Feature Articles

DoD Looks for Disaster Response Solutions Through Artificial Intelligence Innovation Competition

An important new trend in the federal sector involves the use of "innovation competitions" to develop Artificial Intelligence (AI) solutions for major problems that the agencies face. An ongoing AI innovation competition from Centers for Medicare and Medicaid Services (CMS), known as the Artificial Intelligence (AI) Health Outcomes Challenge began in March 2019. While that competition is only open to major consulting firms, the Department of Defense (DoD) was the trendsetter here and has been working on a series of AI competitions that began with the xView competition in March 2018.

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Open Source Governance and the Rise of a New Open Health Movement

It's hard to tell if (or when) new open source foundations will appear and claim a leading role in healthcare. It would be interesting to see one created to scale an existing viable model, such as the one from Oroville Hospital using VistA. Or we could see OSEHRA shifting its focus and expanding its charter beyond just the US government space. Nevertheless, the successful foundation would keep a low barrier to entry for innovators, allowing them to incorporate and scale open source healthcare technologies into commercial products. Time will tell, but what's for certain is that we live in interesting times, and I am looking forward to massive innovation in healthcare in the near future. The time is ripe.

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Using Open Technology To Build a Biodefense Against the Coronavirus

As the number of US cases of the coronavirus rises, how will healthcare professionals be able to tell the difference between which panicked patients with similar symptoms has what? Even if the patient hasn't traveled to Wuhan or China recently, what if they sat at a Starbucks with someone who did? With the incubation time-lag before symptoms appear, who would even know? The challenge of monitoring 330 million people for infectious disease outbreaks is daunting. Take the flu as an example. During the last flu season which, as already discussed, was not as complex as this year's season, approximately 35.5 million Americans had flu symptoms, 16.5 million received medical care, 490,600 were hospitalized and 34,200 died.

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Sure Strategies for Improving Health and Education: 2020 and Beyond

Our paths cross as we enter a new decade and consider the next ten years can lead to marked improvements in healthcare delivery - something sorely needed but doable. To that end, we've identified three key developments that can and should be implemented. These are not the only needed improvements and avenues to be pursued; that would take a book or a longer article. So, we've focused on what are, for us, three of the most innovative possibilities. One added word: some of the three innovations or aspects of them may strike readers as fanciful or imaginary or over-the-proverbial-top. But, the speed with which technology is changing, the pressing need for solutions and our capacities to consider new paradigms for solving old problems are converging.

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How DoD Plans to Leverage Artificial Intelligence and Open Source to Improve Emergency Response and Disaster Relief

Some might not know it, but the US military plays a key role in US disaster response strategy and, accordingly, the Department of Defense (DoD) has prioritized its disaster response mission and is investing heavily towards increasing its capabilities and effectiveness. Technology is a big part of all modern DoD missions, and disaster response is no different. The most promising and transformative technology on the horizon for our future, and for the future of the DoD, is Artificial Intelligence (AI). So how exactly will the DoD leverage Artificial Intelligence technologies in order to meet the demands of the disaster response mission? Is a new Center of Excellence created by DoD, called the Joint Artificial Intelligence Center (JAIC). Read More »

Major Interoperability Initiative Launched During the Annual Meeting of The Sequoia Project

On December 5, 2019, HLN Consulting, LLC participated in The Sequoia Project's day long annual meeting which was held just outside of Washington, D.C. at the Gaylord National Resort & Convention Center. As a leading healthcare informatics consulting company HLN monitors developments in healthcare interoperability nationally with particular emphasis on the impact to public health agencies and their stakeholders. This conference represented a good opportunity to participate in the start of a major interoperability initiative that will play out over the next several years. In addition to this report, the Sequoia Project posted the proceedings of the meeting online, including the recorded discussions as well as the slides from the presentations. Read More »

A Perspective of Open Source Licensing Models for the Health Care Industry

Recently, I've had several interesting conversations about how business models based on open source technologies apply to the healthcare industry. While a lot has been written on the subject, I aim to provide a concise summary and some of my personal perspectives on the matter. This article discusses the definition of open source technology and licensing models; a second article will discuss governance models and applications in healthcare...In reality, it's hard to talk about open source licensing without talking about intellectual property (IP) and copyright. Copyright sums up the rights and obligations that the rightful owner associates with the work. The license describes the rights and obligations of any and everyone else, and can be as broad or as limited as the owner chooses.

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“WE ARE” Data-driven Social Determinants of Health for Life

Who are you? Today the data knows! DataStreams are being created by each of our mobile and Internet-connected devices moment by moment each and every day. In fact, 90% of all data created in the history of humankind has been created in the last few years. We are talking in excess of two quintillion bytes of data a day being generated. A quintillion is a billion, billion; the data generated by our interactions with the Web, Twitter, Amazon, every Google search, text message, photo taken, command sent to Alexa and all our other actions recorded as digital data is the number TWO followed by 18 zeros! The challenge in healthcare today is knowing the elements and characteristics of the DataStreams as they relate to the overall Determinants of Health. The goal is to ethically and legally harness data to develop new products and services that can improve health quality and lower costs, while delivering value and profitability within sustainable organizations.

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Regarding Open Source, Security, and Cloud Migration, Old Prejudices Die Hard in Health Care

Although the health care industry has made great strides in health IT, large numbers of providers remain slow to reap the benefits of a “digital transformation”. Health care organizations focus on what they get paid for and neglect other practices that would improve care and security. At conferences and meetings year and after year, I have to listen to health care leaders tediously explode the same myths and explain the same principles over and over. In this article I'll concentrate on the recent EXPO.health conference, put on in Boston by John Lynn's Healthcare Scene, where the topics of free and open source EHRs, security, and cloud migration got mired down in rather elementary discussions.

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Case Study: Achieving Meaningful Use Targets With careMESH Digital Referrals and Transitions of Care

The Medical Home Development Group (MHDG) is a Washington D.C.-based physician group which qualifies for the Medicaid EHR Incentive Program. On the heels of successfully meeting their Meaningful Use (MU-1) objective with the implementation of an Electronic Health Record (EHR), MHDG focused 2018 on seeking innovative ways to meet MU-2 measures through new digital referral and care transition processes...Quickly nearing the end of the performance period, MHDG chose the careMESH secure, cloud-based communications platform and embedded workflow tools to meet the measure in time. By retrieving patient records from their Sevocity EHR and using the careMESH multi-channel delivery approach to ensure truly digital sharing with all of the receiving providers, MHDG had an opportunity to complete its reporting requirements before year-end.

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Machine Learning in Healthcare: Part 3 - Time for a Hands-On Test

Every inpatient and outpatient EHR could theoretically be integrated with a machine learning platform to generate predictions, in order to alert clinicians about important events such as sepsis, pulmonary emboli, etc. This approach may become essential when genetic information is also included in the EHR which would mandate more advanced computation. However, using machine learning and artificial intelligence (AI) in every EHR will be a significant undertaking because not only do subject matter experts and data scientists need to create and validate the models, they must be re-tested over time and tested in a variety of patient populations. Models could change over time and might not work well in every healthcare system. Moreover, the predictive performance must be clinically, and not just statistically significant, otherwise, they will be another source of “alert fatigue.”

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White Paper: Stop the Referral Problem - Building Digital Care Transitions that Reach Your Entire Network

The healthcare industry has crossed a digital chasm-at least in part. Patient records have moved from paper to computer and many transactions, such as e-prescribing and lab orders have been automated, to accelerate workflows, minimize mistakes and reduce costs. But when it comes to sharing patient records, especially beyond the four walls of a hospital, we remain in the dark ages of paper and fax...In this paper, we will discuss our research about how referrals and care transitions are typically conducted; the financial, non-financial, and quality impacts on patient care; and near-term opportunities for leveraging technology to accelerate these processes to benefit provider organizations and to deliver a high-quality, efficient patient experience.

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Machine Learning in Healthcare: Part 2 - Tools Available to the Average Healthcare Worker

A variety of machine learning tools are now available that can be part of the armamentarium of many industries, to include healthcare. Users can choose from commercial expensive applications such as Microsoft Azure Machine Learning Studio, SAS Artificial Intelligence Solutions or IBM SPSS Modeler. Academic medical centers and universities commonly have licenses for commercial statistical/machine learning packages so this may be their best choice. The purpose of this article is to discuss several free open source programs that should be of interest to anyone trying to learn more about machine learning, without the need to know a programming language or higher math.

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Machine Learning in Healthcare: Part 1 - Learn the Basics

This article is the first in a three-part series that will discuss how machine learning impacts healthcare. The first article will be an overview defining machine learning and explaining how it fits into the larger fields of data science and artificial intelligence. The second article will discuss machine learning tools available to the average healthcare worker. The third article will use a common open source machine learning software application to analyze a healthcare spreadsheet. Part I was written to help healthcare workers understand the fundamentals of machine learning and to make them aware that there are simple and affordable programs available that do not require programming skills or mathematics background...

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Anatomy of a Public Health Open Source Project: HLN's Immunization Calculation Engine (ICE)

An immunization information system (IIS) aggregates immunization information for children (and some adults) living or receiving immunization services in a jurisdiction. One of the core components of an IIS is its immunization evaluation and forecasting system: the computerized algorithm that is used to determine if vaccine doses that were administered to the patient are clinically valid (evaluation) and to project what doses are due now and in the future (forecasting). These algorithms are used to support clinical decision support (CDS) at the point of care and also to help public health agencies understand and manage the immunization status of whole populations.

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