Tag: advanced analytics

Medical Device Development and Data Science Integration

By Dr. Sean Otto, business development, Cyient.

Sean Otto, Phd
Sean Otto

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:

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.

To use a common metaphor, data science is not a single moon shot, but laps around a track. Ultimately your goal is to run progressively faster around the track. An equally major drawback hindering execution is artisan thinking where design is seen as the ultimate end to the data science process. In fact, the most desirable approach is a modular system with emphasis on consistently maintaining and improving what has already been designed. This is particularly true for medical devices where innovation and changes in technology are continuing to better support and enable patients and practitioners.

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Optimize, Analyze and Scrutinize: Key Trends Healthcare CIOs can expect in 2016

Guest post by Kirk Larson, national CIO, healthcare, NetApp Inc.

Kirk Larson
Kirk Larson

As we start a new year, let’s take a moment and take stock of the past 12 months. Like an annual physical, it gives us a chance to take a pulse check on the industry and see what the next year has in store – the opportunities and the obstacles.

During 2015, we had the opportunity to chat candidly with CIOs, healthcare technology partners and healthcare providers to discuss the big questions affecting the industry:

— What are the big topics the industry will be focused on?
— What changes do you see coming?
— What new challenges lay ahead and what new technologies will help us overcome them?

Based on these discussions, here are some of the key trends healthcare CIOs can expect in 2016:

Electronic Health Record (EHR) Optimization

As healthcare organizations move beyond implementation phase of EHRs, CIOs and IT are refocusing their efforts towards enhancing care workflow and benefits realization by way of optimizing the IT infrastructure. Basically, the status quo on overspending on legacy hardware is no longer being tolerated.

While the high availability, performance and security requirements for IT infrastructure certainly aren’t lessening anytime soon, IT is feeling greater cost pressures to run EHRs more efficiently. As a result, organizations are looking to simplify IT operations for running on-premises data centers with improved data management solutions, with the end-goal of moving toward building their own private clouds.

In addition to greater cost efficiency, we are seeing a growing demand for increased agility of IT services. As such, organizations are looking to advanced analytics capabilities as a means of achieving greater responsiveness. But before they can reap the benefits of employing a population health management system, IT needs to shift from tired legacy IT environments to highly agile IT infrastructure.

Population Health Management

Population health management programs have long been used by healthcare insurers to increase wellness and decrease claims cost. Organizations leverage multiple data sources such as EHRs, pharmaceutical data, insurance claims, etc.; to enhance and preserve wellness, as well as, programs that anticipatory and pre-emptive in design.

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