This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data sovereignty and governance considerations unique to healthcare, strategic competitive advantages afforded by proprietary AI infrastructure, and the transformative potential of healthcare-specific foundation models for patient care and organizational operations. Through analysis of empirical evidence, economic frameworks, and organizational case studies, we demonstrate that proprietary multimodal foundation models enable healthcare organizations to achieve superior clinical performance, maintain robust data governance, create sustainable competitive advantages, and accelerate innovation pathways. While acknowledging implementation challenges, we present evidence showing organizations with proprietary AI capabilities demonstrate measurably improved outcomes, faster innovation cycles, and stronger strategic positioning in the evolving healthcare ecosystem. This analysis provides healthcare leaders with a comprehensive framework for evaluating build-versus-buy decisions regarding foundation model implementation, positioning proprietary foundation model development as a cornerstone capability for forward-thinking healthcare organizations.

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.

Organized crime is a continuous issue, and predicting it is always under research. Medical insurance claims are one of the organized crimes related to patients, physicians, diagnostic centers, and insurance providers, forming a chain reaction that must be monitored constantly. These kinds of frauds affect the financial growth of both the insured people and the health insurance companies. The Convolution Neural Network architecture is used to detect fraudulent claims through a correlation study of regression models, which helps to detect money laundering on different claims given by different providers. Supervised and unsupervised classifiers are used to detect fraud and non-fraud claims. By using different attributes of patient case studies, diagnostic reports, and service provider reimbursement claim codes as control variables and attributes of the target class to detect performance metrics, this paper highlights the top reason for organized crime through the public dataset. The claims are filed by the provider, so the fraud can be organized crime. The performance metrics of accuracy, sensitivity, specificity, recall, precision, AUC, and f1-scores are calculated.

Ever since the adoption of Smartphones and further mobile applications have flooded the market, global industries are on their way to offering their services through the adoption of mobile app development trends. One of the prominent and visible growth has been in the healthcare industry, which has gone many miles ahead of where it stood a decade ago. However, that can't be done with just a snap of a finger. You, as a healthcare organization entering into the mHealth service market, must follow healthcare app development trends, which we have discussed over here, to lead your app concept to success. In addition, to know healthcare app development trends, the market study is the need of the hour!!

Millions of people who enrolled in Medicaid during the COVID-19 pandemic risk losing coverage in the spring of 2023, leaving many worried about their healthcare coverage and many healthcare providers struggling to redetermine coverage. It's an anxiety-inducing situation, but robotic process automation (RPA) is on hand to help. States were required to keep people enrolled in Medicaid throughout the pandemic due to a decision made by the HHS declaring COVID-19 as a Public Health Emergency (PHE). However, PHE is set to end on April 11, 2023. And while the HHS has extended the PHE in the past, it's unlikely to do so again.

The novel coronavirus (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) after it was first discovered in Wuhan, China [1]. Over one year, the virus has infected more than 68 million people worldwide [2]. The virus can be fatal for elderly people or ones with chronic diseases [3]. Different countries across the globe have imposed several social practices and strategies to reduce the spread of the infection and to ensure the well-being of the residents. These practices and strategies include but are not limited to social distancing, restricted and authorized travels, remote work and education, reduced working staff in organizations, and frequent COVID-19 tests. These measures have been proved potential in reducing the disease spread and death in the previous pandemics [3], [4]. Several studies have focused on machine learning time series models to forecast the number of COVID-19 infections in different countries [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. This is to aid the government in designing and regulating efficient virus spread-mitigating strategies and to enable healthcare organizations for effective planning of health personnel and facilities resources. Based on the forecasted infections, the government can either make the confinement laws stricter or can ease them.

Growing populations around the world are experiencing (and contributing to) a shortage of healthcare workers, and the gap is expected to widen in the future. "The world will be short of 12.9 million healthcare workers by 2035; today, that figure stands at 7.2 million," as per a recent report by the World Health Organization (WHO). Globally, billions of people will suffer serious health consequences if the findings in today's WHO report are not addressed. Experts believe that integrating technology into healthcare and digitalizing the system can address future challenges. The healthcare sector has been embracing artificial intelligence (AI) by using it to assist doctors, hospitals, pharmaceutical companies, and others in overcoming practical challenges.

Medical billing and coding have been undergoing many changes in recent years as the healthcare industry increases in complexity while the variety of treatments and procedures grow by the minute. The healthcare industry is in urgent need of a scalable solution that can process the vast amount of patient data without compromising speed and accuracy of the billing procedure. The use of AI medical billing and coding can help healthcare organizations facilitate their billing procedures while minimizing costly errors. AI-driven technologies, such as machine learning and natural language processing (NLP), have the ability to interpret and organize a large amount of data quickly and accurately. For instance, an AI medical billing program can arrange data from different records into a logical timeline to make sense of disparate events, diagnoses, and procedures, minimizing coding and reporting errors.

Since the start of the COVID-19 pandemic about two years ago, many facets of life have changed. Healthcare systems have seen many changes throughout the pandemic. We've seen hospitals overloaded with coronavirus patients struggle to keep up with the demand. The pandemic highlighted how inept our healthcare systems were in dealing with this type of crisis, while still providing care to existing patients. It also highlighted the inability to easily collect and analyze data on patients in an efficient matter, thus providing care to people more quickly.

Turning the page on 2022 will be a cause for celebration in the healthcare sector. The past year was one of the worst financial years on record for hospitals, according to Kaufman Hall. New data from the healthcare consulting firm and the American Hospital Association indicates that 53% to 68% of the nation's hospitals will end 2022 in the red. At the same time, hospital employment is down approximately 100,000 from pre-pandemic levels. This is all happening amid a backdrop of growing margin pressures and an aging population.