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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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Editorial

Linking Real-World Clinical Data with Medical Device Data to Demonstrate Device Value

Clinical Data, Medical Device

Real-World Data in the Medical Device Market

Many life sciences organizations are focused on harnessing real-world data (RWD) capabilities to inform every part of the drug development product life cycle. The same types of RWD can, and should, be applied to innovation around the development and measurement of the effects of medical devices. RWD derived from healthcare sources, such as electronic health records, provides insights into the burden of disease, and after a device market launch, provides insights into competitive stance, safety, efficacy, and outcomes. Traditionally, these types of analyses have been difficult to perform for medical devices because the specific details surrounding the medical device make, model, and manufacturer used in procedures are often buried in the electronic health record or other IT systems, and historically have not been collected uniformly. 

Medical Devices in the Real World

Medical Devices have become a critical part of a patient’s daily life, and how patients interact with these devices can tell us a lot about their effectiveness. From joint replacements to life-saving arterial stents and pacemakers, device manufacturers are interested in the benefit-risk profiles to inform future device modifications and technological advancements, Providers can glean important evidence for the clinical effectiveness of these devices, helping to inform what devices to use in their procedures. 

Medical device manufacturers are motivated to invest in new strategies to support success. According to McKinsey, despite a period of growth from 2012 through 2019, S&P medical device investments slowed during the pandemic. Recently, however, signs have been promising; in 2023, the U.S. approved more novel medical technologies than in any single year before, and revenue growth rates are starting to stabilize to pre-pandemic levels. 

Aggregation of RWD to Inform Device Safety and Evidence for Effectiveness

Using relevant and reliable RWD is important for generating RWE to understand and regulate medical device usage and safety. To get a complete picture of the patient’s use and interaction with a medical device, RWD from clinically relevant sources is required to give a holistic view of the clinical experience both before device implant/initiation and after usage has been initiated. The ability to combine disparate data from multiple sources and care settings is where OMNY Health excels. 

The OMNY Health Medical Device Data Advantage

OMNY Health’s Medical Device product offering is a best-in-class view into the healthcare industry’s use of medical devices in patients. Our Medical Device offering is constructed directly from our health provider data sets, with precision down to the patient encounter level. Utilizing the Unique Device Identifier (UDI) and the device version, we have partnered with the FDA’s AccessGUDID database to ensure the highest quality and completeness of data, with unrivaled recency. 

OMNY Health’s Medical Device product offering linked with OMNY Health’s EHR data, can help device manufacturers and provider networks better understand and assist the patients they are servicing by examining: 

  • Medical Device use and trends 
  • Patient journeys and continuity of care
  • Impact of medical devices on clinical, functional, cost, and utilization outcomes
  • Safety/adverse event reporting
  • Providers who are interested in value-based contracting and making procurement decisions

Check out the Total Knee Replacement study utilizing the OMNY Health Medical Device product offering. 

Contact OMNY to learn more about how our Medical Device product offering will boost your real-world evidence generation.

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Editorial

Social Determinants of Health: Generating Accurate Clinical Insights Using Real-World Data

Social Determinants

Recent innovations in healthcare analytics and patient-oriented AI demonstrate the power of leveraging real world data (RWD) to generate ecologically valid actionable insights for life sciences companies to improve patient lives and streamline new therapeutic advances. Such insights are essential for decision-making in health economics and outcomes research (HEOR) and in the clinical trial planning and implementation processes. However, the breadth, depth, and inclusivity of the data used to generate such insights can have a huge impact on both the accuracy and validity of any analyses. Various sources of bias, such as the lack of demographic and socioeconomic diversity in a cohort, can undermine the main benefit of using RWD to supplement life sciences research. To combat this, many researchers and providers have publicized the need for clinically-validated metrics that can be used to measure and quantify cohort-level bias and provide more transparency in the field.

Social Determinants of Health Data Account for Notable Variations in Clinical Outcomes

One commonly referenced set of measures that has received growing recognition is social determinants of health (SDoH) data – a set of criteria used to capture economic and social vulnerabilities across key domains. Various studies have estimated that accounting for SDoH variables, such as income level, housing security, transportation, gender identity, and race and ethnicity, can account for 40 – 60% of the variation in clinical outcomes and care patterns. A recent study examining Antihypertensive and Lipid‐Lowering Treatment to Prevent Heart Attack Trial (ALLHAT) data illustrates this all too well. Researchers found that trial participants in the lowest-income trial sites had poorer blood pressure control and worse outcomes for some adverse cardiovascular events, even when treatment access and adherence were accounted for. The authors concluded that these results clearly illustrate “the importance of measuring and addressing socioeconomic context in [randomized controlled trials] RCTs.” The incorporation of these factors into studies leveraging RWD is just as important.  

United States Government Support for Clinical Research and RWD Inclusivity

Researchers are not the only ones promoting the importance of SDoH – the U.S. government is facilitating efforts to foster greater inclusivity in clinical trial participation across multiple fronts. The Food and Drug Administration has issued new guidelines about the use of RWD in clinical research, pointing to the utility of inclusive data sources to identify clinical trial participants. The Centers for Medicare & Medicaid Services (CMS) are also focused on health equity. At the end of 2023, CMS released an updated framework for health equity with a focus on the collection of interoperable, standardized SDoH. As of January 2024, CMS requires the screening of five SDOH domains for admitted hospital patients.  

Challenges with Obtaining and Utilizing SDoH Data

Despite the power of SDoH to create a more inclusive real-world environment for clinical trials and research analyses, patient-level SDoH data availability to pharmaceutical companies has been lacking. This is in large part due to barriers relating to extracting and aggregating relevant material. In typical healthcare systems, patient-level SDoH data are often difficult to expose and curate as the relevant information is buried in clinical codes and unstructured notes. However, the aforementioned efforts by various government entities have resulted in an exponential increase in the amount of patient-level SDoH data available in medical records – including increased use of structured surveys and other collection tools. 

Much of the SDoH data currently available in the RWD marketplace rely on a combination of 3-digit zip-code-level data and aggregated claims or credit data, which only provide inferred modeled proxies, not insight into actual patient-level information. Those that are able to derive SDoH variables through direct access to EHRs often face issues such as data lags, inconsistent data collection vehicles, and gaps in reporting. OMNY Health has the technology in place at the provider level creating a direct flow of data through the OMNY platform so that data is captured consistently with limited lag time. These unique integrated partnerships with large integrated delivery networks (IDNs) and specialty networks have enabled us to quickly capitalize on the growing availability of SDoH data in patient records.  

OMNY Health Top-tier SDoH Data Sets

OMNY Health is focused on creating a best-in-class SDoH data product to augment our existing EHR-derived, de-identified clinical data sets. Our ​​Social Determinants data offering is constructed from point-of-care documentation aggregated at the patient-encounter level. To ensure deep, multi-faceted coverage, we leverage multiple streams of information, including ICD-10 diagnostic codes, self-reported responses to standardized SDoH surveys, and estimated SDoH burden derived using research-grade, validated NLP models run on unstructured clinical notes.  

OMNY Health’s Social Determinants data offering, when integrated with our extensive clinical data sources, can help pharmaceutical companies and provider networks better help the patients they are serving and be more successful by informing: 

  • More accurate HEOR analyses 
  • Inclusive clinical trial planning and implementation 
  • Analysis of clinical trial results and differences in real-world populations 
  • The development of precision medicine treatments 
  • The understanding of the barriers to drug access and adherence and how to develop targeted programs to reduce those barriers 
  • Different disease severity and outcomes across populations and geographies 
  • Market access and pull-through strategies for contracting and drug promotion. 

SDoH data can offer insight into the everyday non-clinical factors that influence how individuals develop diseases, respond to treatments, and participate in clinical trials.  

Contact OMNY to learn more about how our SDoH data sets will boost your real-world evidence generation.

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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.

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ISPOR Europe 2023: OMNY Health Real-World Data Platform Powering HEOR

ISPOR Europe 2023

HEOR at the Nexus of Policy and Science

OMNY Health believes in the importance of a truly transparent, secure, and connected real-world data (RWD) ecosystem to accelerate life-changing care and treatment innovation between providers, patients, and life science companies.

Comprehensive, representational de-identified data, standardized and aggregated from the complete EHR record can generate health economics and outcomes research that is truly – like the focus of this year’s ISPOR Europe – at the center of policy and science.

ISPOR Europe was an amazing event this year. There were many common themes among the presentations at the conference, including the harmonization of European data practices and RWD collection and standardization. Other presentations focused on the use of RWD to generate real-world evidence, health technology assessment regulations, and the creation of external clinical trial control arms. Other speakers spoke to the need for RWD-informed policy and science.

HEOR Only as Strong as the Data Behind the Analyses

OMNY Health presented five posters at this year’s ISPOR Europe conference. These posters all focused on using RWD to generate real-world evidence around a variety of topics, including long-term COVID-19, social determinants of health (SDoH), and dermatological diseases. For each poster, OMNY Health scientists utilized the rich EHR data assets in our OMNY Health Platform™ to power new and accurate evidence for the clinical studies presented.

The following is a summary of the purpose and findings of each poster.

1) Long COVID: Clinical Characteristics and Management in the Real-World Setting

A total of 17,542 people with long COVID were studied using OMNY Health data to understand the demographics, concurrent diagnoses, and treatment patterns for those with a long COVID-19 diagnosis code (ICD-10-CM: U09.9). Data were studied from the period of 2021 to June 2023 from four integrated delivery networks and one children’s hospital. Ongoing breathing abnormalities and cough were the most common concurrent diagnoses accounting for 43% of people with long COVID in the study. An analysis of prescription drugs and procedures for these people found that asthma and chronic obstructive pulmonary disease drug treatments were the most common therapies given (25%) with antibacterial agents being the second (13%). The top surgical treatment procedure was radiological at 34%.

This analysis demonstrated how EHR data can be leveraged as a valuable tool to provide insights into chronic disease, clinical characteristics, and disease progression.

2) Identifying economic insecurity among psoriasis patients using natural language processing and unstructured clinical notes

Clinical codes for social determinants of health, such as economic insecurity, exist in the International Classification of Diseases, Tenth Revision, but are often underutilized in clinical practice. This often results in a lack of structured SDoH data recorded in clinical records. However, this type of information can also be found in unstructured data such as clinical notes. The objective of this study was to train and apply a natural language processing model (NLP) to extract economic insecurity information from clinical notes and compare its results identifying economic insecurity from structured clinical coding.

A total of 2,007 clinical note sentences were sampled and annotated to train the model. When the model was applied to almost 51,000 people with psoriasis, 686 people were positive for economic insecurity as opposed to zero people having recorded ICD-10 clinical codes for economic insecurity. Of the 100 positive sentences checked for accuracy, sixty percent were true positives.

This study indicates that NLP can be a useful tool to identify SDoH from the unstructured portions of clinical patient records among patients lacking SDoH ICD-10 codes.

3) Trends in the therapeutic management of bullous pemphigoid in the real-world setting

Bullous pemphigoid (BP) is an autoimmune disorder, often found in the elderly, that causes blisters on the skin. The prevalence of BP is increasing in the US and treatments for BP, including corticosteroids, are associated with toxicities and disease recurrence over time. OMNY Health researchers created a profile of real-world BP treatment patterns across diverse treatment settings in the United States to inform the development of future treatments. They did this by accessing electronic health record data from six multi-state specialty dermatology networks and four integrated delivery networks from 2017 to 2023.

Demographics and disease severity at first BP diagnosis were collected along with the percentages of people with prescriptions for topical corticosteroids (TC), systemic corticosteroids (SC), non-immunosuppressive agents, and immunosuppressants.

  • TC prescriptions were the most widely used, increasing from 53% in 2017 to 72% in 2023, peaking at 76% in 2022.
  • SC use ranged from 29% to 41%, peaking in 2021.
  • Non-immunosuppressive agents increased from 30% in 2017 to 35% in 2023, peaking at 37% in 2022.
  • Immunosuppressants were the least used, ranging from 12% to 16%.

These results provided insights into the real-world treatment patterns over time for those with BP. As new treatments are introduced similar analyses could provide insights into uptake and prescription patterns among BP patients at different severity stages.

4) Real-world treatment patterns of ankylosing spondylitis

Ankylosing spondylitis (AS) is an inflammatory disease, affecting the spine, which often spreads to the joints between vertebrae. This disease, which often worsens with age, has treatments that focus on relieving the symptoms of pain and inflammation and preventing longer-term complications. This study examined the real-world treatment patterns of people with AS. These treatments include analgesics and opioids, tumor necrosis factor (TNF) alpha inhibitors, anti-inflammatories, and interleukin 17 (IL-17) inhibitors. Electronic health record data from the OMNY Health RWD platform was accessed for 6,171 people with AS across integrated delivery networks and specialty clinics. Demographic data and the percent of visits with prescriptions were tracked for 5.5 years. Analgesics/opioids were prescribed at 16% of AS-associated patient visits, while TNF alpha inhibitors and IL-17 inhibitors were prescribed at 5% and 1% of AS encounters, respectively.

This study indicates that the long-term electronic health record data found in the OMNY Health RWD platform can be used to provide insights into the therapeutic management of a relatively rare disease such as AS.

5) Itch intensity and therapeutic management of pruritus in the real-world setting

Pruritus, itchy skin, is a cutaneous symptom that presents in a variety of clinical systemic conditions. Its ubiquitous nature is challenging to treat and can greatly impact the lives of those people who have pruritus. Itch intensity scores (1-10) are often documented, especially in dermatological clinics, as a measure of symptom severity. These scores inform pruritus treatment and management.

This study examined the relationship of itch intensity with therapeutic management choices for people with pruritus in six dermatology networks from 2017 to 2013. A total of 7,330 people with pruritus and a recorded 10-point itch intensity assessment associated with a pruritus diagnosis code were studied in the OMNY real-world data platform.

Topical therapies were prescribed at approximately half of pruritus visits, regardless of itch intensity, with topical corticosteroids prescribed at approximately 45% of those visits. Systemic therapy prescriptions were more likely to vary dependent upon itch intensity with 32% of prescriptions associated with the highest itch intensity of 8-10.

This study indicates that the rich dermatological data sets in the OMNY Health RWD platform can be used to provide insights into the management of disease symptoms that greatly impact the quality of life for those who have them.

OMNY Health’s Powerful RWD Platform Informing HEOR and the Impact of SDoH

The 2023 ISPOR Europe conference was an invigorating experience focused on discourse about the changing research landscape in Europe, HEOR, and RWD in the life sciences industry.

OMNY Health values the interactions we had and the opportunities to showcase the strength of our real-world data platform in providing the types of insights that life sciences companies and policymakers seek to understand diseases, treatment patterns, and the impact of disease on the people who have them.

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Editorial

Practical Applications of FDA’s Guidelines on Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products

Practical Applications of FDA’s Guidelines

On August 31, 2023, the U.S. Food and Drug Administration (FDA) released “Considerations for the Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products: Guidance for Industry.”[1] FDA issued this guidance as part of its Real-World Evidence (RWE) program stemming from the 21st Century Cures Act (Cures Act).[2],[3] The Cures Act was signed into law in 2016 to accelerate medical product development. One of the key provisions was for FDA to create the RWE program to evaluate the use of RWE in regulatory decision making.  

The newly released Guidance clarifies FDA’s expectations for clinical studies using real-world data (RWD) but does not establish any legally binding requirements. This Guidance discusses the applicability of 21 CFR Part 312 (investigational new drug application)[4] for studies using RWD and summarizes regulatory considerations for non-interventional studies using RWD. We summarize below the key facets of these considerations and discuss their practical application to the current landscape of available RWD.  

Applicability of 21 CFR Part 312 

21 CFR Part 312 outlines the requirements for Investigational New Drug Applications (IND) for review by the FDA. While they specifically concern interventional studies, these guidelines recognize the utility of RWD in these studies. The example applications of RWD are identifying potential participants for trials, ascertaining outcomes (e.g., diagnoses, hospitalizations), and creating external control arms. The guidelines also note that non-interventional studies are not clinical investigations subject to 21 CFR Part 312. 

Practical Application: The potential use of RWD in clinical research has been long recognized but remains an emerging practice. One challenge to overcome is finding sources of data with enough details to ascertain study inclusion/exclusion criteria, covariates and outcomes. Often, multiple sources must be used. It is also important to understand the underlying characteristics of the RWD source population, as most RWD are samples of convenience. Critical subgroups could be missing from the source, regardless of the level of detail that the RWD source offers. Finally, RWD sources that are readily available are generally de-identified. This presents challenges to finding patients to participate in trials. Careful evaluation of available data sources must be conducted before committing to use RWD in INDs. The FDA’s recommendations noted in these guidelines offer a path to evaluate the usability of RWD sources for this purpose. 

Regulatory Considerations for Non-Interventional Studies 

The majority of the FDA’s new Guidance discusses use of RWD for non-interventional studies. It describes the common sources of RWD for non-interventional studies, including registries, electronic health records (EHRs), and administrative claims. It recognized that while many non-interventional studies involve already collected RWD, some involve collection of additional data (e.g., questionnaires, laboratory tests, and imaging studies), which are subject to regulations protecting human subjects. Data privacy is also noted as a key consideration in using RWD.  

The Guidance discusses requirements to establish transparency in the design of the study. Steps to be taking include sharing drafts of protocols and statistical analysis plans (SAPs), publicly posting protocols, evaluating sources of RWD for fit and assurance that the sources as well as study cohort selection and analysis do not favor a certain conclusion, justification of selected data sources, and maintaining audit trails and documentation of the data extraction and analyses. 

The Guidance recommends that for studies intended for use in a marketing application, sponsors should ensure that they can submit patient-level data and can make the source data available if further verification is necessary. The source data could include “original records of clinical findings, observations, or other activities in a clinical investigation used for reconstructing and evaluating the investigation”, though the Guidance does note that appropriate justification may exist for why the sponsor cannot submit patient-level data. Programming code and algorithms submitted to the FDA must be well documented and complete enough to allow for study replication. 

The Guidance also offers recommendations for study monitoring (from RWD extraction to application of the protocol/SAP), safety reporting, study oversight, and researcher and third-party roles and responsibilities. 

Practical application: Study sponsors typically rely on outside parties for access to RWD. Most of these guidelines cannot be successfully implemented without close partnership with the RWD source. Input from the RWD source can be critical to protocol and SAP design, particularly in determining whether the source meets study requirements. RWD sources vary in their ability (contractual and technical) to track the linage of the patient record, share records with additional parties, or provide detailed documentation. It is important to communicate study requirements and FDA expectations to the RWD source and ascertain their ability to meet these requirements. RWD sources have varying strengths and limitations.  

It is also important to discuss how sources can or cannot meet these guidelines and engage with the FDA in the selection of the optimal source given specific study requirements. To start, the source of RWD is not always the generator of the data. For example, RWD could be sourced from a vendor who is aggregating claims from multiple payers or an aggregator who is procuring EHRs from hospital software vendors. Accessing RWD closer to the source offers greater transparency and traceability, as well as opportunities to collect additional retrospective or prospective data. Also, most sources of RWD include only de-identified patient data. De-identification involves the removal of direct identifiers and the removal or obfuscation of indirect identifiers. Additionally, the RWD source may be required to anonymize providers, facilities, or payers. It is important to understand what data elements have been redacted and consider any impact to the study.  

Conclusion 

Stakeholders in medical product and drug development have long recognized the practical application of RWD to accelerate innovation and bring them to patients who need them faster. The FDA’s Guidelines summarize many of the methods already in practice and stress the importance of planning and communication between the sponsor and regulator. It is equally important to find and partner with the right RWD sources who can offer not just the patient population and data points necessary for a study, but offer enough transparency about the data and their origin to meet the recommendations in this Guidance while respecting patient privacy and ensuring data security.  

[1] https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-real-world-data-and-real-world-evidence-support-regulatory-decision-making-drug

[2] https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf

[3] https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence

[4] https://www.ecfr.gov/current/title-21/chapter-I/subchapter-D/part-312

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Editorial

OMNY Health Presenting and Exhibiting at ISPOR Annual

Visit the Booth

OMNY Health will be presenting and exhibiting at ISPOR Annual on May 7-10, 2023 in Boston, Massachusetts at Booth #1325. Schedule an in-person meeting to learn more about:

  • Clinical measures and social determinants of health data extracted from clinical notes using NLP algorithms
  • Cutting-edge research and studies from our Data Science team, preview some of this research in our podium presentation and poster sessions

View Presentations

OMNY Health will also be presenting a podium presentation and multiple poster sessions.

The podium presentation will be:

Title: Severity Score Extraction from Unstructured Clinical Notes Using a Disease-Agnostic Natural Language Processing Question-Answering Pipeline

  • Presenter: Vikas Kumar, MD
  • Date: Monday, May 8, 2023
  • Time: 4:45PM – 5:45PM
  • Descriptions: In this presentation, Dr. Kumar will describe how OMNY Health uses a natural language processing (NLP) question-answering (QA) pipeline to extract disease severity from electronic health record (EHR) clinical notes.  He will report on the accuracy of the pipeline and demonstrate how findings can be used to perform healthcare resource utilization (HCRU) analyses.

In addition, five posters will be on display:

Categories
Editorial

Top Of Mind in Q1 2023

We are nearing the close of Q1 and our provider ecosystem partners have been actively sharing information with us on their key pain points and strategic priorities for this year. Here are the issues they reported as “top of mind” in our latest strategic discussions:

Increasing Cost Pressures

Concerns about the recent changes to the Medicare Physician Fee Schedule were echoed throughout our conversations with administrators across the healthcare continuum this quarter, from specialty networks to health systems. The 4% rate reduction is not the only financial factor impacting the already frail margins in healthcare. Provider organizations told us they are also wary of inflation and supply chain issues driving the rising cost of supplies and pharmaceuticals as well as costs of labor and the ability to maintain staff. Many were seeking out alternative revenue streams to help offset these additional costs.

Focusing on Improving Health Equity and Incorporating Voice of Patient

The disparities in care exposed during the COVID-19 crisis remain top of mind with some organizations using digital health and quality improvement initiatives to try to identify and address gaps in care for certain populations. Centers for Medicare and Medicaid Services (CMS) recently doubled the weight of patient experience in the Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey, and our provider ecosystem partners have taken notice. That combined with the patient reported outcomes (PROs) component of CMS’ Merit-Based Incentive Payment System (MIPs) program has elevated the impact of patient experience in care delivery more than ever. CMS and the National Quality Forum (NQF) have a major initiative underway to promote the benefits and actively encourage the completion of PROs to support value-based care and alternative payment models. Our provider partners are motivated and see the value of collecting additional measures on or from patients that would give them greater insight into disease severity and quality of life, driving better patient outcomes.

Staying Current with New Treatment Options

The ever-expanding assortment of new treatment options available across many prevalent and rare diseases is exciting and challenging for providers and patients. Accelerated Approval, Breakthrough Therapy, Fast Track, and Priority Review programs from the US Food and Drug Administration (FDA) have introduced more treatment options, and healthcare providers (HCPs) are feeling the pressure to stay current on available treatments, monitor changes to treatment guidelines, and keep up with the abundance of prior authorization procedures required for these new therapies across payer organizations. Most of our provider partners are exploring how to best educate themselves on these new products and communicate more broadly within their organizations on best practices, while simultaneously balancing the need to meet their patients’ unique and individual treatment needs.

Participating in Research Activities

Provider interest in participating in clinical trials and other research initiatives is on the rise. Many of the organizations in our provider ecosystem have been involved in research activities, but many are asserting that they want to grow their footprint, expand the types of research activities, and be a more active participant in innovation. Research and innovation are now being seen by regional community health care providers as a differentiator that attracts new patients, retains patients and providers, and drives new revenue dollars into the organization. This sentiment is consistent with the latest guidance from the FDA to begin requiring sponsors to incorporate diversity planning for late-stage clinical trials. In a news release from last year, the FDA cited the need to “facilitate the development of better treatments and better ways to fight diseases that often disproportionately impact diverse communities.”

To learn more about how OMNY Health is helping provider organizations proactively address these top-of-mind issues, stop by to see us at  ViVe on March 26-29 in Nashville, TN, Becker’s on April 3-6 in Chicago, IL, or ARVO on April 23-27 in New Orleans, LA. We also invite you to continue the conversation by reaching out to join@omnyhealth.com to schedule some time to discuss these topics further. We look forward to assisting our data ecosystem partners as we work to navigate these issues with you in 2023.

About the Authors:
Matia Cary is Chief of Staff at OMNY Health. As Chief of Staff she leads the Delivery and Customer Success teams responsible for supporting all of OMNY’s provider ecosystem partners and strategic initiatives. Internally, she helps lead people operations, strategy, cross-functional collaboration across teams, and investor relations. Matia holds a Bachelor’s degree in Biochemistry, Cellular, & Molecular Biology from Drake University and an MBA for Leaders in Healthcare from the University of Kentucky.

Jessica Walling is Director of Product Management at OMNY Health. She is currently working on a new suite of SaaS products to help healthcare providers and life sciences customers glean insights from OMNY Health’s real world data. She is responsible for product strategy and product marketing and enjoys working closely with  customers on product launches and continuous product improvement. Jessica holds a Bachelor’s degree in Industrial Engineering from Georgia Institute of Technology and an MBA from Georgia Institute of Technology.

Categories
News

Atropos Health Expands Evidence Network by Teaming with OMNY Health

Collaboration enables Atropos to generate real-world evidence from OMNY’s vast portfolio of data, opening the door for more data-driven medical breakthroughs

PALO ALTO, Calif., March 8, 2023 – Atropos Health, a leader in generating personalized real-world evidence at the point and pace of care, announces a teaming alliance with OMNY Health, a healthcare ecosystem facilitating cross-industry data sharing. This relationship will unlock novel insights from the de-identified data shared by participants within OMNY Health’s network, creating more opportunities for evidence-based practice and medical research.

Life science and healthcare provider groups rely on OMNY Health to facilitate secure and mutually-beneficial data sharing and research collaborations. OMNY augments provider organizations’ ability to leverage their data – for research, clinical decision support, population health program design, and more. For life sciences, OMNY offers a vast portfolio of data conducive to downstream analytic needs. The Atropos-OMNY teaming alliance will enable data activation across their respective platforms by catalyzing transformation of real world data into usable insights. With Atropos Health’s Green Button Informatics Consult Service, OMNY and Atropos clients will have the option to leverage advanced solutions that provide novel real-world evidence (RWE) responses to clinical questions, called Prognostograms, in under 48 hours.

This collaboration also advances Atropos’ evidence generation capabilities. Answering previously unanswerable questions with insights from medical data, as Atropos does, depends on a blend of factors – sophisticated technology, clinical expertise, and the right data as its substrate. Leveraging OMNY’s curated clinical and specialty data means more of the “right data” is available. Resulting Prognostograms will add to Atropos’ growing library of novel RWE – a crucial step toward bridging the evidence gap in medicine.

“Atropos is a catalyst from medical data to actionable evidence. When paired with OMNY’s data ecosystem, the opportunities are remarkable,” said Brigham Hyde, Ph.D., CEO and Co-Founder of Atropos Health. “We will be able to generate insights that do not exist in current medical literature. Making this accessible at scale can lay the groundwork for medical breakthroughs, scientific discoveries, and informed care decisions that save lives.”

“Our mission is to free data from silos and allow it to be shared, analyzed, and transformed into life-saving treatments and better care for patients,” says Mitesh Rao, CEO, OMNY Health. “Life science organizations and providers often struggle with time and budget constraints that hinder their ability to learn from their data. The work Atropos and OMNY are doing can unblock this process by providing on-demand answers to pressing medical questions.”

The collaboration with OMNY Health comes after news of Atropos Health’s recent partnerships with the Mayo Clinic, a non-profit global leader in clinical care; Syntegra, a generative AI company producing synthetic data; and CancerLinQ, a technology platform for cancer research and treatment, provided by the American Society of Clinical Oncologists (ASCO).

Atropos Health’s core technology began as a research project at Stanford University that postulated a future where answers to complex clinical questions would be available at the touch of a “Green Button” in the EHR. This idea evolved into the Green Button Consult Service Atropos Health offers to clinicians and researchers today.  (See here.)

About Atropos Health

Atropos Health is the developer of the first physician consultation service powered by research-grade real-world data. Using hundreds of millions of anonymized patient records, we help providers answer clinical questions that have fallen through the cracks of the evidence-based literature. By leveraging clinical insights from a growing span of real-world data in a rapid, methodologically-transparent manner built on dozens of peer reviewed publications, Atropos empowers more robust research and evidence-based care delivery.

To learn more about Atropos Health, visit www.atroposhealth.com or connect through LinkedIn or follow on Twitter @AtroposHealth.

About OMNY Health

OMNY Health connects patients, providers, and life sciences companies through data and insights to accelerate life-changing innovation.  Our easy-to-use, secure platform enables life sciences organizations to engage in collaborative data and research partnerships efficiently, compliantly, and at scale.

The team at OMNY Health imagines a future of patient-centered and evidence-based care. We’ll achieve that mission through a seamless workflow of analysis-to-insight-to-decision-making based on a connected ecosystem of healthcare data and collaboration across the healthcare ecosystem.

For more information visit www.marketing-dev.omnyhealth.com or e-mail: join@omnyhealth.com. 

Media Contact

Bruno Solari

atroposhealth@autumncommunications.com

Categories
News

OMNY Health announces $17 Million Raise to Lead the Next Generation of Real-World Data-Driven Decision-Making and Collaboration Across Healthcare

OMNY Health compliantly connects a diverse network of healthcare stakeholders with research-ready data and insights, significantly reducing the burden of data acquisition and curation, and accelerating the time to decisions and action  

Atlanta, GA, November 30, 2022 /PRNewswire/ — OMNY, Inc. (d/b/a OMNY Health), a leader in compliant data and platform solutions for connecting healthcare stakeholders, today announced a $17 million Series B round led by Chicago Pacific Founders (CPF). Dr. Vance Vanier, MD, Co-Founder and Co-Managing Partner of CPF will join the OMNY Board of Directors. 

OMNY Health was created to enable any provider organization in the healthcare ecosystem to benefit from the value of Real-world data (RWD), while helping to reduce bias in data-driven decision-making across healthcare stakeholders. RWD and real-world evidence (RWE) play an essential and increasing role in health care decisions, but the current sources of administrative and payor data can create a biased, incomplete view of the care and associated outcomes. Similarly, clinical trials and other research studies often focus on patients receiving care at large academic centers or research sites that specialize in recruiting patients for studies, leaving patients from rural settings or those treated in community-based settings under-represented. OMNY Health answers these challenges with a proprietary unified national data layer, providing an efficient mechanism for Life Sciences companies to source research-ready data and insights from a broad mix of provider organizations across the country, with additional capability to compliantly connect with these provider organizations for follow-up research activities and collaborations. 

“Timely RWD and RWE have become the foundation for critical functions for countless healthcare companies. Unfortunately, teams today are stuck with data that doesn’t meet their needs, processes that waste time and money, and few options for acting on the insights generated,” said Mitesh Rao, MD, Founder and CEO of OMNY Health. “This situation is no longer tenable in today’s fast-paced data-driven environment. Healthcare stakeholders shouldn’t be forced to make decisions based on inadequate information. This new investment will fuel our growth as we continue to meet the accelerating demand for more seamless data-driven decision-making in healthcare and opportunities to better engage with patients and providers through the common language of data.” 

OMNY’s proprietary DaaS and SaaS solutions spearhead the evolutionary transition to dynamic data procurement and insight generation by seamlessly normalizing data from disparate electronic health record systems across diverse sets of provider sizes and types. As a result, researchers can gain the flexibility they need to acquire fit-for-purpose data that can scale up or down based on their needs. This allows them to significantly reduce data acquisition costs and alleviate the stress of managing multiple data vendors and the compliance activities associated with provider relationships. Today, OMNY’s dynamic data ecosystem represents more than 56 million individuals from health systems and specialty provider networks across 42 states, with many more scheduled to join in 2023. More than a dozen Life Sciences companies are leveraging the OMNY Platform and data network to meet their research and analytic needs since the platform commercially launched in May 2021.  

“OMNY’s demonstrated capability of enabling providers and Life Sciences companies to create partnerships around real-world data will transform patient care. We are thrilled to have the opportunity to participate in the next stage of OMNY’s journey,” said Vance Vanier, M.D. Co-founder and Co-Managing Partner of CPF. 

About OMNY Health

The OMNY Health ecosystem is the premiere real-world data layer, securely connecting researchers and healthcare providers through a common language of deidentified data. Serving as a key resource and central intermediary, OMNY Health revolutionizes the potential for providers and life sciences organizations to align goals across the healthcare industry, forging transformative data relationships and fueling innovation where patients need it the most. For more information on the OMNY Health network, please visit www.marketing-dev.omnyhealth.com.  

About Chicago Pacific Founders 

Based in Chicago and San Francisco, Chicago Pacific Founders (“CPF”) is a leading strategic private equity firm focused on investing in growth companies within value-based care, healthcare services, AI and tech enabled services, and caring for aging populations. CPF believes that the most significant societal impact and investment returns from healthcare for the next decade will be generated by investment in delivery model innovation. CPF’s leadership team is comprised of former healthcare founders, senior executives, and investment professionals with a passion and track record of building healthcare businesses. For more information, please visit www.cpfounders.com. 

MEDIA INQUIRIES:  please contact Stacey Long, stacey@omnyhealth.com

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