News

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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From Data Silos to Black Holes...the Story of America's Healthcare System?

The scary thing about black holes is that their gravity inexorably drags in everything within its reach. Unless you are very far away or have sufficient escape velocity, you will get pulled in, and, once you are sucked in, you are never getting out. We call it our "healthcare" system, but usually what we mean is medical care. It treats illnesses, it puts us under the care of medical professionals, it turns us into patients. A doctor's visit begats prescriptions, and perhaps some testing. Testing leads to procedures. Procedures lead to hospital stays. Hospital stays lead to....you get the idea. What we might once have thought of as "health" -- or never thought about at all -- becomes "health care," a.k.a. medical care. And once you transform from a person, whose health belongs to you, to a patient, your health is never quite your own again. You've been sucked into the medical care black hole.

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How Open Data and Open Tools Can Save Lives During a Disaster

If you've lived through a major, natural disaster, you know that during the first few days you'll probably have to rely on a mental map, instead of using a smartphone as an extension of your brain. Where's the closest hospital with disaster care? What about shelters? Gas stations? And how many soft story buildings-with their propensity to collapse-will you have to zig-zag around to get there? Trying to answer these questions after moving back to earthquake-prone San Francisco is why I started the Resiliency Maps project. The idea is to store information about assets, resources, and hazards in a given geographical area in a map that you can download and print out.

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Health Organizations Implore Congress to Fund Public Health Surveillance Systems

HLN Consulting joined more than eighty organizations, institutions, and companies in imploring Congress to fund public health surveillance systems. The appropriations request letters – one to the House and one to the Senate – seek one billion in funding over ten years (and $100 million in FY 2020) for the Centers for Disease Control and Prevention (CDC). This funding would allow CDC, state, local, tribal, and territorial health departments to move from sluggish, manual, paper-based data collection to seamless, automated, interoperable IT systems and to recruit and retain skilled data scientists to use them.

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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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Is WeWork's Ecosystems Approach a Model for Healthcare Platforms?

Maybe you don't work in a WeWork office setting. Maybe you haven't ever visited one. Maybe you haven't even heard of WeWork. In that case, then you'll probably be surprised that this audacious real estate start-up now has a valuation close to $50b, with over 400,000 "members" in 100 cities across 27 countries (and they claim to "touch" 5 million people worldwide). Or that their plans go well beyond their unique twist towards office sharing. Who in healthcare is thinking about them, and who should be worried...or intrigued?...WeWork was never just about finding people and companies office space: it wanted to "help people work to make a life, not just a living." It focused on building a culture in its spaces, complete with amenities and events to help build a community among its members.

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9 Resources for Data Science Projects

Data science, machine learning, artificial intelligence, and deep neural nets are all hot topics these days (and key terms that might help this post with some SEO, unless the AI sees through my attempts). Below I've shared several of the resources I use regularly while working on data science projects over the last few years. I don't read many books, so that I've shared even one is evidence of how important it is. There are enough resources here to get even the most novice engineer started on a path towards data science mastery in this new age where data science skills will be needed at every level. There is a tool for performing the work, a class taught by a renowned Stanford professor, websites with tutorials to give you real-life experience, and a site dedicated to making the latest research available to all for free so you can learn more if you want.

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GAO Report on Patient Matching: Nothing New Under the Sun

On January 15, 2019 the US Government Accountability Office (GAO) released a new report to Congress, Health Information Technology: Approaches and Challenges to Electronically Matching Patients' Records across Providers. This report is in response to the mandate in the 21st Century Cures Act for the GAO to study patient matching. To develop this report, GAO reviewed available literature and interviewed more than thirty-five stakeholders (who are not identified) over the course of a year. I have written several blogs and a feature article on patient matching developments in the US. Similarly, this new GAO report is an excellent retrospective on industry efforts over the past several years.

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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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