More Related Content
Similar to HEALTH SECTOR AND DATA SCIENCE.pptx
Similar to HEALTH SECTOR AND DATA SCIENCE.pptx (20)
Recently uploaded
Recently uploaded (20)
HEALTH SECTOR AND DATA SCIENCE.pptx
- 2. Table of contents Introduction to Health sector Challenges in Indian health sector Why is data science required in health sector ? How data science helps in resolving the challenges ? conclusion
- 3. The health sector comprises of hospitals, patients, doctors, companies that render medical services, produce medical devices or medications, offer medical insurance, or in some other way assist in the delivery of healthcare to patients. Healthcare markets are characterized economically by a few key elements, they are: Government involvement in healthcare markets and operations is widespread, in part because of the economic considerations and Price inelasticity for healthcare services is very high. Regarding demands, outcomes, and the costs of services, both consumers and producers must contend with inherent uncertainty. Principal-agent issues are pervasive, and patients, providers, and other industry actors all have access to highly asymmetric information. Health sector
- 4. Challenges in Indian health sector: . Almost 1.3 billion people in India are served by a complex and multifaceted healthcare system that includes both government-run and private facilities. The healthcare system still faces a variety of problems, such as: Inadequate medical infrastructure Lack of Standardisation of Quality Care Non-Communicable Diseases Lack of Adequate Mental Healthcare Gap in Doctor-Patient Ratio
- 5. The competitive need for valuable information in the health market is the most significant of the many variables that make data science essential to healthcare today. consumers may receive better-quality healthcare with the aid of the correct data collection procedures. To make educated decisions on the health conditions of their patients, everyone from doctors to health insurance companies to institutions depends on the collection of factual data and its precise analysis. With the aid of data science in healthcare, diseases may now be anticipated as early as possible, and that too remotely with cutting-edge appliances powered by machine learning. In order for doctors to develop therapies, mobile applications and smart gadgets continuously gather data regarding heartbeat rates, blood pressure, sugar levels, and other metrics. Why data science is required in Health sector?
- 6. How data science helps in resolving the challenges ? - In detail 1. Discovering drugs: The foundation for medication synthesis utilizing artificial intelligence is what data science contributes most to the pharmaceutical sector. Compounds that address the statistical link between the parameters are developed using patient metadata and mutation profiling. This will help in the process of curing non -communicable diseases which is one of the major challenges that Indian health sector faces. 2. Virtual assistance and tracking patients health: These days, chatbots and AI platforms are created by data scientists to assist people in better understanding their health by entering specific health data about oneself and receiving a precise diagnosis. 3. Wearable gadgets: This enable clinicians to gather the majority of this data, including heart rate, sleep patterns, blood glucose, stress levels, and even brain activity, have been developed by data scientists for public health. Doctors are able to identify and monitor widespread disorders like cardiac or respiratory diseases with the aid of data science tools and machine learning algorithms.
- 7. 4. Diagnostics: Data science applications in healthcare can facilitate and expedite diagnosis, a vital component of medical services. Medical heatmaps referring to demographic patterns of illnesses can be created, in addition to helping with early diagnosis of health issues by analysis of the patient's data. 5. Predictive Analytics in Healthcare: This helps to determine the stage of a disease, the degree of harm, and the best course of action. Predictive analytics is beneficial in healthcare because: Managing chronic illnesses. the need for pharmaceutical logistics should be tracked and analyzed. the likelihood of subsequent patient crises and speedier delivery of hospital data documentation. 6. Medical Image Analysis : Medical personnel frequently employ imaging techniques like X-rays, MRIs, and CT scans to see the inside organs and systems of your body. Aids clinicians in creating successful treatment plans. Many of the frequently used machine learning algorithms are as follows: An algorithm for image processing is used for picture analysis, augmentation, and denoising. For detecting bone displacement and fractures, an anomaly detection algorithm. The descriptive image recognition method extracts and interprets data from photos and combines many images to create a larger overall picture.
- 8. Conclusion To conclude this presentation we can say that data science has a vital role to play in the health sector and it helps in resolving the challenges that are faced in this sector they are: accessibility but with the help of policy measures it should be affordable as well, timely treatment with out any delays due to various reasons, this way we can handle the issue of doctor –patient ratio gap as well to certain extent, new medicine preparation, diagnostics etc . Data science helps in providing efficiency and efficacy in the healthcare services provided to the patients by the doctors and various other related services in this sector.
- 9. THANK YOU