Vision deterioration is among the common implications of aging, though young people may also experience eyesight problems. Thankfully, innovative corrective procedures have made these problems easy to resolve. While LASIK is a popular option, refractive lens exchange is fast emerging as a more appropriate solution. It is a surgical procedure that involves the replacement of the defective lens with an artificial one to correct farsightedness by improving focus. But RLE is relatively lesser known than LASIK, so understanding it better can help you learn more about its benefits. Let us explain why you should consider RLE surgery to restore your vision.
Lasting solution with the better visual quality
Many patients know laser vision correction is an ideal solution for eyesight issues. While it has an impressive success rate, RLE scores better on the vision quality front. Since the RLE procedure replaces the natural eye lens with an intraocular lens, you can expect better outcomes in the long run. Moreover, the lens does change with time, so it is a permanent solution to restore your vision for good. You need not worry about regression of results as with laser surgery.
Minimal recovery time
RLE is a quick procedure, as it takes less than thirty minutes to complete. During the procedure, the doctor uses eye drops to numb the eye. The next step is to create a circular opening with the laser to access, remove, and replace the lens. The recovery period is minimal, and you can expect to resume your daily routine within a day after the procedure. Most patients experience immediate vision improvement after the treatment.
By Gevik Nalbandian, vice president of software engineering, Lyniate.
As healthcare providers manage market shifts such as value-based care, increased consumer expectations, staffing shortages, changing reimbursement models, and competition from non-traditional healthcare players including Alphabet, Amazon, Apple, and Microsoft — what will it take to compete?
Providers must strengthen the internal IT infrastructure systems to better manage patient relationships. This all begins with easy access to accurate patient data. But with the explosion of data in the healthcare ecosystem, this is no small feat.
Interoperability doesn’t end with integration
Reducing friction in health data exchange requires seamless interoperability among different systems. Interoperability is often viewed as accessing and exchanging data, typically through an integration engine for extracting, composing, standardizing, and passing data between disparate systems. This is a necessary component, but it is not sufficient to achieve a full and accurate picture of your patients and patient populations.
A second component is patient identity management. An identity layer, managed through an enterprise master person index (EMPI), is critical to knowing which patients the data is tied to. In an April 2022 report, Gartner describes EMPIs as “crucial tools for reconciling patient identity and addressing medical record matching challenges needed for high-quality healthcare delivery and health information exchange.”
Accurate patient identification ensures every interaction in which data about an individual is captured — regardless of system or location — is linked correctly for a single, up-to-date view of one’s care. This includes diagnosed medical conditions, lab work, imaging, diagnostic tests, medications, allergies, and family medical history. When a patient’s data is trapped in various systems across the continuum, it can have potentially disastrous downstream clinical, operational, and financial effects.
Gaps or errors in the patient identity management process can have serious consequences for patients. According to a recent survey, nearly 40% of U.S. healthcare providers have incurred an adverse event in last two years as the result of a patient matching issue.
More than half of hospitals in the U.S. are projected to experience negative margins in 2022, with expenses estimated to increase by nearly $135 billion over 2021 levels, according to a recent Kaufman, Hall & Associates report.[1]
While health systems have no direct means of controlling the rising rate of inflation, they are able to reduce the impact of losses through the use of utilization management strategies and tools designed to ensure that patients get the care that they require, without excessive testing and unnecessary costs associated with care they don’t need.
Utilization management, while effective at addressing the most obvious sources of waste within a health system, has been less successful at a more granular level, due in part to the disconnect between those who create and enforce clinical cost guidelines and those who actually provide the care. Hospital-based utilization reviews grew in popularity throughout the 1960s and 1970s, as a result of growing doubts that greater medical care expenditures resulted in improved health status.
By the 1980s, utilization efforts began to transition to third-parties, such as health plans, in response to research that suggested that many medical services were unnecessary or inappropriate; an increased emphasis by purchasers on linking cost containment with quality assurance; and a proliferation of information resources and assessment tools that made case-by-case review of proposed services feasible on a large scale.[2]
Throughout its history, utilization efforts have placed increasing pressure on providers with regards to the cost of care, starting with prospective pay, then HMOs, and now value-based care and bundled payments. Each new effort has sought to transfer greater economic risk onto providers, perhaps because those who administer costs take the perspective that since providers are the ones making decisions about what to spend, they should also manage the implications of their decisions.
Over the same period, provider access to cost data remained very limited, so decisions about what drugs to prescribe or treatment to undertake were made with no exposure to the related costs. As a result, clinicians have long been outspoken critics of utilization management because it’s been seen as limiting their clinical autonomy and contributing to an ever-increasing administrative burden.[3]
By Rinat Akhmetov, product lead and ML solutions architect, Provectus.
The use of artificial intelligence (AI) is growing across all sectors, and healthcare is no exception. In fact, AI is particularly well-suited to healthcare applications due to the vast amount of data — from electronic health records (EHR) and clinical trials, to disease registries and claims — that is generated in the industry on a daily basis.
Ophthalmology is one area where the application of AI technology is more than justified. Faster and more accurate, at-scale eye screening can help diagnose and prevent such eye conditions as amblyopia, strabismus, diabetic retinopathy, glaucoma, age-related macular degeneration, and many others. AI holds the potential to improve patient diagnosis, reduce cost per screening, and expand the availability of eye screening to all.
This article explores how AI can be used in ophthalmology. We will consider the benefits and challenges of AI, outline prospective use cases, and offer a framework for adopting AI.
Ophthalmology is ready for AI innovation
Artificial intelligence is beginning to be used in ophthalmology for a reason.
A 2020 study researching the use of AI to screen for diabetic retinopathy, a leading cause of blindness, found that AI was able to achieve an accuracy of around 95%, which is comparable to that of expert human graders. Another study used AI to detect glaucoma, also a leading cause of blindness. The AI system was able to achieve an accuracy of over 90% in detecting the disease.
These studies show that the amount of real-world data is enough to develop highly accurate algorithms that can detect disease as well or even better than humans — in all types of eye screens, and at a speed and scale that exceed human potential many times over.
Given the global shortage of ophthalmologists and optometrists, and the widespread availability of technology (from ready-to-use algorithms to cloud computing), introducing AI to augment the work of ophthalmologists seems like a sensible solution.
Despite benefits, AI remains a challenge
The potential benefits of using AI in ophthalmology are significant. The improved accuracy and scale of disease detection lead to earlier diagnosis and treatment, which improves patient outcomes. Automated disease screening frees up time for ophthalmologists to focus on other tasks.
However, there are also some challenges associated with using AI.
AI requires high-quality data for training. And while the volume of data is usually not a problem, finding the right talent to prepare it can be problematic. Only professional ophthalmologists are qualified to label training data in a manner that ensures high accuracy on real-world data in production.
There are risks of false positives or false negatives. Some diseases may be incorrectly diagnosed, while others may be missed altogether. Hence, the importance of prepped data, an infrastructure for AI monitoring and re-training, and human-in-the-loop (HITL) for processing user feedback.
Thankfully, AI technologies are developing so quickly that it becomes easier for practitioners to build eye screening applications from scratch, using open-source tools and cloud services from AWS, Google, or Microsoft.
Practical applications of AI in ophthalmology
There are a number of ways in which AI can be used for disease screening in ophthalmology.
One example is fundus photography, which is a type of medical imaging that captures an image of the back of the eye. For instance, AI can help capture and interpret the retinal vasculature, to determine risk or presence of diabetes. Similarly, AI can preemptively reveal pathologies that cause blindness and vision loss by enabling at-scale screening for fundus and retina abnormalities at birth.
Another example is the use of Optical Coherence Tomography (OCT). This is a non-invasive imaging technique that uses light waves to take pictures of the retina. These pictures are processed and analyzed by AI to detect any signs of anomalies associated with disease.
AI can also be used to augment photoscreening applications. GoCheck Kids, a company assisting primary care networks, implements cost-effective pediatric vision screening, and utilizes AI to supplement image analysis and improve user actions, to help ophthalmologists capture the best image possible for further analysis.
The paradigm for AI adoption in ophthalmology
The power of AI lies in its ability to identify patterns and anomalies in data that may be difficult for humans to spot. Nowhere is this more apparent than in the field of ophthalmology, where AI is used for disease screening — detecting anomalous parts of eye screens that may indicate a specific eye condition.
For AI in ophthalmology to work effectively, however, certain conditions must be met.
Any disease screening system or application has to have an image labeling component. AI is a work in progress, a system that evolves over time on new data, and users should be able to label new screens and verify low accuracy screens that were previously taken.
End-to-end infrastructure for AI has to be in place so that models can be built, trained, deployed, monitored, re-trained, and fine-tuned. Any types of data or model drift, or bias, should be monitored and countered by cyclic model updates.
It is better for the solution to live in the cloud. It helps realize such benefits as automatic scalability, high flexibility, and reduced IT costs. It also ensures collaboration efficiency and business continuity. For instance, an eye screen taken with an app by an optometrist in Chicago can be labeled by a highly trained ophthalmologist in LA, with both of them contributing to the improvement of the application’s AI.
Having the right UI matters. Doctors taking eye screens should have access to a section that explains why AI made certain decisions, to better understand the signs of detected abnormalities. The labelers should be able to sort existing screens, and markup and feed new screens to the system. A customer-centric UI ensures that doctors do not have to spend time examining screens with no signs of pathology, so they can focus on patients who need assistance.
Conclusion
The potential of AI in healthcare is immense. From streamlining administrative tasks to providing insights for clinical decision-making, AI can help to improve patient outcomes, increase productivity and efficiency of care delivery, and make it easier for wider categories of the population to access healthcare services.
In ophthalmology, AI-powered disease screening is the future. By automating pattern identification, AI can help to increase accuracy while saving time. It can identify individuals who are at risk of developing, or who already have a certain disease, as well or better than human doctors.
It is estimated that by 2050, over 1.8 billion people will suffer from some form of vision impairment. This number could be reduced drastically if preventable vision loss was detected and treated early on. The way forward is to scale disease screening with AI, to enable doctors to focus on patient care while leaving routine work to the machine.
Health equity is a focus of providers, regulatory agencies, and payers as they seek ways to eliminate care disparities across race and ethnicity, gender, sexual orientation, and socioeconomic status lines. Its significance is further impacted by new quality-based care models beyond those established by the Patient Protection and Affordable Care Act of 2010.
The challenge for many healthcare organizations participating in these new reimbursement models is how to view health equity and social determinants of health (SDoH) to understand the actual value of this information. Often overlooked is that healthcare organizations’ coding and revenue cycle management (RCM) departments already aggregate information that can help better understand inequities in care delivery and health equity across their patient populations.
A Primer on SDOH Impacts
SDoH impact many health risks and outcomes, which is why this data is vital for clinical care and reimbursements. Defining factors can include anything from geography, race, gender, and age to disability, health plan, or any other shared characteristic. Of increased importance, SDoH issues are most often experienced by the most vulnerable members of society: the poor, less educated, and other disadvantaged groups.
SDoH is linked negatively with outcomes, including higher hospital readmissions, length of stay (LOS), and increased need for post-acute care. Value-based payment programs, therefore, may penalize organizations that disproportionately serve disadvantaged populations if they do not collect and respond to SDoH data.
For example, addressing food insecurity — a key SDoH data point — by connecting patients to programs like Meals on Wheels, Supplemental Nutrition Assistance Programs (SNAP), or food pantries is proven to reduce malnutrition rates and improve short and long-term health outcomes.
In the case of SNAP, which is the primary source of nutrition assistance for more than 42 million low-income Americans, participants are more likely to report excellent or very good health than low-income non-participants. Low-income adults participating in SNAP incur about 25% less medical care costs (~$1,400) per year than low-income non-participants.
With 82% of 2022 claims denials associated with Medicare, and third-party audit volume rapidly climbing, hospitals and health systems are under intense pressure to protect and grow revenues.
These were among the key findings of the 2022 MDaudit Annual Benchmark Report released today by MDaudit, the healthcare technology company that harnesses the power of analytics and its proven track record to allow the nation’s premier healthcare organizations to retain revenue and reduce risk.
Peter Butler
“Our analysis suggests that the post-pandemic era has given rise to a new phenomenon for healthcare. Medical spending is more discretionary for consumers impacted by inflation, driving dramatic reductions in revenues generated by physician office and hospital visits for the third quarter of 2022,” said Peter Butler, president and CEO, MDaudit. “Exacerbating this situation is the need to successfully defend against more third-party audits amidst chronic personnel and resource shortages.”
Driving Smarter Audits
Payers are investing in predictive modeling and artificial intelligence (AI) tools to scrutinize claims more closely before adjudication to reduce improper payments. The 2023 Department of Health and Human Services budget requests $2.5 billion in total investments for the Healthcare Fraud and Abuse Control and Medicaid Integrity Programs, $900 million of which is allocated for discretionary spending to advance technologies to scrutinize payment accuracy — up $26 million from 2022.
This should be a concern for healthcare organizations – and the push compliance leaders need to find more efficient ways to retain at-risk revenues. Per the MDaudit analysis:
Billing compliance leaders mustleverage data and analytics as catalysts to proactively detect risks and perform audits for corrective action. Data-driven, risk-based audits (up 28% in 2022) can complement the annual compliance plan to ensure effective audit scope coverage.
By deploying prospective (up 31% in 2022) and retrospective auditing methods, compliance teams can drive cross-functional initiatives that mitigate compliance and revenue risks.
Defending Revenues
A key element of a successful revenue defense is to help compliance teams become more efficient in managing external payer audit requests to retain at-risk revenues. The role of billing compliance needs to be increasingly data-driven and cross-functional, as well as serving as a business partner to other teams including coding, revenue integrity, finance, pharmacy, and clinical, to meet changing and more complex risks. The MDaudit analysis also found that:
Artificial intelligence (AI) is a driving force in the future of technology, often enhancing the speed, precision, accuracy and effectiveness of human efforts. As a result, AI has had a tremendous impact on nearly every working industry, including healthcare and its specialties. In recent years, there has been an increased adoption rate of AI in the healthcare industry.
Although this growth may be related to the need for telehealth and other remote tools as a result of the COVID-19 pandemic, recent estimates predict a more than tenfold growth in the market for AI in medical imaging over the next decade.
Although there is a projected growth for AI in healthcare, if this technology cannot be efficiently implemented into existing daily workflows, then these AI tools will not be practical in a real-life setting. Understanding how healthcare practitioners use AI in a clinical setting and how AI can help solve real-life challenges are crucial to increasing further adoption rates. This will ultimately drive innovation around this technology and will lead to improved quality of patient care.
What clinicians want from AI-powered technology
A recent survey by the American College of Radiology (ACR) indicated that 30% of respondents use AI to enhance image interpretations across all modalities. The modalities most commonly identified were computed tomography (CT) scans and mammography scans. When asked what they would specifically like AI technologies to do to enhance their clinical practices, respondents indicated that lesion detection (73%) and anatomic measurements (71%) were most important.
These responses indicate that clinicians are most interested in reducing the need for manual tracing and measurements of medical imaging, which takes a significant amount of time and effort. The survey’s findings also indicate that respondents would prefer additional support, or a “second opinion”, when detecting and identifying lesions – a task known to be difficult. The clinicians’ responses also indicated that the development of a method to evaluate an AI algorithm within the workplace setting before purchase is of utmost importance.
With these findings in mind, software developers need to prioritize building technology to help provide a practical solution to clinicians’ most important needs. The use of artificial intelligence may be the best way to ensure clinicians’ needs are met in an effective and practical manner. With the use of AI, the software can continuously learn from collective insights of multiple experts, effectively offering practitioners thousands of “second opinions.”
This type of technology can also help to provide interpretations and analyses of medical imaging, significantly reducing the amount of time it takes for clinicians to measure and trace these images. With less time and effort spent on interpretation and analysis, clinicians are able to spend more time with patient facing tasks – ultimately leading to higher quality care for a lower cost while reducing employee burnout.
Although these solutions exist, there are still several barriers that prevent the successful implementation of AI in clinical practices from occurring. For example, clinicians want to ensure that the AI is safe, effective and solves specific needs before the technology is purchased. However, of those providers who currently use AI in their practice, most were satisfied with their overall experience and found that AI provided value to them and their patients. Therefore, it seems that education about the potential benefits of AI in all practice types will continue to be important (Allen et al, 2021).
Healthcare operations can be extensive, and proper medical data management entails multi-layered security to protect patients and the business. Many aspects of healthcare operations involve manual and repetitive tasks. However, this is currently changing due to the advancement in digital technology.
Healthcare facilities and organizations use sophisticated software and hardware systems to automate as many tasks as possible to improve efficiency. But safeguarding healthcare networks and data requires advanced technical expertise. And that’s when managed IT services come into play.
What Are Managed IT Services?
Managed IT services refer to outsourcing information technology services to a third-party company to improve operations and reduce overhead costs and other business expenditures. In the healthcare industry, managed IT services streamline the operations of healthcare organizations. They promote workforce transparency and address key challenges, such as compliance, scalability, and mobility, for healthcare efficiency.
Learn how managed IT services drive healthcare efficiency by reading this article.
Healthcare operations can be extensive, and proper medical data management entails multi-layered security to protect patients and the business. Many aspects of healthcare operations involve manual and repetitive tasks. However, this is currently changing due to the advancement in digital technology.