Senjuty Sarkar

Lots of Data and Tools: Predictive analytics in healthcare is not entirely new. The novelty is the abundance of data and tools, and the growing interest in using them to generate insights. Most hospitals and physicians in US offices now use electronic health records, according to the American Hospital Association. Many healthcare organizations have implemented business intelligence systems and are now looking to get more value out of their data. It remains to be seen exactly which applications are most effective and relevant and which areas will yield the greatest ROI.

Strong interest: The transition from paid services to value-based health care can make advanced analysis a necessity for decision-makers, not just a "nice to have" thing. Today's healthcare leaders show a strong interest in predictive analytics to protect their patients more efficiently. According to the Hospital & Health Network article, more than 30 per cent of hospitals have used some sort of predictive analytics for a year or more. Most healthcare executives (80%) say they believe technology can improve patient care. To meet the pent-up demand for data scientists, many universities in the United States and Europe are now offering degrees and certificates in health analytics. A growing number of predictive analytics conferences give professionals and academics opportunities to share their healthcare use cases. Investors believe in the long-term potential of predictive analytics: between 2011 and Q3 2014, they poured $1.9 billion into companies that purport to deliver predictive analytics, according to Rock Health`s digital health funding database.

Actionable insights: Predictions, of course, are most useful when they can be turned into action. In the healthcare setting, the possibilities for predictive analytics are almost endless. US hospitals are currently experimenting with predictive analytics applications in surgery, finance, population health management, and clinical surgery. Predictive analytics offers a tremendous opportunity for hospitals to reduce waste and proactively deliver patient care before they need more expensive interventions. Predictive analytics, done right, can connect data points throughout the health cycle and convey information far beyond the human mind's ability and speed to process it on its own.

 Health Care Rooted in Predictive Analytics May Become an Inevitable Reality: Gartner predicts that some form of the predictive or prescriptive algorithm will be incorporated into 75% of all healthcare delivery processes by 2020. Now is the time for exploration and experimentation. To this end, Dimensional Insight is exploring opportunities to provide predictive capabilities to our customers. We partnered with Ventana Systems, a company with deep MIT roots and more than 30 years of experience providing predictive solutions.

 Artificial intelligence in healthcare is on the agenda: Implementing artificial intelligence in healthcare is nothing new. A report by Sage Growth Partners shows that 90% of taxpayers and healthcare providers with annual revenues of more than $800 million are proactively exploring artificial intelligence and automation. One of the main applications of AI in medicine is predictive analytics, with 60% of professionals already using predictive analytics.

What is predictive analytics?

Predictive analysis is the discipline in which computer programs analyze past events, occurrences, or patterns to predict the future logically. The process is effective, but not without difficulties. Insights then help practitioners to develop treatment strategies that are tailored both at the individual and patient cohort levels. While doctors can use their experience to evaluate patients, there is a limit to the amount of data a person can process.

Data is helping healthcare providers prevent rapid health deterioration as much as intervening at a time that counts. Healthcare organizations rely on multiple EHRs, independent imaging devices, labs, and a colossal volume of unstructured data (like medical notes, drug prescriptions, medical service alerts, and research reports), which presents a considerable challenge in itself. It`s no easy feat to extract data from various sources, then structure it in such a way that a machine learning algorithm can make sense of the inputs.

 Predictive analytics has three central uses in healthcare: personal care, cohort treatment, and intelligent operational management. The health sector, with its many stakeholders, is one of the main beneficiaries of predictive analytics, with state-of-the-art technology being recognized as an integral part of healthcare delivery.

All of these achievements have multiple health benefits, including easier workflows, faster access to information, lower healthcare costs, better public health, and an overall improvement in quality of life. To better understand the various possibilities of predictive analytics in healthcare, it is first important to recognize the various ways in which health care can benefit from this discipline.

Using predictive analytics will help ensure that healthcare facilities can provide outstanding services for a long time in an environment of a growing population, while also addressing the issue of rapid treatment for patients and providing more accurate diagnoses for patients. Predictive analysis of large population studies using volumes of health system data, including geographic, demographic, and medical condition information, can generate community profiles and other cohort health models and inform health organizations and government agencies about were to better target interventions such as “smoking cessation campaigns" or "obesity", thereby increasing effectiveness.

Projects using predictive analytics in healthcare must be aligned with the goal of patient-centred care to remain ethically sustainable. The establishment and introduction of ethics committees in government agencies, regulatory bodies and associations can be done in the modern era to address potential inequalities and biases when using predictive analytics in healthcare.

What challenges is facing the healthcare sector today and how can new technologies transform healthcare in India? challenges in the system The demand for personalized care according to the psychological structure of the patient is the hour requirement that is met by AI and ML. Health care professionals are now leveraging this technology to increase efficiency, streamline workflows, reduce costs associated with healthcare, and offer personalized treatment plans to improve outcomes.

The advent of digital technology in healthcare is helping to transform a healthcare system that was not sustainable yesterday into a system that is sustainable tomorrow. Today, as the healthcare industry continues to innovate and ensure a hassle-free patient experience.