Feature Articles

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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The Story of How our Health Informatics Textbook Came into Being

I have been asked many times how and why I became interested in Health Informatics and how that led to the writing and self-publication of our textbook, Health Informatics: Practical Guide. The textbook is now in its 7th edition and has been adopted by a large number of universities for their health informatics courses. More co-authors have come on board, and we are now looking at publishing other textbooks. Thus we thought this would be a good point to tell the story.

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Using LibreHealth EHR for Education in Academic Settings

Traditionally, access to EHRs has been viewed as important only for software training, particularly order entry. What seems to be overlooked is the potential for education, analytics and research. Additionally, one could argue that there should be an open-source “EHR Sandbox” so multiple external EHR integrations could be studied and reported. Furthermore, many EHR users view the software as a means to enter or extract data on one patient at a time and fail to see the benefit in analyzing their entire clinic population (population health). The following diagram displays how an EHR could be used for education, training, analytics and research.

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How Cyber Hardening Can Protect Patient Privacy And Treatment

The abundance of internet-connected devices that collect and share patient data has greatly increased the “attack surface” (where an attacker inserts or extracts data) and number of possible vulnerabilities within a system. Now that medical devices can connect to home-based routers, public Wi-Fi or cellular networks to relay data to hospitals, specialists, and care providers. In addition, the software in those devices lacks cybersecurity and can be updated and reprogrammed remotely. Thus, sensitive patient information is even more prone to data breaches, and the safety of the devices can be compromised. Recent supply chain compromises, and the migration of health applications and platforms to the cloud, also add to the threat equation. This article looks at why the medical community is so vulnerable and suggests how it can better protect life-saving equipment and sensitive data from unprecedented cyberattacks.

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Medicaid as a Service Part II: The State’s Perspective

This article is the second in a series of four proposing a revolutionary new direction in Medicaid Management Information Systems (MMIS) structure and pricing...Our first article, outlining the general scheme, may be read here. To summarize, we propose that states treat MMIS as a service, as opposed to treating it as a procurement. Put another way - MMIS is a verb (Something you do) vice a noun (something you possess). In the current paradigm, individual States (to include the District of Columbia) create independent, stand-alone MMIS platforms with long contract lead- and execution- times.

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HIMSS 18 and the Disruption of the Traditional Office Visit

Healthcare is evolving quickly and HIMSS 18 offers a broad range of healthcare issues to explore, but will it recognize the disruption of the traditional office visit? New requirements for implementing HIT systems are changing as new health IT priorities and procedures emerge. Convergence in the health care sector is accelerating the need for interoperability, not just for EHRs, but also across clinical, financial, and operational systems. This need is also challenging and changing one of the biggest traditions in healthcare—the doctor-patient medical visit. 

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DHIS2 - Transforming Health IT Standards in the Developing World (Part 2)

Rwanda's 2012 implementation of DHIS2 is one of at least 16 completed national rollouts of this free and open source health data management. A total of 54 countries are deploying DHIS2 on a national scale, 30 of which are in the pilot stage or early phase in their rollouts. Since DHIS2's release in 2006, NGOs and national governments in 60 countries have deployed DHIS2 for health-related projects, including patient health monitoring, improving disease surveillance and pinpointing outbreaks, and speeding up health data access.

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Drones and the Future of Disaster Response

Four continental states and one U.S. territory took a beating this fall as one natural disaster after another rocked communities in Northern California and along the Gulf Coast, spreading disaster relief resources and personnel thin as federal, state, and local governments scrabbled to address the crises. Wildfires in California's wine country claimed at least 42 lives, 8,400 structures, and 245,000 acres of land in October. Hurricanes Harvey and Irma pummeled Louisiana, Texas, and Florida only to be followed by Hurricane Maria, which slammed Puerto Rico on September 20 and left much of the U.S. territory without communications systems, electricity, clean water, or functioning hospitals....

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HLN's Report on Patient Centered CDS Learning Network Conference

The Learning Network is an initiative funded by the Agency for Healthcare Research and Quality (AHRQ) whose goal is to encourage collaboration and development of tools and resources to support clinical decision support (CDS) as it relates to individual patients and their caregivers. This work is an offshoot of the Patient-Centered Outcomes Research (PCOR) initiative which focuses on the relationship between healthcare providers in patients in making better, more informed, healthcare decisions. A sister project to the Learning Network is the AHRQ-funded CDS Connect Repository which has begun to house shareable CDS artifacts which represent evidence-based standards of care.

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Developing Nations Improving Health Communication Through the Use of DHIS2 (Part 1)

DHIS2 implementations are spreading steadily among national health services in developing countries as well as among international non-governmental organizations (NGOs) working to improving health in the developing world through the use of health information technology. As an open source solution, DHIS2 offers developing countries the advantage of adopting a cost-effective and flexible solution for aggregate statistical data collection, validation, analysis, management, and presentation as well as for data sharing between healthcare professionals and facilities. Organizations and individuals who work with humanitarian software solutions will need to know what DHIS2 is, how it works, and how it might be implemented by national health services and other health-related projects across the globe...

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Why Cloud-Based Public Health Solutions are a Good Option for Clinical Decision Support

Cloud computing is one of the most powerful technology deployment strategies in use today. In fact, the notion of cloud resources has become prominent in consumer computing with only a limited understanding of what it means or does (e.g., Apple iCloud and Google Cloud). In this article we will examine some of the key reasons to host public health clinical decision support (CDS) solutions in the cloud. In a recent national survey, 95% of respondents indicated that they were using some form of cloud computing in their environment...The first step is to to understand the difference between cloud computing and traditional computing.

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