5 Ways Artificial Intelligence May Affect Health Care in the Near Future and What That Means for You

Technology is changing fast, and the world is changing with it. Concepts that were mere science fiction only a couple of decades ago -- like artificial intelligence (AI) -- are quickly becoming commonplace. Computers have become powerful enough to handle complex AI computations; machine learning algorithms are more accurate and faster than ever; and the cloud and the internet of things have made it possible for even small devices to access artificial intelligence's enormous capabilities.

That's why responsible use of AI solutions in health care could improve, and even save people's lives. On the other hand, health care is an area where recklessness can occur; that's why new developments are regulated and implemented slowly and cautiously.

Here are five ways that AI and machine learning will likely be affecting your health care in the very near future:

• Digital Consultations
• Radiology and Images
• Personalized Medicine: Faster, More Accurate Diagnoses
• Robot Surgeons
• Cybersecurity

The full Entrepreneur article can be viewed at this link.  

Slides from presentation by Priyanka Surio, Director, Data Analytics & Public Health Informatics, Association of State and Territorial Health Officials (ASTHO) at eHI's 12.4.18 Executive Advisory Board on Data Governance roundtable meeting.

Cost of Care Conversations: Practice Briefs

As part of the Cost Conversation projects, Avalere worked closely with the Robert Wood Johnson Foundation project grantees to synthesize the key themes and findings across their exploratory studies and create 7 Practice Briefs. These briefs are intended to act as actionable resources to clinicians, staff, and practice administrators interested in increasing the value and frequency of cost-of-care conversations in the clinical setting. The briefs cover the 7 key topics below.

• Why Do Cost-of-Care Conversations Matter?
• What Your Patients Aren’t Telling You: How To Partner with Patients To Help Manage the Hidden Costs of Healthcare
• How To Welcome Cost-of-Care Conversations in Your Practice
• Structuring the Conversation: How To Talk To Your Patients About the Costs of Their Care
• How To Integrate Cost-of-Care Conversations into Workflow
• Considerations for Facilitating Cost-of-Care Conversations with Vulnerable Patients
• Addressing the Most Common Barriers to Implementing Cost-of-Care Conversations

These briefs on the America's Essential Hospitals site can be viewed at this link.

A SCOPING REVIEW EXPLORATION OF THE INTENDED AND UNINTENDED CONSEQUENCES OF EHEALTH ON OLDER PEOPLE: A HEALTH EQUITY IMPACT ASSESSMENT

eHealth is one perceived mechanism to extend the range and reach of limited health-care resources for older adults. A decade-scoping review (2007–2017) was conducted to systematically search and synthesize evidence to understand the intended and unintended consequences of eHealth initiatives, informed by a health equity impact assessment framework. Scoping review sources included international academic and grey literature on eHealth initiatives (e.g., eHealth records, telemedicine/telecare, and mobile eHealth application) focused on the varying needs of older adults (aged 60+), particularly individuals experiencing sociocultural and economic difficulties. Findings suggest that eHealth has several potential benefits for older adults, but also the possibility of further excluding already marginalized groups, thereby exacerbating existing health disparities. Ongoing evaluation of eHealth initiatives for older adults is necessary and requires attention to unique individual-level, socioeconomic, and cultural characteristics to heighten benefits and better capture both the intended and unintended outcomes of advanced eHealth systems.

The full article can be downloaded below.  

Computing Statistics from Private Data

In several domains, privacy presents a significant obstacle to scientific and analytic research, and limits the economic, social, health and scholastic benefits that could be derived from such research. These concerns stem from the need for privacy about personally identifiable information (PII), commercial intellectual property, and other types of information. For example, businesses, researchers, and policymakers may benefit by analyzing aggregate information about markets, but individual companies may not be willing to reveal information about risks, strategies, and weaknesses that could be exploited by competitors. Extracting valuable utility from the new “big data” economy demands new privacy technologies to overcome barriers that impede sensitive data from being aggregated and analyzed.

Secure multiparty computation (MPC) is a collection of cryptographic technologies that can be used to effectively cope with some of these obstacles, and provide a new means of allowing researchers to coordinate and analyze sensitive data collections, obviating the need for data owners to share the underlying data sets with other researchers or with each other. This paper outlines the findings that were made during interdisciplinary workshops that examined potential applications of MPC to data in the social and health sciences.

The primary goals of this work are to describe the computational needs of these disciplines and to develop a specific roadmap for selecting efficient algorithms and protocols that can be used as a starting point for interdisciplinary projects between cryptographers and data scientists.

The full article can be downloaded below.  

Patient and public involvement in medical performance processes: A systematic review

The significance and recognition of patient and public involvement (PPI) have grown in many domains of health care in recent years propagating an evolution of “patient-centred care” and shared clinical decision making. This review indicates a need for a similar level of integration for PPI within medical performance processes as existing models are both fragmented and inadequate to have a meaningful impact on systems and processes that assess and monitor performance.

Feedback and complaints have both summative and formative elements, though the balance varies between different systems and even within systems. PPI can make a positive contribution to developing both elements, although the evidence presented in this review suggests that most doctors would prefer patient feedback and complaints to provide a primarily formative assessment of their performance and are cautious about the use of such data for summative purposes. Developing the formative element of feedback and complaints mechanisms with patients involved in the design of their structures and systems may have a greater impact on the professional development of doctors.

More broadly, quality improvement may act as a driver for PPI in medical performance processes to evolve beyond the level of providing feedback and lodging complaints, forming the foundation of a transition from a culture of contractual PPI that exists as part of the clinical interface between the doctor and patient, to that of collaboration that enhances the profession-society relationship.

The full article can be downloaded below.  

Current challenges in health information technology–related patient safety

We identify and describe nine key, short-term, challenges to help healthcare organizations, health information technology developers, researchers, policymakers, and funders focus their efforts on health information technology–related patient safety. Categorized according to the stage of the health information technology lifecycle where they appear, these challenges relate to (1) developing models, methods, and tools to enable risk assessment; (2) developing standard user interface design features and functions; (3) ensuring the safety of software in an interfaced, network-enabled clinical environment; (4) implementing a method for unambiguous patient identification (1–4 Design and Development stage); (5) developing and implementing decision support which improves safety; (6) identifying practices to safely manage information technology system transitions (5 and 6 Implementation and Use stage); (7) developing real-time methods to enable automated surveillance and monitoring of system performance and safety; (8) establishing the cultural and legal framework/safe harbor to allow sharing information about hazards and adverse events; and (9) developing models and methods for consumers/patients to improve health information technology safety (7–9 Monitoring, Evaluation, and Optimization stage). These challenges represent key “to-do’s” that must be completed before we can expect to have safe, reliable, and efficient health information technology–based systems required to care for patients.

The full article can be downloaded below.  

Split learning for health: Distributed deep learning without sharing raw patient data

Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.

The full article can be downloaded below.  

Questions for Artificial Intelligence in Health Care

AI is a promising tool for healthcare, and efforts should continue to bring innovations such as AI to clinical care delivery. However, inconsistent data quality, limited evidence supporting the clinical efficacy of AI, and lack of clarity about the effective integration of AI into clinical workflow are significant issues that threaten its application. Whether AI will ultimately improve quality of care at reasonable cost remains an unanswered, but critical, question. Without the difficult work needed to address these issues, the medical community risks falling prey to the hype of AI and missing the realization of its potential.

The full article can be downloaded below.  

Using AI to Improve Electronic Health Records

While AI is being applied in EHR systems principally to improve data discovery and extraction and personalize treatment recommendations, it has great potential to make EHRs more user friendly. This is a critical goal, as EHRs are complicated and hard to use and are often cited as contributing to clinician burnout. Today, customizing EHRs to make them easier for clinicians is largely a manual process, and the systems’ rigidity is a real obstacle to improvement. AI, and machine learning specifically, could help EHRs continuously adapt to users’ preferences, improving both clinical outcomes and clinicians’ quality of life.

The full Harvard Business Review article can be viewed at this link.