI, when coupled with accurate terminology, can help reduce the time it takes to identify patients with rare diseases.
Blog | It’s time to start dealing with unstructured SDOH data
Learn how the Centers for Medicare & Medicaid Services (CMS) is attempting to tackle healthcare disparities by requiring organizations to collect more SDOH data.
Data quality in healthcare
In the world of health IT, data quality is foundational. Without top-notch data, any initiatives an organization does want to use that information might not be as precise or useful, which can impact patient care and decision-making. Below are our most popular pieces on the tedium of manual data cleansing and how data quality is impacting initiatives ranging from public health to tech investments.
Blog | Demystifying value set management through improved data quality
In health informatics, accurate value set management is the key to efficiently identifying patients within a target population, simplifying a historically complex process.
Insight brief | Moving beyond data cleansing: How data scientists are reclaiming their time
Data scientists spend around 40% of their time on data preparation, diverting their focus from more engaging tasks like modeling and analysis. This insight brief explores how healthcare and health IT leaders tackle time-consuming data cleansing tasks.
Blog | The CDC’s DMI: A turning point for data quality in public health
The Centers for Disease Control and Prevention (CDC) has advanced national health threat surveillance via the Data Modernization Initiative (DMI), but there’s still room for progress.
Survey | Intelligent insights: Data challenges, tech investments, and shifting provider priorities
IMO conducted an in-depth survey of more than 300 leaders responsible for implementing and purchasing technology for US hospitals and health systems to see how they identify what the right solutions are amid fluctuating priorities.
