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.

PWC makes eight such predictions based on insights from AI visionaries, as well as what PwC leaders see as they advise clients around the world who are putting AI to work in their own organizations.

Presentation slides and recording from 8/22/18 Webinar.

For many stakeholders, the healthcare experience can feel siloed and fragmented. In reality today, care management is limited to the windows of time patients and providers have during in-person appointments to discuss quality of life, lifestyle habits, monthly device readings, and adjust treatment. This is because providers only have access to a fraction of the valuable information about patients’ health outside the four walls of the hospital.

However, the use of digital health devices and apps – like in-home medical devices and wearables – can extend care beyond these walls and provide better insights into patients’ health. These devices and apps generate valuable patient data that, when incorporated into the clinical workflow and a continuous program of care, lead to more efficient and effective treatment that can lower costs and improve outcomes.

In this webinar, three stakeholders – including a patient with type 2 diabetes, a provider who designed and managed remote care programs, and a health technologist specializing in data workflows and analytics – will share their perspectives on the value that patient-generated health data (PGHD) and digital health devices offer remote care programs.

Participants will also gain practical guidance on:

-Best practices for implementing and scaling a remote care program with digital tools
-How best to engage and empower patients with data-driven remote care programs
Join the webinar to hear from a patient discussing their firsthand experience participating in a remote care program, and the perspectives of a technologist and provider on the implementation and operation of such programs.

Speakers:
-Drew Schiller, CEO, Validic
-Steve Van, Patient Advocate
-Martin Entwistle, President and CEO, Ares Health Systems

Best Practices in Technology and Analytics

Optum Predicts Risk of Atrial Fibrillation Using Artificial Intelligence

An estimated 3-6 million people in the United States have (AFib), an irregular heartbeat that increases the risk of stroke and heart disease. Due to the aging population, the numbers are expected to increase and many patients are unaware of their condition. If AFib is detected early on, the risk of stroke and heart disease is reduced by >75% with proper care and medications. The department of Data Science and Transformation of Optum Enterprise Analytics is exploring the ability of machine learning and artificial intelligence to identify patients who might have an abnormal heart rhythm. In a recent study, Optum trained a tree-based model using a de-identified dataset and identified patterns of data that are associated with the presence of ICD coding for atrial fibrillation. If patients are determined to be at risk for AFib, they can be referred for further testing and participation in anticoagulant drug therapy. Identifying AFib and preventing potential heart disease and stroke presents health organizations with opportunities for medical cost savings. The end goal of Optum’s research is to improve the health of AFib patients, decrease medical spending, and possibly detect other chronic diseases and complications through machine learning, artificial intelligence, and predictive analytics.

This best practice was discussed as part of eHealth Initiative’s July 2018 Technology & Analytics Workgroup.

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.

Perspective: Coupling Policymaking with Evaluation- The Case of the Opioid Crisis

The authors examine various opioid-control interventions to evaluate their effects.  These policies and interventions include prescription drug monitoring programs (PDMPs), prescription limits, restrictions on doctor shopping, abuse-deterrant formulations, and notification letters for high-volume prescribers.  The article does not shy away from describing ineffective or mixed results. 

The full article can be viewed below.  

The Opioid Epidemic: Data-Driven Case Studies

The KPMG Government Institute released an issue brief covering the opioid epidemic.  This provides context and background while also highlighting data-driven success stories from Rhode Island, Massachusetts, Pennsylvania, Oklahoma.

The full brief is below.  

An integrated big data analytics-enabled transformation model: Application to health care

A big data analytics-enabled transformation model based on practice-based view is developed, which reveals the causal relationships among big data analytics capabilities, IT-enabled transformation practices, benefit dimensions, and business values. This model was then tested in healthcare setting. By analyzing big data implementation cases, we sought to understand how big data analytics capabilities transform organizational practices, thereby generating potential benefits. In addition to conceptually defining four big data analytics capabilities, the model offers a strategic view of big data analytics. Three significant path-to-value chains were identified for healthcare organizations by applying the model, which provides practical insights for managers.

The full article can be viewed below.