The digital health space refers to the integration of technology and health care services to improve the overall quality of health care delivery. It encompasses a wide range of innovative and emerging technologies such as wearables, telehealth, artificial intelligence, mobile health, and electronic health records (EHRs). The digital health space offers numerous benefits such as improved patient outcomes, increased access to health care, reduced costs, and improved communication and collaboration between patients and health care providers. For example, patients can now monitor their vital signs such as blood pressure and glucose levels from home using wearable devices and share the data with their doctors in real-time. Telehealth technology allows patients to consult with their health care providers remotely without having to travel to the hospital, making health care more accessible, particularly in remote or rural areas. Artificial intelligence can be used to analyze vast amounts of patient data to identify patterns, predict outcomes, and provide personalized treatment recommendations. Overall, the digital health space is rapidly evolving, and the integration of technology in health

Tuesday, November 26, 2024

MIQ | MI10

Clinicians, Advance Practice Professionals, Allied Health Professionals, and Faculty School Administrators:

This is Your Organization's Roadmap to an AI-Enabled Future


MIQ | MI10

With the advent of more advanced artificial intelligence (AI) technology, there is increasing interest for executives in different organizations to initiate efforts to adopt these AI tools. There is an increasing number of “AI Maturity” models for organizations, including a few such models for hospitals and healthcare organizations. These AI maturity models, however, are usually not well suited for healthcare systems and/or are excessively tedious to use. In addition, these AI maturity models often lack an objective measurement of the AI readiness of the healthcare organization, the very important first step of a healthcare organization’s AI journey.

We prefer a different strategy: an “AI Readiness” approach and we term an objective data-based assessment the “Medical Intelligence Quotient (MIQTM)” of the healthcare organization. AI readiness is the very first and most important step of a healthcare organization’s AI journey. Just like the human intelligence quotient (IQ), this is the beginning baseline of the AI effort. In short, AI readiness assessment in the form of MIQTM is where the healthcare organization is as it begins this AI journey. In addition, as an AI enterprise, we established a data science approach to this assessment process not only for each individual hospital but also for a small cohort of diverse healthcare organizations for comparative purposes. Lastly, this assessment score or MIQTM is designed by actively practicing clinicians who are leading the AI efforts at their hospitals rather than consulting technologists or administrators who are not actively engaged in AI efforts in hospitals or health systems.






Monday, November 25, 2024

Why Healthcare Systems Are Prime Targets for Cyber Attacks


Cybercrime is rampant on the internet. It occurs in financial transactions, viruses, spam, and industries such as agriculture, health, law, government, and public utilities. It casts a shadow over the internet. Why?  The internet offers a veil of anonymity. It even extends to outer space, and sabotage of satellite communications. It may also pose a great risk to NASA, or SpaceX.

The Healthcare Sector: A Goldmine for Cybercriminals

Healthcare systems represent an alluring target for cybercriminals due to the sheer volume and sensitivity of the data they manage. Globally, healthcare expenditures exceed trillions of dollars annually, making the sector a vital part of national economies. Yet, this critical infrastructure is riddled with vulnerabilities, stemming from its reliance on legacy systems, complex workflows, and a vast number of interconnected devices. Hospitals, clinics, insurers, and pharmaceutical companies maintain troves of patient information, financial data, and intellectual property, often without sufficient safeguards. Cybercriminals recognize these weaknesses and exploit them to infiltrate networks, disrupt operations, and steal data. Healthcare organizations frequently operate under intense time pressures, with limited opportunities to implement robust security protocols, creating a perfect storm for attackers. Additionally, the legal and regulatory requirements to protect sensitive data, while intended to safeguard privacy, can sometimes hinder rapid responses to emerging threats, further increasing the risk of successful breaches.




Cybercriminals will use ransomware to steal a victim’s medical records from a healthcare provider. Ransomware is a type of malware that prevents organizations from accessing their data or devices by encrypting it. Cybercriminals promise to decrypt the data or device once the ransom is paid. They will threaten the organization to pay the ransom or else they will never give back the stolen data or leak the data to the public. According to Healthcare Dive, ransomware has cost the healthcare industry around $77.5 billion since 2016.

Ransomware attacks have become a dominant threat in the healthcare sector, often bringing operations to a standstill and putting patient lives at risk. Attackers use sophisticated malware to encrypt critical systems, preventing access to electronic medical records, scheduling systems, and even life-saving medical devices. Healthcare providers are particularly vulnerable to ransomware because of the urgency of their work; even a few hours of downtime can result in delayed surgeries, missed treatments, or misdiagnoses. Cybercriminals exploit this urgency to demand exorbitant ransoms, knowing that many organizations will pay rather than risk prolonged disruptions. The financial impact of ransomware extends beyond the ransom itself, including costs associated with restoring systems, regulatory fines for data breaches, and long-term damage to an organization’s reputation. In 2023 alone, ransomware attacks on healthcare entities cost billions globally, with many organizations still struggling to recover months after the initial attack.



Expanding the Attack to Patients

The ripple effects of healthcare data breaches often extend far beyond the initial compromise of systems. Once attackers gain access to PHI, they can directly exploit patients for additional financial gain or leverage. One common tactic is using stolen data to launch phishing campaigns that impersonate healthcare providers, tricking patients into providing further sensitive information or making fraudulent payments. Criminals can also use medical data to commit insurance fraud by submitting fake claims for high-cost procedures, leaving patients to navigate billing nightmares. Another alarming trend is the use of compromised medical information for extortion. Hackers may threaten to disclose private health details, such as a diagnosis of a stigmatized condition unless the victim pays a ransom. In some cases, fraudulent medical records created with stolen PHI can jeopardize a patient’s care, as healthcare providers may rely on inaccurate or incomplete information when making treatment decisions. These scenarios illustrate the high stakes involved in protecting patient data from cyber threats.
Reference:

Medical devices are particularly vulnerable. Hospitals use monitoring equipment with wearables, monitors, and communication systems. Internal LANs and wireless access points provide back door entrances to the entire hospital network. External USB memory sticks can deliver a powerful virus, and introduce ransomware.  The Defense Department disables USB ports in the Medical records system. (MHS GENESIS) The Veterans Administration uses VISTA and is also transitioning to a new system called Oracle Cerner as part of a modernization effort to improve health care delivery and interoperability with the Department of Defense's EHR system, MHS GENESIS.

The acquisition and mergers of multiple software vendors also create confusion for cybersecurity. 

The efficiency positive interconnectivity, and interoperability increase the risk of transmission of viruses, and ransomware.

The recent adoption of artificial intelligence creates an unknown vehicle for cybercrime.

Friday, November 22, 2024

Does AI-Powered Clinical Documentation Enhance Clinician Efficiency? A Longitudinal Study | NEJM AI

Introduction

In recent years, the integration of artificial intelligence (AI) into various facets of health care has promised to revolutionize clinical practices and patient care. One area experiencing significant transformation is clinical documentation, where AI-powered tools are increasingly used to automate the process of documenting patient encounters.1,2 These tools leverage advanced machine learning algorithms and natural language processing capabilities to capture and summarize conversations, thereby streamlining workflows and potentially improving the accuracy of captured information compared with clinician recall. By alleviating administrative burdens and enabling clinicians to focus on patient care, AI-powered clinical documentation tools could play a role in mitigating physician burnout and making healthcare delivery more efficient.3,4

Nuance’s Dragon Ambient eXperience (DAX) Copilot is an electronic health record (EHR)–integrated AI-enabled scribe software. It synthesizes a preliminary outpatient clinical note by “listening” to the conversation between a clinician and a patient during their visit. Starting in June 2023, Atrium Health conducted a rigorous outcomes evaluation of using DAX in primary care settings, to determine whether using DAX improves efficiency for clinicians (as measured by EHR use metrics) and financial performance for the health system. A separately reported substudy evaluated the effects on clinician-reported experience outcomes.5,6

The New England Journal of Medicine reports that Ambient AI scribing does not markedly improve efficiency in electronic health record transcribing.

In this evaluation, we found no statistically significant differences in EHR-related and financial metrics between DAX users and the control group. However, exploratory results suggested that modest reductions in note time could result from using DAX at a high utilization level or deploying DAX to select clinician subgroups. Taken as a whole, these findings suggest that AI-enabled documentation’s efficiencies may translate to decreased markers of burnout for a subset of clinicians, and perhaps more broadly when the implementation of DAX achieves a higher adoption level. However, widespread implementation of DAX in its current form is unlikely to generate appreciable gains for healthcare systems looking to increase productivity.

These results fly in the face of common sense to me. The study was limited to the use of NLP for the EHR. There are many other uses of artificial intelligence where it may be productive. These uses must. also be studied.

The following tables reveal the results

                          Participant Selection Flowchart

                              Characteristics of DAX Copilot Participants.

Linear Mixed Models on EHR Use Metrics and Financial Metrics.

Linear Mixed Models on EHR Use Metrics and Financial Metrics by Patient Volume.

Participants

This study enrolled 238 clinicians specializing in family medicine, internal medicine, and general pediatrics (including physicians and advanced practice providers) from outpatient clinics in North Carolina and Georgia. The intervention group was stratified into five waves between June and August 2023 based on their clinic locations. Before their accounts were activated, clinicians underwent a 1-hour training session on DAX. A control group not utilizing DAX was also recruited in five waves using two methods aimed at matching practice locations and specialties with the intervention group: (1) identification and encouragement of clinicians by service-line leaders to participate as controls; and (2) inclusion of clinicians who initially showed interest in DAX but later opted out (Fig. 1). Among all DAX users, clinicians who transferred more than 25% of their DAX notes to EHR system were defined as active DAX users, and those who transferred more than 60% of their DAX notes were considered high DAX users.8 Clinicians were excluded from the analysis for the following reasons: they were preceptors (n=11), they were identified as DAX participants and either never opened DAX or did not open DAX after their training date (n=10), or their age was missing (n=2). The final analytic sample included 112 clinicians in the intervention group and 103 clinicians in the control group. (See Table 1.)

Two sets of primary outcomes were assessed: EHR use metrics and financial metrics. EHR use metrics include time in EHR (EHR-Time8), work time outside of work (WOW8), time in note (Note-Time8), completed appointment rate, same-day closure rate, and note length. Financial metrics include gross revenue per visit and work relative value units (wRVUs) per visit. The index date was defined as the start date of using DAX in the intervention group and the first date of each wave for the control group. Our quantitative endpoints measured 180 days post-index date among both groups. EHR-Time8, WOW8, and Note-Time8 used 8 hours of scheduled patient time to normalize time spent in the EHR as described by the American Medical Association (AMA).9 The rest of the metrics were analyzed using nonnormalized data

Discussion

In this evaluation, we found no statistically significant differences in EHR-related and financial metrics between DAX users and the control group. However, exploratory results suggested that modest reductions in note time could result from using DAX at a high utilization level or deploying DAX to select clinician subgroups. Taken as a whole, these findings suggest that AI-enabled documentation’s efficiencies may translate to decreased markers of burnout for a subset of clinicians, and perhaps more broadly when the implementation of DAX achieves a higher adoption level. However, widespread implementation of DAX in its current form is unlikely to generate appreciable gains for healthcare systems looking to increase productivity.
To our knowledge, this is the first study to investigate whether this ambient-listening AI tool improves both efficiency and financial metrics from a healthcare system standpoint. These findings have important and timely implications as healthcare systems weigh the cost of adopting new technology. Indeed, the hype and novelty of ambient-listening AI tools have outpaced the evidence to support or refute claims that these tools are transformational in terms of time savings and efficiency. Consequently, healthcare systems, which already operate on small margins in hypercompetitive environments, run the risk of overpaying and not realizing the expected benefits. This study also contributes to the evidence-based incorporation of EHR use metrics into the evaluation of emerging technology and creates a foundational standard for comparison of outcomes across future studies.
There are several possible explanations for why we did not see large improvements in either EHR time or financial metrics. For example, while overall note time decreased, clinicians may have simply repurposed that time for other EHR activity they might otherwise not have done. In terms of financial metrics, clinicians with more free time may choose to use that time for other clinical activities or to leave work sooner. While our primary outcome found modest overall time saved (as viewed through a health system lens); our analysis identified subgroups among DAX users that saved substantial time. For example, 18% of participants saw a reduction of more than 1 hour a day in the EHR. While exploratory and unadjusted, these findings raise additional questions for future research to explore which user subgroups might achieve disproportionate benefits from using DAX. Additionally, a subset of clinicians noted that the documentation tool did indeed save them time; but rather than seeing more patients, these time savings allowed them to sleep more, spend less time working at home, and make existing encounters more focused and personal.5 Future research will need to evaluate whether modest time savings can add patient capacity or improve the quality of care for existing patients, to support the expense of adding new technology. Additionally, in our organization, ambulatory clinicians are responsible for determining the level of service for billing encounters. Clinicians indicated that DAX captures more details from the clinician-patient interaction and gives clinicians documentary support for all the condition management they performed, often resulting in more comprehensive notes.5 With the expansion to a larger cohort of users and over a longer time frame, this greater comprehensiveness could lead to more complete billing for services rendered, but additional study will be required to track its financial impact.



Does AI-Powered Clinical Documentation Enhance Clinician Efficiency? A Longitudinal Study | NEJM AI

HealthCare IT Today


The annual AdvaMed MedTech Conference brings medical device makers, entrepreneurs, strategic, and investors together. For the first time, the 2024 conference was held outside of the US in Toronto, ON.  

Key Takeaways

Innovation in medical devices is alive and well. The exhibit hall was full of companies showing off their latest medical products and digital solutions. The breadth of devices was impressive – everything from a patient transport system from Able Innovations to a new type of low-frequency ultrasound treatment.  One topic of discussion was the upcoming changes to medical device regulations in the EU and potentially in the US.

Surprisingly there was not a lot of discussion about cybersecurity or sessions on that topic. This might be because this is already something that is table stakes in the medtech industry or perhaps it was discussed at length at prior conferences.

Able Innovations

One of the most impressive devices on display was Able Innovations’ ALTA platform for patient transfers. The platform is incorporated into a stretcher and it “crawls” underneath a prone patient, lifts them up, and slides them gently from the bed to the stretcher. This is all accomplished with the touch of a button.

This represents a significant improvement over manually shifting a patient – which not only is physically demanding, it also frequently requires more than two staff members. With the ALTA platform, a patient can be transferred by a single staff member.

[Note: Able Innovations is a company that is part of the ventureLAB innovation hub. The author of this article is an advisor at ventureLAB]

Philips


An equally impressive innovation – albeit one with a much smaller footprint – was the Philips patient avatar. Shaped like a gingerbread man, this avatar shows patient vitals like heart rate and respiration. As these rise or fall, the icon changes colors to make it easy for clinicians and caregivers to see if something is wrong.

The avatar was the idea of two anesthesiologists who were also pilots. In a cockpit, there are too many dials and displays for a pilot to monitor at once. Modern cockpits have alert systems and visualizations to make it easy for pilots to be aware of everything that is happening to the plane – without the need to scan all the individual displays.

This was the inspiration for the patient avatar.

A Great Learning Experience

The MedTech Conference was very educational and a great learning experience. It was especially exciting to see several digital health companies in attendance at the event – proving that the lines between medical devices and health IT are blurring.