In 2021, 37.1 million Americans, or 11.6% of the population, had diabetes. Of those, 77.0% (28.5 million) have diagnosed and 23.0% (8.6 million) have undiagnosed. Moreover, approximately 96 million adults (38% of US adults) have prediabetes, but more than 80% of these adults are unaware they have the condition. Early detection of these can improve adoption of lifestyle changes that slow the progression of diabetes and prevent diabetes complications. In recent years, machine learning (ML) has emerged as a powerful tool in healthcare, offering new ways to analyze complex datasets and uncover patterns that were previously undetectable. According to the Journal of Diabetes Science and Technology, ML algorithms improved early detection rates of diabetic nephropathy by 15% compared to conventional methods. By leveraging ML, researchers and clinicians can identify predictive biomarkers that signal the onset of complications in diabetic patients, allowing for timely and personalized interventions.

The market size of big data in healthcare is expected to reach $78.03 billion by 2027. However, the ever-increasing volume of processed information, including unstructured healthcare data, makes it challenging to organize and systematize. Over 80% of healthcare data is unstructured and it’s increasing at the rate of 47% every year. Structured information is crucial for efficient data processing and analysis, enabling seamless integration. NCBI study suggests that structured EMR data helps reduce the risk of errors in decision-making by 57%. Medical entity abstraction leverages technologies like Natural Language Processing and machine learning to extract and structure information from unstructured data. This process is vital in healthcare and life sciences for improving data accuracy, enhancing clinical decision support, and streamlining administrative tasks

Hospital readmissions are a significant issue in the United States, with approximately 20% of Medicare patients being readmitted within 30 days of discharge. Despite advancements in medical technology and patient care, the revolving door phenomenon of patients returning to hospitals shortly after discharge continues to burden both patients and healthcare systems alike

The combined team will support Reveal’s mission to unleash the full potential of AI technology for healthcare and life sciences organizations

According to recent studies, healthcare data breaches have been on the rise, with the number of reported incidents increasing by 107% from 2018 to 2022 and over 66% patients worry about the security of their health information when shared electronically. This alarming trend underscores the pressing need for robust data security measures to protect patient confidentiality and prevent unauthorized access to medical records. Data security stands as a cornerstone of Health Information Exchange (HIE), holding critical importance in safeguarding sensitive patient information across healthcare systems. As healthcare organizations increasingly rely on HIE to facilitate care coordination and improve patient outcomes, striking a delicate balance between privacy and data exchange becomes imperative. The very nature of HIE aims to promote interoperability and seamless sharing of patient data across disparate systems and healthcare providers. While this interoperability is essential for enhancing care coordination and improving patient outcomes, it also introduces inherent risks to patient privacy. Reveal HealthTech emerges as a beacon of innovation, providing a unique angle with its Reveal POV to address these pressing issues head-on.

Advancements in deep learning algorithms have sparked a revolution in the field of drug discovery, offering unparalleled opportunities to expedite the identification of novel therapeutics. According to a report by the Pharmaceutical Research and Manufacturers of America (PhRMA), it can take an average of 10 to 15 years and cost over $2.6 billion to bring a new drug to market. Moreover, the success rate of clinical trials remains low, with only about 10% of drugs entering clinical testing ultimately receiving approval. One of the primary challenges lies in identifying promising drug candidates and targets amidst the vast space of potential compounds and biological pathways. In contrast, deep learning applications offer unprecedented potential to address these challenges by leveraging large-scale biological and chemical data to expedite the drug discovery process. Deep learning algorithms, such as neural networks and convolutional neural networks (CNNs), excel at extracting complex patterns and relationships from data, enabling more accurate prediction of drug-target interactions, molecular properties, and adverse effects.

Effective staff allocation is paramount in ensuring the smooth functioning of healthcare operations. It involves strategically deploying healthcare professionals to match the ever-changing patient care demands, resource availability, and regulatory requirements. Traditional methods of staff allocation in healthcare often struggle to keep pace with the dynamic nature of patient care needs and operational demands. These methods rely heavily on manual processes, static scheduling systems, and subjective decision-making, which can lead to inefficiencies and suboptimal outcomes. . However, the emergence of artificial intelligence in healthcare (AI) offers a promising solution to this long standing challenge, revolutionizing how healthcare organizations optimize their workforce to meet patient needs while minimizing costs.

Cloud computing holds immense importance in healthcare and life sciences due to its transformative impact on data management, collaboration, and innovation. Its adoption enables healthcare providers to deliver high-quality, cost-effective care while driving advancements in medical research and patient care. The global healthcare cloud computing market size is expected to grow at 20.3% from 2024 to 2030 CAGR while the healthcare cloud computing market size which is estimated at USD 10.1 billion, is growing at 11.8% CAGR.

Today, the digitization of healthcare practices and processes exhibits a major momentum. Especially in times of partially endangered access to care and the unequal distribution of healthcare providers and specialists, the comprehensive provision of healthcare services to safeguard societal health, patient satisfaction, and safety, as well as positive therapy outcomes, requires innovative ways of treatment delivery and execution. The USA digital healthcare market is forecasted to grow at 16.6% CAGR from 2022 to 2023. Healthcare spending in the USA has been growing at a faster rate than the overall economy for many years. Recently, wearable technology has become one of the leading and considerably most valuable assets within the digital health solutions category.

In the complex realm of healthcare, interoperability emerges as a beacon of promise, offering the seamless transmission of vital patient data across diverse healthcare systems and platforms. Nowhere is its significance more pronounced than in oncology, where every moment is precious, and interoperability stands as the linchpin to elevating patient care and driving better outcomes. This importance arises from the inherent complexity of cancer treatment, where informed decisions hinge on the availability of accurate and up-to-date patient data. From diagnosis to treatment planning and ongoing care management, interoperability ensures that healthcare providers have access to comprehensive patient information at the point of care. This facilitates timely interventions, personalized treatment strategies, and enhanced care coordination, ultimately leading to improved patient outcomes and quality of life.

In today's healthcare landscape, a patient-centric approach has become increasingly crucial for improving health outcomes and enhancing overall satisfaction. However, the current healthcare system often falls short of meeting patient preferences, leading to frustrations and inefficiencies. One significant area where the system lags is the incorporation of Artificial Intelligence (AI) technologies. Despite the potential benefits, AI is not yet fully integrated into many healthcare practices. AI has the potential to revolutionize patient care by personalizing treatment plans, streamlining processes, and improving overall efficiency. AI-based innovations like virtual health assistants and remote monitoring systems not only enhance the patient experience but also allow healthcare professionals to overcome the limitations of physical proximity, enabling them to provide care to patients wherever they may be.

In today’s ever-evolving corporate landscape, employee well-being emerges as a critical cornerstone for organizational success. Defined as the holistic health encompassing mental, physical, emotional, and economic dimensions, it directly influences productivity, innovation, and talent retention. According to the Global Happiness and Well-Being Policy Report published in 2019 by the Global Happiness Council, well-being is positively correlated with employee productivity, organizational profitability, customer satisfaction, and employee retention.

Read Swapnil Sharma’s Journey at Reveal HealthTech

Readmitting a patient just after they have been discharged from the hospital is not an ideal scenario. It bears a cost implication for the patient as well as the provider. Our client wanted to leverage predictive modeling to determine the likely risk of patient readmission.

Our client used pharmacokinetic and pharmacodynamic (PK/PD) modeling in drug discovery to determine optimal dosing for patients. This was a very time-consuming process for our client's expert clinical pharmacologists. They were looking for a solution that would support rapid experimentation and help speed up the drug discovery process.

The COVID-19 pandemic disrupted most aspects of the healthcare industry including how patients could access care. As social distancing norms were put in place to mitigate the spread of the virus, our client understood that patients are either not able or unwilling to visit clinics in person. They wanted a virtual patient intake mechanism for sleep apnea patients that would collect the critical details and identify the appropriate Continuous Positive Airway Pressure (CPAP) device size.

Our client wanted Reveal’s support to identify biomarkers of patients who are likely to respond to a treatment. Knowing these biomarkers would significantly improve patient outcomes. However, the challenge was that every patient is genetically unique and identifying biomarkers responsible for a good prognosis is not easy.

Read how Reveal collaborated with a leading medical device manufacturer, by training a machine learning model to increase treatment adherence in patients and improve the effectiveness of frontline providers.

Clinical trials are expensive and take many years. Our client had developed multiple drugs to treat lupus. The clinical trial results of these drugs were mixed. Despite investing several years and significant funds into drug development, they did not have conclusive data to proceed to further stages of the trial.

The client’s commercial and medical teams needed access to fast and actionable insights to function effectively. This proved challenging when the backend data was unstructured and did not have an easy-to-use data extraction interface.

Managing unstructured medical records is always a cumbersome process, even more so when it is being done manually. Extracting clinical information from unstructured data takes too much time and adversely impacts patient care.

A large number of patients have difficulty finding the information they are looking for about their medical device. Either they don’t know what to search for or they don’t have a grasp on how to go about their search or even where to search for relevant content. Secondly, trust is also a factor. Patients do not have the expertise to distinguish trusted sources from untrusted ones.

The primary challenge for our client was to process and analyze data of over 20 million patients. They needed a platform that could process the data at scale and leverage AI to provide patient-specific personalized treatment.

Our client, a leading pharmaceutical company, was looking to gain relevant data insights to run efficiently and grow. However, getting insights from data silos was proving to be a considerable challenge, that was also taking up too much time. In addition, the accuracy of these insights was questionable due to multiple, inconsistent data sources. All this led to inefficiencies within the organization.

Understand how Reveal’s MLOps platform helped a leading pharmaceutical company train, deploy, run, manage, and monitor hundreds of machine learning (ML) models to facilitate drug discovery.

There is a progressive imbalance between the availability of resources and the ever-evolving needs of different stakeholders in healthcare. A wide variety of people avail healthcare services and products which presents an opportunity for innovative healthcare solutions that use artificial intelligence and robotic process automation (RPA). These new technologies can play a vital role in understanding human needs, adapting to an ever-changing landscape, and designing interfaces that cater to these needs. The integration of RPA in different facets can go a long way in achieving a revolutionary human-centric design (HCD) in healthcare. Inculcating a ‘Human-Centric Design’ to improve the overall effectiveness and quality of services is a worthy goal for most leading healthcare organisations.

According to latest findings by the WHO, 1 in 5 people develop cancer, and 1 in 9 men and 1 in 12 women succumb to it. This burden can be attributed to late-stage diagnosis which influences patient outcomes drastically. Delayed detection of cancer limits available therapies, and onset of therapy. It is seen that with every month of delay in therapy, the risk of death increases by 10%. Tumors vary phenotypically and genotypically. Thus, one solution may not fit all. A more personalized therapy that is based on genetic or molecular features of a tumor is necessary.

The rise of Electronic Health Records (EHRs) in the US healthcare industry was like that of a juggernaut - it took a while to gain momentum from the 80s, when researchers brought up the concept of EHRs. It picked up pace by the 2000s, and by 2015, close to 96% of American hospitals had adopted EHRs as part of their health tech. However, the matter of interoperability soon came to the fore. Care providers and infrastructure architects did not prioritize patient care coordination, where a patient could visit any provider around the country and easily access their health records. Back then, EHRs were primarily adopted to help with recording and security of the medical data, and each provider arbitrarily selected formats that were incompatible with most of the other providers in the country. Thus, the term ‘data silos’ entered healthcare.

The history of healthtech , The WHO defines healthtech to be the "application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve quality of lives." However, to put a date or time to when technology has been used in healthcare would be a fruitless effort. In the early stages of civilization, human technology was still nascent, and medical knowledge was a lot more traditional. But at some point in history, technology began to shape our understanding of our body and its biological functions.

Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. With extensive expertise in digital front door and health stack modernization services, we work with providers across the US and globally to help set up custom digital-first healthcare platforms to provide the best care for their patients.

Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. With decades of expertise in healthcare data protection, we work with providers across the country to help them set up robust cybersecurity infrastructure to stay resilient to cyber threats.

Watch Dr. Salim Afshar, Chief Medical & Innovation Officer, Reveal HealthTech, and Robin Farmanfarmaian, renowned author and healthcare entrepreneur, discuss ‘How AI Can Democratize Healthcare’ in our latest ‘Reveal AI in Healthcare’ webinar series.

Watch Dr. Salim Afshar, Chief Medical & Innovation Officer, Reveal HealthTech, and Dr. Daniel Nigrin, CIO, MaineHealth, discuss ‘The CIO Perspective’ in our ‘Reveal AI in Healthcare’ webinar series.

Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. With decades of expertise in designing predictive models for disease risk assessment, we work with healthcare providers to integrate analytic models into their existing EHRs.

Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. With decades of expertise in healthcare management and AI, we work with facilities to create custom decision support systems and analytics algorithms that work for their respective specialties.

We walked into the office, a blank canvas buzzing with nervous excitement. None of us, from CEO to newest intern, had been here before…

Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. As leading AI healthcare pioneers, we take a curated approach to identify your business problems accurately and build custom enterprise-grade AI solutions to tackle them effectively.

International patient relationship platform LeadSquared announces a partnership with leading technology services firm, Reveal HealthTech, which provides cutting-edge product development, implementation, & advisory support to the healthcare industry

Reveal HealthTech, a leading healthcare technology company, today announced a new partnership with CancerX, a public-private partnership to accelerate cancer innovation. Through this partnership, Reveal HealthTech will be a member of the CancerX community.

Watch Dr. Afshar, Chief Medical & Innovation Officer at Reveal HealthTech, and Dr. Chang, Founder of AIMed, discuss 'The Emerging Landscape of AI in Healthcare' in our 'Reveal AI in Healthcare' webinar series.

It’s no secret that healthcare is filled with challenging problems. The status quo just isn’t good enough for patients, providers, or anyone else who interacts with the healthcare system.

Imagine a world in which healthcare software is so intuitive and seamless that it allows medical professionals to concentrate on the human aspects of patient care. Envision digital tools that are capable...

The transformational power of technology to improve the delivery of care and make a real difference in the lives of patients and providers alike has been widely documented. There is also abundant coverage of the...

Reveal HealthTech, a healthcare technology firm, has raised $4 million in seed funding from W Health Ventures, a healthcare-focused VC that invests in and grows tech-enabled early-stage healthcare compani...