Segmed Blogs

A radiologist is not someone who typically comes to mind when people think of a doctor. Radiologists most often diagnose and treat patients through the help of medical images, such as x-rays, computed tomography (CT), and ultrasound. ‍ Dr. Michael Larson , a radiology resident at the Banner University Medical Center Medical Imaging Department in Arizona, enjoys his job because it is like “being a medical Sherlock Holmes.” Radiologists observe details and patterns that help them form a coherent story about a patient, sometimes from a single image. They are often unseen leaders, piecing through puzzles they are trusted to figure out. As technology advances, healthcare is relying more heavily on imaging data. Radiologists are being called to action as imaging volume increases. Unfortunately, this heightened workload removes them from their detective work, replacing the feeling of passion with that of exhaustion. In the annual Medscape National Physician Burnout and Suicide Report of this...

This article delves into how AI is revolutionizing Radiology and the potential future of healthcare. Learn how AI is streamlining diagnosis and revolutionizing the way medical professionals interact with patient data. Introduction With the advancement of Artificial Intelligence (AI) prevalent in recent years, it isn’t only IT spheres that can benefit from it. Healthcare is a massive industry, always looking for ways to progress and optimize their processes to ensure patients are treated quickly and correctly. By applying AI to healthcare, and by extension radiology, there are massive opportunities to revolutionize the industry in many ways, by using deep learning algorithms, convolutional neural networks and other data science techniques. In this article, the focus will be on radiology and what can be gained from AI, whilst identifying potential pitfalls. AI’s Potential Impact on Radiology So, what impact could AI have on Radiology itself? Other than broader benefits from its applicati...

About Aidence : Netherlands-based Aidence was founded 5 ½ years ago with the grand goal of applying AI to benefit mankind. The team decided their outlet was through healthcare, with lung nodules being a particular area of interest. Since its inception, Aidence has grown its team to over 50 members representing 15 nationalities, and has become one of the leaders in lung cancer screening across Europe, ascending to number one in the UK. Their lung nodule management tool already has grateful physicians calling to thank them for eliminating errors and increasing accuracy for their patients and efficiency for physicians- but Aidence has grander visions. Their goal is to move beyond purely diagnostic tools and instead follow patients along the entire lung cancer pathway, providing guidance and support at each step of the way. The Beginnings : Getting an AI-based healthcare startup off the ground is no simple task. Issues such as security and compliance, funding, training and development are ...

The rapid development of Artificial Intelligence (AI) has opened up exciting new possibilities in medicine. There are a multitude of similar projects underway that employ AI in the early detection of macular degeneration, acute kidney failure, skin cancer, sepsis, and Alzheimer’s disease, among others. In healthcare, AI has been used in a myriad of ways, yet it is still far from reaching its potential. Already, AI has been used to quickly obtain useful information from patient populations to assess - in real-time - risks for the general population (truer than ever now in the COVID-19 age) and to carry out highly repetitive tasks such as analysis of tests, X-rays, or CT Scans. Clinically, it has proven to lower the risk of diagnostic mistakes, promoting a precision medicine approach and implementing the best course treatments based on a rapid assessment of clinical condition. For health systems, it can help organize clinical charts while reducing needless administrative work, and analyz...

In today’s healthcare environment, the general consensus is that the exchange and flow of data is crucial and beneficial to developing better care of the patients. Healthcare information exchanges (HIEs), as an example, have shown to be effective in improving public health, reducing healthcare redundancies, and providing clinical providers with more support tools in their day-to-day work. As much as we have realized the importance of data sharing, many barriers still remain - namely legal, motivational, and technical. Legal barriers. Legal barriers to data sharing run the gamut from being too restrictive to being completely non-existent. While HIPAA is the overarching data governance strategy in the U.S., the interpretations of HIPAA and the level of comfort with de-identification under HIPAA still vary. For example, disparate agencies have disparate policies with differing levels of restriction. Some agencies have had experiences that were negative in the past, and that in turn influe...

At SCOPE Summit '24, join Segmed at booth #101 to know our cutting-edge solutions that are revolutionizing healthcare. Meet our team to explore innovative possibilities and redefine the future of clinical insights and research design. ‍ 1. Revolutionizing Clinical Insights: Witness Segmed's groundbreaking innovation in integrating Real-World Data (RWD) with Randomized Control Trials (RCT) at SCOPE. In the dynamic landscape of healthcare advancements, our pioneering solutions redefine how clinical executives approach patient insights and research design. 2. Overcoming Data Challenges: Discover how Segmed addresses the challenges of accessing imaging Real-World Data alongside Electronic Medical Records (EMR). Our commitment to overcoming these obstacles propels Life Science and MedTech forward, accelerating the development of new therapies and devices for the benefit of patients. 3. Supporting Clinical Operations Excellence: As a proud sponsor of SCOPE, Segmed aims to showcase ho...

The healthcare industry generates a massive amount of data, and medical imaging is no exception. With the advent of AI-driven algorithms, the need for data security and privacy has become even more crucial. Data tokenization is a technique that has been gaining traction in the medical imaging space to ensure data protection and confidentiality. In this blog post, we will explore what data tokenization is, its benefits and difficulties, and how it can be applied to medical imaging data. What is Data Tokenization? Data tokenization is a process that replaces sensitive data elements with non-sensitive, unique tokens that have no intrinsic value. The original data is securely stored in a separate, protected environment, while the tokens are used for processing and analysis purposes. This approach minimizes the risk of unauthorized access or data breaches and ensures compliance with privacy regulations. Benefits of Data Tokenization in Medical Imaging a. Enhanced Data Security: Data tokeniz...

Before delving into the history of Electronic Health Records (EHR), let’s see what an Electronic Health Record (EHR) is? According to the organization HealthIT.org , “An electronic health record (EHR) is a digital version of a patient’s paper chart. EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users. While an EHR does contain patients' medical and treatment histories, an EHR system is built to go beyond standard clinical data collected in a provider’s office and can be inclusive of a broader view of a patient’s care. EHRs are a vital part of health IT and can:Contain a patient’s medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory and test results Allow access to evidence-based tools that providers can use to make decisions about a patient’s care Automate and streamline provider workflow” EHR advancement is a story of technological evolution, pol...

Although federated learning (FL) is becoming an increasingly popular form of machine learning, it is still considered a new domain. FL was only recently coined in 2016 when it was introduced in a paper published by Google AI called “Communication-Efficient Learning of Deep Networks from Decentralized Data.” As the name suggests, federated learning’s advantage point over other forms of machine learning is its ability to work using decentralized data. It is extremely important for decentralized data to be used—especially in the field of healthcare, where patient medical data is sensitive—in order to avoid privacy breaches or violations. Additionally, federated learning can be used to train data with rare cases, as it is a result of the large amounts of varying medical data from different data sources. When there is enough of this diverse data, the algorithms are able to identify more cases and give more accurate predictions and/or conclusions. Federated learning works by using a...

In the previous blog post I covered a little bit of the history of EHR data. You may be wondering - how were the early EHR systems implemented? The key technology for EHR was called MUMPS (yes, a little unfortunate), which stands for Massachusetts General Hospital Utility Multi-Programming System. It was developed in the late 1960s at Massachusetts General Hospital. It was designed as a programming language and database system optimized for the healthcare environment, capable of handling medical records, billing information, patient tracking, and more. Over the years, MUMPS has been known simply as 'M'. Key Role in EHR Development One of the main reasons MUMPS played a critical role in developing EHR systems is its capacity for rapid development and customization. This was essential in the early days of EHRs when there were no one-size-fits-all solutions, and systems needed to be tailored to the specific needs of different healthcare practices. MUMPS is unique because it integ...

About a year and a half ago, I was a rising senior at the University of Waterloo with no real idea of what I was going to do post-graduation. I was in the process of applying for a summer internship when I came across Segmed. It seemed like a solid fit - a product management role in a fast-growing start-up in the health-tech space. I applied for the job, and in summer 2021, I started as a PM intern at Segmed. What a Segmed Internship Looks Like Interns at Segmed aren’t just “interns”. They’re valuable employees that are given high-impact work. As a small team, nobody is just a cog in the machine - each person owns a specific project, product area or customer/partner segment, and each team member's contributions are critical. In summer 2021 we had 10+ interns, all working on features that were either productized or led to insights that were woven into our roadmap. And of course - working on cool stuff is important, but having some fun on the job doesn’t hurt. And the Segmed team ma...