Cutting the Administrative and Operational Burden in Healthcare with AI - Lessons Learned
Our Take:
While AI holds the potential to radically transform clinical protocols and care, potentially bringing such things as AI-driven precision medicines and AR/VR-assisted remote surgeries to name a few, perhaps AI holds its greatest near-term promise in addressing the vast administrative and operational challenges in healthcare. For example, according to one report, non-capital-non-labor costs constituted almost 55% of hospital total operating costs with administrative and general costs making up over 20% of that. In addition to provider costs, payers incur substantial costs in marketing, administering, and adjudicating claims as well. In fact, according to National Health Expenditure Data, administrative costs relating to health insurance totaled over $280 billion in 2019 alone.
Key Takeaways:
Even after doing work to standardize and label patient data, in at least one broad study almost 10% of items in the data repository didn’t have proper identifiers (Perspectives on Health Information Management)
One study of 14 AI products in radiology found most products had no information on training data collection and population characteristics, making it difficult to assess the risk of bias (Frontiers in Digital Health).
AI utilizing voice-enabled technology saved clinicians 3.3 hours per week and reduced the amount of time physicians spent reviewing old notes by 60% by producing a clinical summary (AAFP)
Each additional standard deviation improvement score that hospitals received in cultural competency, translated into an increase of 0.9% in nurse communication and 1.3% in staff responsiveness on patient satisfaction surveys (Medical Care)
The Problem:
Given that AI is good at time-consuming, repetitive tasks like summarizing clinical appointments (via ambient clinical intelligence) and streamlining administrative processes like scheduling, billing, and patient management, AI holds the potential to dramatically reduce costs and improve efficiency in non-clinical areas of healthcare. In fact, an argument can be made that in demonstrating its ability to tackle these “front of the house tasks” AI tools can combat some elements of fear and distrust, thereby winning over AI skeptics who will then embrace the technology to begin tackling clinical issues.
However, the successful deployment of AI technologies in these applications in healthcare faces significant hurdles, including fragmented data, biases in training datasets, and the need for better workflow integration. Even here IT departments must deal with overcoming challenges, such as ensuring data access, maintaining high data quality, addressing privacy concerns, and achieving regulatory oversight.
Lessons from the implementation and application of sports analytics can offer valuable insights into achieving broader acceptance and effective use of AI in clinical settings. Meanwhile, AR and VR technologies are emerging as transformative tools in healthcare, with applications in medical training, mental health treatment, and patient care. These technologies can enhance the training of healthcare professionals, improve treatment outcomes for chronic conditions, and provide innovative solutions for addressing mental health issues. However, challenges such as infrastructure limitations, the need for evidence-based VR programs, and rapid technological advancements must be addressed for these technologies to fully realize their potential in healthcare.
The Backdrop:
The rapid advancements in technology have paved the way for innovative solutions in healthcare, particularly through the integration of artificial intelligence (AI) and augmented/virtual reality (AR/VR). AI has been increasingly recognized for its potential to streamline administrative tasks, enhance clinical decision-making, and improve patient outcomes by analyzing vast amounts of healthcare data.
For example, while still early, AI has demonstrated its capacity to alleviate physician burnout by automating documentation and reducing bureaucratic tasks through ambient clinical intelligence, allowing physicians to focus more on patient care. In addition, AI can help bridge language and cultural barriers in healthcare, improving communication and patient satisfaction by providing culturally appropriate interactions and translation services. In addition, given its ability to rapidly process and analyze large volumes of complex data, AI has powered the necessary insights and real-time feedback for AR/VR applications like immersive and interactive experiences for medical training, patient treatment, and mental health therapies. As such, AI is creating advanced, efficient, and personalized healthcare solutions that address the growing demands of the industry.
Lessons Learned:
What have been some of the “lessons learned” regarding the development and acceptance of AI in healthcare, and how AR/VR can also improve outcomes, from our “Our Take” posts over the years?
Deploying AI in healthcare requires addressing challenges such as integrating insights into workflows, consolidating fragmented data, and ensuring data quality and reliability.
According to Forrester's "The Cloud, Data, and AI Imperative for Healthcare" Report the 3 greatest challenges to implement AI are: 1) integrating insights into existing clinical workflows; 2) consolidating fragmented data; and, 3) achieving clinically reliable clean data
Researchers working to uncover insights into prescribing patterns for certain antipsychotic medications found that approximately 27% of prescriptions were missing dosages
Even after doing work to standardize and label patient data, in at least one broad study almost 10% of items in the data repository didn’t have proper identifiers
Academic publications in biology around AI technologies such as deep learning, natural language processing (NLP), and computer vision have grown over 50% a year since 2017
For AI to be optimized in healthcare, policymakers need to focus on improving data access, interdisciplinary education, and regulatory oversight.
77% of healthcare organizations either already leverage AI to support clinical decision-making or are likely to do so
The most fundamental issue for AI integration in healthcare is the lack of uniform access to high-quality, representative data
The FDA's current regulatory approach often uses the less stringent 510(k) pathway for AI products instead of the more rigorous premarket approval process
A significant portion of FDA-cleared AI products lack transparency regarding the demographic composition of their training datasets, raising concerns about bias and accuracy
AI can significantly reduce physician burnout by minimizing time spent on documentation and bureaucratic tasks, allowing doctors to focus more on patient care.
Physician burnout costs the U.S. approximately $4.6B/yr. due to reduced hours, physician turnover, and expenses of finding and hiring replacements (Harvard Business School)
60% of physicians agree that bureaucratic tasks, including note writing, are the top contributor to physician burnout (Medscape)
Physicians spend almost 50% of their time on the electronic health record (EHR) and desk work with 1-2 hours of after-hours work each night dedicated to EHR tasks (Annals of Internal Medicine)
AI utilizing voice-enabled technology saved clinicians 3.3 hours per week and reduced the amount of time physicians spent reviewing old notes by 60% by producing a clinical summary (AAFP)
AI can help bridge language and cultural barriers in healthcare, improving communication and patient satisfaction by providing culturally appropriate interactions and translation services.
African Americans and Latinos experience 30% to 40% poorer health outcomes than White Americans
Research shows poor care for the underserved is because of fear, lack of access to quality healthcare, distrust of doctors, and often dismissed symptoms and pains
One study found that black patients were significantly less likely than white patients to receive analgesics for extremity fractures in the emergency room (57% vs. 74%), despite having similar self-reports of pain
Each additional standard deviation improvement score that hospitals received in cultural competency, translated into an increase of 0.9% in nurse communication and 1.3% in staff responsiveness on patient satisfaction surveys
AI and AR/VR technologies hold significant promise in healthcare with the integration of advanced analytics into medical practices.
AI can alleviate physician burnout by reducing time spent on documentation and bureaucratic tasks, enhancing patient care
Successful AI deployment in healthcare requires addressing privacy concerns, potential errors, misinformation, and biases in AI models
AI tools like speech recognition and natural language processing can save clinicians time and improve work-life balance
Businesses can learn from sports teams' success with analytics by applying focused, integrated, manageable, and well-communicated data strategies
VR and AR technologies are revolutionizing healthcare by improving training and treatment, despite infrastructure and standardization challenges.
The virtual reality market has a projected compound annual growth rate of over 30% between 2020 and 2025 (PWC)
Some major trends in healthcare VR applications include neurological and developmental therapy, pain reduction through distractions, exposure therapy for phobias, and psychological applications (JMIR Biomedical Engineering)
VR training improved physicians’ surgical performance by 230% compared to traditional training programs (Harvard Business Review)
Barriers to adoption of VR therapy in clinical settings include the lack of evidence-based VR programs, infrastructure to collect and analyze VR data, software standardization, and technological obsolescence
Final Thoughts:
The integration of AI and AR/VR technologies in healthcare presents a promising future where efficiency, patient care, and treatment outcomes can be significantly enhanced. While there are challenges to be addressed, such as data quality, regulatory oversight, and infrastructure development, the potential benefits far outweigh these obstacles. By learning from the successful application of analytics in other fields, such as sports, and by focusing on cultural competence and patient-centric solutions, healthcare providers can harness these technologies to create a more effective and empathetic healthcare system. The ongoing advancements and the lessons learned so far underscore the importance of a thoughtful and strategic approach to implementing AI and AR/VR in healthcare, ensuring that these innovations truly transform the industry for the better.
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