September 2018 report from the Office of the Chief Technology Officer on the state of data sharing between agencies of the U.S. Department of HHS.

Across the twenty-nine distinct agencies of the United States Department of Health and Human Services (HHS), data essential to understanding the nation’s health are collected every day. 

 Whether surveillance, survey, or claims data, HHS expends an enormous amount of financial resources to report on the state of the health of the population it serves.

These data, however, are largely kept in silos with a lack of organizational awareness of what data are collected across the Department and how to request access. Each agency operates within its own statutory authority and each dataset can be governed by a particular set of regulations. As such, each discrete analysis of the data often gets reviewed for legal purposes and leads to data sharing occurring largely on a project-by-project basis. The individuals involved negotiate the nature and extent of data sharing arrangements often

A cohesive enterprise-wide data governance strategy that promotes data sharing, drives business value from leveraging data as an asset, and bases policies on evidence is essential to a long-term data-driven vision of HHS.

Precision medicine—the approach to disease prevention and treatment that takes into account people’s individual variations in genes, environment, and lifestyle—is game-changing. But to achieve groundbreaking insights and impact clinical care, organizations must pick up the pace. Research with a decade-long timeline will not suffice. Nor will failing to define a clear strategy and path forward. Turn to Booz Allen to generate momentum and maximize your impact in precision medicine.

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July 2018 report from Michelle De Mooy, Center for Democracy & Technology

This report explores the potential for harmful bias in mHealth interventions and considers the impact of such bias on individuals, companies, and public health, ultimately providing recommendations for app developers to ensure that the tools they build are inclusive and nondiscriminatory. This report seeks to advance the conversation about — and implementation of — equity and inclusivity in automated decisions in the health sector in ways that benefit both the public and the companies using data to make decisions by: (a) providing a landscape of the mHealth ecosystem; (b) synthesizing research and investigations to draw out key issues and
concerns related to bias in automated decision-making in the commercial health context; and (c) making recommendations that advance identification and mitigation of bias and discrimination in processes that produce commercial health app content.

Part II of this report provides an overview of the mHealth marketplace, covering the types of mHealth apps available, how data flows in and out of these apps, who uses these apps, how these apps are regulated, and how effective these apps are. Part III discusses the efficacy of mHealth and suggests that reducing bias is vital to delivering effective health interventions with these tools. Part IV examines how and when bias can be introduced into mHealth interventions.

Part V provides a recommended roadmap of inquiry for developers and others involved in mHealth to identify and mitigate bias. Part VI is a review of areas for future research, and Part

VII is a brief conclusion.

In today’s digital world, different systems interact with each other for data and information exchange. We expect each interaction / transaction between the systems to be secure and reliable. Blockchain is a new technology that promises an efficient, cost-effective, reliable, and secure system for conducting and recording any transaction without the need of middleman.

The lack of "alignment" between providers and payers hampers integration of care and services and is a primary driver of our high healthcare costs.

Lynda Rowe of InterSystems reviews a recent NEJM Heath Catalyst study on the topic and outlines thoughts and hypothesis on how to “cross the chasm."

Data is one of the most valuable assets in any organization and is necessary to sustain current and future business models. As healthcare transitions into a more analytically driven industry, managing data is especially relevant. Organizations are grappling with ways to manage continual changes in health information technology (IT), IT infrastructure, and the huge volume of data collected across the healthcare industry. The push toward value-based care has amplified the need for efficient exchange of quality patient data, which fills gaps in information and offers providers and payers a more complete picture of the patient. Data-centric strategies focused on managing the entire lifecycle of healthcare data are particularly important in today’s environment.

The policies and procedures to manage, protect, and govern information across a healthcare enterprise falls under data governance. Data governance includes data modeling, data mapping, data audit, data quality controls, data quality management, data architecture, and data dictionaries. A strong data governance structure is a critical component of any healthcare organization, as it provides a structure for analytics and other complex data initiatives.

In Spring 2018, eHealth Initiative Foundation and the LexisNexis® Risk Solutions healthcare business hosted the first in a series of roundtable meetings on data governance in healthcare. The meeting convened senior executives from stakeholder groups, including payer, provider, professional organizations, health information exchanges (HIEs), research, public health, laboratory, and pharmaceuticals. The goal of the meeting was to gather expert opinions on how to make data accessible, close quality gaps, turn insight into action, and protect sensitive patient information. This brief addresses the value of data governance in healthcare; existing challenges related to data governance; and key takeaways from the meeting.

Healthcare was a key discussion topic in the White House and Congress for most of 2017. In 2018, the healthcare industry will continue to experience rapid change. From blockchain tech to precision medicine an artificial intelligence, change in the healthcare system shows no signs of slowing down.

Click on the link below to view the detailed infographic posted by the Duquesne School of Nursing.

Author: Ralph Johnson III

Federal and state mandates have compelled healthcare systems to adopt “meaningful use” electronic health record (EHR) systems. Off-the-shelf, onthe-spot, one-source EHR systems such as EPIC® have become popular choices. Indeed, EPIC® recently captured a substantial proportion of the Houston Texas Medical Center (TMC), CVS Pharmacy mini-clinics, and extended into academic institutions. Current reported estimates are contentious but vary between 2047% of the EHR market share. Therefore, it is only sensible to conduct a review of EPIC.  

Hospitals’ and health systems’ ongoing prioritization of health information technology (IT) tools continues to expand patients’ ability to engage with their providers, access their health data, and interact with the health care system electronically. It also allows providers to more readily communicate across settings of care, supporting greater care coordination. In a patient-centered, value-driven care model, the ability of patients to interact and engage with both their health data and the health care delivery system electronically is a key driver of high-quality health care. 

This is the first in a series of issue briefs highlighting data from the 2016 AHA Annual Survey Information Technology Supplement for community hospitals
collected November 2016 – April 2017.1 This brief focuses on the state of patient's access to and engagement with their health data through health IT. Results are grouped into three categories of activity: accessing health data, interacting with health data, and obtaining health care services.

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Authors: Wullianallur Raghupathi and Viju Raghupathi

The healthcare industry historically has generated large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care. While most data is stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data. Driven by mandatory requirements and the potential to improve the quality of healthcare delivery meanwhile reducing the costs, these massive quantities of data (known as ‘big data’) hold the promise of supporting a wide range of medical and healthcare functions, including among others clinical decision support, disease surveillance, and population health management. Reports say data from the U.S. healthcare system alone reached, in 2011, 150 exabytes. At this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (10²¹ gigabytes) scale and, not long after, the yottabyte (10²⁴ gigabytes). Kaiser Permanente, the California-based health network, which has more than 9 million members, is believed to have between 26.5 and 44 petabytes of potentially rich data from EHRs, including images and annotations.

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