Innovation: Health Technology and Systems

HBHI convened supply chain scholars and industry leaders to discuss how to prevent supply chain interruptions from occurring when the next pandemic invariably arrives.

A symposium co-hosted by HBHI and the Hopkins Economics of Alzheimer's Disease and Services Center covers the ramifications of the approval of controversial Alzheimer's Disease drug Aduhelm for patients enrolled in approved clinical trials.

Albert Wu for Journal of Patient Safety and Risk Management

Speaker: Dr. Mark H. Dredze, PhD, is the Interim Deputy Director of the Data Science and AI Institute and the John C. Malone Professor in the Department of Computer Science at Johns Hopkins University

A huge thank you to Dr. Santiago Romero-Brufau of the Mayo Clinic and Dr. Christina Yuan of the Bloomberg School of Public Health for their wisdom and insights!

Lindsay Thompson in Frontiers in Public Health

Haiyang Yang in Handbook of Qualitative Research Methods in Marketing: Second Edition

Risa Wolf in JAMA Network Open

Featured speaker Dr. Nigam Shah from Stanford discussed the importance of aligning AI models with healthcare realities, particularly around capacity constraints and financial sustainability.

Dr. Charlotte J. Haug, MD, PhD, is the Executive Editor of NEJM AI and an International Correspondent for the New England Journal of Medicine.

HBHI convened an expert panel at the Hopkins Bloomberg Center in Washington, D.C. to discuss how the healthcare industry at large and providers at the grassroots level are working to bring about change.

Several members of HBHI's core faculty have received new or continued funding in this round of awards.

Dr. Manisha Loss presented the exciting ongoing GenAI implementations at Johns Hopkins, and Dr. Mario Macis, discussed the research opportunities arising from these implementations.

The Nexus Awards are a universitywide initiative launched in 2023 to support convening, research, and teaching anchored at the new Johns Hopkins University Bloomberg Center in Washington, D.C.

Standard economic theory would predict that offering financial incentives to complete an altruistic act should increase individuals' willingness to donate.

In this scoping review, we evaluated the performance of artificial intelligence (AI) in clinical radiology practice and examined health professionals' perspectives regarding AI use in radiology.

The role of altruism in the acceptance of novel preventive healthcare technologies like vaccines has not been thoroughly elucidated.

Significant variation in coding intensity among hospitals has been observed and can lead to reimbursement inequities and inadequate risk adjustment for quality measures.

Tinglong Dai, Ph.D., and one of our new Core Faculty members, Tej Azad, Ph.D., co-authored an op-ed on AI regulation that was just published in Medical Economics.

James Zou, PhD, Associate Professor of Biomedical Data Science, Chan-Zuckerberg investigator, and Sloan Fellow, Stanford University

Earth Day was the perfect occasion for the Hopkins Business of Health Initiative (HBHI) to host a first-of-its-kind conversation with HHS Secretary Xavier Becerra about addressing healthcare’s climate footprint in the context of environmental justice.

Dr. Suchi Saria a healthcare AI expert, was highlighted in a conversation with Drs. Tinglong Dai and Risa Wolf, emphasizing the importance of interdisciplinary collaboration, patient-centered care, and continuous learning.

Diabetic eye disease (DED) is a leading cause of blindness in the world. Early detection and treatment of DED have been shown to be both sight-saving and cost-effective

To examine telerehabilitation utilization in the United States (US) during the first 2 years of the pandemic.

Why doesn't healthcare in the US improve in quality while becoming more affordable, as we see in other industries? Join us for an enlightening discussion with James (Jim) and Robert (Bob) Rebitzer, authors of "Why Not Better and Cheaper?"

Arjun (Raj) Manrai, Ph.D., Assistant Professor of Biomedical Informatics, Harvard Medical School, Deputy Editor, NEJM AI

Founder & CEO Bayesian Health, Endowed Chair/AI Prof Johns Hopkins

Arjun (Raj) Manrai, PhD is an Assistant Professor in the Department of Biomedical Informatics at Harvard Medical School

Dr. Xiaoxi Yao is a Health Services Research Professor and Endowed Scientific Director of Pragmatic Trials and Evaluation at Mayo Clinic's Kern Center.

Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population

Accolades

Professor Nicholson Price, J.D., Ph.D., Professor of Health, University of Michigan Law School

Rama Chellappa, Ph.D., Bloomberg Distinguished Professor, Johns Hopkins University, who discussed the university’s major investment in artificial intelligence and its implications for the future of medicine

Professor Danton Char, Stanford University School of Medicine; Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)

HBHI is pleased to announce our five pilot grant awardees for our 2024 cohort.

Guest Speaker: Chien-Ming Huang, John C. Malone Assistant Professor, Johns Hopkins Department of Computer Science; Director, Johns Hopkins Intuitive Computing Laboratory; and Member, Johns Hopkins Laboratory for Computational Sensing and Robotics.

Why doesn't healthcare in the US improve in quality while becoming more affordable, as we see in other industries? Join us for an enlightening discussion with James (Jim) and Robert (Bob) Rebitzer, authors of "Why Not Better and Cheaper?" We explored the unique challenges of healthcare innovation in the US, discussing why there's a lack of incentives for cost-reducing innovations and the ease of profiting from low-value innovations. The Rebitzers also shared their perspectives on directing healthcare innovation towards more efficient and cost-effective solutions. This is a crucial conversation for anyone interested in the future of American healthcare.

Artificial intelligence and machine learning (AI/ML) applications in healthcare could have algorithmic biases that perpetuate health disparities.

As artificial intelligence (AI) applications become more pervasive, it is critical to understand how knowledge workers with different levels and types of experience can team with AI for productivity gains. We focus on the influence of two major types of human work experience (narrow experience based on the specific task volume and broad experience based on seniority) on the human-AI team dynamics.

A public conversation around generative artificial intelligence (AI) in medicine is both long overdue and increasingly urgent. In health care applications, the stakes are heightened because of the intricate privacy considerations and the critical nature of decision-making. The potential positive impact is also enormous.

The emergence and increasing use of artificial intelligence and machine learning (AI/ML) in healthcare practice and delivery is being greeted with both optimism and caution. We focus on the nexus of AI/ML and racial disparities in healthcare: an issue that must be addressed if the promise of AI to improve patient care and health outcomes is to be realized in an equitable manner for all populations.

In August 2022, the US Food and Drug Administration issued the final rule to establish a new category of over-the-counter (OTC) hearing aids. Over-the-counter hearing aids are intended for use by adults with mild-to-moderate hearing loss without the need for an audiologist to adjust and program the device for the user’s level of hearing as is currently done for most conventional hearing aids. One key aspect of the effectiveness of these future OTC hearing aids is provision of the correct level of sound amplification based on the individual’s level of hearing. Thus, a feature to import prior hearing test (audiogram) results into these devices may be an important consideration to ensure optimal amplification of sound.

Millions of older adults co-manage or delegate health responsibilities to one or more family caregivers. Patient portals facilitate health care management tasks, but little is known about portal use arrangements (i.e., individual or joint use) among older adults and their caregivers.

It’s clear that changes in telehealth policy, mobile technology development, and consumer demand have fundamentally reshaped mental health treatment and substance use care in the US and around the world. What does this mean for people’s outcomes, specifically behavioral health care access, quality, and cost?

There is increased acceptance that social and behavioral determinants of health (SBDH) impact health outcomes, but electronic health records (EHRs) are not always set up to capture the full range of SBDH variables in a systematic manner. The purpose of this study was to explore rates and trends of social history (SH) data collection—1 element of SBDH—in a structured portion of an EHR within a large academic integrated delivery system.

Interactions with wearable devices, smartphones, and social media generate user data, which can reveal sensitive health information. Those data can be used to improve individual and community health, but also raise security and privacy concerns. Given potential benefits alongside substantial privacy risks, it is important to evaluate how consumers view organizations and entities that collect and re-use their personal digital health data. We surveyed US consumers to assess their confidence in sixteen public and private organizations to use their digital health data responsibly.

We conducted a randomized controlled trial involving nearly 700 customer-service representatives (CSRs) in a Canadian government service agency to study whether providing CSRs with performance feedback with or without peer comparison affected their subsequent organ donor registration rates. Despite having no tie to remuneration or promotion, the provision of individual performance feedback three times over one year resulted in a 25% increase in daily signups, compared to otherwise similar encouragement and reminders.

While providers are aware of the financial burden of healthcare for patients, there is a lack of actionable price transparency at the point of prescribing. Real-time prescription benefit (RTPB) tools are new electronic clinical decision support tools that retrieve patient- and medication-specific out-of-pocket cost information and display it to clinicians at the point of prescribing.

For many years, there has been a collective excitement about using artificial intelligence in healthcare delivery. Still, AI technology is often talked about as if it is science fiction and the future of healthcare. Despite the FDA's approval of more than 500 medical AI systems by July 2022, many experts question if AI is a reality in the medical arena, or if it remains far in the future—and how to overcome the obstacles to adopting the new technologies?

Around 150 million Americans rely on employers for access to health care. Can the companies who employ them help foster an affordable system that improves our health and doesn't drive us crazy in the process?

As the health of Americans gets worse, with chronic disease and physical and mental illnesses on the rise, do corporations have a role to play in addressing the health crisis?

In 2020, HBHI awarded a pilot grant to Jodi Segal, MD, MPH, a HBHI Leadership Committee member and Professor of Medicine at the Johns Hopkins School of Medicine and the Bloomberg School of Public Health to understand what clinicians and patients consider to be appropriate use of telemedicine in primary care to inform future development of a framework that should be valuable to diverse stakeholders.

HBHI pilot study publication on a proposed framework for the appropriate use of telemedicine in primary care

HBHI pilot study publication on interview findings of what clinicians and patients consider to be appropriate use of telemedicine in primary care

Artificial intelligence on call: The physician’s decision of whether to use AI in clinical practice