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Visualization: Meaningful Use Hospital Beds by Vendor, -Only Complete EHRs

visualization of EHR adoption for stage 1 meaningful use by vendor. Includes Epic, Meditech Cerner, Allscripts. McKesson, Siemens, CPSI, and HMS
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List of Different Types of Health Data - HealthDataKnowledge

List of Different Types of Health Data - HealthDataKnowledge | Healthcare Data | Scoop.it
List of types of data found in health care electronic health records, EHRs, including definitions of data. LOINC, ICD-9_CM, ICD-10-CM, NDC, SNOMED, HCPCS, CPT,
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Big data: are we making a big mistake?

Big data: are we making a big mistake? | Healthcare Data | Scoop.it
“ Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world’s top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of”
Via Sílvia Cordomí
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Viz: Average Meaningful Use Payments to Elligible Providers by State - HealthDataKnowledge

Viz: Average Meaningful Use Payments to Elligible Providers by State - HealthDataKnowledge | Healthcare Data | Scoop.it
Meaningful use stage 1 and stage 2 average medicare payment for attestation
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Viz: Meaningful Use Attestation and Payments by Eligible Hospitals in Virginia - HealthDataKnowledge

Viz: Meaningful Use Attestation and Payments by Eligible Hospitals in Virginia - HealthDataKnowledge | Healthcare Data | Scoop.it
“ This map shows Meaningful Use attestation in Virginia by Eligible Hospitals (EHs) over the past few years.”
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Four types of data analytics that providers are using to improve population health

Four types of data analytics that providers are using to improve population health | Healthcare Data | Scoop.it
The push by the government to reduce healthcare costs and the increased liability providers have is forcing them to more easily identify and help chronic care patients to better manage their conditions. At the same time healthcare IT vendors are expanding their big data armories to help providers, particularly accountable care organizations mine claims and clinical data to get a better sense of patient outcomes, performance and how and where they can reduce costs. As more providers convert from paper to electronic records they are working with health IT vendors that can help them produce more accurate assessment of their patient populations to mine patient data to help predict outcomes. At HIMSS earlier this month Dr. Anil Jain, the CMIO of Explorys, a spinout from the Cleveland Clinic, highlighted some of the different analytics approaches it is offering clients as they get more involved in population health. Descriptive Analytics This accounts for the biggest chunk of big data across industries and it tends to focus on what went wrong or assessing why outcomes are more or less than what was expected. “Most people are pretty well covered when you think of descriptive analytics,” says Jain. One example of descriptive analytics is giving hospitals a better understanding of current assessments, like how many of its patients should have received a pneumococcal vaccine or how many diabetes patients in an endocrinology department have their blood sugar under control? Predictive AnalyticsBig data is chiefly being used to identify patterns, predict how to predict future outcomes, and avoid preventable events as a way to reduce healthcare costs. Jain says the most frequently asked question, particularly from accountable care organizations is, “‘What percent of our patients will be re-admitted?’ They are also looking at how many patients will use the emergency room.” Explorys’ big data platform includes a tool that can score patients based on their risk profile, such as whether they have chronic conditions, so providers can develop more effective approaches to care. Prescriptive Analytics One of the most noticeable trends at HIMSS this year was the increasing interest in prescriptive analytics. A recent report from Gartner looking across business intelligence said that only 13 percent of organizations are using predictive analytic but even fewer — 3 percent — are using prescriptive analytics, so there is plenty of opportunity for growth and the demand is increasing. Prescriptive analytics involves helping a provider measure and manage a patient population. For example, one tool from Explorys’ big data platform allows users to focus on patients with obesity, add a morbidty like diabetes and assess their LDL levels or other measurement to determine where they need to focus attention. “When you have an ACO that is trying to change the cost curve it is about good data but once the data is in, most providers look at the computer screen and try to figure out what the focus should be.” Jain likens shifting from a descriptive to prescriptive data analytics platform to the equivalent of going from a broad, fluorescent light to a laser beam focus. “We don’t bring customers on if they are not ready to address population health as a solution. Provider groups have the same end-goal in mind: How do we stay relevant as pay-for-performance models change?” Comparative Analytics One of the most interesting ways providers can use big data is to compare their performance to other healthcare facilities. Explorys expanded into the comparative analytics market this year with its National Benchmarks platform. The platform uses comparative metrics throughout more than 92 billion clinical, financial, and operational data sets across a continuum of care. By combining clinical data with claims and administrative data, it gives insights into patterns and trends. Providers can compare their performance with a particular patient population compared with the aggregate network, made up of providers such as the Cleveland Clinic, St Joseph Health System and Legacy Health. Patient information is de-identified and made HIPAA compliant while keeping participating providers private. For example, providers can see how the LDL levels of their patients compares with that of the network and can use different sets of criteria across age, race, geography and gender. Providers can use the information to develop insights to improve performance.
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FDA Unveils Draft Guidance on Drugmakers' Social Media Use

FDA Unveils Draft Guidance on Drugmakers' Social Media Use | Healthcare Data | Scoop.it
On Monday, FDA released draft policy guidance indicating that pharmaceutical drug manufacturers and distributors would not be held responsible for information posted on social media about their products by consumers or providers. Background According to Modern Healthcare, the pharmaceutical industry has been reluctant to use social media platforms because it lacked FDA guidance on how such platforms could be used without violating certain prohibited promotion. For example, pharmaceutical firms were concerned about being held accountable for social media posts by clinicians or patients promoting off-label use of their products. In addition, drugmakers are required to disclose side effect information when promoting their products, but social media posts by third parties might not include such data. Details of Draft Policy Guidance In the draft policy guidance, FDA notes that such companies are generally not accountable for user-generated content that is "truly independent," meaning it was not produced or solicited by the companies. The draft guidance also states that pharmaceutical companies are not responsible for content published on websites they financially support but do not control editorially. In addition, such companies will not be held accountable for promotional materials found on third-party websites as long as the pharmaceutical firms did not direct the promotion's placement on the website and did not have any other control or influence over the site. source: http://www.ihealthbeat.org/articles/2014/1/15/fda-unveils-draft-guidance-on-drugmakers-social-media-use
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Visualization: Meaningful Use Hospital Beds by Vendor, -Complete and Modular EHRs

Visualization: Meaningful Use Hospital Beds by Vendor, -Complete and Modular EHRs | Healthcare Data | Scoop.it
Meaningful use graphic visualization of EHRs attesting to stage 1 by vendor. Done in tableau. Includes, Cerner, Meditech, Epic, McKesson, Allscripts, HCA, Siemens, CPSI, HMS, and Iatric.
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Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework - Springer

Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework - Springer | Healthcare Data | Scoop.it

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Sílvia Cordomí's curator insight, May 14, 2014 7:56 AM

Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of personal health and healthcare-related data. Imagine visiting your physician’s office with a list of concerns and questions. What if you could walk out the office with a personalized assessment of your health? What if you could have personalized disease management and wellness plan?  The timing is right for such a research direction—given the changes in health care, reimbursement, reform, meaningful use of electronic health care data, and patient-centered outcome mandate. We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates.

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EHR Analysis More Effective, Cost Efficient Than Clinical Trials

The use of electronic health records to identify the best treatment option for patients is more efficient and less costly than the current clinical trial process, according to a study published in the journal Health Technology Assessment Study Details For the study, which was funded in part by the National Institute for Health Research and the Welcome Trust, researchers from several universities in the United Kingdom, used a new computer program in 23 approved general practitioners across England and Scotland. The first part of the study used 300 patients' electronic health records, which are stored in the Clinical Practice Research Datalink and updated during routine medical visits, to monitor the effects of their prescribed treatments. A second part of the study, which involved 31 participants, looked at the use of antibiotics among patients with chronic obstructive pulmonary disease. Study Findings The researchers determined that they were able to understand health patterns related to specific prescribed medications and determine which treatments were more effective by analyzing EHRs. They added that the EHR analysis offers a larger and more diverse overview of the general population than current clinical trial methods. The researchers also noted that using EHRs allows the analysis to be conducted with minimal effects on the lives of the patients, whose involvement in the process stops after their initial consent. According to the researchers, 26 out of 27 general practitioners who participated in the study expressed strong support for the use of patients' EHRs for research purposes. In addition, 10 patients who were interviewed by the researchers all said that their involvement in trial was an acceptable practice
Via nrip, Sílvia Cordomí
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Apple Wants To Be 'A Hub Of Health Data' And Is In Talks With Top Hospitals - Business Insider

Apple Wants To Be 'A Hub Of Health Data' And Is In Talks With Top Hospitals - Business Insider | Healthcare Data | Scoop.it
Business Insider Apple Wants To Be 'A Hub Of Health Data' And Is In Talks With Top Hospitals Business Insider While the talks may not amount to anything concrete, they underscore how Apple is intent on making health data, such as blood pressure,...
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Anonymizing Health Data - PDF Free Download - Fox eBook

Anonymizing Health Data - PDF Free Download - Fox eBook | Healthcare Data | Scoop.it
“ Anonymizing Health Data PDF Free Download, Reviews, Read Online, ISBN: 1449363075, By Khaled El Emam, Luk Arbuckle”
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100 Healthcare And Digital Health Influencers To Follow In 2014

A list of healthcare and digital health influencers to follow in 2014.
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