Blogs
Welcome to our blog section on Artificial Intelligence (AI)! Here, we will explore in-depth one of the fastest and most exciting technological fields of the modern era.
Blogs
29/11/2023
In the vast sea of global data, the healthcare industry stands as a towering giant, generating a staggering 30% of the world's data volume. According to the International Data Corporation (IDC), the industry is predicted to amass over 2,314,000,000 terabytes of data, showcasing an astounding 11,000% growth since 2013. Projections indicate that by 2025, healthcare will emerge as the fastest-growing data source worldwide. At SignalFire, we recognize this data surge not only as a challenge but as a monumental opportunity for startups to revolutionize healthcare data infrastructure.
The sudden data explosion in U.S. healthcare is attributed to the escalating adoption of electronic health records (EHRs), regulatory enforcement, and the burgeoning popularity of wearables and health tracking devices. These advancements contribute to a diverse array of data types, encompassing patient information, clinical notes, test results, imaging data, and claims data.
However, managing and extracting meaningful insights from this vast data pool poses significant challenges. Addressing the interoperability problem, ensuring data normalization, and tackling privacy and security concerns are prerequisites before delving into the sophisticated applications of data science.
The time is ripe for transformative change. The advent of cutting-edge AI tools capable of deciphering the intricate web of healthcare data has opened new avenues. As the world shifts its focus to AI, there is a sense of urgency among slower-moving industry players to modernize their data infrastructure.
In this post, we will explore the major technological and regulatory shifts impacting the healthcare data landscape. Additionally, we'll highlight a dozen specific opportunities where SignalFire is poised to invest. Drawing on our decade-long experience developing our proprietary AI data platform, Beacon, tracking over half a trillion data points, we offer unique insights into market intelligence for our portfolio companies. Our in-house expertise in data and machine learning provides us with a distinctive perspective on the transformative power of data in healthcare, guiding our excitement to support visionary founders.
Now, let's delve into the trends and opportunities surrounding AI and analytics for hospitals, payors, pharma, and patients, paving the way for a future where innovative solutions harness the full potential of healthcare data.
In the world of AI healthcare, data quality reigns supreme, with the mantra "garbage in, garbage out" guiding our journey. To lay the foundation for advanced models and analytics, we must tackle key questions:
In 2016, the healthcare data landscape underwent a seismic shift with the introduction of the 21st Century Cures Act. This legislation mandated bi-directional data exchange through the Trusted Exchange Framework and Common Agreement (TEFCA), fostering accessibility across healthcare organizations and provider networks.
To access shared data, entities must now secure the Qualified Health Information Network (QHIN) designation, setting a high standard for companies navigating data infrastructure challenges. Health Gorilla, a SignalFire portfolio company, stands as a QHIN licensee, showcasing a robust platform with access to the complete medical records of over 90% of the U.S. patient population.
By overcoming raw data challenges, ensuring cleanliness, facilitating integration, and implementing privacy-safe storage, Health Gorilla serves as a beacon for companies collaborating seamlessly with regulators. This journey illustrates that conquering data infrastructure hurdles is pivotal for propelling the next generation of healthcare solutions, where AI and high-quality healthcare data converge for innovation and improved patient outcomes.
Improved infrastructure empowers companies to create unique analytics and AI models in specialized healthcare niches. These targeted applications, relying on specific datasets, enable startups to establish data moats for defensibility. In the sensitive realm of patient data, distinguishing solutions lies in prioritizing top-tier privacy and security practices.
Providers play a pivotal role in generating valuable clinical data with each patient visit, capturing essential information for healthcare practice. SignalFire is particularly enthusiastic about the following areas:
Personalized Patient Engagement:
Leveraging comprehensive medical history, demographic details, and consumer preferences, how can we proactively engage patients for preventive visits, provide education on high-risk conditions, and offer insights on available healthcare options? This proactive approach fosters long-term patient engagement, enhancing brand loyalty for providers while reducing overall costs.
Clinical Intake Intelligence:
Health Note transforms the traditional intake process by sending patients a digitally powered dynamic questionnaire via SMS before their visit. This time-efficient solution not only streamlines front desk administration but also auto-generates a portion of the clinical note, optimizing the overall patient experience.
Clinical Decision Support:
Access to the entire patient medical record, coupled with AI tools, enables precise diagnoses and real-time interventions with higher accuracy than human capability alone. Tools like Recora Health's virtual cardiac platform offer valuable insights to clinicians, enhancing diagnostic speed and accuracy without replacing human judgment.
Coding Automation:
AI models, such as those from CodaMetrix (SignalFire Series A lead), facilitate faster payments and accurate billing by autogenerating billing codes from unstructured doctor's notes. CodaMetrix's unique advantage, stemming from its origin at Mass General Brigham, establishes a data moat around high-quality training data.
SignalFire remains at the forefront of healthcare innovation, driving advancements in personalized patient care, streamlined clinical processes, enhanced decision support, and efficient coding automation.
The business models of payers, essentially operating as insurance entities, inherently foster alignment with solutions utilizing AI and analytics to reduce healthcare costs while enhancing outcomes. Presented below are various challenges addressed by companies employing data and AI:
Medication Adherence and Management:
The payer ecosystem expends an estimated $300 billion annually on issues such as unconsumed medications, preference for pricier drugs over generic alternatives, and prescriptions that are no longer necessary. Enhanced data capabilities enable a comprehensive understanding of a patient's conditions, facilitating personalized engagement. Leveraging behavioral economics principles, interventions can be tailored to encourage timely and appropriate medication adherence, exemplified by our investment in Wellth.
Population Health Management:
Payers, overseeing hundreds of thousands to millions of lives, bear the responsibility of understanding the overall health of their population. A data-driven solution like Color can analyze patient population data comprehensively, aiding in proactive health management by directing individuals to the appropriate care.
Payment Integrity:
The annual expenditure of $200–300 billion on claims of waste, fraud, and abuse underscores the critical need for addressing inefficiencies. CodaMetrix's autonomous coding solution directly tackles the prevalent issue of overspending due to human errors on the provider side. Beyond rectifying this problem, widespread adoption could establish a standardized language, fostering equitable transactions between payers and providers.
In the pharmaceutical industry, where the development of a successful drug demands an investment exceeding $1 billion and a span of 10 years, the allure of data-driven and AI solutions lies in their potential to expedite drug development timelines and reduce costs significantly:
Drug Discovery:
A crucial initial step in drug development is identifying conditions that are both prevalent and economically impactful. Access to anonymized patient data provides a panoramic overview of prevalent conditions, allowing for in-depth exploration of the patient's journey under various treatments. Leveraging robust datasets, such as that offered by Ovation.io, accelerates the identification of potential drugs—a vital advantage given the extended timeline required to bring new drugs to market.
Synthetic Clinical Trial Arm:
Real-world evidence data plays a pivotal role in streamlining patient recruitment for clinical trials. By constructing a "synthetic control arm," pharmaceutical companies can model comparators using data, eliminating the need to collect data from patients specifically recruited for the control group. This innovative approach not only expedites the trial process but also yields substantial cost savings in the extensive and resource-intensive trial phases.
Post-FDA Targeting:
Beyond the stage-four approval that allows a new drug to enter the market, the challenge lies in identifying the most promising target market. Utilizing data can pinpoint key opinion leaders and prescribing physicians for patients whose profiles align with the drug. This targeted approach ensures a strategic marketing focus post-approval, optimizing the drug's reach and impact.
While the solutions discussed earlier primarily benefit patients indirectly by enhancing healthcare processes, patients remain the focal point of our healthcare ecosystem. Here are additional ways in which data insights and accessibility directly contribute to patient well-being:
Individual Medical Record Access:
Currently, patients with chronic or rare diseases face the challenge of manually compiling their health information for optimal treatment. Under TEFCA, approved data sharing use cases only allow providers to access patient information. However, we anticipate unlocking individual use cases in the coming year, empowering patients to access their own health information. This development will prove invaluable for individuals managing clinical illnesses and those striving to keep track of their immunization records.
Patient Payments:
Improved data utilization can alleviate the financial burden on patients seeking healthcare services. Payzen harnesses extensive patient data, including medical history, demographics, and visit frequency, to devise personalized medical bill payment plans featuring a 0% interest rate. By leveraging this data-driven approach, patients can more easily manage and afford their healthcare expenses.
At VinBrain, we create DrAid™ Enterprise Data Solution where we accompany you in defining, analyzing, and implementing the strategy for your medical work. “Transforming raw data into valuable knowledge is the core value of comprehensive digital transformation” Steven Truong, CEO of VinBrain, says. In DrAid™ Enterprise Data Solution, we offer you 4 solutions including: Smart Automatic Report Generation, EMR Smart Analytics, Hospital Dashboard and Predictive Analytics, and Data Lake, Data Management & Knowledge System.
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