group:let-s-get-technical - The NIC Collaboration Hub2024-03-29T15:41:36Zhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/feed/tag/group%3Alet-s-get-technicalMobile health and privacy: cross sectional studyhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/mobile-health-and-privacy-cross-sectional-study2021-06-28T18:51:13.000Z2021-06-28T18:51:13.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Objectives: To investigate whether and what user data are collected by health related mobile applications (mHealth apps), to characterise the privacy conduct of all the available mHealth apps on Google Play, and to gauge the associated risks to privacy.</p>
<p>Main outcome measures: Primary outcomes were characterisation of the data collection operations in the apps code and of the data transmissions in the apps traffic; analysis of the primary recipients for each type of user data; presence of adverts and trackers in the app traffic; audit of the app privacy policy and compliance of the privacy conduct with the policy; and analysis of complaints in negative app reviews.</p>
<p>Conclusions: This analysis found serious problems with privacy and inconsistent privacy practices in mHealth apps. Clinicians should be aware of these and articulate them to patients when determining the benefits and risks of mHealth apps.</p>
<p><a href="https://www.bmj.com/content/373/bmj.n1248" target="_blank">Read the study >></a></p></div>Adjusting Quality Measures For Social Risk Factors Can Promote Equity In Health Carehttps://hub.nic-us.org/groups/let-s-get-technical/resources1/adjusting-quality-measures-for-social-risk-factors-can-promote-eq2021-04-20T18:39:36.000Z2021-04-20T18:39:36.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Risk adjustment of quality measures using clinical risk factors is widely accepted; risk adjustment using social risk factors remains controversial. We argue here that social risk adjustment is appropriate and necessary in defined circumstances and that social risk adjustment should be the default option when there are valid empirical arguments for and against adjustment for a given measure. Social risk adjustment is an important way to avoid exacerbating inequity in the health care system.</p>
<p><a href="https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2020.01764" target="_blank">Read the article >></a></p></div>Determining the feasibility of an index of the social determinants of health using data from public sourceshttps://hub.nic-us.org/groups/let-s-get-technical/resources1/determining-the-feasibility-of-an-index-of-the-social-determinant2021-03-23T17:43:48.000Z2021-03-23T17:43:48.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Abstract:</p>
<p>Examining the feasibility of developing an index measure for the social determinants of health using public data is needed. We examined these characteristics at the ZIP code in California and New York using public data extracted from the US Census, American Community Survey, the USDA Food Research Access Atlas, and the Dartmouth Atlas. We conducted a retrospective study from 2000 to 2017. The main outcome was a novel index measure representing six domains (economic stability, neighborhood and physical environment, education, community and social context, food access, and health care) and encompassing 13 items. The index measure at the ZIP code was created using principal component analysis, normalized to “0” worse and “1” better in California (ZIP codes n = 1,447 to 1,515) and New York (ZIP codes n = 1,211 to 1,298). We assessed the reliability and conducted a nonparametric comparison to the Robert Wood Johnson Foundation County Health Rankings, Area Deprivation Index, Social Deprivation Index, and GINI Index. These measures shared similarities and differences with the novel measure. Mapping of this novel measure showed regional variation. As a result, developing a universal social determinants of health measure is feasible and more research is needed to link it to health outcomes.</p>
<p><a href="https://www.tandfonline.com/doi/full/10.1080/17538157.2021.1880413" target="_blank">Read the article >></a></p></div>Examining the Interfacility Variation of Social Determinants of Health in the Veterans Health Administrationhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/examining-the-interfacility-variation-of-social-determinants-of-h2021-02-15T21:20:16.000Z2021-02-15T21:20:16.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Introduction: Recently, numerous studies have linked social determinants of health (SDoH) with clinical outcomes. While this association is well known, the interfacility variability of these risk favors within the Veterans Health Administration (VHA) is not known. Such information could be useful to the VHA for resource and funding allocation. The aim of this study is to explore the interfacility variability of 5 SDoH within the VHA.</p>
<p>Methods: In a cohort of patients (aged ≥ 65 years) hospitalized at VHA acute care facilities with either acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2012, we assessed (1) the proportion of patients with any of the following five documented SDoH: lives alone, marginal housing, alcohol use disorder, substance use disorder, and use of substance use services, using administrative diagnosis codes and clinic stop codes; and (2) the documented facility-level variability of these SDoH. To examine whether variability was due to regional coding differences, we assessed the variation of living alone using a validated natural language processing (NLP) algorithm.</p>
<p>Results: The proportion of veterans admitted for AMI, HF, and pneumonia with SDoH was low. Across all 3 conditions, lives alone was the most common SDoH (2.2% [interquartile range (IQR), 0.7-4.7]), followed by substance use disorder (1.3% [IQR, 0.5-2.1]), and use of substance use services (1.2% [IQR, 0.6-1.8]). Using NLP, the proportion of hospitalized veterans with lives alone was higher for HF (14.4% vs 2.0%, P < .01), pneumonia (11% vs 1.9%, P < .01), and AMI (10.2% vs 1.4%, P < .01) compared with International Classification of Diseases, Ninth Edition codes. Interfacility variability was noted with both administrative and NLP extraction methods.</p>
<p>Conclusions: The presence of SDoH in administrative data among patients hospitalized for common medical issues is low and variable across VHA facilities. Significant facility-level variation of 5 SDoH was present regardless of extraction method.</p>
<p><a href="https://www.mdedge.com/fedprac/article/234322/health-policy?sso=true" target="_blank">Read the report >></a></p></div>Global data and analytics diversity reporthttps://hub.nic-us.org/groups/let-s-get-technical/resources1/global-data-and-analytics-diversity-report2021-02-15T20:55:49.000Z2021-02-15T20:55:49.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>About:</p>
<p>The article examines how structural inequalities, biases and racism can be easily encoded in data sets and in the application of data science.</p>
<p><a href="https://www.harnham.com/harnham-data-analytics-diversity-report-2021" target="_blank">Read the report >></a></p></div>Silo Busting: The Challenges and Success Factors for Sharing Intergovernmental Datahttps://hub.nic-us.org/groups/let-s-get-technical/resources1/silo-busting-the-challenges-and-success-factors-for-sharing-inter2021-01-05T18:40:05.000Z2021-01-05T18:40:05.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Executive summary:</p>
<p>The work of knitting together large government data systems so that they seamlessly connect and provide customer-friendly services to the public is difficult yet achievable and valuable. If this type of data sharing was easy, everyone would be doing it. Instead, there are only a handful of outstanding examples of success and a lot of barriers to achieving it.</p>
<p>The state of data-driven government has advanced rapidly over the past decade, but it has not yet achieved its full potential. Single-agency successes are prevalent, with rapid acceleration of the use of data to solve problems within an agency. Peer-to-peer data sharing across units of government is increasing. This horizontal sharing among peers in one government (city, state, federal agency) is often orchestrated either by the agency itself or by a shared data or IT organization, often to solve a particular and clearly defined problem. This type of data sharing remains far more common than vertical sharing across layers of government, say from city to county and state or to federal. And yet, solutions to the most complex and vexing public problems require data sharing that spans boundaries of government agencies.</p>
<p>As an example, addressing homelessness can’t be solved with just housing data but also requires data about an individual’s situation and needs across employment and education, health and mental health or substance use, and criminal justice sectors. Yet, in each of those service delivery silos, most staff have no incentive to go outside of their sphere of responsibility to get at the root cause of the problem, nor do they typically have authority to access external data sources. As a result, data analytics projects that are cross-departmental require alignment across many factors and are both uncommon and inspiring.</p>
<p>With digital data being created at a dizzying rate with every mouse click, or swipe of a device to enter a building and credit card transaction, the world is awash in data but not yet keeping up with analysis of this data. How is data connected and used to drive action in government? Not nearly enough. And yet, there are some positive points of reference.</p>
<p><a href="http://www.businessofgovernment.org/sites/default/files/Silo%20Busting.pdf" target="_blank">Read the report >></a></p></div>Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Carehttps://hub.nic-us.org/groups/let-s-get-technical/resources1/artificial-intelligence-in-health-care-benefits-and-challenges-of2020-12-17T17:14:44.000Z2020-12-17T17:14:44.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Executive Summary</p>
<p>This report is being jointly published by the Government Accountability Office (GAO) and the National Academy of Medicine (NAM). Part One of this joint publication is the full presentation of GAO’s Technology Assessment: Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care. Part Two is the full presentation of NAM’s Special Publication: Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic. Although GAO and NAM staff consulted with and assisted each other throughout this work, reviews were conducted by GAO and NAM separately and independently, and authorship of the text of Part One and Part Two of this Executive Summary and the following report lies solely with GAO and NAM, respectively.</p>
<p><a href="https://www.gao.gov/assets/720/710920.pdf" target="_blank">Read the report >></a></p></div>Using Administrative Big Data to Solve Problems in Social Science and Policy Researchhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/using-administrative-big-data-to-solve-problems-in-social-science2020-12-08T19:59:52.000Z2020-12-08T19:59:52.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Abstract:</p>
<p>This article describes an explosion in the availability of individual-level public administrative data in the United States and worldwide. These datasets can be used as stand-alone resources or linked across different sources. These new resources will facilitate transformative research on social, demographic, and economic changes, policy evaluation, and other experimental analyses. We discuss the current status of administrative big data in the United States, their potential to advance social science and policy studies, and advantages and challenges for using these data in practice. We showcase a few ongoing large-scale U.S. administrative data initiatives and hope to spark future parallel endeavors in other countries.</p>
<p><a href="https://repository.upenn.edu/cgi/viewcontent.cgi?article=1057&context=psc_publications" target="_blank">Read the article >></a></p></div>Person Matching for Greater Interoperability: A Case Study for Payershttps://hub.nic-us.org/groups/let-s-get-technical/resources1/person-matching-for-greater-interoperability-a-case-study-for-pay2020-12-01T14:53:31.000Z2020-12-01T14:53:31.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>The Sequoia Project patient identity management experts partnered with Blue Cross Blue Shield System to apply A Framework for Cross-Organizational Patient Identity Management to the payer community and develop person matching strategies across its 36 independent companies.</p>
<p>Together, they announced publication today of Person Matching for Greater Interoperability: A Case Study for Payers found a 99.5% accuracy rate, and provides actionable insights for improving patient matching across the payer community, a critical component of successful health information exchange and interoperability.</p>
<p><a href="https://sequoiaproject.org/resources/patient-matching/blue-cross-blue-shield-case-study/" target="_blank">Read the case study >></a></p></div>10 Patient Data Sharing, Interoperability Principles for Providershttps://hub.nic-us.org/groups/let-s-get-technical/resources1/10-patient-data-sharing-interoperability-principles-for-providers2020-11-24T17:56:28.000Z2020-11-24T17:56:28.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>November 18, 2020 - Stakeholders and healthcare professionals across the country developed 10 patient data sharing principles. These principles intend to help guide health organizations and universities toward the appropriate use of data sharing to boost patient care, research, and innovation, according to an article published in the Journal of the American Medical Informatics Association (JAMIA).</li>
<li>Stakeholders created these 10 principles to maintain the ethics and responsibilities of patient data sharing.
<ul>
<li>ENSURE DATA SHARING SUPPORTS THE ORGANIZATION’S MISSION</li>
<li>AVOID FINANCIAL COMPENSATION FOR DATA SHARING</li>
<li>USE MINIMUM DATA ELEMENTS, DEIDENTIFIED DATA</li>
<li>ESTABLISHING EXPIRING AND NONEXCLUSIVE DATA SHARING AGREEMENTS</li>
<li>DATA OWNERSHIP CANNOT BE TRANSFERRED OR REDISTRIBUTED</li>
<li>RESEARCHERS SHOULD NEVER REIDENTIFY DEIDENTIFIED DATA</li>
<li>DO NOT MIX PREDETERMINED PATIENT DATA WITH OUTSIDE DATA SETS</li>
<li>PROVING TRANSPARENCY WITH STAKEHOLDERS</li>
<li>MAINTAINING TRANSPARENCY WITH CONFLICTS OF INTEREST</li>
<li>DEVELOPING A DATA SHARING REVIEW COMMITTEE</li>
</ul>
</li>
</ul>
<p><a href="https://ehrintelligence.com/news/10-patient-data-sharing-interoperability-principles-for-providers?eid=CXTEL000000590793&elqCampaignId=16894&utm_source=nl&utm_medium=email&utm_campaign=newsletter&elqTrackId=efc4bf7236064baeb0e09176468ff80a&elq=f27d74c43da6498b922b0c0d12023b76&elqaid=17672&elqat=1&elqCampaignId=16894" target="_blank">Read the article >></a></p></div>Regardless of the presidential election outcome, here are 5 health IT issues to watchhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/regardless-of-the-presidential-election-outcome-here-are-5-health2020-11-09T19:40:55.000Z2020-11-09T19:40:55.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>COVID-19 has highlighted the need to invest in a robust public health system that supports health equity. “It’s critical that we bolster the secure flow of de-identified health information into public health surveillance systems,” said Wylecia Wiggs Harris, Ph.D., chief executive officer of the American Health Information Management Association.</li>
<li>Health IT issues and policies to watch include:
<ul>
<li>Patient identification</li>
<li>Telehealth</li>
<li>Interoperability</li>
<li>Security and privacy</li>
</ul>
</li>
</ul>
<p><a href="https://www.fiercehealthcare.com/tech/regardless-presidential-election-outcome-here-5-health-it-issues-to-watch-at-federal-level" target="_blank">Read the article >></a></p></div>How Social Determinants Data Can Enhance Machine Learning Toolshttps://hub.nic-us.org/groups/let-s-get-technical/resources1/how-social-determinants-data-can-enhance-machine-learning-tools2020-11-03T17:04:59.000Z2020-11-03T17:04:59.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>To help providers identify primary care patients with social risks, researchers at Regenstrief Institute and Indiana University turned to machine learning technology. The team developed Uppstroms, a machine learning application that identifies patients who may need referrals to wraparound services.</li>
<li>To further improve the performance of the Uppstroms app, researchers recently incorporated additional social determinants of health data into the algorithm, including insurance, medication history, and behavioral health history.</li>
<li>“When patients come in for a primary care appointment, the model will pull all of the data available on their clinical background, demographics, and social factors, and use it to identify additional services they may need,” said Kasthurirathne. “Maybe you’re at high risk for depression, or you recently became unemployed. Maybe you're suffering from abuse. With the Uppstroms app, providers can point patients to additional services that will help take care of these non-clinical needs.”</li>
</ul>
<p><a href="https://healthitanalytics.com/news/how-social-determinants-data-can-enhance-machine-learning-tools?eid=CXTEL000000590793&elqCampaignId=16596&utm_source=nl&utm_medium=email&utm_campaign=newsletter&elqTrackId=c3ba783384cd4e789ada70336ec151df&elq=b47b5244835f4c3893797c1531f1b01d&elqaid=17386&elqat=1&elqCampaignId=16596" target="_blank">Read the article >></a></p></div>Effective patient matching is fundamental to interoperabilityhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/effective-patient-matching-is-fundamental-to-interoperability2020-11-03T17:01:28.000Z2020-11-03T17:01:28.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>There is new momentum around finding more effective means of patient identity and matching, and Verato CEO Mark LaRow says the company's referential matching technology can help.</p>
<p><a href="https://www.healthcarefinancenews.com/video/effective-patient-matching-fundamental-interoperability" target="_blank">Watch the video >></a></p></div>Siloed approaches to social determinants of health aren't enoughhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/siloed-approaches-to-social-determinants-of-health-aren-t-enough2020-10-26T17:47:47.000Z2020-10-26T17:47:47.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>About:</p>
<p>Alliance for Better Health CEO Dr. Jacob Reider discusses the data and IT demands of coordinating SDOH efforts – and the imperative to serve underserved communities, where effects from COVID-19 will be felt long after the pandemic has subsided.</p>
<p><a href="https://www.healthcareitnews.com/video/siloed-approaches-social-determinants-health-arent-enough" target="_blank">Watch the video >></a></p></div>Sequoia Project sets sights on semantic interoperability with new guidance efforthttps://hub.nic-us.org/groups/let-s-get-technical/resources1/sequoia-project-sets-sights-on-semantic-interoperability-with-new2020-10-19T14:08:05.000Z2020-10-19T14:08:05.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>The Sequoia Project announced Wednesday that it's forming a new Data Usability Workgroup to continue removing barriers to interoperability, and is calling for participants in advance of its first meeting later this month.</li>
<li>The workgroup, part of the Sequoia Project’s Interoperability Matters cooperative, is focused first on developing three implementation guides to data usability requirements for provider-to-provider, provider-to-public health agency and healthcare entity-to-consumer information exchange.</li>
</ul>
<p><a href="https://www.healthcareitnews.com/news/sequoia-project-sets-sights-semantic-interoperability-new-guidance-effort" target="_blank">Read the article >></a></p></div>New framework helps streamline EHR data extractionhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/new-framework-helps-streamline-ehr-data-extraction2020-10-19T14:00:16.000Z2020-10-19T14:00:16.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>The framework, which the researchers call FIDDLE (Flexible Data-Driven Pipeline), has the power to greatly speed up EHR data preprocessing and assist machine learning (ML) practitioners working with health data, according to a study published this week in the Journal of the American Medical Informatics Association.</li>
<li>"While FIDDLE is by no means the single best way to preprocess data for all use cases, it facilitates reproducibility and the sharing of preprocessing code (oftentimes overlooked or not fully described in the literature)," wrote the research team. "We hope that FIDDLE will be useful to other researchers; ultimately, once the community starts using the tool, we will be able to collectively refine and build on it together," they added.</li>
</ul>
<p><a href="https://www.healthcareitnews.com/news/new-framework-helps-streamline-ehr-data-extraction" target="_blank">Read the article >></a></p></div>Cerner unveils new interoperability tools, as CEO Brent Shafer says 'innovation is accelerating'https://hub.nic-us.org/groups/let-s-get-technical/resources1/cerner-unveils-new-interoperability-tools-as-ceo-brent-shafer-say2020-10-19T13:53:08.000Z2020-10-19T13:53:08.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>
<p>At the 35th annual Cerner Health Conference, which launched virtually on Tuesday, CEO Brent Shafer described the many ways the company has been helping providers around the world deliver high-quality healthcare more efficiently during the COVID-19 pandemic.</p>
</li>
<li>
<p>Cerner also unveiled a pair of new technology suites on Tuesday: Cerner Unite, which the company describes as a group of interoperability tools designed to take data exchange "beyond just connectivity to true usability," and Cerner Discover, a new portfolio of designed to work alongside Unite to "improve data quality, simplify data reconciliation and seamlessly integrate data-driven insights into clinician workflows – on any health platform." </p>
</li>
</ul>
<p><a href="https://www.healthcareitnews.com/news/cerner-unveils-new-interoperability-tools-ceo-brent-shafer-says-innovation-accelerating" target="_blank">Read the article >></a></p></div>A Guide to Cybersecurity in Healthcarehttps://hub.nic-us.org/groups/let-s-get-technical/resources1/a-guide-to-cybersecurity-in-healthcare2020-10-19T13:39:03.000Z2020-10-19T13:39:03.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Protect your electronic information and assets from unauthorized access, use and disclosure. Discover how to identify cyberthreats, understand best practices, and demystify laws and regulations related to cybersecurity in healthcare.</p>
<p>In this Guide:</p>
<ul>
<li>What is Cybersecurity in Healthcare?</li>
<li>Understanding Threats</li>
<li>Cybersecurity in Healthcare Best Practices</li>
<li>Cybersecurity in Healthcare Laws and Regulations</li>
<li>Healthcare Security Forum: Protect Your Organization</li>
</ul>
<p><a href="https://www.himss.org/resources/cybersecurity-healthcare" target="_blank">Read the guide >></a></p></div>Top EHR Implementations of 2020, So Farhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/top-ehr-implementations-of-2020-so-far2020-10-06T16:13:00.000Z2020-10-06T16:13:00.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>When the novel coronavirus began its spread across the United States, it transformed healthcare in its entirety. That includes the health IT sector, where EHR vendors had to brainstorm and develop new EHR implementation ideas.</li>
<li>AdventHealth, a Florida-based health system and one of the nation’s largest faith-based health systems, will gradually transition away from Cerner and it will implement Epic Systems EHR across its 50 hospital campuses and 1,200-plus care sites that range over nine states. AdventHealth, expects the project will be completed within three years.</li>
<li>The Epic implementation will allow the giant health system to be on a single, integrated platform. This platform will include its EHR and a revenue cycle management system that covers its acute care, physician practice, ambulatory, urgent care, home health, and hospice facilities.</li>
</ul>
<p><a href="https://ehrintelligence.com/news/top-ehr-implementations-of-2020-so-far?eid=CXTEL000000590793&elqCampaignId=16063&utm_source=nl&utm_medium=email&utm_campaign=newsletter&elqTrackId=e53f1e62e8564bb18c7d28db27b99457&elq=881aa81e6694476ebd8bfd75e7c28178&elqaid=16839&elqat=1&elqCampaignId=16063" target="_blank">Read the article >></a></p></div>Can Social Informatics Improve Social Determinants of Health Data?https://hub.nic-us.org/groups/let-s-get-technical/resources1/can-social-informatics-improve-social-determinants-of-health-data2020-10-06T16:05:06.000Z2020-10-06T16:05:06.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Highlights:</p>
<ul>
<li>Gathering and integrating social determinants of health (SDOH) data are becoming more common, but the study of social informatics could help ease the integration process.</li>
<li>The rise of value-based reimbursement has led the medical field to increasingly recognize the importance of meeting not only patients’ clinical needs, but also their social needs. As social services and SDOH programming crop up in practices and hospitals nationwide, providers need the SDOH data itself to determine how to best refer patients to services.</li>
<li>Current health IT and data processing systems aren’t quite equipped to do that. Interoperability and integration from multiple data sources hamstring efforts to understand the full scope of SDOH and create appropriate social services recommendations to patients. The burgeoning field of social informatics may be the answer to that, as well as federal calls for better use of SDOH data, like those from the Office of the National Coordinator for Health Information Technology (ONC).</li>
</ul>
<p><a href="https://ehrintelligence.com/news/can-social-informatics-improve-social-determinants-of-health-data" target="_blank">Read the article >></a></p></div>Could Customer Reviews Give Transparency into Health IT Tools?https://hub.nic-us.org/groups/let-s-get-technical/resources1/could-customer-reviews-give-transparency-into-health-it-tools2020-09-22T17:40:39.000Z2020-09-22T17:40:39.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>Crowdsourced ratings face three key barriers to providing transparency for interoperability and health IT products, according to a study published in the Journal of the American Medical Informatics Association (JAMIA).</p>
<p>From Yelp to Glassdoor, crowdsourced consumer ratings are a common and essential avenue to show transparency in product quality. However, in the healthcare industry, crowdsourced ratings have yet to see similar or substantial results.</p>
<p>That is about to change as developers become subject to new health IT policies and regulations.</p>
<p><a href="https://ehrintelligence.com/news/could-customer-reviews-give-transparency-into-health-it-tools?eid=CXTEL000000590793&elqCampaignId=15978&utm_source=nl&utm_medium=email&utm_campaign=newsletter&elqTrackId=d464ccfc5da74f54bb3d7083f581f2de&elq=c85e1383a7d145ea98a1cc7822ed6668&elqaid=16746&elqat=1&elqCampaignId=15978" target="_blank">Read the full article >></a></p></div>Zeroing in on Racial Health Disparities Related to Patient Matchinghttps://hub.nic-us.org/groups/let-s-get-technical/resources1/zeroing-in-on-racial-health-disparities-related-to-patient-matchi2020-09-14T15:20:38.000Z2020-09-14T15:20:38.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>COVID-19 has shown the importance of patient matching, but gathering patient data from all ethnicities is not a simple matter.</p>
<p><a href="https://ehrintelligence.com/news/zeroing-in-on-racial-health-disparities-related-to-patient-matching?eid=CXTEL000000590793&elqCampaignId=15884&utm_source=nl&utm_medium=email&utm_campaign=newsletter&elqTrackId=ea04f829a24f4bf5b0492c0a9c669433&elq=16b3626d85a94aaf96ee99264614aeab&elqaid=16660&elqat=1&elqCampaignId=15884" target="_blank">Read the article >></a></p></div>21st Century Cures Act: Interoperability, Information Blocking, and the ONC Health IT Certification Programhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/21st-century-cures-act-interoperability-information-blocking-and-2020-03-12T15:57:00.000Z2020-03-12T15:57:00.000ZAmanda Taylorhttps://hub.nic-us.org/members/AmandaTaylor<div><p>The Office of the National Coordinator for Health IT (ONC) and the Centers for Medicare & Medicaid Services (CMS) released final regulations related to driving more interoperability and data exchange across the healthcare ecosystem. The purpose of this regulation is meant to implement provisions of the 21st Century Cures Act, to include Conditions and Maintenance of Certification requirements for health information technology (health IT) developers under the ONC Health IT Certification Program. According to this final rule, implemenation of this regulation will improve interoperability and the use of electronic health information.</p><div><a href="https://www.healthit.gov/cerus/sites/cerus/files/2020-03/ONC_Cures_Act_Final_Rule_03092020.pdf" target="_blank">Check out the regulation >></a></div></div>2020-2025 DRAFT Federal Health IT Strategic Planhttps://hub.nic-us.org/groups/let-s-get-technical/resources1/2020-2025-draft-federal-health-it-strategic-plan2020-02-19T13:29:09.000Z2020-02-19T13:29:09.000ZAdam Pertmanhttps://hub.nic-us.org/members/AdamPertman<div><p><span id="ember77" class="ember-view">The draft of the National Coordinator's 2020-2025 Federal Health IT Strategic Plan is open for public comment until March 18, 2020. This plan defines a set of goals, objectives, and strategies the federal government intends to pursue to empower patients, deliver high-quality care, and improve health for individuals, families, and communities through the use of health IT. <br /> </span></p>
<p><span class="ember-view"><a href="https://www.linkedin.com/posts/office-of-the-national-coordinator-for-health-information-technology_draft-2020-2025-federal-health-it-strategic-activity-6625492754644422656-ZnQ6" target="_blank">Here's the link</a> to the draft Federal Health IT Strategic Plan. I urge everyone to review it and suggest we plan a Friday session during which Let's Get Technical participants (and anyone else who wants) can engage in a conversation about it and shape some comments for submission.</span></p></div>How States Use Data to Inform Decisions: A national review of the use of administrative data to improve state decision-makinghttps://hub.nic-us.org/groups/let-s-get-technical/resources1/how-states-use-data-to-inform-decisions-a-national-review-of-the-2020-01-21T23:31:11.000Z2020-01-21T23:31:11.000ZNavah Steinhttps://hub.nic-us.org/members/NavahStein<div><p>Every day, state governments make decisions that affect the lives of their citizens. Legislators and governors determine which policies to enact and what public problems to address. State agencies establish how programs should be run and where budget dollars are best spent, as well as who qualifies for government assistance.</p><p>To effectively serve the public, state officials at every level of government are tasked with ensuring that these daily decisions are prudent and well-informed. Consequently, states are increasingly turning to administrative data, or information—such as vital records, college enrollment data, and Medicaid utilization statistics, collected and maintained primarily for the routine management of programs and services—to make strategic data-informed decisions. This information can include any data that are necessary to implement and oversee a program, such as demographics, outcomes, and enrollment details.</p><p><a href="https://www.pewtrusts.org/-/media/assets/2018/02/dasa_how_states_use_data_report_v5.pdf" target="_blank">Read More >></a></p></div>In the NIC of Time: Six Domains of Primary Focus for the National Interoperability Collaborativehttps://hub.nic-us.org/groups/let-s-get-technical/resources1/in-the-nic-of-time-six-domains-of-primary-focus-for-the-national-2020-01-21T23:27:17.000Z2020-01-21T23:27:17.000ZNavah Steinhttps://hub.nic-us.org/members/NavahStein<div><p>The six domains are: human and social services; public health; public education; public safety; emergency medical services; and health information technology, which differs from the others in that it cuts across domains and is critical to their operations. The mission and work of each domain are summarized in this document, which was researched and written by the Stewards of Change Institute (SOCI), the Healthcare Information and Management Systems Society (HIMSS), and several subject matter experts to whom we are very grateful for the knowledge and guidance they contributed.</p><p><a href="https://stewardsofchange.org/wp-content/uploads/sites/2/2018/08/Six-Domains-Full-Document-Final-03-19-18.pdf" target="_blank">Read More >></a></p></div>ONC Fast 101https://hub.nic-us.org/groups/let-s-get-technical/resources1/onc-fast-1012020-01-21T23:23:19.000Z2020-01-21T23:23:19.000ZNavah Steinhttps://hub.nic-us.org/members/NavahStein<div><p>The FHIR at Scale Taskforce (FAST), convened by the Office of the National Coordinator for Health IT (ONC), brings together a highly representative group of motivated healthcare industry stakeholders and health information technology experts.</p><p>The group is set to identify HL7® Fast Healthcare Interoperability Resources (FHIR®) scalability gaps and possible solutions, analysis that will address current barriers and will accelerate FHIR adoption at scale.</p><p><a href="https://st2.ning.com/topology/rest/1.0/file/get/3676443366?profile=original" target="_blank">Read More >></a></p></div>Pilot of a Data Quality Framework to Support Patient Matchinghttps://hub.nic-us.org/groups/let-s-get-technical/resources1/pilot-of-a-data-quality-framework-to-support-patient-matching2020-01-21T23:20:17.000Z2020-01-21T23:20:17.000ZNavah Steinhttps://hub.nic-us.org/members/NavahStein<div><p>High-quality patient demographic data are fundamental to accurate patient identification and matching. Similarly, accurate patient identification and matching is pivotal to interoperability, patient safety, and research, such as patient-centered outcomes research (PCOR). With the adoption of rapid developments in health information technology (IT) and advancements in electronic health record (EHR) systems, the scale of growth of the data captured, stored, and exchanged continues to increase. Because of the constant growth in patient populations and their diversity, along with increasing complexity of health care networks, health care staff must have the capabilities and best practices to capture high-quality data on the front line.</p><p><a href="https://www.healthit.gov/sites/default/files/page/2019-09/PMAL_PDDQ_Paper_08292019.pdf" target="_blank">Read More >></a></p></div>Patient Matching, Aggregation, and Linking (PMAL) Projecthttps://hub.nic-us.org/groups/let-s-get-technical/resources1/patient-matching-aggregation-and-linking-pmal-project2020-01-21T23:18:05.000Z2020-01-21T23:18:05.000ZNavah Steinhttps://hub.nic-us.org/members/NavahStein<div><p>The Department of Health and Human Services (HHS) Office of the Assistant Secretary for Planning and Evaluation (ASPE) engaged the Office of the National Coordinator for Health Information Technology (ONC) to study and advance patient matching, aggregation, and linking (PMAL, or the PMAL Project) through the Patient-Centered Outcomes Research (PCOR) Trust Fund. ASPE oversees federal health programs funded through the PCOR Trust Fund to build data capacity for research. PCOR studies are designed to produce new scientific evidence that informs and supports the health care decisions of patients, families, and their health care providers. PCOR studies examine the effectiveness of prevention and treatment options while taking into consideration the preferences, values, and questions that are important to patients when they make health care choices</p><p><a href="https://www.healthit.gov/sites/default/files/page/2019-09/PMAL%20Final%20Report-08162019v2.pdf" target="_blank">Read More >></a></p></div>ONC PMAL Project: Creeping Forward on Patient Matchinghttps://hub.nic-us.org/groups/let-s-get-technical/resources1/onc-pmal-project-creeping-forward-on-patient-matching2020-01-21T23:15:07.000Z2020-01-21T23:15:07.000ZNavah Steinhttps://hub.nic-us.org/members/NavahStein<div><p>Last week, the Office of the National Coordinator for Health Information Technology (<a href="https://www.healthit.gov/topic/about-onc" target="_blank">ONC</a>) released the <a href="https://www.healthit.gov/sites/default/files/page/2019-09/PMAL%20Final%20Report-08162019v2.pdf" target="_blank">final report</a> from its Patient Matching, Aggregation, and Linking (<a href="https://www.healthit.gov/topic/scientific-initiatives/pcor/patient-matching-aggregating-and-linking-pmal" target="_blank">PMAL</a>) Project, as well as an <a href="https://www.healthit.gov/sites/default/files/page/2019-09/PMAL_PDDQ_Paper_08292019.pdf" target="_blank">additional report</a> describing a pilot project to test the <a href="https://www.healthit.gov/playbook/pddq-framework/" target="_blank">Patient Demographic Data Quality Framework</a> (PDDQ) to Support Patient Matching that was released several years ago. Funded from June 2015 through September 2018 by the HHS Office of the Assistant Secretary for Planning and Evaluation (ASPE) through the Patient-Centered Outcomes Research (PCOR) Trust Fund, PMAL was one of the activities I described in an <a href="https://www.hln.com/update-on-patient-matching-activities/" target="_blank">earlier blog entry</a>.</p><p><a href="https://www.hln.com/onc-pmal-project-creeping-forward-on-patient-matching/" target="_blank">Read More >></a></p></div>