Know The Application of Data Science in the Healthcare Industry
Tags: datascience, datasciencecourse, datasciencecourseinbangalore, artificialintelligencecourse
Published: 2022-10-04 06:14:27



The individual's health is now separated from the frequent life-or-death decisions made at the insurance level of the healthcare system by many levels. While it makes sense for insurance companies, which are trying to limit costs, to serve as a gatekeeper for authorizing medical treatments, the process is usually managed by people who are neither licensed medical professionals nor experts in the field of medicine. The severe compliance requirements placed on medical personnel are one of the main problems with Health care. There should be checks and balances between the medical profession and the pooled risk insurance corporations.

For example, obtaining pre-authorizations for medical treatment is still frequently done via fax machines. It seems incredible that such a dated approach, which can decide whether or not someone receives medical treatment, is still in use in the 21st century when almost everything is now shared digitally. As each health insurance has variable degrees of coverage for a wide range of procedures and treatments, doctors, physician assistants, and other medical office personnel spend upwards of 20 hours each week contacting and arranging with the many health insurers.

How Can Data Science Help Improve the Health Care System?

The high cost and fragmented nature of American healthcare may be addressed by technology in general and machine learning or AI in particular. Massive amounts of data are generated as a result of the health care system. Data scientists can use them to enhance patient outcomes, help people more likely to develop chronic diseases change their behaviors, advance precision medicine, and simplify the digital sharing of patient records while still adhering to HIPAA regulations.

**Smart Rooms**
IBM and the University of Pittsburgh Medical Center started working together in 2005 to build a hospital "smart room" where connected devices would aid in streamlining the workflow of the front-line employees. The suggested features for the smart rooms range from voice-activated temperature settings alerting nurses when a patient leaves their bed and recognizing employees when they enter a patient's room. The series of tasks for a caregiver—based on their assigned role for patient care—will be analyzed and automatically prioritized concerning the specific patient's condition and treatment protocol, thanks to the use of machine learning and AI technologies.

Advanced algorithms can monitor caregiver workload management and notify patient care management when staffing levels need to be increased, when routine work is likely to be behind schedule, and when workloads should be automatically redistributed to available medical personnel. According to IBM's white paper, such implementations demonstrate a 60% improvement in nursing documentation. The majority, if not all, of data scientists' principal duties, include developing the prediction algorithms that serve as the brains of a fully functional smart room system. Although data scientists don't build front-end technological instruments, they create the algorithms needed to react to human contact, forecast and change human behavior, and provide recommendations.

**AI and Robotic Surgery**
One of the most complicated and dangerous specialities in medicine is surgery. Depending on the type of surgery, the patient may spend an hour or many hours on the operating table as the surgeon and their surgical teamwork to protect the patient's life before, during, and after the procedure. However, a surgeon's ability and physical capabilities can differ. Although it is extremely unsettling, if not terrifying, to consider that surgeons can make errors, they do. After all, they are people. Enter the world of artificial intelligence and robotics, which can keep an eye on a surgeon's movements, help with precision decision-making by giving the surgeon prompt feedback throughout the surgical process and for the patient following the surgery, and collaborate with the surgeon by performing particular surgical techniques.

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This is a simple algorithmic implementation at first glance. The complexity of human physiology, however, necessitates collecting and analyzing vast information regarding the patient, the surgeon, and the robotic component of this intricate equation. This is where data scientists can create an intelligent algorithmic analytics system that continuously self-updates depending on the continuous environmental data stream, bridging the gap between human and robotic engagement. Data scientists can do much more than only develop AI that can teach itself to play chess by working with medical and robotics professionals; they can also contribute to saving lives.

**Wearable Technology and Behavior Modification**
Fitbits, Apple Watches, heart rate monitors, and other medical gadgets or fitness trackers that provide consumers with rapid feedback are already in popular usage, so this initially appears to be a simple algorithm. Millions of individuals use smartphones and accompanying apps to monitor their activity levels, sleeping habits, water consumption, macronutrients, blood glucose levels, and calorie expenditure. AI algorithms can be used to inform users about the predictive chance that a behavior will not only raise the risk of acquiring a chronic health condition but also increase their health care expenses. This is relevant to behavior modification and its relationship to healthcare costs. Insurers and healthcare providers can use this information to change health insurance premiums automatically and co-pay amounts or, more precisely, regulate a treatment regimen for an existing ailment. 

The user is swiftly informed of their choice's financial and health repercussions, but they are still free to continue the activity or stop it immediately.


**Precision Medicine and Digital Health Records**
Training an algorithm to recognize and correctly categorize a set of photos is one of the first topics covered in many machine learning courses. The direct connection to using AI for medical imaging, where the algorithm offers real-time analytics of the CT scan, X-ray, MRI, or another image type, makes this pertinence relevant. By providing a predicted diagnosis, determining whether additional tests are necessary, outlining which tests should be included, and recommending a course of treatment, this procedure can be taken many stages further. 

The patient's primary care doctor, a medical specialist, or any other healthcare practitioner to whom they have already granted access to their private health information may receive all of this information. In the end, healthcare professionals and patients must maintain their autonomy in decision-making and collaborate with AI rather than being subjected to an algorithm that, despite being programmed by people, lacks the full range of human emotions such as empathy and compassion. 

Data scientists are more than just computational quantifiers which are kept in the dark about the outcomes of their labor. They serve as a human bridge between the complicated world of human psychology and physiology and the computational world of computers. 

**Conclusion**
The collaborative support of data scientists who have developed experience inside the health care business can help decisions at all levels more rapidly, precisely, and with fewer layers of bureaucracy. One career path is to start as a data analyst or junior data scientist for a health insurer or other healthcare organization if you're interested in becoming a data scientist in the field but haven't yet had any exposure to it. Make sure to take [data science courses in Bangalore](https://www.learnbay.co/data-science-course-training-in-bangalore) if you are just starting your formal education as a data scientist in a healthcare organization.