By Kevin von Keyserling, chief strategy officer, Keyfactor.
As value-based care becomes more prevalent, healthcare delivery organizations (HDOs) are continuously looking to transform the patient care model with goals of reducing costs, optimizing patient outcomes and driving better financial performance. As new channels, delivery agencies, and patients taking greater responsibility in managing their care in the continued shift to telemedicine or virtual healthcare, digital security has become an even more important component of this evolving ecosystem.
Given the growth of connected medical devices, the potential for security lapses from release through use is considerable. While implanted devices draw the most attention, the broader universe of medical care gadgets can also warrant concern. In the U.S. alone, hospitals can average anywhere between 10 to 15 connected devices per-bed. With this kind of scale, the number of security gaps can be significant.
Medical devices that feature wireless connectivity, remote monitoring, and near-field communication technology allow health professionals to adjust and fine tune implanted devices remotely and in real-time. Devices capture and transmit data across many channels and receiving parties, but many fail to incorporate data security protocols and standards. Older devices that remain in the field may be using outdated security software. There is also significant ambiguity on who owns the data, which can result in nobody taking the lead on managing current security practices. Put these factors together and it’s easy to understand why healthcare data is highly susceptible to security failures.
Optimizing Data and Device Security
Healthcare has the highest breach-related costs of any industry at $408 per-stolen record. As patients willingly share personally identifiable information (PII) reliable controls must be in place to protect patient privacy. Every identity within an organization must be covered by layers of digital security, and the process can be broken down into smaller bites to ensure you’re setting the stage for optimized data and device security without taking everything on all at once.
Establish necessary barriers: Improve the strength of targeted devices between devices and outside threats and be aware of your device inventory always – no device should ever be left unattended! Don’t leave cell phones or laptops out in open spaces, but if you do, utilize features such as auto-lock. Ensure that all devices are locked in a restricted and secure area when not in use.
Define your security protocol: Make sure to have a regular cadence in which passwords need to be updated and utilize multi-factor authentication when possible.
By Sean Otto, vice president of business development, Cyient.
The approval of electrocardiogram’s (EKG) through the FDA that enables atrial fibrillation detection right from a patient’s watch band is just one example of how the digitization of medical devices, a part of the Internet of Things movement, is leading product development and innovation in medicine. However, while medical devices built on a connected services platform include components for data storage, security, accessibility, and mobile applications, along with advanced analytics, successfully implementing artificial intelligence to drive actionable intelligence remains a challenge from an execution perspective.
According to Gartner, 85 percent of data science projects fail. Successful integration of data science into medical device development requires a rethinking around the role of data science in product design and life-cycle management.
Viewing data science as a product
While data science is rightly defined as the process of using mathematical algorithms to automate, predict, control or describe an interaction in the physical world, it must be viewed as a product. This distinction is necessary because, like any medical product, data science begins with a need and ends with something that provides clear medical utility for healthcare providers and patients.
It is erroneous to restrict the realm of data science to just the designing of algorithms. While data scientists are good at fitting models, their true value comes from solving real-world problems with fitted data models. A successful algorithm development process in data science includes business leaders, product engineers, medical practitioners, and data scientists collaborating to discover, design and deliver. For instance, a typical data science integration with a medical device product would include many of the following activities:
Identifying the medical need
Identifying proper data variables
Developing the right analytic models
Designing analytic algorithm integrations
Performing testing and verification
Deploying beta versions
Monitoring real-time results
Maintaining and updating algorithms
Considering data science as a product or feature of a product provides organizations with a different paradigm for execution focused on a tangible outcome. Data scientists are trained to develop accurate models that solve a problem, but the challenge many companies face is operationalizing those models and monetizing their outputs. Furthermore, conceptualizing data science as a product will ensure companies focus on its implementation, rather than just its development.
Advanced analytics: Part of the process, not an afterthought
Designing intelligence (even AI) into a connected medical device first depends on whether the data is being used to make a real-time decision or report on the outcome of a series of events. Most companies don’t realize the layers of advanced analytics that create actionable intelligence. By understanding these layers, which range from simple rule- and complex rule-based analytics to asynchronous event rules, complex event processing, and unsupervised learning models, companies can move quickly into developing mature analytics that have an impact from day one. As a company matures its analytics system from descriptive and diagnostic to predictive and prescriptive, it should also evolve to include strategic opportunities to provide business value, including automating decisions that can be delegated to a smart decision-support system.
Successful integration involves viewing advanced analytics as an architecture and not as a single solution to be implemented. The best way to make sure that you are successful in analytic development is to follow a continual process of discovery, design and delivery. For instance, data science architecture may begin with a business question, requiring you to determine if you have the right data and can actually leverage that data in the existing IT system. If you don’t answer this basic question, you will have challenges fully vetting the analytic opportunities available to you.
Recognizing common challenges in data science execution
Data science execution is often impaired by common missteps, like incongruence between customer and business needs and solving technical problems when it’s too late to have a positive impact. Another significant mistake from the business side is treating data science like a one-time accomplishment and not realizing it is a continuous process, or like a software development process with an unwarranted fixation on tools rather than skills and capabilities.
Greenlight Guru is the only quality management software designed specifically for the medical device industry.
Get to market faster with less risk and achieve true quality.
The seeds for Greenlight Guru were planted back in 2006 by Jon Speer, a medical device engineer turned consultant as a result of a simple observation: paper-based quality management systems are painful, risky & wildly inefficient. Commercial quality management software solutions have been available for over 20 years now, yet only about 30 percent of medical device companies that should be using them were. This observation and question led Jon Speer to team up with David DeRam to create the vision for a beautifully simple quality management software.
Greenlight Guru partners with trade publications and frequently hosts webinars to help medical device startup founders plot a clear course through the complicated regulatory environment.
Quality management solutions existed, or could be engineered to work, for nearly every industry. Because of to the complicated nature of medical device regulatory compliance in the United States, Canada and the European Union one of two things was happening: 1. Systems not meant for medical devices were being rigged to work or, 2. An unorganized, not easily searched paper-based QMS was developed.
Greenlight Guru was developed to help medical device manufacturers manage documents, manage risk, perform quality management and log and address customer complaints in an easy to use cloud-based platform.
Who are your competitors?
Greenlight Guru is the only QMS system built specifically for the medical device industry. Non-industry specific QMS systems exist; however, they often have to be heavily modified to handle even the most mundane tasks in the medical device industry. As a result, Greenlight Guru helps device manufacturers spend more time on their product, and less time on paperwork.
How your company differentiates itself from the competition and what differentiates Greenlight Guru?
Greenlight Guru consists of three systems meant to help device manufacturers “GO” to market, “GROW” in the market, and “GURU” to provide regulatory expertise to device makers. This three pronged approach helps manufacturers through the full life-cycle of the product.
Greenlight Guru has a B2B business model with systems mean to help device makers “GO” to market, “GROW” in the market and “GURUs” to help stay in the market.
Greenlight Guru is always looking for talented individuals with a willingness to work hard and improve the quality of life for our users.