Predictive analytics is a form of advanced analytics that utilizes historical data to forecast future events. It leverages a set of techniques from data mining, statistical algorithms & machine learning for analyzing current data in order to predict future events.
Predictive Analytics in Healthcare
Predictive analytics in Healthcare has evolved by bettering the aforementioned features in decision-making, the earlier detection of illness, and the personalization of treatment plans for greater efficiency.
The variety of sources continuously generates health data from EHRs, wearable devices that can monitor in real time every vital statistic on a patient, to a host of other sources. Predictive analytics in Healthcare utilizes the boundless information for the identification of trends and to make forecasts. Drawing from patterns of past experiences of patients, for instance, health providers can anticipate possibilities that their patients may develop particular medical conditions, thus affording early interventions, which greatly improve patient outcomes.
Some of the major uses of predictive analytics in healthcare are for predicting outbreaks of diseases. Studying data from various regions helps healthcare providers to identify those areas that are at a high risk for an outbreak and take necessary steps. It is especially useful regarding infectious diseases, where time can make the difference between life and death. Predictive analytics is also being used in the identification of high-risk patients. These are the patients who are more liable to get readmissions, chronic diseases, or complications in treatment. It helps in the early identification of such patients and assists healthcare providers in resource allocation to these cases for better patient care.
Predictive analytics is not just about the prediction of diseases but also about operational efficiency within the organization. For instance, hospitals can develop predictive models to anticipate rates of admission and, therefore, be better prepared with the right staff and resources to handle such admissions. This can help reduce wait times and further improve the patient experience.
What is a Downside of Predictive Analytics?
However, predictive analytics also raises a number of challenges. Included in these is the quality and accuracy of the data. Data in healthcare could be incomplete or inconsistent, or plain wrong. Such may lead to flawed predictions. For instance, if some biased or incorrect data are inputted into a predictive model, it could yield misleading results that may sometimes give rise to poor decisions.
Other concerns involve the ethical effect of predictive analytics in healthcare. The predictive models may inadvertently reinforce biases that are already present, such as socio-economic or racial disparities that may be present in healthcare. In such instances, these discrepancies could lead to unequal treatment of patients, with some groups perhaps being cared for better than their other counterparts. Another very important issue related to health care analytics is privacy and security. Health data is highly sensitive, and breaches or misuse have significant consequences for patients. Data protection, transparency of predictive models, and accountability will retain trust in the technologies.
Predictive analytics also faces resistance from healthcare professionals. Usually, a new technology that means training and workflow changes tends to be more reluctantly adopted. There is also fear that one day the predictive models will supplant human decision-making and take over people’s jobs in healthcare. Predictive analytics application thus needs a balancing with the expertise of healthcare professionals for its successful implementation.
Which Type of Question Does Predictive Analytics Address?
Predictive analytics, for the most part, answers questions that start with ‘what will happen?’ and are informed by historical data. It is used to answer such questions as: What is the likelihood of a patient being readmitted to a hospital within 30 days? Which patients are at high risk of developing a chronic condition like diabetes or heart disease? What is the likelihood of a disease outbreak in a particular region within the next six months?
How many patients will come to the emergency room during the next flu season?
How many of them will require a longer hospital stay?
These are some examples of “what will happen” questions. Predictive analytics needs to find patterns and trends in data on which such predictions can be made. The outcome of predictive analytics informs decisions that enable care providers to take effective prevention measures well before a situation gets worse.
As predictive analytics focuses on “what will happen,” it usually complements other forms of analytics. Descriptive analytics is one good example that answers “what has happened” through the analysis of past data, while prescriptive analytics offers suggestions based on the outcome of your predictions, answering “what should be done.”
Is Predictive Analytics AI?
Predictive analytics shares close relations with AI, especially in the use of machine learning algorithms in generating predictions. However, predictive analytics and AI do not have similar meanings. AI encompasses several technologies in its outlook, such as natural language processing, computer vision, robotics, and others, whereas predictive analytics emanates focus on the forecast of events through data analysis.
Machine learning, which falls within AI, is an important ingredient in predictive analytics. In machine learning, predictive models train entities of large volumes to recognize patterns and predict the future. With increased exposure to data, these models get improved continuously and their forecasts more accurate. Predictive analytics can also employ traditional methods of statistics without belonging to AI, such as regression analysis.
One of the most important distinctions between predictive analytics and AI involves the breadth and range of techniques or applications concerned. AI might conceivably refer to many more general classes of techniques and applications, whereas predictive analytics is narrowly focused on prediction. In practice, however, both are often partly related, especially in the use of machine learning to extend the reach of analytics models.
Conclusion
Predictive Analytics in Healthcare has the potential to bring about a sea change, enabling proactive, informed decision-making and improvement in patient outcomes. It does, however, have its negative side: data quality, ethical issues, and resistance to change are challenges that need to be recognized and overcome to allow for the successful implementation of predictive analytics into healthcare systems. By truly understanding the types of questions predictive analytics addresses and its relationship with AI, healthcare professionals and students alike can gain an appreciation both for the impact and the limitations of this powerful tool.