UNITED
STATES
SECURITIES
AND EXCHANGE COMMISSION
Washington,
D.C. 20549
FORM
10-K
(Mark
One)
For
the fiscal year ended December 31, 2024
or
For
the transition period from ___ to ___
Commission
File Number: 001-40386
ONEMEDNET
CORPORATION
(Exact
name of registrant as specified in its charter)
(Address of principal executive offices) (Zip Code)
Registrant’s
telephone number, including area code: (800)918-7189
Securities
registered pursuant to Section 12(b) of the Act:
Title of each class Trading Symbol(s) Name of each exchange on which registered
Common stock, par value $0.0001 per share ONMD The Nasdaq Stock Market LLC
Securities
registered pursuant to Section 12(g) of the Act: None
Indicate
by check mark if the registrant is a well-known seasoned issuer, as defined in Rule 405 of the Securities Act. Yes ☐ No
☒
Indicate
by check mark if the registrant is not required to file reports pursuant to Section 13 or Section 15(d) of the Act. Yes ☐ No
☒
Indicate
by check mark whether the registrant (1) has filed all reports required to be filed by Section 13 or 15(d) of the Securities Exchange
Act of 1934 during the preceding 12 months (or for such shorter period that the registrant was required to file such reports), and (2)
has been subject to such filing requirements for the past 90 days. Yes ☒ No ☐
Indicate
by check mark whether the registrant has submitted electronically every Interactive Data File required to be submitted pursuant to Rule
405 of Regulation S-T (§ 232.405 of this chapter) during the preceding 12 months (or for such shorter period that the registrant
was required to submit such files). Yes ☒ No ☐
Indicate
by check mark whether the registrant is a large accelerated filer, an accelerated filer, a non-accelerated filer, a smaller reporting
company, or an emerging growth company. See the definitions of “large accelerated filer,” “accelerated filer,”
“smaller reporting company” and “emerging growth company” in Rule 12b-2 of the Exchange Act.
Large accelerated filer ☐ Accelerated filer ☐
Non-accelerated filer ☒ Smaller reporting company ☒
Emerging growth company ☒
If
an emerging growth company, indicate by check mark if the registrant has elected not to use the extended transition period for complying
with any new or revised financial accounting standards provided pursuant to Section 13(a) of the Exchange Act. ☐
Indicate
by check mark whether the registrant has filed a report on and attestation to its management’s assessment of the effectiveness
of its internal control over financial reporting under Section 404(b) of the Sarbanes-Oxley Act (15 U.S.C. 7262(b)) by the registered
public accounting firm that prepared or issued its audit report. ☐
If
securities are registered pursuant to Section 12(b) of the Act, indicate by check mark whether the financial statements of the registrant
included in the filing reflect the correction of an error to previously issued financial statements. ☒
Indicate
by check mark whether any of those error corrections are restatements that required a recovery analysis of incentive-based compensation
received by any of the registrant’s executive officers during the relevant recovery period pursuant to §240.10D-1(b).☒
Indicate
by check mark whether the registrant is a shell company (as defined in Rule 12b-2 of the Act). Yes ☐ No ☒
As
of June 28, 2024, the aggregate market value of the common equity of the registrant held by non-affiliates was $9.7 million (based on
the closing sales price of the shares of common stock on June 28, 2024). For the purpose of the foregoing calculation only, all directors
and executive officers of the registrant and owners of more than 10% of the registrant’s common stock are assumed to be affiliates
of the registrant. This determination of affiliate status is not necessarily conclusive for any other purpose.
As
of March 26, 2025, there were there were 30,760,576 shares of common stock, par value $0.0001 per share, issued and outstanding, and
0 shares of preferred stock, par value $0.0001 per share, of the registrant issued and outstanding.
DOCUMENTS
INCORPORATED BY REFERENCE
None.
ONEMEDNET
CORPORATION
ANNUAL
REPORT ON FORM 10-K FOR THE YEAR ENDED DECEMBER 31, 2024
INDEX
Page
PART I
Item 1. Business 1
Item 1A. Risk Factors 17
Item 1B. Unresolved Staff Comments 28
Item 1C. Cybersecurity 28
Item 2. Properties 29
Item 3. Legal Proceedings 29
Item 4. Mine Safety Disclosures 29
PART II
Item 6. [Reserved] 30
Item 7A. Quantitative and Qualitative Disclosures About Market Risk 39
Item 8. Financial Statements and Supplementary Data 40
Item 9A. Controls and Procedures 41
Item 9B. Other Information 41
Item 9C. Disclosure Regarding Foreign Jurisdiction that Prevent Inspections. 41
PART III
Item 10. Directors, Executive Officers and Corporate Governance 41
Item 11. Executive Compensation 47
Item 14. Principal Accountant Fees and Services 55
PART IV
Item 15. Exhibits and Financial Statement Schedules 56
Signatures 58
CAUTIONARY
NOTE REGARDING FORWARD-LOOKING STATEMENTS
Certain
statements that we make from time to time, including statements contained in this Annual Report on Form 10-K (the “Annual Report”)
constitute “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995, and of
Section 27A of the Securities Act of 1933, as amended (the “Securities Act”), and Section 21E of the Securities Exchange
Act of 1934, as amended (the “Exchange Act”). All statements other than statements of historical facts contained in this
Annual Report are forward-looking statements. The forward-looking statements in this Annual Report are only predictions. We have based
these forward-looking statements largely on our current expectations and projections about future events and financial trends that we
believe may affect our business, financial condition, and results of operations. In some cases, you can identify these forward-looking
statements by terms such as “anticipate,” “believe,” “continue,” “could,” “depends,”
“estimate,” “expects,” “intend,” “may,” “ongoing,” “plan,” “potential,”
“predict,” “project,” “should,” “will,” “would” or the negative of those
terms or other similar expressions, although not all forward-looking statements contain those words. We have based these forward-looking
statements on our current expectations and projections about future events and trends that we believe may affect our financial condition,
results of operations, strategy, short- and long-term business operations and objectives, and financial needs.
Our
operations involve risks and uncertainties, many of which are outside our control, and any one of which, or a combination of which, could
materially affect our results of operations and whether the forward-looking statements ultimately prove to be correct. We have based
these forward-looking statements largely on our current expectations and projections about future events and trends that we believe may
affect our financial condition, results of operations, business strategy, short-term and long-term business operations and objectives,
and financial needs. Forward-looking statements in this Annual Report include, without limitation, statements reflecting management’s
expectations for future financial performance and operating expenditures (including our ability to continue as a going concern, to raise
additional capital and to succeed in our future operations), expected growth, profitability and business outlook, and operating expenses.
Forward-looking
statements are only current predictions and are subject to known and unknown risks, uncertainties, and other factors that may cause our
actual results, levels of activity, performance, or achievements to be materially different from those anticipated by such statements.
These factors include, among other things, the unknown risks and uncertainties that we believe could cause actual results to differ from
these forward looking statements as set forth under the heading, “Risk Factors” and elsewhere in this Annual Report. New
risks and uncertainties emerge from time to time, and it is not possible for us to predict all of the risks and uncertainties that could
have an impact on the forward-looking statements, including without limitation, risks and uncertainties relating to:
● our projected financial position and estimated cash burn rate;
● our estimates regarding expenses, future revenues and capital requirements;
● our ability to continue as a going concern;
● our reliance on third-party suppliers and manufacturers;
● the success of competing products or services that are or become available;
These
forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described in “Risk
Factors.” Moreover, we operate in a very competitive and rapidly changing environment. New risks emerge from time to time. It is
not possible for our management to predict all risks, nor can we assess the impact of all factors on our business or the extent to which
any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward-looking statements
we may make. In light of these risks, uncertainties and assumptions, the forward-looking events and circumstances discussed in this Annual
Report may not occur and actual results could differ materially and adversely from those anticipated or implied in the forward-looking
statements.
You
should not rely upon forward-looking statements as predictions of future events. Although we believe that the expectations reflected
in the forward-looking statements are reasonable, we cannot guarantee that the future results, levels of activity, performance or events
and circumstances reflected in the forward-looking statements will be achieved or occur. Moreover, except as required by law, neither
we nor any other person assumes responsibility for the accuracy and completeness of the forward-looking statements. We undertake no obligation
to update publicly any forward-looking statements for any reason after the date of this Annual Report to conform these statements to
actual results or to changes in our expectations.
You
should read this Annual Report and the documents that we incorporate by reference in this Annual Report with the understanding that our
actual future results, levels of activity, performance and events and circumstances may be materially different from what we expect.
As a result of a number of known and unknown risks and uncertainties, our actual results or performance may be materially different from
those expressed or implied by these forward-looking statements including those described in the “Risk Factors” section of
this Annual Report beginning on page 18 and elsewhere in this Annual Report.
PART
I
Item
1. Business
Company
Overview
OneMedNet
is a global provider of clinical imaging innovation and curator of regulatory-grade Imaging Real World Data (“iRWDTM”). OneMedNet’s
innovative solutions connect healthcare providers and patients satisfying a crucial need within the life sciences field offering direct
access to clinical images and the associated contextual patient record. OneMedNet’s innovative technology proved the commercial
and regulatory viability of imaging Real World Data (as defined below), an emerging market, and provides regulatory-grade image-centric
iRWDTM that exactly matches OneMedNet’s life science partners case selection protocols and paves the way for Real World Evidence
(as defined below).
OneMedNet
was founded to solve a deficiency in how clinical images were shared between healthcare providers. This resulted in OMN’s initial
product BEAMTM image exchange that enabled the successful sharing of images for more than a decade with OMN’s largest customer
being the Republic of Ireland.
OneMedNet
continued to innovate by responding to the demand for and utilization of Real World Data (as defined below) (“RWD”) and Real
World Evidence (“RWE”), specifically data that focused on clinical images with its associated contextual clinical record.
We were able to leverage internal technological competencies along with OneMedNet’s formidable healthcare provider installed base
from its first product with BEAMTM to become the first RWD solution for life science companies with its launch of iRWDTM in 2019.
OneMedNet
provides innovative solutions that unlock the significant value contained within clinical image archives. With a growing network of 1,400
healthcare sites, OneMedNet has the immediate ability to quickly search and extensively curate multi-layer data from a federated group
of healthcare facilities. The term “healthcare sites” refers specifically to the hospitals, integrated delivery networks
(“IDNs”) and imaging centers that provide imaging to OneMedNet, which represent the core source of our data. At present,
OneMedNet has access to more than 1,400 sites who provide regulatory grade data to us.
Initially,
it was all about solving the diverse access needs of patient care providers. This focus systematically evolved to addressing the rapidly
growing needs of image analysis and researchers, clinicians, regulators, scientists and more. The federated network allows OneMedNet
to access the following data to provide to research as RWD.
Real
World Data is any data that is collected in the context of the routine delivery of care, in contrast to data collected within a clinical
trial where study design controls variability in ways that are not representative of Real World care and outcomes.
A
key component driving its mission is that OneMedNet believes we have a unique opportunity to affect a material positive impact on the
lives of tens of millions of people while improving our customers’ business productivity. First and foremost, OneMedNet’s
iRWDTM offering plays a significant role in enabling life science companies to bring safer and more effective patient care to market
sooner. Using our highly curated de-identified clinical data in our iRWDTM offering in life science product development, validation,
and regulatory approval processes, life science companies contribute to patient care advancements in more meaningful ways. Moreover,
life sciences improve life science companies’ product development and validation processes, which benefits all parties.
Significant
documentation exists that shows that Real World Data can provide expanded insights across broader and more representative patient populations.
For this reason, the Food and Drug Administration (“FDA”) has instituted Real World Data guidelines for regulatory approvals.
Utilization of highly reliable and quality Real World Data that strictly adheres to all of the very specific data stratification requirements
can supplement or supplant clinical trials.
OneMedNet
covers the complete value chain in imaging Real World Data; it begins with our 10+ year federated network of providers and is supported
by a multi-faceted data curation process managed by an expert in-house clinical team. Additionally, we work hand-in-hand with our life
science partners regarding the case selection protocol and, when required, producing case report forms for regulatory clearance. We are
focused on delivering value by supporting life science advancements with OneMedNet’s iRWDTM which holds the key to unlocking boundless
patient care advances. We believe we unleash the power of research-grade image-centric iRWDTM that is curated to meet every cohort requirement
and stand up to the rigors of prospective clinical trials.
Today,
life science companies, including pharmaceutical companies, artificial intelligence (“AI”) developers, medical device businesses,
and clinical research organizations, share the same widespread challenge in obtaining insight-rich, high-quality patient data that explicitly
matches their precise cohort specifications. A substantial portion of patient diagnosis involves clinical imaging, and approximately
90% of healthcare data, by size, is associated with imaging. Historically, much of imaging value has been derived from its initial review,
and further gains from the image archives have been very limited.
We
help providers to “Unlock the Value in Imaging Archives.”TM By utilizing OneMedNet’s iRWDTM offering, providers can
greatly improve their research efforts with streamlined data access. Health care providers such as hospitals, clinics, and imaging centers
can also accelerate life science patient care innovations by sharing de-identified data in a well-defined and de-identified and secure
manner. In return for doing so, income is generated and applied to critical and possibly unfunded provider projects.
The
OneMedNet Difference
We
believe OneMedNet has been a leader in the business of extracting, securing, and transferring medical data for 10+ years. Doing so requires
specialized expertise in:
● Compliance (HIPAA (as defined below), GDPR, 21 CFR Part11)
● Advanced privacy & security measures
● Clinical patient condition(s) and hospital processes
● Radiology interpretation
● AI/ML (Artificial Intelligence and Machine Learning) technology
Attaining
in-house expertise in all essential elements is a challenge and we believe deters many organizations from attempting such a venture.
We take pride in this ambitious achievement - while continually working to maintain state-of-the-art expertise. OneMedNet strictly adheres
to the highest level of professional and ethical standards and applicable regulations throughout all interactions and activities.
We
believe OneMedNet is a leader in the field of regulatory-grade imaging RWD curators. Doing so requires specialized expertise in AI/ML
technology, data privacy/security, as well as expertise in clinical patient condition(s) and healthcare record keeping. Having, or achieving,
expertise in all essential disciplines is a challenging achievement. OneMedNet had a significant head start with our clinical image exchange
solution which served to launch the Company over a decade ago. All data remains “native” within the federated OneMedNet iRWDTM
provider network - meaning all the data remains locally onsite until specific de-identified data is licensed for a particular life science
research opportunity.
OneMedNet’s
Competitive Advantages
We
believe that OneMedNet iRWDTM offers the best of advanced technology, clinical expert curation, and service. Medical imaging and associated
clinical data are indexed at each network site using state-of-the-art AI/ML technology. This typically includes electronic health records
(“EHR”), radiology, cardiology, lab, pathology and more. Our in-house clinical team performs intensive curation of the data
so that results meet the specifications and requirements of life science Data Collection Protocol (“DCP”) - regardless of
the complexity.
We
believe that OneMedNet unlocks the value in imaging and EHR data in the following three principal ways:
OneMedNet’s
data is fully de-identified using a multi-step quality control process and goes beyond protected health information (“PHI”)
to include personally identifiable information (“PII”), site identifiable information, and more. Importantly, life science
users receive the data in the exact format that they require. No data sifting or manipulation is needed. The data is simply ready for
use. Moreover, OneMedNet has the unique combination of knowledge, tools, and experience to:
● Access and harmonize complete patient profiles across fragmented data silos;
● Provide unmatched data accuracy and completeness;
● Ensure the security and privacy of patients’ PHI;
● Imaging RWD is our singular passion and focus and no one does it better.
Finally,
OneMedNet has a team of highly experienced and clinically trained data curators. This team appreciates the complexity and criticality
of clinical data and can effectively communicate with both providers and life science specialists.
Industry
Background
A
2016 analysis published in the Journal of Health Economics and authored by the Tufts Center for the Study of Drug Development placed
the cost of bringing a drug to market, including post-approval research and development, at a staggering $2.87 billion. Meanwhile, a
2018 study from the Tufts Center for the Study of Drug Development noted that the timeline for new drug development ranged from 12.8
years for the average drug to 17.2 years for ultra-orphan drugs that only affect several hundred patients. This places the onus on life
science organizations to find ways to deliver treatments to patients faster - especially those who cannot wait 17 years
for a potentially life-saving treatment. Knowing how a medicinal product is actually used by patients can help stakeholders across the
healthcare ecosystem make important and potentially life-saving real-time decisions.
Real
World Data is observational data typically gathered when an approved medical product is on the market and used by “real”
patients in real life, as opposed to clinical trials or real world images for real patients. The FDA cites several potential sources
of Real World Data, including EHR, claims, and disease and product registries. There are multiple types of data including structured
and unstructured data, clinical and billing data, transactional and claims data, patient-generated data, and data gathered from additional
sources that can shed light on a patient’s health status and more. As reliance on healthcare data grows exponentially, OneMedNet
has observed that the reliance on information has increased coming from multiple additional sources including EHR, claims, registries,
clinical trials, patient and provider surveys, wearable devices and more. These additional sources include the internet of things (“IoT”),
social media forums and blogs. Real World Data has the potential to break down inefficiencies and fill gaps in information silos among
stakeholders throughout the healthcare ecosystem of providers, payers, manufacturers, government entities and patients. This information
sharing, in turn, enables all parties to derive new insights, support value-based care and deliver better health outcomes.
Commercializing
a drug requires its developer to harness various sources of Real World Data to identify patient populations and refine sales and marketing
strategies for those populations among many other undertakings. Historically, this practice involved purchasing large amounts of data
from data aggregators or data platforms, if not directly from the source itself, sometimes without much knowledge about the quality of
the data. Preparing this data for analysis is both expensive and time-consuming; thus, many organizations would outsource the process
to consultants or third-party vendors. Moreover, the process of preparing this data for analysis by untrained consultants can yield a
static analysis that is difficult to modify or rerun in response to follow-up questions or potential discrepancies.
Definitions
of Real World Data and Real World Evidence
Real
World Data has become a powerful tool in the life sciences industry. After decades of relying on clinical data as the gold standard for
decision making, industry leaders now recognize how data collected in the real world adds valuable context and insight to their efforts.
From identifying unmet medical needs and defining the patient journey, to supporting regulatory submissions, proving value to payers,
and shaping market strategies, Real World Data adds value at every stage of the drug development lifecycle. Real World Data also sets
the foundation for Real World Evidence, and while the terms are often used interchangeably, they are distinct, and they are changing
health care. Here’s how it happens:
The
availability of medical imaging in Real World Data such as that provided by OneMedNet is facilitated by the development of digital image
analysis to increase the accuracy of diagnostics and conduct passive screening on large databases of medical images using AI algorithms
such as those applied by OneMedNet. Algorithms can also help identify additional diagnostic tests of value from medical images with pathology.
Real
World Evidence is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis
of Real World Data, as defined by the FDA. Real World Evidence can be generated by different study designs or analyses, including but
not limited to randomized trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective).
The difference in Real World Evidence and Real World Data focuses on the end use case. Real World Data can take the form of claims, EHR,
labs, data etc. Often this insight is used to better understand a patient’s journey or a natural history of a disorder (how does
a disease progress if left untreated.)
Real
World Evidence in contrast builds upon many of these data sets and prepares them for submission, as part of regulatory review such as
to the FDA or the European Medicines Agency (“EMA”), for example, in support of a customer’s clinical trial application.
When data and, in particular, imaging data is submitted to the FDA, the agency requires the following:
One
area where Real World Evidence has been relied on heavily relates to oncology approvals. The FDA’s Oncology Center of Excellence
presented an analysis of this at the American Society of Clinical Oncology in 2021, looking at oncology applications containing Real
World Data and Real World Evidence. That analysis looked at 94 applications that were submitted from 2011–2020 and showed that
inclusion of Real World Data to support regulatory decision-making has increased dramatically over that period. In 2020 alone, there
were 28 submissions for oncology products that contained Real World Data. Outside of the oncology context, probably the most notable
recent example of an approval relying on Real World Evidence is the FDA’s July 2021 approval of a new indication for Astella Pharma
Inc.’s’ drug program (or tacrolimus) for the prevention of organ rejection in lung transplant patients. The approval there
was based on a non-interventional study providing Real World Evidence of effectiveness. FDA’s press release announcing the approval
noted that the approval was “significant because it reflects how a well-designed, non-interventional study relying on fit-for-purpose
Real World data, when compared to a suitable control, can be considered adequate and well-controlled under FDA regulations.”
An
additional recent approval of note was the FDA’s December 2021 approval of the supplemental BLA (Biological License Application)
for Orencia® to prevent graft versus host disease. The application included data from a randomized clinical trial, with additional
evidence of effectiveness provided by a registry-based clinical study that was conducted using Real World data from the Center for International
Blood and Marrow Transplant Research. That registry study analyzed outcomes of 54 patients treated with Orencia® for the prevention
of graft versus host disease, in combination with standard immunosuppressive drugs, versus 162 patients treated with the standard immunosuppressive
drugs alone, and showed efficacy in that indication.
AI
is employed in Real World Data to enhance data anomaly detection, standardization, and quality checking at the pre-processing stage.
AI is expected to offer pharma and biotech companies the ability to increase meaningful Real World Evidence output, decrease time to
insights, and make the most of the available vast data sources. A Real World Evidence technology platform that delivers smart data processing,
analysis, and outcomes offers an unparalleled opportunity to capitalize on these computing advancements.
When
used as part of an overall comprehensive Real World Evidence strategy, AI innovations can enhance drug development, improve patient treatment
and access, and drive valuable new business opportunities.
In
post-marketing studies, adverse events reporting is an area where AI is used, creating greater automation and efficiency in historical
data sets. Techniques like natural language processing (“NLP”) enable AI to scan tens of thousands of records and quickly
find adverse event details. AI-integrated analytics and automation provide access to crucial insights from historical clinical trial
Real World Data and Real World Evidence, expanding end-to-end clinical trial capabilities:
● Data ingestion - publicly/historically available Real World Data
AI
is driving ground-breaking leaps in protein structure identification, and advances in regulations are providing healthcare research organizations
with access to Real World data to accelerate clinical trial processes. We believe that AI-enabled technologies have unparalleled potential
to offer innovative trial design and collection, organizing, and analyzing the increasing amount of data generated by clinical trials.
AI has many applications in clinical trials, both short and long-term. AI technologies make possible innovations crucial for transforming
clinical trials, such as seamlessly combining Phases I and II, developing novel patient-centered endpoints, and collecting and analyzing
Real World Data.
OneMedNet
believes that AI tools also have wider benefits for hospitals and health systems. Professor Alexander Wong, University of Waterloo Canada
Research Chair in AI and Medical Imaging, points out that AI benefits include the potential to ease the burden on radiology departments
in terms of assessing scans and predicting upcoming demand for general hospital and intensive care beds, and demand for equipment such
as respirators and ventilators, medicines, masks, and ventilator mouthpieces, as well as aiding workforce planning.
Across
a diverse set of imaging modalities, digital images typically include metadata and/or annotations that may include protected health information
(e.g., patient name, date of birth). Although diagnostic images generally do not warrant the same level of privacy concerns as
genomic data, researchers must also remove facial characteristics or other features that could identify a patient.
Digital
image analysis can be used to support research and development by analyzing large volumes of tissue specimens or other medical images
to run molecular screens that model biomarkers and treatment responses by transplanting a portion of a patient’s tumor into humanized
mice or 3D tissue cultures derived from stem cells that resemble miniature organs. These models allow researchers to conduct controlled
laboratory experiments that can inform treatment approaches and link predicted treatment response to actual clinical outcomes by linking
this data to EHR, claims, and other sources of Real World Data. Similarly, preclinical studies can be informed by safety assessments
conducted in animal models or studies of animal molecular biomarkers or anatomic abnormalities to minimize the burden on human study
participants. Findings can also inform clinical trial optimization by stratifying participants according to predicted response and determining
appropriate eligibility criteria.
Evaluating
Real World Evidence in the context of regulatory decision-making depends not only on the evaluation of the methodologies used to generate
the evidence but also on the reliability and relevance of the underlying Real World Data; these constructs may raise different types
of considerations. Real World Evidence refers to evidence about the risks and benefits of a product derived from analysis of the Real
World Data. For example, the FDA has used Real World Data and Real World Evidence, derived from its Sentinel System, the largest multisite
distributed database in the world dedicated to medical product safety, for monitoring the safety of regulated products, in place of post-marketing
studies. It has carried this out for nine potential safety issues involving five products.
Real
World Evidence is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis
of Real World Data. Real World Evidence can be generated by different study designs or analysis, including but not limited to, randomized
trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective).
Unlike
traditional clinical trials, where necessary data elements can be curated and collection mandated, the creation of Real World Evidence
requires assessing, validating and aggregating various, often disparate, sources of data available through routine clinical practice.
Real World Evidence is used by different stakeholders in many different ways.
● It gives life sciences companies insight into how their drugs are being used.
● It helps providers improve the delivery of care.
● It helps payers assess outcomes from treatments.
From
Real World Data to Real World Evidence
The
creation of Real World Evidence requires a combination of high-powered analytics, a validated approach and a robust knowledge of available
Real World Data sources (e.g., what data is captured within existing quality registries, what data can be captured through EHR
and case report forms or claims, and which patient organizations capture data on relevant patient cohorts). This process includes several
steps, which are summarized here:
1. Defining a study protocol answering relevant clinical questions.
5. Validating and supplementing blended data through editable eCRFs.
6. Defining and calculating clinically relevant outcomes and measures.
Real
World Evidence has been proven to fill a gap between research (what we learn) and everyday practice (what we do) in healthcare, and it
creates a difference between what is expected to happen and what really happens. Driving measurable improvements in healthcare requires
us all to be rooted in the reality of what actually happens before, during, and after clinical procedures, interventions, and office
visits. Real World Evidence fills those gaps and documents the truth by establishing definitively what really happens when doctors treat
a wide range of patients that do not look like the homogeneous patient groups in a clinical trial. Because of this, Real World Evidence
serves many uses and provides many benefits across the healthcare ecosystem.
As
more countries battle to contain healthcare costs, and as the population ages and the number of patients with chronic diseases increases,
the need to remove inefficiencies and upgrade the delivery of coordinated care that improves outcomes is more pressing. At the same time,
life sciences companies are facing tumultuous times. Industry globalization, the end of the blockbuster era, and an increasingly complex
regulatory environment all add to the difficulty of bringing products to market. And across the board, companies are moving toward a
patient-centric and outcome-focused model. In this environment, Real World Evidence can be transformative for the industry when Real
World Data is combined with the right technology framework and the regulatory intelligence to make sense of it. As data is consumed across
life sciences in different ways and by different stakeholders, it can provide valuable insights and “evidence” across the
product life cycle. In addition, stakeholders across the healthcare ecosystem use this new knowledge to support decision-making and improve
safety and effectiveness, and ultimately, patient outcomes.
Uses
of Real World Evidence in Life Sciences, Among Regulators, Clinicians, Researchers and Healthcare Systems
According
to repeated studies by Deloitte, the importance of Real World Evidence continues to rise as it promises to accelerate regulatory decision-making
and support the approval of new indications for drugs already on the market. Life sciences, pharmaceutical and medical device companies
are significant consumers of Real World Evidence because it can provide value across the entire product lifecycle from pre-trial design
to clinical studies and trials to post-market surveillance. Medical product developers are using Real World Evidence to support clinical
trial designs (e.g., large simple trials, pragmatic clinical trials) and observational studies to generate innovative, new treatment
approaches.
Real
World Evidence can be used to make clinical trials more effective and efficient, for example in patient recruitment or label extension,
Real World Evidence gathered from other studies or from currently marketed products in a similar category, for example, can have a positive
effect on the product portfolio by exposing positive side effects as new potential indications. The most famous example is Viagra, which
was initially studied as a drug to lower blood pressure, but an unexpected side effect led to the drug ultimately being approved for
erectile dysfunction.
The
benefits of Real World Evidence derived from Real World Data are increasingly being recognized by regulatory authorities. The FDA released
a framework for using Real World Evidence to support the process of drug regulation and submission. This is a major step toward recognizing
that clinical trials, while still relevant, are not the only way to assess the efficacy and safety of a product. Indeed, the FDA is soon
expected to conduct its first full post-market safety approval using only Real World Evidence.
Real
World Evidence is now accepted as a reliable source of information for regulatory decision making in certain circumstances. A primary
rationale for the FDA to use Real World Evidence is to help support the approval of a new or extended use for a drug approved under the
FD&C Act and to help support or satisfy post-approval study requirements always with the condition that the data quality is up to
the standard required. In a recent statement, the FDA even noted how new tools for capturing data in the post-market period, including
more sophisticated use of Real World Data and Real World Evidence are providing new approaches to address important questions about the
safety and benefits of new drugs in real world settings and that these approaches have the potential to do to so more rapidly and with
greater efficiency than traditional methods.
Why
Do We Need Real World Evidence?
There
is a gap between research (what we learn) and everyday practice (what we do) in healthcare, and it creates a difference between what
is expected to happen and what really happens. But it is what really happens that matters. Driving measurable improvements in healthcare
requires us all to be rooted in the reality of what actually happens before, during, and after clinical procedures, interventions, and
office visits. Real World Evidence is here to fill those gaps and root us in truth. It tells us what really happens when doctors treat
a wide range of patients that don’t look like the homogeneous patient groups in a clinical trial. Because of this, Real World Evidence
serves many uses and provides many benefits across the healthcare ecosystem.
Uses
of Real World Evidence in Pharmaceutical and Device Companies
Pharmaceutical
and medical device companies are major consumers of Real World Evidence, as it can provide value across the entire product lifecycle.
Real World Evidence plays an important role for research across the product lifecycle for both pharmaceutical and device companies. It
can inform pre-trial study design by helping researchers identify potential patients and create proper inclusion criteria for clinical
trials. Much of medical innovation is driven by traditional clinical trials, where new pharmaceuticals and devices are rigorously studied
and tracked before they can be sold and widely distributed.
Although
clinical trials are incredibly important to determine the safety and efficacy of new technologies, when compared to Real World Evidence,
they do have some limitations. For example, a traditional clinical trial can have strict inclusion criteria that makes it challenging
for providers to accurately extrapolate the results of a clinical trial to a broader population. Clinical trial participation is often
limited by who the study administrators are able to recruit, and various demographics are often not able to participate. This again challenges
the generalizability of clinical trial results across patient populations. Real World Evidence can help overcome the limitations of clinical
trials by providing information about a broader cross-section of society. This can help clinicians, researchers, and industry partners
better understand their products and how they work.
Once
a product is approved and marketed, Real World Evidence assists pharmaceutical or medical device companies understand their products’
relative safety, effectiveness, value, off-label use and more. This post-market surveillance, or post-marketing surveillance, is valuable
to stakeholders across the healthcare industry.
The
AI-enabled patient enrichment and recruitment process can improve suitable cohorts and increase clinical trial effectiveness, data management,
analysis, and interpretation of multiple Real World Data sources, including EHR and medical imaging data. This presents a unique opportunity
for NLP to perform the sophisticated analysis necessary to combine genomic data with electronic medical records (“EMR”) and
other patient data, present in various locations, owners, and formats - from handwritten paper copies to digital medical
images - to surface biomarkers that lead to endpoints that can be more efficiently measured, and thereby identify and characterize
appropriate patient subpopulations. AI-enabled systems can help to improve patient cohort composition and aid with patient recruitment.
AI
technologies can help biopharma companies identify target locations, qualified investigators, and priority candidates and collect and
collate evidence to satisfy regulators that the trial process complies with good clinical practice requirements. One of the most important
elements of a clinical trial is a selection of high-functioning investigator sites. Site qualities such as resource availability, administrative
procedures, and experienced clinicians with in-depth knowledge and understanding of the disease can shape study timelines and data quality,
accuracy, completeness, and consistency.
AI
integrated clinical trial programs can help monitor and manage patients by automating Real World Data capture, sharing data across systems,
and digitalizing standard clinical assessments. AI technologies and wearable technologies can help enable continuous patient monitoring
and generate real-time insights into the safety and effectiveness of treatment while predicting the possible risk of dropouts, thereby
enhancing patient engagement and retention. To comply with trial adherence criteria, patients must keep detailed records of their medication
intake and other data points related to their bodily functions, response to medication, and daily protocols. This can be an overwhelming
and tedious task, leading to 40% of patients becoming non-adherent after 150 days into a clinical trial. Wearable devices/sensors and
video monitoring are used to collect patient data automatically and continuously, thereby relieving the patient of this task. In combination
with wearable technology, AI techniques offer new approaches to developing real-time, power-efficient, mobile, and personalized patient
monitoring systems.
Among
regulators, clinicians, academic researchers and healthcare systems, the reliance on curated Real World Evidence has grown significantly
because of the value it can provide, which is unique relative to each parties’ objectives and mandates. It also helps that the
FDA has also sharpened its focus on Real World Data and Real World Evidence. For example, in late 2022, the FDA published proposed guidance
related to data standards for product submissions with Real World Data and also weighed in on the use of Real World Data and Real World
Evidence to support regulatory decision-making for drugs and biological products with specific advice for data from EHR and medical claims.
In addition, the FDA uses Real World Data and Real World Evidence to monitor post-market safety and adverse events and to make regulatory
decisions. The health care community is using these data to support coverage decisions and to develop guidelines and decision support
tools for use in clinical practice.
AI
with deep-learning capability is also helpful in organizing and translating a vast amount of structured and unstructured data to Real
World Evidence. The human mind can possibly manage 4-5 variables; therefore, AI-enabled data mapping and integration and their normalization
into a common data model according to disease pathway and workflow will likely be useful for both quality management in clinical trials
and generating meaningful insight for human disease by providing a broader perspective based on Real World data.
Market
Size
The
global Real World Evidence solutions market size was estimated at USD $2.6 billion in 2023 and is expected to grow at a compound annual
growth rate (CAGR) of 8.4% from 2024 to 2030. The market growth is driven by rising demand for enhanced Real World Evidence capabilities
within the life science industry, reflecting an increasing market shift from volume to value-based care. Advancements in data analytics
and Real World Evidence contribute to supporting regulatory compliance, research, and solution development efforts in medical device
and life sciences organizations. For instance, the increased demand for Real World Evidence solutions is prompting players to introduce
new products, fostering market growth. In October 2023, Maxis Clinical Sciences launched Real World Evidence Solutions, providing diverse
Real World Data capture and analysis to improve clinical research and care.
Government
initiatives supporting Real World Evidence programs, evolving regulations, and actionable Real World Data enable organizations to conduct
outcomes-based analyses, contributing to the overall market expansion. For instance, in December 2022, the FDA launched the Real World
Evidence Program. This program aims to raise awareness that Real World Evidence can support regulatory decisions, identify approaches
for generating Real World Evidence to meet post-approval study requirements or effectiveness labeling and develop agency processes that
foster consistent decision-making and shared learning regarding Real World Evidence.
The
COVID-19 pandemic further accelerated the adoption of Real World Evidence solutions, with governments collaborating with market players
to implement these solutions. For instance, in June 2021, ConcertAI and the FDA initiated a five-year collaborative research program,
Evaluation of Real World Outcomes and Safety in the Treatment of Cancer. The partnership leverages ConcertAI’s oncology
Real World Data and advanced AI technology solutions to generate Real World Evidence for various clinical and regulatory use cases.
Real
World Evidence solutions services allow pharmaceutical companies and healthcare providers as well as payers by providing efficient management
of operations and accelerating the process of drug development and its approval, which fuels market growth. Support from regulatory bodies
for using Real World Evidence solutions and an increase in research and development spending are anticipated to boost the market growth.
The
Real World Evidence solution providers are increasingly forming strategic partnerships with AI solution providers to offer integrated
solutions. For instance, in April 2023, ConcertAI, a player in AI SaaS technology and Real World Evidence solutions for healthcare and
life sciences, partnered with PathAI, an AI-powered pathology provider, to introduce a first-in-class quantitative histopathology and
curated clinical Real World Data solution. This collaboration integrates ConcertAI’s Patient360 and RWD360 products with PathAI’s
PathExplore tumor microenvironment panel. Based on end user, the global Real World Evidence solutions market is segmented into pharmaceutical,
biotechnology, and medical device companies; healthcare payers; healthcare providers; and other end-users (academic research institutions,
patient advocacy groups, regulators, and health technology assessment agencies). The large share of this segment is primarily attributed
to the increasing importance of Real World Evidence studies in drug development and approvals and the growing need to avoid costly drug
recalls and assess drug performance in Real World settings.
With
the growing need for evidence generated from Real World Data, the increasing importance of epidemiological data in decision making, and
a shift from volume to value-based care, there has been an increased focus on patient registries, a rise in the adoption of EMR in hospitals,
and exponential growth in mobile health data and social media, which have resulted in the generation of huge amounts of medical data.
In 2021, the Real World datasets segment is estimated to account for the larger share of 51.2% of the global Real World evidence solutions
market. According to Coherent Market Insights, the global Real World Data market is estimated to be valued at $7.51 billion in 2024 and
is expected to exhibit a CAGR of 9.1% during the forecast period (2024-2031).
Our
Long-Term Growth Strategies
Our
long-term growth strategy is anchored on the following key pillars:
Corporate
Information
Data
Knights was originally incorporated in Delaware on February 8, 2021 under the name “Data Knights Acquisition Corp” as a special
purpose acquisition company, formed for the purpose of effecting a merger, capital stock exchange, asset acquisition, stock purchase,
reorganization or similar business combination with one or more businesses.
On
November 7, 2023, a subsidiary of Data Knights merged with and into OneMedNet Solutions Corporation (formerly named OneMedNet Corporation)
(“Legacy ONMD”), with Legacy ONMD surviving as a wholly-owned subsidiary of Data Knights (the “Business Combination”).
In connection with the Business Combination, Data Knights changed its name to “OneMedNet Corporation.”
We
are located at 6385 Old Shady Oak Road, Suite 250, Eden Prairie, MN 55344 and reachable by telephone on 800-918-7189.
Legacy
ONMD was incorporated in the State of Delaware on November 20, 2015. Its wholly-owned subsidiary, OneMedNet Technologies (Canada) Inc.
(“ONMD Canada”), was incorporated on October 16, 2015 under the provisions of the Business Corporations Act of British Columbia.
ONMD Canada’s functional currency is the Canadian dollar.
Recent
Developments
Closing
of Business Combination
On
November 7, 2023, following the approval of the Merger Agreement and the transactions contemplated thereby at the special meeting of
the shareholders of Data Knights held on October 17, 2023 (the “Special Meeting”), Merger Sub merged with and into Legacy
ONMD pursuant to the Merger Agreement (the “Merger”), with Legacy ONMD surviving the Merger as a wholly-owned subsidiary
of Data Knights. Following Closing, Data Knights changed its name to “OneMedNet Corporation.”
The
Business Combination was accounted for as a reverse recapitalization in accordance with U.S. generally accepted accounting principles
(“GAAP”). Under this method of accounting, Data Knights was treated as the acquired company and Legacy ONMD was treated as
the acquirer for financial statement reporting purposes.
Standby
Equity Purchase Agreement
On
June 17, 2024, we entered into a Standby Equity Purchase Agreement, or the SEPA, with Yorkville. Under the SEPA, we have the right to
sell to Yorkville up to $25.0 million of our common stock, subject to certain limitations and conditions set forth in the SEPA, from
time to time, over a 24-month period. Sales of our common stock to Yorkville under the SEPA, and the timing of any such sales, are at
our option, and we are under no obligation to sell any shares of our common stock to Yorkville under the SEPA except in connection with