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The American Healthcare Industry has experienced many large-scale changes in the past few decades. This timeframe has afforded us many drastic reforms in the industry such as the Affordable Care Act (ACA) or the widespread shift towards Value-Based Care. However, the most noteworthy and significant change is the gradual adoption of software solutions into the healthcare industry. The digitization of healthcare has brought numerous benefits to healthcare organizations that are able to streamline their day-to-day operations. More importantly, these solutions have made life easier for care providers and patients by simplifying the delivery of care. In order for these complex systems to operate, they need to display competency in Interoperability.

How Interoperability Ties It All Together

Interoperability in the context of healthcare refers to the use of many complex systems and information technology (IT) to exchange and interpret health-based data. As many software systems were designed for specific tasks, the transfer of data between different systems emerged as a significant challenge. Interoperability allowed for different computer systems that operate on different platforms to interact with each other. This gave health organizations the ability to employ multiple systems for their varying needs. At the foundational level, interoperability is present in roughly 75% of health systems in the US. The incorporation of more advanced levels allows organizations to expand the scale of their services.

How Technology is Combatting COVID-19

The COVID-19 Pandemic has proved to be a challenging obstacle for the healthcare industry. While the pandemic continues to test the industry’s existing abilities, the prevalence of computer systems currently in use have helped in the fight to control COVID-19. The use of virtual health services has skyrocketed since the outbreak as clinics across the country shift their focus to COVID-19. Patients are able to access health services like routine check-ups from their tablet or computer. The significance of this service is that it ensures patients with chronic conditions can receive medical services without the risk of being infected with COVID-19. It also helps clinics establish stable cash flow and make up for revenue shortfall due to the pandemic.

Examples of Interoperable Health Tech Solutions:

Telehealth

Telehealth involves the transfer of healthcare services through a telecommunications platform. While the primary use of telehealth is for virtual conferencing between patients and physicians, it is also used for monitoring and educating patients. The most popular form of telehealth is video conferencing where patients and physicians can perform most tasks required in a typical check-up. According to the American Hospital Organization (AHA), 3 out of every 4 hospitals offer some form of telehealth service. Telehealth has proven to be a valuable tool in the fight against COVID-19, while also eliminating long wait times and nonessential clinical visits. Telehealth must be interoperable with other platforms in order to share Electronic Health Records (EMR). Reviewing these records is crucial for physicians who are deciding the next course of action for a patient.

Remote Patient Monitoring

Remote Physiological Monitoring (RPM) uses real-time technology to collect vital parameters such as heart rate, blood pressure, weight, or any other relevant health-based measure. These devices are worn by patients to track the parameters of their health while simultaneously sending the results to a qualified health professional. This professional can analyze the information and intervene if there is any abnormal data. These gadgets have been extremely helpful for chronic care patients who can avoid the hassle of regular clinical visits. Clinics who effectively use these devices can significantly reduce the number of readmissions, which costs the industry over $41 billion a year. Interoperability is crucial in the RPM care delivery as data must be transferred from the patient’s device to the health system without any errors.

Workflow and Referral Management

The goal of Workflow Management is to streamline the patient workflow by eliminating inefficiencies in the process. Tech solutions such as Smart Rooming help nurses room the patient and transfer the responsibility of care in a time-efficient manner. Referral Management is also an extremely crucial part of clinical operations. Referral Leakage, which occurs when a patient’s Referral loop is not closed, costs the industry millions of dollars a year. Interoperable platforms would transfer information from the physician to the specialist in a timely manner and without any gaps.

Artificial Intelligence and Machine Learning

While still extremely developmental in nature Artificial Intelligence (AI) and Machine Learning (ML) provide a glimpse into the future of healthcare. AI and ML both use machines to perform human activities such as comprehension, interpretation, and analysis. Despite a limited role, they are both currently used for routine activities like streamlining workflows, patient education, diagnosis, and predictive analysis. AI/ML can help health tech innovators attain interoperability by assisting computer systems in receiving and analyzing data.

Primary Benefits

The influx of interoperable systems has revolutionized the healthcare industry. Listed below are the main benefits of these solutions.

Improved Patient Experience: One of the main focuses of these innovative software solutions was to improve the overall experience of patients. The introduction of Telehealth and RPM increases access to healthcare for all patients. Tools such as AI and ML are life-saving as they quickly and accurately diagnose conditions.

Simplifying the Care Journey: In the traditional Care Journey, patients may have to spend an entire day in a clinic while physicians shuttle back and forth to tend to them. Software Solutions have streamlined this process by assisting clinics with scheduling, rooming, and diagnosis. Nurses, Physicians, and Clinical staff can allocate their time more efficiently, resulting in a smoother Care Journey for patients.

Optimal Operational Efficiency: Health Organizations are able to maximize the use of their resources thanks to health tech solutions. Using tools like Referral Management and Care Orchestration allows organizations to streamline patient workflows. This helps them serve more patients without having to expand or increase costs.

Increased Profit: Perhaps the greatest benefit for organizations is the ability to increase clinical profits. Efficient software solutions help organizations identify and eliminate inefficient practices. At the same time, solutions like RPM provide additional revenue streams for clinics with little additional cost. While Interoperable solutions may incur an initial cost, effective development and use of the product will have a positive impact in the long run.

Talk to us to understand more about the advancements in the healthcare industry and we will guide you to achieve our common goal “Quality Care for All” seamlessly.

Current Status of AI in Healthcare

AI is still in the early stages of development in the health tech industry and it has yet to fully penetrate the market. However, AI investment is projected to grow from $600 million to $6.6 billion between 2014 and 2021, indicative of the large and growing demand for such services. AI is already used by many health systems for everyday activities such as streamlining workflows, patient education, diagnosis, and predictive analysis. Including these practices has helped clinics save millions of dollars and serve patients more efficiently. Thanks to the rapid growth of AI, the healthcare industry will experience an influx of innovative techniques to help solve modern healthcare problems.

Machine Learning in Healthcare

Machine Learning (ML) is a method within AI in which machines are given the opportunity to learn through experience rather than constant programming. In essence, this trains machines to think like humans and learn from practical examples. Areas of healthcare where ML is already prevalent include data collection, diagnosis, and clinical trials. This method is being experimented in the health industry due to the abundance of data needed to make informed decisions. ML can allow computers to process millions of data points in just seconds, resulting in a faster and more efficient result. In the future, effective use of ML could hold the key to vaccine development and cancer treatment. One hurdle ML faces is that it would need large-scale testing in order to become readily available for use in all areas of healthcare. This is due to ML being rooted in experience-based learning rather than rigid programming.

Precision Medicine

Precision Medicine involves diagnosis and treatment plans that are specialized to the individual patient. This method greatly differs from traditional diagnosis and treatment as it analyzes millions of relevant variables to produce a patient-specific care plan. AI/ML-based machines can analyze more variables than humans could in a fraction of the time. One intriguing aspect of this technique is Whole-Genome Sequencing, which involves the analysis and discovery of an individual’s entire DNA sequence. AI/ML makes this technique possible by simplifying an extremely complex process. Ultimately, a streamlined version of Precision Medicine can shift healthcare away from standardization and towards personalized care. Like many AI techniques, Precision Medicine is highly developmental and will likely require large financial investments. Additionally, this method is quite controversial as it is still unproven and involves information about patients’ DNA.

Robotics

Robots are a clear example of how AI could be put into practice in the near future. Many large or high-budget clinics already employ the use of robotic machines. These instruments can carry out different tasks depending on their design. During the COVID-19 Pandemic, robots are being used to direct patients within a health facility to eliminate the risk of patient to care provider transmission. They have proven to be very effective in guiding patients when a human is unavailable. In a non-Pandemic context, robots would be useful in rural or undermanned health clinics, where similar situations could arise. Robotic AI machines could also be used for long term care patients who need daily monitoring and reminders related to their treatment. One area where Robotic-based AI can drastically reduce discrepancies in rural health accessibility is through Remote Treatment. Robotic devices could allow doctors to operate on patients without being physically present. The incorporation of Virtual/Augmented Reality devices could help with both clinical training as well as virtual appointments. The main obstacle associated with robots is that providers must make a significant financial commitment. This will subsequently make healthcare costlier for all parties involved, including patients and the Federal Government.

Artificial Intelligence is opening the door for more efficient and accessible health care. The astronomical increase in AI investment proves the effectiveness of new developmental methods. If the industry is able to address the remaining financial obstacles, we can experience AI leading the healthcare industry into the future.