Big data in healthcare
Healthcare is one of the industries where big data’s abilities to improve patient care, provide the best healthcare at an affordable cost to everyone across geographical locations, and predictive diagnostics to save human lives is redefining the perspective.
Insights of patient touchpoints are analyzed from a large data pool using algorithms to reflect on patterns in treatment plans. The Healthcare industry needs decisions backed by evidence-based practice to solve complex cases. Scientific data and physicians' opinions have to converge for an effective prognosis.
Artificial intelligence (AI) is merging these two elements to predict health potential risks and devising treatment plan recommendations for doctors to refer to. Large amounts of data can be crunched efficiently by saving time and delivering accurate results. This ability is creating waves now and in the future will turn indispensable as a critical component in diagnosis, treatment, and patient care outcomes. From delivering critical patient care, robotic surgeries, and workflow management to healthcare policy decision framework ai in healthcare is significantly impacting the industry.
The future of healthcare
AI is in its nascent stage of use in industry and human lives but has traversed from a scientific prototype and beta versions to applied technology. It has spread from scientific labs where it was conceived as an idea and conceptualized to larger sections of industry, society, and even our home. The reliance on AI and other digital solutions like big data, blockchain, cloud computing, and machine learning is propelling the fourth industrial revolution.
Let us examine the impact of AI on healthcare industry deliverables :
Impacting clinical decisions
Healthcare is one industry where confirmation bias is a good one. However, healthcare providers do not always have access to a second expert's opinion when they need it. But with AI-driven solutions there is hope. It aids in the clinical decision-making process for healthcare providers while minimizing risks in patient care through effective predictive precision diagnostics, cross-referencing treatment plans, and disease prevention management.
Deep learning in genomics
Genomics research uses large sets of data to understand DNA sequences. The complexity of human genome studies coupled with vast data is a double whammy for scientific researchers. They rely on deep learning tools of AI to compute and interpret valuable information used in healthcare innovations.
The use of AI and deep learning tools in healthcare and pharma research is burgeoning and the future holds promising possibilities to improve patient care models around the globe. A few examples of the use of AI in genomic research include:
Facial recognition and analysis to predict genetic disorders even in carriers
Identify cancer in early stages through liquid biopsy
Predictive mapping of the spread of cancer in an affected patient
Identify rough genes that cause diseases from benign variants
If human populations can check birth defects during the gestation period then the option of birthing a child with congenital birth issues can be reduced.
Medical imaging
Healthcare diagnosis relies on medical imaging. An experienced radiologist examines the ultrasound scanning images to detect abscesses, cysts, and other disease-related complications. But the inference of a radiologist is subjective and constrained to his perspective based on experience and education. The possibilities of misdiagnosis are high. Interestingly, AI emulates the human mind and is not subject to the weariness that affects humans when making decisions. It breaks down complex data and understands the patterns of a medical image to provide a quantified evaluation that is accurate and automated. AI to develop early-stage diagnoses in the fields of ophthalmology, radiology, and biopsy studies is already in use in major healthcare facilities.
Clinical workflow management
AI can help administrative workflow and ease operations. With natural language processing reading clinical footnotes in electronic health records (EHR) becomes easy and one-time data input activity. Future references can be collated from existing data in EHR with other medical records like scanned images, and remote patient monitoring devices. The correlation of the data and medical transcription becomes an easy process for concerned healthcare assistants and administrative staff who manage patient data. It helps in creating personalized treatment plans for effective patient care.
Conclusion:
There is a particular sense of hostility towards the use of AI in workplaces induced by fear of job places. AI makes repetitive tasks easy through automation and its scope and advancement in healthcare innovation are indispensable for sustainable development. And it will be treated as a tool that makes human jobs easy with the ability to focus on trivia that matter.
