In recent years, the health care industry has been grappling with numerous challenges, including rising costs, inefficiencies, and a growing demand for high-quality care. As populations age and chronic diseases become more prevalent, the strain on health care systems intensifies. However, the advent of artificial intelligence (AI) promises to revolutionize health care, offering innovative solutions to enhance efficiency, reduce costs, and create opportunities for greater profitability.
Improving the Current Health Care Landscape
I often use business case studies outside of eye care to drive home conceptual parallels to our profession. Amazon started as an online bookstore. Today, even though Amazon sells over 80% of all online books, it is less than 5% of its total annual revenue. In the 1990s, this “disruption” hardly seemed like anything at all, but as we all are well aware, it changed everything. It made shopping consumer centric as opposed to store centric.
Today, health care systems globally face significant hurdles. Escalating costs, limited resources, and increasing patient loads contribute to inefficiencies that often result in not only suboptimal care delivery but ultimately in lower profitability. As eye care providers, we need to understand the dynamics of the upcoming (here already) AI disruption to traditional care delivery models and how it will impact our patients, our practices, and our profits by making the eye care model patient centric rather than practice centric.
For years, the broader health care professions have been plagued by inefficiencies and escalating costs. Administrative burdens such as differentiating managed vision care and medical carriers, fragmented care, and lengthy diagnostic processes have all contributed to a system that often feels overwhelmed and cumbersome. Look at something as simple as patient communications and recalls. Twenty years ago, a recall system was nothing more than mailing the patient a “postcard” reminding them that it was time for their annual appointment, followed up by making telephone calls to follow up on the postcard reminder. Efficient — hardly, effective — even worse.
Today, automated communication methods, such as texting, automated phone calls, and other systems incorporated into the EHR have made the “old way of doing things” nothing but a distant memory. Imagine a disruptive approach that can transform the entire practice, from patient interaction to clinical efficiencies and better patient outcomes. The need for a solution that can streamline operations, reduce costs, and improve patient outcomes has never been more urgent. That is the promise of AI.
AI in Health Care – Creating the Patient-Centric Experience
Artificial intelligence, with its capacity to analyze vast amounts of data and learn from patterns, offers a powerful tool to address the existing challenges in health care. AI technologies encompass a range of applications, from machine learning and natural language processing to robotics and predictive analytics. These technologies hold the potential to improve diagnostic accuracy, streamline administrative processes, and personalize patient care.
Enhancing Diagnostic Accuracy
One of the most significant impacts of AI in health care is its ability to enhance diagnostic accuracy. AI algorithms can analyze medical images, such as OCTs, visual fields, and fundus images with remarkable precision, often surpassing human capabilities. For instance, AI-powered systems can detect early signs of diseases such as diabetic retinopathy, enabling timely intervention and improving patient outcomes. I have seen systems such as AMIKnowS that use patient information to provide a diagnosis and treatment algorithm in seconds.
Furthermore, AI’s ability to continuously learn and adapt means that diagnostic tools become more accurate over time. This dynamic learning process allows for the identification of patterns and anomalies that might be missed by human eyes, leading to earlier and more accurate diagnoses.
Streamlining Administrative Processes
Administrative tasks in health care, such as scheduling, billing, coding, and record-keeping, often consume valuable time and resources. AI-powered automation can streamline these processes, reducing the administrative burden on health care professionals and their teams. We will get to a point where natural language algorithms will be able to transcribe and manage patient records efficiently, freeing up doctors and staff to focus on direct patient care. Additionally, AI-driven chatbots can handle appointment scheduling and patient inquiries, enhancing the overall efficiency of health care facilities.
The integration of AI in administrative processes also enhances data accuracy and reduces the risk of human error. New automated systems can handle vast amounts of data with precision, ensuring that patient records are up to date and accurately maintained. This reduces the time spent on manual entry and corrections, allowing health care professionals to devote more time to patient care.
Reducing Costs and Increasing Profit
Currently, based on national averages for income and expenses, the average chair cost per hour for an optometric practice is approximately $99.20. This represents the breakeven point for the costs associated with providing professional services, excluding optical revenues and costs. In this scenario, the eye care professional must generate at least $99.20 per hour in professional service revenue to break even — no profit or loss.
With the trend of decreasing reimbursements from the primary source of optometric income, managed vision care plans, this is one of the areas that AI will have the ability to immediately impact and transform. For example, currently if “Eye Care Plan A” pays $40 per examination, and the practice is only seeing two patients per hour, then the practice revenues are $80 per hour against a cost of $99.20 per hour, a loss of $19.20 per hour. The adoption of AI technologies can disrupt this declining profit spiral and restore better foundational revenue and cost structures.
It is my prediction that within five to seven years at least 80% of the eye examination components will be done prior to the patient walking through the practices’ doors. Advances in handheld devices and diagnostic data collection will allow many data components of the eye examination to be performed and captured within the patient’s home. Patient demographics such as insurance information, date of last exam, medication history, etc. will be interchangeable between the practice and the patient, with instantaneous updates. When the patient arrives in the practice, the doctor will have to review information, verify or validate test results, and perform a health examination as per state board requirements. AI will assist in validating clinical findings, diagnosing the patient, and developing the most accurate treatment programs based upon the patient’s whole health record.
Talk about efficiency and effectiveness! Entire patient encounter time can be reduced by nearly two-thirds, thus increasing productivity from two exams per hour to six exams per hour. Based on the exact same parameters discussed earlier, this translates into income of $240 per hour in revenue against a cost of $99.20 per hour. In this model, profitability moves from a loss of $19.20 per hour to a profit of $140.80 per hour — with no sacrifice to quality of care and most likely with better patient outcomes.
While it is obvious that the average practice’s care patterns would differ, if one were to perform just a full day of comprehensive eye examinations based upon the above scenario, it would translate into $1,126.4 per day of net profit, $5,632 per week of net profit, or $281,600 per year of net profit. This is with the lowest paying plan, no medical eye care at all, no additional diagnostic testing, no contact lens services and no optical.
Optimizing Resource Allocation
Optimizing the clinical experience is just one benefit of AI. AI can also analyze data from various sources, such as electronic health records, to predict patient demand and optimize resource allocation. By anticipating patient needs, health care providers can better manage staffing levels, reduce wait times, and minimize the use of unnecessary tests and procedures. This optimization can lead to substantial cost savings and more efficient use of health care resources.
For example, based upon your own practice schedule dynamics, predictive analytics can develop customized scheduling to maximize productivity and profitability by allocating staff and other clinical resources more effectively. By increasing productivity, the predicted shortage of physicians will have significantly less impact as each practice gains in efficiencies and better outcomes.
Challenges and Considerations
While the potential benefits of AI in health care are immense, several challenges and considerations must be addressed to ensure its successful implementation.
Data Privacy and Security
When choosing an AI ecosystem, you must consider the security of the data and the algorithms. AI relies on vast amounts of data to function effectively. Ensuring the privacy and security of patient data is paramount to maintaining trust and compliance with regulations. Robust data protection measures and transparent policies are essential to safeguard sensitive information. Medical equipment companies that employ “image management systems” must be scrutinized for security and integrity of the AI process. We are now seeing some of the top equipment manufacturers partner with technology security services to protect the ecosystem.
Implementing AI in health care requires a delicate balance between data accessibility and privacy. Eye care practices must adopt stringent security protocols to protect patient information from breaches and unauthorized access, and their technology partners must also allow the free flow of data sharing and integration without compromising security of the data or algorithms. Additionally, transparent data usage policies help build trust with patients, ensuring that their data is used responsibly and ethically.
Ethical and Bias Concerns
AI algorithms are only as good as the data they are trained on. If the training data is biased or incomplete, the AI system may produce biased or inaccurate results. Addressing these ethical concerns and ensuring the fairness and transparency of AI systems is crucial to their widespread adoption in health care. To mitigate bias, it is essential to use diverse and representative datasets during the training phase of AI development. Moreover, companies that employ multiple algorithms for various diseases should be looked upon favorably as they would allow the practitioner the flexibility to “model” outcomes for patients to determine the best course of treatment. Constant monitoring and evaluation of AI systems will help identify and correct biases, ensuring that the technology delivers equitable and accurate outcomes for all patients. To prevent unintended consequences, It goes without saying that ethical considerations must remain at the forefront of AI implementation.
The Future of AI in Health Care
The potential of AI to transform health care is undeniable. As technology continues to advance, AI will play an increasingly central role in improving efficiency, reducing costs, and enhancing patient care. Eye care practices need to be on the forefront of this as the eye will truly be looked at as the window to the body for AI-driven processes for determining everything from cardiac issues to neurological disorders.
The future of AI in health care is bright, with ongoing advancements promising even greater benefits. Emerging technologies such as AI-powered diagnostics and virtual health assistants are poised to revolutionize the way health care is delivered, turning the current clinical delivery system on its head. Practitioners who embrace disruption and are continually evaluating new methods to increase access, patient care, productivity, and profitability will be richly rewarded. Anticipating, embracing, and directing these changes creates opportunity to deliver the highest level of care, the very best outcomes at a rate of profitability that makes our current models instantly obsolete. Partnering with those companies that have a secure, non-biased ecosystem will lead to greater economies of scale and efficiency as well.
In conclusion, the integration of artificial intelligence into health care systems presents a promising solution to the eye care industry’s challenges of patient demand, high quality outcomes, and stemming the decreased profitability paradigm. By enhancing diagnostic accuracy, streamlining administrative processes, and personalizing patient care, AI can significantly improve efficiency, reduce costs, and increase profits. While challenges remain, the ongoing development and thoughtful implementation of AI technologies hold the key to a more efficient, effective, and profitable health care future.
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