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OMNY Health’s Real-World Data is Redefining Clinical Trial Control Arms

The pharmaceutical industry is rapidly embracing real-world evidence (RWE) to accelerate drug development and regulatory approvals. External control arms (ECAs), built using real-world data (RWD), are transforming clinical trials by offering an alternative to traditional placebo groups. By reducing recruitment burdens, increasing statistical power, and aligning with regulatory expectations, ECAs have the potential to redefine late-stage drug development. 

OMNY Health is at the forefront of this shift, providing research-ready EHR datasets that seamlessly integrate into clinical trial designs. Our recent study demonstrates how EHR-derived control arms can effectively mirror traditional placebo groups, offering a robust and scalable solution for regulatory submissions. 

Building an External Control Arm with OMNY Health’s RWD 

To evaluate the feasibility of an ECA in a late-stage clinical trial, OMNY Health leveraged six specialty dermatology networks and six integrated delivery networks (2017-2024) from its real-world data platform. The study constructed an ECA for the Phase 3 POETYK PSO-1 trial, which evaluated deucravacitinib versus placebo for moderate-to-severe plaque psoriasis.

The ECA was built using precise patient selection criteria that ensured alignment with the trial’s placebo group. Patients were included if they met physician global assessment (PGA) score eligibility, ensuring comparable disease severity. Baseline treatment history was carefully controlled, and topical medication use was restricted prior to the index severity visit to reflect treatment-naïve status. By tracking patients longitudinally, we assessed week-16 outcomes using structured EHR data, mirroring the trial’s methodology. 

While some demographic differences were observed—ECA patients were older, more likely to be female, and had a different racial distribution compared to the placebo arm—the disease severity and baseline clinical characteristics were well matched. 

Key Findings: OMNY Health’s ECA vs. Trial Placebo Arm

The real-world ECA demonstrated a significantly higher response rate compared to the traditional placebo arm, reinforcing its validity as a comparator. 

  • 18.5% of ECA patients achieved PGA 0/1 (clear or almost clear skin), versus only 7.2% in the placebo arm. 
  • Despite differences in age, gender, and race, disease burden and severity scores aligned closely between the ECA and placebo group. 
  • Stratification by prior biologic use, systemic therapy history, and weight showed no notable impact on outcomes. 

One notable finding was racial disparities in achieving the primary endpoint within the ECA, suggesting that differences in patient demographics between the ECA and placebo group may have contributed to the observed differences in response rates. Further research could explore adjustments such as population weighting to refine ECA comparability even further. 

These results confirm that OMNY Health’s real-world dataset can be used to generate ECAs that replicate clinical trial placebo groups, while also revealing new insights into patient diversity, treatment history, and long-term outcomes. 

Why OMNY Health’s Data is a Game-Changer for External Control Arms 

Traditional clinical trials face high recruitment costs, ethical concerns over placebo use, and long enrollment timelines. OMNY Health helps to eliminate these barriers by offering regulatory-grade EHR data that aligns with clinical trial endpoints. 

With 85M+ patients, 1B+ encounters, and 4B+ unstructured clinical notes, our dataset provides a scalable and statistically powerful alternative to traditional control groups. By incorporating structured disease severity scores, prescribing patterns, and physician-reported outcomes, our ECAs offer more efficient and cost-effective alternative to prospectively collected placebo data.

Beyond reducing recruitment time, OMNY Health’s real-world ECAs improve trial generalizability, capturing diverse patient populations often underrepresented in traditional studies. As the FDA increasingly endorses RWD for regulatory decision-making, the ability to integrate ECA’s into pivotal trials is becoming a competitive advantage for pharmaceutical companies.

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From Symptom to Strategy: What Itch Intensity Data Tells Us About Pruritus Management

Pruritus, or chronic itch, is one of the most common symptoms reported in dermatology, yet real-world treatment patterns remain poorly characterized. While topical therapies are typically the first-line approach, severe cases often require systemic interventions. But how do clinicians determine when to escalate therapy? OMNY Health conducted a real-world evidence study leveraging research-ready EHR data from six specialty dermatology networks to examine how itch intensity, a critical but underutilized measure, influences real-world prescribing patterns. By integrating structured and unstructured clinical data, OMNY provides new insights into the relationship between symptom severity and treatment decisions.

Quantifying Pruritus Severity in Real-World Data 

While pruritus is a symptom, its severity can significantly impact quality of life and treatment choices. This study focused on patients with a documented 10-point itch intensity score, captured through physician assessments and patient-reported outcomes. The dataset included 7,330 patients with 8,115 encounters, capturing the following structured severity measures:

  • Itch intensity scores (0-10 scale) recorded alongside pruritus-related encounters. 
  • Pruritus-related prescriptions categorized into topical and systemic treatments. 
  • Demographic characteristics, including age, gender, and race, to understand patient stratification. 
  • Treatment patterns by disease severity, assessing whether increasing itch intensity influenced therapy selection. 

By leveraging EHR-derived severity assessments, the study provides a more granular understanding of how treatment decisions align with symptom burden.

How Itch Intensity Drives Treatment Decisions

Findings revealed a clear relationship between itch severity and systemic therapy use, while topical therapies were prescribed at consistent rates across all levels of severity.

  • Topical corticosteroids were the most prescribed treatment, used in nearly half of all pruritus-related visits, regardless of itch intensity. 
  • Topical calcineurin inhibitors were prescribed far less frequently, at around 10% of cases, with minimal use of alternative topical therapies. 
  • Systemic therapy prescriptions increased as itch severity worsened, with sedative antihistamines emerging as the most commonly used option. 
  • Prescription rates for sedative antihistamines climbed from 13% in mild cases to 26% in severe cases, while other systemic treatments—including non-sedative antihistamines, systemic doxepin, SSRIs, and opioid receptor antagonists—were prescribed far less frequently. 

The increasing use of sedative antihistamines in severe cases suggests a reliance on limited systemic options, leaving a gap in alternative therapies for patients with refractory pruritus.

Why Itch Intensity Matters for Clinical and Research Applications

The underutilization of structured itch severity scores in real-world studies has historically limited the ability to quantify treatment impact. Our findings reinforce why itch intensity should be a standard measure in both clinical decision-making and drug development. For clinicians, understanding real-world prescribing trends tied to symptom severity can optimize treatment pathways and inform escalation decisions. For researchers and trial sponsors, itch intensity scores provide an opportunity to refine patient segmentation, support cohort identification, and assess real-world treatment responses. For drug developers, the limited use of alternative systemic therapies highlights a need for novel treatments, particularly for patients with refractory pruritus.

Advancing Dermatology Research with Real-World Data

This EHR-based study provides new insights into how structured itch intensity measures correlate with real-world prescribing behavior. By integrating structured severity assessments with prescribing data, OMNY Health’s research-ready datasets offer a unique lens into treatment trends, patient stratification, and disease burden.

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Advancing SDoH research with unstructured clinical notes to improve patient care

Every patient’s story extends beyond structured medical records. Social determinants of health (SDoH), such as economic insecurity, often go unrecorded in traditional coding systems, leaving critical gaps in understanding patient needs. A recent study utilizing OMNY Health’s real-world data platform showcases the power of unstructured clinical notes in identifying financial hardship among psoriasis patients—insights that structured EHR data alone did not capture. 

Leveraging NLP to Extract Real-World Patient Challenges 

The OMNY Health Platform was used to access electronic health record (EHR) data for patients with International Classification of Diseases, Tenth Revision (ICD-10) codes related to economic insecurity. These codes, outlined in Table 1, include classifications for financial instability, lack of adequate food and safe drinking water, extreme poverty, low income, and material hardship. However, structured data alone failed to capture the full scope of patient struggles. 

By applying natural language processing (NLP) to unstructured clinical notes from five specialty dermatology networks (2017-2019), researchers uncovered 686 patients with financial hardship indicators that would have otherwise gone undetected. These were patients whose struggles—such as insurance challenges, difficulty affording medications, and financial stress impacting care decisions—were only documented in free-text notes, never coded in structured fields. 

At a probability threshold of 0.91, the model achieved a 91% precision rate, though manual review found that 60% of flagged sentences were true positives. This highlights the need for continued refinement while demonstrating how clinical notes provide deeper, more patient-centric insights than structured data alone. 

What Structured Data Misses: A Holistic Approach to SDoH 

Traditional structured EHR data often fails to capture the full scope of social determinants of health (SDoH), creating critical blind spots in patient care. Many healthcare decisions—such as delaying treatments, switching medications, or discontinuing care altogether—are influenced by social and financial challenges that remain undocumented in structured records. 

For example, economic insecurity is a major yet often invisible factor shaping healthcare journeys. By analyzing clinical notes, researchers have identified patients facing financial hardship that was not reflected in coded data, underscoring the need for a more comprehensive approach to SDoH research. But financial instability is just one piece of a much larger puzzle.  

OMNY Health’s dataset goes beyond economic hardship, capturing a wide range of social determinants that impact health outcomes, including:   

  • Housing Instability – Identifying patients experiencing homelessness or frequent relocations that may affect continuity of care. 
  • Food Insecurity – Detecting concerns related to nutritional deficiencies and limited access to healthy food.  
  • Transportation Barriers – Recognizing challenges patients face in accessing healthcare facilities. 
  • Education and Health Literacy – Understanding how limited education levels influence patient adherence and treatment outcomes. 
  • Social Support and Caregiver Burdens – Capturing notes related to lack of family or community support, impacting long-term disease management. 

By incorporating unstructured clinical notes into SDoH research, OMNY Health’s data helps: 

  • Identify at-risk patients who may otherwise be invisible in structured records. 
  • Support more effective intervention strategies tailored to individual social challenges.   
  • Enhance real-world evidence (RWE) generation to guide healthcare policy and decision-making. 

As healthcare organizations seek to advance health equity, leveraging unstructured data provides a more complete view of patient experiences—ensuring that social determinants are not just acknowledged, but actively addressed in care strategies and policy development. 

Expanding the Impact of Unstructured Data  

This study is just the beginning. Researchers are expanding NLP-driven approaches to explore other SDoH domains, including housing instability and undereducation. With continued model refinement, these insights will help healthcare providers, researchers, and policymakers gain a more complete understanding of patient experiences beyond structured EHR limitations. 

As healthcare shifts toward patient-centered solutions, unstructured clinical notes will be key to closing critical data gaps. The ability to capture these hidden patient struggles has the potential to transform care delivery and research.

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Why Clinical Measures Matter: Linking Disease Activity to Treatment Decisions in Actinic Keratosis

In dermatology, real-world data plays a crucial role in understanding disease progression and optimizing treatment strategies. Actinic keratosis (AK), a common precancerous skin condition, requires tailored management approaches based on disease severity. OMNY Health’s vast dermatology dataset provides new insights into AK treatment patterns by leveraging structured electronic health records, including lesion count and patient-reported pain scores.

Measuring Disease Activity: A Data-Driven Approach

One of the unique aspects of OMNY Health’s dataset is the inclusion of real-world AK disease activity measures. Traditionally, treatment decisions for AK have been guided by lesion count, but OMNY’s data also incorporates pain scores using the 0-10 Visual Analogue Scale (VAS). This dual-measure approach provides a more comprehensive understanding of how AK severity impacts treatment decisions.

Linking Disease Severity to Treatment Patterns

Analyzing data from six specialty dermatology networks within the OMNY Health platform (2017-2024), researchers examined how lesion count and pain VAS influence real-world treatment strategies. The study included 334,410 patients with 704,665 assessments, highlighting distinct trends in treatment selection.

Key findings include:

  • Fluorouracil prescriptions increased with lesion count but decreased with pain severity. 
  • Lesion destruction procedures (e.g., cryosurgery, electrosurgery) and photodynamic therapy became more common as pain scores increased. 
  • Treatment approaches remained relatively stable across different lesion counts, suggesting that pain level plays a more significant role in guiding intervention choices. 

Clinical Implications: The Power of Real-World Dermatology Data

These findings emphasize the value of structured EHR measures in refining dermatological treatment strategies. By incorporating both lesion count and pain VAS, OMNY Health’s dataset enables providers and researchers to:

  • Identify patterns in real-world clinical decision-making. 
  • Optimize treatment plans based on both physical disease burden and patient-reported symptoms. 
  • Treatment approaches remained relatively stable across different lesion counts, suggesting that pain level plays a more significant role in guiding intervention choices. 

Next Steps: Expanding Real-World Evidence in Dermatology

OMNY Health continues to enhance its dermatology RWD offerings by integrating unstructured clinical notes and refining disease activity metrics. Future analyses could leverage clinical notes to provide richer insights into treatment rationale and long-term outcomes.

As dermatology evolves, real-world evidence will be essential in bridging the gap between clinical research and everyday patient care. OMNY Health’s commitment to data-driven insights ensures that providers have access to the most comprehensive, research-ready EHR datasets to inform their decisions.

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OMNY Health Achieves HITRUST e1 Certification: A Milestone in Healthcare Data Security

We at OMNY Health are thrilled to announce that the OMNY Health Platform has successfully achieved HITRUST e1 Certification, a landmark achievement in our ongoing commitment to foundational cybersecurity controls and information risk management in healthcare. 

HITRUST e1 Certification focuses on foundational cybersecurity and the most critical set of controls for essential cybersecurity hygiene. This certification demonstrates that OMNY’s Ecosystem Platform has a comprehensive set of rigorous controls and best practices in place for essential for cybersecurity hygiene and protecting sensitive information. 

“The HITRUST e1 Validated Assessment is a good tool for cyber-aware organizations like OMNY HEALTH that want to build assurances and progressively demonstrate due diligence around information security and privacy,” said Robert Booker, Chief Strategy Officer at HITRUST. “We applaud OMNY HEALTH for their commitment to cybersecurity and successful completion of their HITRUST e1 Certification.” 

Achieving HITRUST Certification is no small feat. It represents countless hours of hard work, meticulous attention to detail, and an unwavering commitment to excellence from our entire team. This certification validates our robust approach to data security and privacy, covering 19 domains of information security. 

“The HITRUST e1Certification is more than just a badge of honor – it’s a rigorous, comprehensive validation of our security practices,” said Dr. Maik Lindner, OMNY’s Chief Information Security Officer.  “This achievement demonstrates that we’ve implemented a robust set of controls that meet the unique challenges of protecting sensitive healthcare data. It’s a reflection of our proactive approach to security and our commitment to staying ahead of evolving cyber threats in the healthcare industry.” 

For our health system partners, the HITRUST Certification of our platform offers several key benefits:

  • Enhanced Trust: You can be confident that your data is protected by security measures that meet or exceed industry standards.
  • Simplified Compliance: Our certification helps streamline your compliance efforts, particularly with regulations like HIPAA. 
  • Reduced Risk: With our certified security framework, the risk of data breaches and associated costs is significantly reduced.
  • Improved Interoperability: Our certification enhances our ability to securely share and process data across the healthcare ecosystem.

“Achieving HITRUST certification is a testament to our unwavering commitment to data security and privacy in healthcare. This milestone reflects the dedication of our entire team and reinforces our position as a trusted partner in the healthcare data ecosystem. We’re proud to offer our health system partners the highest level of assurance in data protection, allowing them to focus on what matters most – improving patient outcomes,” said Dr. Mitesh Rao, CEO. 

At OMNY, we view this certification not as a final destination, but as a milestone in our ongoing journey of excellence. We are committed to:

  • Continuously improving our security measures 
  • Staying ahead of emerging threats in the digital healthcare landscape
  • Providing our partners with the highest level of data protection 

As we celebrate this achievement, we want to thank our dedicated team and our valued partners for their trust and support. We look forward to continuing our mission of advancing healthcare through secure, innovative data solutions.

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Does Implant Design Matter? Studying Total Knee Arthroplasty in Clinical RWD

Knee replacement surgery has advanced significantly over the years, with innovations in implant design aimed at improving patient mobility and long-term outcomes. However, when it comes to single-radius (SR) vs. multi-radius (MR) femoral implants, how much of an impact does design really have on patient recovery and healthcare utilization? While clinical trials provide controlled comparisons, real-world evidence offers a broader perspective on outcomes across diverse patient populations. 

 To better assess these differences, OMNY Health analyzed real-world data from its orthopedic-focused medtech dataset, evaluating clinical, functional, and economic outcomes in TKA patients. 

Comparing Implant Design in Real-World Settings

This study leveraged data from the OMNY Health Medical Device Database (2017-2021), examining 1,464 patients who underwent unilateral TKA. Patients were categorized into SR (N=1,135) and MR (N=329) cohorts, allowing for direct comparisons of key outcomes.

bar chart showing demographic characteristics among patients with sickle cell

Patient Demographics:

  • Most patients were born between the 1950s and 1960s (SR: 59%, MR: 57%). 
  • Women accounted for the majority of cases (SR: 63%, MR: 70%). 
  • The SR cohort had a higher proportion of White patients (85%) compared to the MR cohort (75%). 
  • More MR patients underwent outpatient procedures (53%) compared to SR patients (43%). 

Key Findings: Minimal Differences Between Implant Designs 

Despite prior speculation that implant design could significantly impact outcomes, this real-world analysis found that SR and MR implants performed similarly across key measures. 

bar chart showing demographic characteristics among patients with sickle cell

Clinical Outcomes: 

  • Mortality rates were low in both cohorts (SR: 1.1%, MR: 0.3%). 
  • Postoperative knee pain was reported at comparable rates (SR: 0.6%, MR: 1.2%). 
  • Implant removal was rare, with no significant difference observed (SR: 0.4%, MR: 0.0%). 

Functional Outcomes: 

  • Non-routine discharge disposition (NRDD) rates were identical (SR: 16.4%, MR: 16.4%), suggesting that implant design did not influence post-surgical mobility. 

Economic & Utilization Outcomes:

  • Length of stay (LOS) was similar across groups (SR: 0.98 days, MR: 0.96 days).
  • Gross charges were slightly higher for SR patients (Median: $43,879 vs. $39,255 for MR), though differences may be driven by factors beyond implant design.

What This Means for Clinical Decision-Making 

The findings suggest that implant design alone does not significantly impact clinical or functional outcomes in TKA patients. Instead, factors such as surgical technique, rehabilitation protocols, and patient-specific factors play a more substantial role in determining recovery and long-term success.

For healthcare providers and medtech companies, these results highlight the value of real-world data in refining orthopedic product development and post-market surveillance. While MR designs have been thought to provide more natural knee movement, this study suggests that real-world functional outcomes do not differ significantly between SR and MR implants.  

Additionally, the slight difference in cost between implant types warrants further investigation to determine cost-effectiveness in value-based care models.

The Role of Real-World Data in Orthopedic Research

By integrating structured EHR data with curated clinical measures, OMNY Health provides real-world insights into medical device performance. This data-driven approach enables:

  • Comparisons of implant designs and surgical techniques to refine best practices. 
  • Better understanding of patient recovery patterns and healthcare utilization. 
  • Support for evidence-based decision-making to optimize orthopedic device selection and patient outcomes.

With orthopedics as a key therapeutic area for medtech innovation, leveraging real-world evidence is essential for ensuring high-quality, cost-effective decision-making in TKA and beyond.

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Breaking Barriers in Sickle Cell Care: Real-World Insights on Treatment & Healthcare Utilization

Breaking Barriers in Sickle Cell Care: Real-World Insights on Treatment & Healthcare Utilization

Sickle cell disease (SCD) presents unique challenges for patients and healthcare providers alike. To better understand these challenges, OMNY Health analyzed real-world data (2017-2024), leveraging its unique ability to integrate structured EHR data with curated clinical measures to provide a comprehensive view of SCD patients. This study examined the demographics, healthcare utilization, and treatment patterns of 10,958 individuals diagnosed with SCD.

Disproportionate Burden and Frequency of Healthcare Use in Sickle Cell Disease

SCD remains a condition that disproportionately affects minority populations. Among the studied population:

  • 82% identified as Black, with additional representation from Hispanic and Asian/Pacific Islander groups, emphasizing racial disparities in disease burden and healthcare access.
  • The average age of patients was 37 years, showing that SCD continues to impact individuals well into adulthood.

Figure 1. Demographic Characteristics of Patient Population

bar chart showing demographic characteristics among patients with sickle cell

API = Asian Pacific Islander
Note: Percentages were based on non-missing data

At the same time, these patients experience high levels of healthcare utilization:

  • 66% had an emergency room visit.
  • 65% required outpatient care.
  • 4.3 inpatient admissions per patient on average, though some required over 185 hospital stays.

These figures suggest that many individuals with SCD struggle with disease management, pain crises, and complications that lead to recurring hospital visits. The high frequency of inpatient admissions, even among younger adults, indicates that preventive strategies may not be reaching those who need them most. Many hospital stays are driven by acute complications, highlighting the importance of early intervention and better access to disease-modifying treatments that could reduce the need for emergency care.

Table 1: HCRU Among Individuals with SCD

 Inpatient AdmissionsEmergency VisitsOutpatient Visits
Mean (SD)4.3 (10.6)7.0 (15.4)34.1 (74.3)
Median (Q1, Q3)2.0 (1.0, 3.0)3.0 (1.0, 7.0)8.0 (2.0, 29.0)
Min, Max1, 1851, 3681, 1147

Comorbidities and Limited Treatment Utilization for Sickle Cell Disease

OMNY Health’s curated datasets capture a wide range of comorbidities, helping to identify broader clinical patterns that impact SCD progression. Among the most common conditions observed:

  • Anemia, chronic pain, and hypertension, which are linked to SCD progression.
  • Gastroesophageal reflux disease (GERD), urinary tract infections, and vitamin D deficiency, highlighting broader health vulnerabilities.

Figure 2: Top clinical comorbidities among individuals with SCD

bar chart showing top clinical comorbidities among individuals with sickle cell

URI: Upper respiratory infection; GERD: gastro-esophageal reflux disease without esophagitis

Despite available treatments, adoption remains low:

  • 11.3% of eligible patients received hydroxyurea, the most used therapy. 
  • Less than 1.1% of patients received newer disease-modifying treatments, such as Crizanlizumab, Voxelotor, L-glutamine, or Stem Cell transplants.

This raises concerns about barriers to access, including cost, provider awareness, and prescribing patterns. Through OMNY Health’s ability to track real-world prescribing trends and treatment adherence, stakeholders can better understand where interventions are needed to improve access.

Moving Forward with Data-Driven Solutions

By leveraging OMNY Health’s robust real-world data, we can provide critical insights into treatment patterns, healthcare utilization, and patient needs. OMNY Health’s curated data and clinical measures enable a more complete understanding of SCD care gaps, supporting efforts to inform policy changes, optimize treatment strategies, and enhance healthcare provider decision-making

For more information about OMNY Health’s product offerings, let’s connect!

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Unlocking Real-World Insights into IBD Treatment: The Power of Clinical EHR and Physician Notes Integration

At OMNY Health, we believe that unlocking the full potential of healthcare data means going beyond structured fields in Electronic Health Records (EHRs). By combining EHR data with unstructured clinical notes, we can uncover insights that truly reflect the complexities of patient care—insights that are often hidden in claims or EHR data alone.

Our recent study on Inflammatory Bowel Disease (IBD) treatment patterns highlights the unique value of integrating EHR data with clinical notes. Using Large Language Models (LLMs), we analyzed over 10.6 million clinical notes from our health systems to explore why IBD patients switch or discontinue biologic treatments. This approach gave us a comprehensive, real-world view of treatment decisions. We identified 7 distinct reasons for treatment alteration across 7 biologics.

Key Findings: Uncovering the Real Reasons for Treatment Changes

While traditional EHRs track structured data like medication prescriptions and diagnoses, they often miss the “why” behind treatment decisions. Our study revealed seven key reasons why biologic treatments are altered, based on unstructured notes that provide deeper context:

  • Adverse Drug Events (16-28%)
  • Finance-Related Reasons (4-24%)
  • Patient-Related Factors (2-9%)
  • Lack of Efficacy (1-14%)
  • Symptom Resolution (1-4%)
  • Drug-Disease Interactions (1-3%)
  • Obstetric Concerns (0-2%)

Figure 1. Reasons for Biologic Switching in IBD

BiologicAdverse Drug Event (%)Drug-Disease Interaction (%)Symptom Resolution (%)Finance Related (%)Patient Related (%)Not Effective (%)Obstetric (%)
Infliximab28.261.922.8724.428.6214.370.48
Adalimumab26.822.871.4412.938.142.871.92
Golimumab0.9600.481.440.4800
Certolizumab1.92000.96000
Vedolizumab16.280.964.317.186.701.441.92
Ustekinumab11.010.4803.832.3900
Risankizumab0.960000.4800

Transforming IBD Care: From Data to Insight

By analyzing clinical notes, OMNY Health’s AI models identified reasons for treatment alterations with 94.5% accuracy. This level of insight isn’t typically captured in structured EHR fields, and it has significant implications for improving patient care.

Understanding the real-world reasons behind treatment changes is essential for refining treatment strategies, improving adherence, and ultimately achieving better patient outcomes. For IBD patients, this means more personalized care tailored to their unique circumstances—whether that’s a financial hurdle, an adverse drug reaction, or a need for a more effective therapy.

The Future of Healthcare Data: A New Era of Personalized Care

At OMNY Health, we’re excited about the future of healthcare data. By integrating EHRs with unstructured clinical notes and leveraging the power of AI, we can identify patterns that were previously overlooked. This approach not only improves our understanding of IBD treatment but also has the potential to transform how we approach care across a wide range of conditions.

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OMNY Health launches new GI real-world data solutions in partnership with leading US gastroenterology practices

new GI real-world data solutions

OMNY Health’s GI partnerships are a pivotal step in its mission to deliver data and insights to accelerate life-changing innovation. With a focus on catalyzing research and improving outcomes, OMNY aims to make a tangible difference for the millions of individuals in the US impacted by gastroenterology diseases

ATLANTA, GA, April 22, 2024 – OMNY Health is pleased to announce its partnership with the nation’s leading community-based independent gastroenterology (GI) practices and integrated delivery networks (IDNs) to launch a novel set of real-world data and evidence solutions for research. Over 5,000 GI providers serving more than 10 million patients are now part of OMNY’s research network, representing care delivery by over 380,000 providers and 75 million patients across the US. The focus on GI practice data is a notable expansion to OMNY’s real-world data ecosystem, which works with providers to support compliant research partnerships at scale. OMNY works with the health systems to enable insights on patient population characteristics, care delivery patterns, and treatment outcomes based on data extracted from de-identified inpatient and ambulatory care electronic health records (EHRs). OMNY’s solutions deliver valuable information on disease severity, rationale behind treatment decisions, treatment efficacy, and impact of social determinants on treatment selection and disease progression. By integrating disparate EHRs into unified, de-identified research data products, OMNY offers researchers and providers the breadth and depth of information needed to address their biomedical and population health research questions.

“United Digestive remains committed to offering our patients the most suitable and cost- effective healthcare solutions. Our dedication to delivering best-in-class GI care is intricately linked with our data-driven approach,” says United Digestive’s Chief Medical Officer, Dr. John Suh. “We are thrilled to announce our partnership with OMNY Health, which allows us to drive further insights from our data to advance patient care.”

“As Florida’s leading gastroenterology provider, Borland Groover’s mission is to provide exceptional care and improve the lives of our patients. Achieving this objective requires a deep understanding of our patients, patterns of care, outcomes, and opportunities for further improvement through data analysis,” said Borland Groover’s CEO, Dr. Kyle Etzkorn. “Our collaboration with OMNY not only aligns with our mission, but it also presents avenues to identify and pursue future opportunities for elevating GI patient care.”

“Enhancing patient care isn’t just a goal; it’s our responsibility, guided by the personal experiences of every patient we serve. Each piece of patient data is not just a statistic; it is a pathway to better outcomes, a testament to our commitment, and a source of inspiration for ongoing progress” said Christa Newton, MBA, CEO, OneGI. “We are excited to partner with OMNY as a tangible step in realizing these guiding principles for the benefit of our patients.”

“It is important to One GI that our patients are represented when it comes to emerging research, care improvement initiatives, and best in class care. Our partnership with OMNY helps ensure our providers have the data and support they need to deliver easy to access exceptional GI care.” – Dr. Michael Dragutsky, Founder, Chairman, OneGI. Key populations available in the GI specialty offerings are aligned with the significant research and development efforts underway in gastroenterology, including ulcerative colitis, Crohn’s disease, celiac disease, short bowel syndrome, reflux disorders, and many others. Like other OMNY Health specialty-focused solutions, the GI offering includes information covering pharmacy orders, lab and diagnostic test results, comorbidities, symptom checklists, and provider clinical assessment scores. “We are thrilled to welcome our new gastroenterology providers to our network. Over the past two years, we have made great strides connecting researchers and providers participating in our dermatology and ophthalmology research networks. We look forward to building on that impact in the field of gastroenterology,” said Mitesh Rao, MD, CEO, OMNY Health.

About OMNY Health

OMNY Health connects the healthcare ecosystem through data and insights to transform healthcare delivery, improve clinical outcomes, and address patients’ unmet needs.   The OMNY platform serves as a centralized resource for healthcare stakeholders to participate in data sharing and research services at scale, fueling innovation where patients need it the most. For more information, go to www.marketing-dev.omnyhealth.com.

MEDIA INQUIRIES: media@omnyhealth.com 

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Five Natural Language Processing Subtasks that Large Language Models Can Perform to Improve Healthcare

Five Natural Language Processing Subtasks that Large Language Models Can Perform to Improve Healthcare

In late 2022, the technology world was turned upside down as OpenAI released ChatGPT, its new artificial intelligence (AI) model.  Unlike most previous natural language processing (NLP) models, this model contained billions of parameters, was trained on a corpus of unstructured data with unprecedented size, and underwent a novel alignment process to better orient the model towards human needs.  The result was a model that provided superior performance for various NLP-related tasks and professional certification exams (including medical ones) and could produce realistic-sounding text that even the most hardened AI cynics could not deny.  Technological and legal impacts were felt almost immediately. Many technology companies began to invest in the new field of generative artificial intelligence (genAI) with an eye toward the potential for financial benefits. 

However, lost in all the excitement, is the fact that genAI relies largely on the bread-and-butter NLP methods and subtasks that have supported and improved many industries, including healthcare, over the past decade or two, with the additional capability of text generation.  Unlike previous NLP models, newer large language models (LLMs) can perform many different NLP subtasks with one single model. 

So, what are the different subtasks that a state-of-the-art LLM can perform? Without further ado, here are five NLP subtasks that can be programmed into LLM to improve healthcare:

Text Classification

Text classification, or the task of distributing text into categories, is often seen as the most primitive NLP subtask.  A classic non-healthcare example of this subtask is categorizing reviews (e.g., critiquing products or movies) into having positive or negative sentiments. Text can be classified at the sentence or paragraph level, depending on the use case.   

In healthcare, the clinical notes for patients can be classified as to whether they identify patients as having certain attributes or disease characteristics.  Using that information, appropriate personalized interventions and treatments can then be used to improve health.  Over the past year, work at OMNY Health has focused on several text classification projects, including the following:

  1. Classifying generalized pustular psoriasis (GPP) patients or patients with a rare, devastating form of the skin disease psoriasis, by their disease status (flare versus non-flare) in collaboration with a life sciences partner. 
  2. Classifying psoriasis patients as to whether they received joint assessments and/or rheumatology referrals in the presence of psoriatic arthritis symptoms, in collaboration with life sciences partners. 
  3. Identifying patients adversely affected by social determinants of health, including illiteracy and financial and housing insecurity. 

The results of the study are available (View Report).

Named Entity Recognition

Also known as token classification, named entity recognition (NER) involves classifying individual words or phrases as reflective of an entity, for example, a person, place, or organization.  It differs from text classification in that every word is classified, unlike text classification in which sentences and paragraphs are classified.

In healthcare, common entities detected include symptoms, medications, and protected health information (PHI). PHI may be related to individuals, such as names, addresses, and contact information, in larger efforts to deidentify clinical notes before their input in an NLP model.  In fact, at OMNY Health, more than 20 PHI entities are identified and removed from clinical text before NLP to protect patient privacy and to comply with the Health Insurance Portability and Accountability Act (HIPAA) of 1996.

Relation Extraction

Relation extraction (RE) represents a relatively more complex subtask in which the relationships between various entities are determined.  For example, if two “person” entities are detected in the text, an RE task may be used to identify whether the two entities are married.  It is usually performed downstream of an NER step.  Previously, RE tasks often required expensive annotation and labeling processes to be performed successfully, although this requirement has faded with the advent of newer LLMs that can label and annotate notes automatically.

In healthcare, RE applications include identifying patient-doctor relationships for clinical note understanding, as well as more general clinical RE to model medical knowledge ontologies such as the Systematic Nomenclature in Medicine – Clinical Terms (SNOMED-CT).  At OMNY Health, work has been performed to identify relations between detected symptom and medication entity-pairs to determine if they comprised an adverse drug event (ADE).

Closed-domain Question Answering

Closed-domain question answering is an NLP subtask in which a text is paired with a question about the text, like a reading comprehension question on a standardized exam.  The question-context pair is then passed to an NLP model, which then extracts the answer to the question from the text.

In healthcare, question-answering pipelines can be performed to extract practically any conceivable information about a patient from clinical notes, when paired with the correct prompt.  The answer can then be processed and refined using additional NLP steps.  As an example, a clinical note about medication discontinuation could be passed as input to an NLP model paired with the question, “Why was the medication discontinued?”  The answer could then be passed to a text classification model which categorizes the reason for treatment discontinuation.  At OMNY Health, we have constructed such a pipeline to determine reasons for treatment discontinuation for various medications (View Report).  A second application of question-answering is the extraction of severity scores from clinical notes for various diseases.

Text Generation

Finally, text generation is the process of composing new, original text in response to a provided prompt.

The basic mechanism revolves around predicting the next word based on the input prompt and the sequence of previously generated words.  As alluded to in the introduction, modern LLMs undergo alignment to prevent toxic, irrelevant, and/or unhelpful text from being generated. This has resulted in improved output.  Text generation can be used for open-domain question answering, in which a general prompt is provided with no corresponding answer in the context (e.g. “What is the purpose of life?”).  Text generation is also used on the downstream end of retrieval-augmented generation pipelines, in which a query first causes the NLP pipeline to filter the relevant documents in a database, extract an answer, and then generate text to present the answer to the user.

In healthcare, potential applications of text generation include helping physicians write clinical notes to ease documentation burden, as well as simplifying routine tasks for health analysts at pharmaceutical companies.  A true challenge is to accomplish these use cases while protecting patient privacy and preventing any PHI leakage in responses to prompts.  At OMNY Health, we strive to accomplish these and related use cases responsibly.

Conclusion

In conclusion, there is more to LLMs than generating text.  Combining text generation functionality with the subtasks mentioned in this article potentiates LLMs to complete powerful tasks that can help improve healthcare.