Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

Almost all the papers in this review applied AI, machine learning, and NLP techniques to extract structured data from unstructured clinical narratives in both English data sources as well as sources in other languages. Tasks such as applying billing codes or populating cancer registries or assisting with clinical research are key roles for HIM professionals. Collectively, the set of papers show the potential for these techniques to improve the efficiency of what have been laborious manual processes.

In the future, the uses of AI and machine learning methods to mine structured, and increasingly, unstructured, data from EHRs are likely to expand. Such expansion, in addition to clinical and health services research that make use of data in EHRs, might also include risk scoring and other predictive modeling, population health management, analyses for revenue enhancement, and quality assurance activities. As the survey paper of the HIM section of the IMIA Yearbook, authored by Stanfill et al. makes clear, when the use of these methods becomes more integrated into research and clinical activities, the need to address a variety of technical and ethical issues, including those related to data quality, as well as privacy and security, will be increasingly recognized. HIM professionals can play a key role in addressing these issues, but the issues themselves are important to many professions and multiple and diverse research domains.

Given the importance of AI methods and approaches to the field of Health Information Management, it was striking that the MeSH headings of papers that represent cutting edge work in the use of AI concepts rarely included MeSH headings related to HIM, although these articles could be found with searches that included the AI concepts and EHRs. Similarly, the set of papers that included HIM-related MeSH headings did not include papers on AI methods. It is difficult to tell whether the lack of overlap of the AI literature and HIM is a result of how the article authors chose key words, how the MeSH coders assigned headings, or the fact that HIM professionals are not involved in this research and the researchers do not identify with HIM. Whatever the cause, the results of the 2018 literature search as well as the discussion in the survey paper highlight the need for HIM professionals to become more knowledgeable about these new approaches and to bring their expertise to the research applying these methods in practice.

The full article can be downloaded below.  

AI in Health: State of the Art, Challenges, and Future Directions​

Artificial intelligence (AI) technologies continue to attract interest from a broad range of disciplines in recent years, including health. The increase in computer hardware and software applications in medicine, as well as digitization of health-related data together fuel progress in the development and use of AI in medicine. This progress provides new opportunities and challenges, as well as directions for the future of AI in health.

The goals of this survey are to review the current state of AI in health, along with opportunities, challenges, and practical implications. This review highlights recent developments over the past five years and directions for the future.

Publications over the past five years reporting the use of AI in health in clinical and biomedical informatics journals, as well as computer science conferences, were selected according to Google Scholar citations. Publications were then categorized into five different classes, according to the type of data analyzed.

The major data types identified were multi-omics, clinical, behavioral, environmental and pharmaceutical research and development (R&D) data. The current state of AI related to each data type is described, followed by associated challenges and practical implications that have emerged over the last several years. Opportunities and future directions based on these advances are discussed.

Technologies have enabled the development of AI-assisted approaches to healthcare. However, there remain challenges. Work is currently underway to address multi-modal data integration, balancing quantitative algorithm performance and qualitative model interpretability, protection of model security, federated learning, and model bias.

The full article can be downloaded below.  

Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management

This paper explores the implications of artificial intelligence (AI) on the management of healthcare data and information and how AI technologies will affect the responsibilities and work of health information management (HIM) professionals.

A literature review was conducted of both peer-reviewed literature and published opinions on current and future use of AI technology to collect, store, and use healthcare data. The authors also sought insights from key HIM leaders via semi-structured interviews conducted both on the phone and by email.

The following HIM practices are impacted by AI technologies: 1) Automated medical coding and capturing AI-based information; 2) Healthcare data management and data governance; 3) Patient privacy and confidentiality; and 4) HIM workforce training and education.

HIM professionals must focus on improving the quality of coded data that is being used to develop AI applications. HIM professional’s ability to identify data patterns will be an important skill as automation advances, though additional skills in data analysis tools and techniques are needed. In addition, HIM professionals should consider how current patient privacy practices apply to AI application, development, and use.

AI technology will continue to evolve as will the role of HIM professionals who are in a unique position to take on emerging roles with their depth of knowledge on the sources and origins of healthcare data. The challenge for HIM professionals is to identify leading practices for the management of healthcare data and information in an AI-enabled world.

The full article can be downloaded below.

Traditional and Digital Biomarkers: Two Worlds Apart?

The identification and application of biomarkers in the clinical and medical fields has an enormous impact on society. The increase of digital devices and the rise in popularity of healthrelated mobile apps has produced a new trove of biomarkers in large, diverse, and complex data. However, the unclear definition of digital biomarkers, population groups, and their intersection with traditional biomarkers hinders their discovery and validation. We have identified current issues in the field of digital biomarkers and put forth suggestions to address them during the DayOne Workshop with participants from academia and industry. We have found similarities and differences between traditional and digital biomarkers in order to synchronize semantics, define unique features, review current regulatory procedures, and describe novel applications that enable precision medicine.

The full article can be downloaded below.  

AI And Healthcare: Is The Bloom Finally Off The Rose?

What a rough few weeks it’s been for AI and healthcare. In just the last ten days, we’ve seen the publication of a number of commentaries that collectively express a significant degree of caution, if not outright concern, about the extravagant expectations around the application of AI to healthcare and drug discovery.

The full Forbes article can be viewed at this link.  

U.K. Firm Creates 'Operating System' To Handle Massive Genomic Patient Data Sets

Every aspect of our existence on this planet is being digitized. We’re making work digital on every desktop, we’re empowering digital sensors in the Internet of Things (IoT) on every street corner, we’re building smart digital AI-powered IT systems with machine learning and decision-making abilities to automate our lives… and we’re digitizing ourselves as well.

The full Forbes article can be viewed at this link.  

AI, Health, And The Future Of Human Agency

The 21st century has ushered in a new age where all aspects of our lives are impacted by technology. How will humanity anticipate, mitigate, and manage the consequences of AI, robots, quantum computing and more? How do we ensure tech works for the good of all? This Ashoka series sheds light on the wisdom and ideas of leaders in the field.

The full Forbes article can be found at this link.

Mobile clinical communications saves Valley Medical Center tens of millions of dollars

The operating room unit includes 18 surgery rooms on two floors, and delays in any activities necessary to expedite pre-op preparation, the speed of the procedure and post-op care – for example, room set-up, instrument sterile processing or transport help – are expensive.

Until 2015, the clinicians, nurses and support staff who had to coordinate these activities communicated with each other using a complex mix of pagers, legacy phones and whiteboards. In this environment, it was difficult to even track the location of valuable operating room equipment.

“We needed a faster, easier way for everyone to connect with each other and share patient and workflow information,” said Jeremy Wyatt, manager of perioperative services and manager of sterile processing at the UW Medicine Valley Medical Center. “In 2015, we consolidated operating room perioperative communications and alarm/alert notifications on the HIPAA-compliant Voalte smartphone system.”

This was part of a hospital-wide rollout of Voalte communications technology with goals aligned with the medical center’s four “Patients Are First” strategic pillars – provide the highest quality care, improve patient experiences, lower costs, and become an employer of choice by improving clinician and staff experiences.

The full Healthcare IT News article can be viewed at this link.

Mobilizing Primary Care to Address the Opioid Use Disorder Treatment Gap

In this Executive Perspective, we describe how federally qualified health centers, funded by the Health Resources and Services Administration (HRSA; hereinafter referred to as health centers), have responded to the opioid use disorder treatment gap among the most vulnerable and underserved populations in the United States. We describe HRSA’s use of the Patient-Centered Medical Home advanced primary care model for persons with opioid use disorders, and we posit that the experience of health centers can inform an expansion of behavioral health and primary care integration in other primary care settings.

The full article can be downloaded below.