Healthcare is one industry that always strives and thrives for advancements and has been embracing AI at an accelerated rate. However, the current healthcare system needs a major technological uplift to solve some of the most critical problems humanity is facing.
From chronic diseases and cancer to radiography and risk assessment to disease prediction, there are countless opportunities to leverage technology to deliver more precise, effective, and impactful interventions at the right time in a patient’s treatment journey. In fact, the global healthcare IT market size was valued at USD 135.6 billion in 2021 and is projected to exhibit a CAGR of 29.3% in the forecast period.
People especially post-pandemic, are more concerned than ever about their health and want to be as aware of their health vitals as they can. The recent improvements in payment installments and insurance systems have helped patients to worry less about the costs and get involved in more frequent health checkups and required treatments.
With such processes in place, healthcare institutions now have amassed huge data sets in the form of demographic data, claims data, clinical trial data, and health records and medical images, establishing the perfect platform and need for artificial intelligence to bring out all the necessary innovations across the healthcare industry.
AI offers several advantages over traditional analytics and clinical decision-making techniques. Learning algorithms can become more exact and accurate as they interact with more genuine collected datasets, providing access to previously unattainable insights into diagnosis, care procedures, treatment variability, and patient outcomes. Healthcare providers can swiftly harvest reliable, pertinent, evidence-based information by utilizing machine learning technology on the most recent biomedical data and electronic health records.
Today, artificial intelligence sits on top of any technological advancements in healthcare systems and has enabled healthcare professionals in various ways, such as improved accessibility, early diagnosis, increased speed in treatments, reduced costs, efficient and unique assistance in surgery, enhanced human abilities, and mental health support, and many more. Following are some of the major areas where it contributes to healthcare in a significant way:
AI improving medical imaging for accurate diagnosis
AI can not only help to automate abnormalities detection in radiological images obtained by MRI machines, CT scanners, and x-rays but researchers show that the upcoming radiology tools can accurately and precisely replace the need for tissue samples in some cases eliminating the potential risks for infection through biopsies.
We recently helped our client automatically assist in radiograph diagnostics where our AI model detected abnormalities in chest X-Rays. A triage system was built and installed that sorts the cases based on abnormality scores for multi-disease detection
Fulfilling staff shortage in healthcare
Shortages of trained healthcare providers, including ultrasound technicians and radiologists can significantly limit access to life-saving care in tier2 or tier 3 cities. Digital health through AI can help mitigate the impacts of this severe deficit of qualified clinical personnel by taking over some of the diagnostic duties that typically are allocated to humans.
For instance, automated imaging instruments are also utilized for eye disease screening with eye images, often achieving a level of accuracy comparable to humans. This capability can be deployed through an application accessible to providers in low-resource areas, decreasing the requirement for a trained diagnostic radiologist on site.
Predicting high-risk conditions of patients
AI can help to warn physicians about patients’ high-risk conditions, such as sepsis and heart failure, and can help identify the patients most at risk as well as those most likely to respond to treatment protocols. Each of these could provide decision support to the doctors seeking the best diagnosis and treatment for patients as for ECG, sensors attached to the skin are used to detect the electrical signals produced by the heart each time it beats and is fed as input to the AI model.
Since many cancers have a genetic basis, physicians have found it increasingly complex to understand all cancer genetic variants and their response to new drugs and protocols where automation of the process and eliminate manual errors. For example, the gene sequence is fed as input for the AI model for cancer detection.
Influencing patient behavior
Another growing focus in healthcare is on effectively designing the ‘choice architecture’ to nudge patient behaviour in a more anticipatory way based on real-world evidence. Through information provided by provider EHR systems, biosensors, watches, smartphones, conversational interfaces, and other instrumentation, the software can tailor recommendations by comparing patient data to different effective treatment pathways for similar cohorts. The recommendations can be provided to providers, patients, nurses, call-centre agents, or care delivery coordinators.
AI Improving medical device intelligence
In the medical environment, using smart devices is critical for monitoring patients in the ICU and elsewhere. Using artificial intelligence to enhance the ability to identify deterioration can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties. Also, many medical devices are now integrated with AI voice assistant functions reducing the physician’s efforts to manually operate the machine hence, optimizing the operation theatre experience.
AI for treatments
Robots driven by AI are capable of doing simple tasks like precision cutting and stitching with increased accuracy and shrinking while also providing three-dimensional magnification for articulation. Also, Machine Learning can automate the complicated statistical work for developing a personalized treatment plan for a patient and can help discover which characteristics indicate that a patient will have a particular response to a particular treatment. So the algorithm can predict a patient’s probable response to a particular treatment.
AI for healthcare administration
Many healthcare organizations are now equipped with chatbots for patient interaction, mental health and wellness, and telehealth. These NLP-based applications are useful for simple transactions like refilling prescriptions or making appointments.
Not only this but, this technology with relevance to claims and payment administration can be used for probabilistic matching of data across different databases. Insurers must verify whether the millions of claims are correct. Reliably identifying, analyzing, and correcting coding issues and incorrect claims saves all stakeholders – health insurers, governments, and providers alike – a great deal of time, money, and effort.
Identifying digital biomarkers for disease detection
Correctly diagnosing diseases takes years of medical training. Even then, diagnostics is often an arduous, time-consuming process. Deep Learning algorithms – have recently made huge advances in automatically diagnosing diseases, making diagnostics cheaper and more accessible. Similarly, digital biomarkers are a set of rules made as per the diseases that provide high certainty as to whether or not a person has a disease. They make the process of diagnosing a disease secure and cheap.
But discovering suitable digital biomarkers for a particular disease is a hard, expensive, and time-consuming process that involves screening tens of thousands of potential candidates. AI can automate a large portion of the manual work and speed up the process. The algorithms classify molecules into good and bad candidates – which helps clinicians focus on analyzing the best prospects.
Optimizing Revenue Cycle Management
The administrative and clinical tasks connected to claim processing, payment, and revenue production are managed by facilities using a financial process called healthcare revenue cycle management. This process consists of identifying, managing, and collecting patient service revenue.
Prior authorizations, claim status checks, and out-of-pocket cost estimations are three of the most urgent revenue cycle management problems that automation can efficiently resolve while also sending the information that requires human involvement to the appropriate person at the right time.
Akaike, over the years, has helped multiple providers and pharma companies overcome the above challenges by providing customized AI solutions to cater to their advanced healthcare needs. As per the research, market players are integrating massive data, artificial intelligence, and machine learning algorithms in their existing solutions to enhance health & medical processes, which is fueling the market growth.
To conclude, artificial intelligence improves productivity and treatment efficacy in the healthcare industry. Additionally, it can enable medical professionals to devote more time to providing the best patient care, lowering their risk of burnout.