In predictive analytics, healthcare stands to improve patient care and outcomes. With the aid of historical data, this type of analytics provides a view to health systems to know how things might pan out in the future, with respect to operations and clinicians.

The skills comes mostly in handy for those health organisations which practise the value of care, since knowing the predicted outcome before it’s happening lends the stakeholders towards identifying where their current strategies have been unsuccessful and working towards the turn of those strategies.

This is quite meaningful in risk stratification and management of chronic diseases, where effective implementation could greatly benefit adverse outcomes and cost reduction.

What Is Predictive Analytics in Healthcare?

Predictive analytics is a discipline in the data analytics world that relies heavily on modelling, data mining, AI, and machine learning techniques. It is used to evaluate historical and real-time data to make predictions about the future.

Predictive analytics in healthcare is the analysis of current and historical healthcare data that permits healthcare professionals to find opportunities to make operational and clinical decisions that would be more effective and efficient, predict trends, and even manage the spread of diseases.

Healthcare data means any kind of data relating to the state of health of an individual or population collected through administrative and medical records, health surveys, disease and patient registries, claims-based datasets, and EHRs. Healthcare analytics is a tool anyone within the healthcare industry may avail themselves of and benefit from to provide better-quality care; that is, healthcare organisations, hospitals, doctors, physicians, psychologists, pharmacists, pharmaceutical companies, and even healthcare stakeholders.

Goal of predictive analytics in healthcare

With technological advancement, analytics can tremendously impact the healthcare industry. AI and machine learning techniques can use data to diagnose diseases, determine the best treatment for each patient’s use case, and much more. Here are the most important primary goals in which healthcare organisations can benefit from predictive analytics:

1. Improved Patient Care

One major beneficial goal of predictive analytics for the healthcare services would be the data’s availability, wherein all types of information, such as medical history, demographics, economics, and comorbidities, are given access. This amount of data gives the doctor or healthcare person insightful understanding to weigh their choices. Hence, better, smarter, and data-driven decisions constitute better patient care.

An example of predictive customer analytics usage where patient outcomes are improved would involve looking at the treatment data and outcomes of patients before’ activities. The machine learning algorithms, then, can be coded to provide insight into what treatment options are likely to be more effective for that individual patient.

2. Personalised Treatments

Medicine has conventionally been largely the same for different people. Treatments and drugs are chosen based on very limited information-at-best statistics of a broad population rather than on an individual patient. But patient diagnosis based on tests and indicators gradually introduced medical professionals to the precise and correctly suited line of treatment depending on the unique health situation of the patient.

3. Population Health Management

Conversely, predictive analytics can also be used for population health management. If a healthcare organisation has data about patient symptoms, medications, and personal history, analytics can find groups of patients with similar characteristics within that population cohort. It can start looking for cohorts with a potential disease outbreak. In such a case, health professionals could immediately start to look for treatment modalities that can increase the chances of survival for those in that cohort.

4. Identification of At-Risk Patients

Predictive analytics in healthcare can predict which patients are at a higher risk and could therefore be anticipated for early interventions to avert serious problems. For example, it can predict patients with cardiovascular disease who are most likely to be hospitalised based on age, any coexisting chronic illness, and adherence to medications. Likelihood predictions about disease and chronic illness can empower doctors and healthcare organisations to offer preventive care rather than wait for the at-risk patient to walk in for a routine checkup.

There are other at-risk groups aside from the chronically ill, including the elderly population and patients recently discharged from the hospital following invasive manipulations.

5. Chronic Disease Management

Chronic diseases are the leading causes of death and disability as well as the major burden behind $3.5 trillion in yearly health spending. Five chronic diseases account for 75% of healthcare spending: cancer, cardiovascular disease, diabetes, obesity, and kidney disease.

One of the best things about chronic disease management is that it is based on the understanding of how one can prevent and manage these diseases on the part of the professionals providing care. However, chronic disease management and prevention come with their own set of problems. This is where predictive analytics comes in: the healthcare providers can thus make informed decisions in the better interest of the patients, in augmented time, assisted with fact-based analytics to provide more effective treatments while minimising the cost of care from the patients’ perspectives. Advance your career with London’s premier hub for training and consulting where medical professionals gain the skills to lead, innovate, and make a real difference.

6. Forecast Equipment Maintenance Needs Before They Arise

In industries such as manufacturing and telecommunication, predictive analytics for maintenance foresight have been in use for a long time. The same type of prognostics could be of good use to the healthcare system. Certain machine parts do wear out or begin to deteriorate, and this is where expert predictive analytics will allow the observation of specific data through sensors in an MRI machine so they can predict when it may fail and when an element may need to be replaced. Therefore, working with such foresight, the hospitals could pre-schedule maintenance when the machine is not in use so that the minimum disruption to the workflow is incurred by care teams and patients.

7. Healthcare Tracking and Digitalisation

Digitalisation of health services is transforming the interface of interaction between patients and health professionals. Nowadays we can keep devices on our bodies that help us track our health at any given point, anywhere, from our mobile phones. For instance, diabetics can track the elevation of blood sugar anytime without actually pricking a finger.

8. Fraud Detection

Unfortunately, fraud in healthcare is a real problem. Health fraud takes many forms: individuals obtaining totally subsidised or covered prescription pills that actually are unneeded and then selling them on the black market; billing for a noncovered service as a covered service; maybe even modification of medical records; intentional misreporting of a diagnosis or procedure to maximise payment; etc. Predictive analytics identify certain aberrations that would flag these fraudulent actions and thus make it easy to deal with early.

9. Reduces Overall Healthcare Costs

Predictive analytics can also be utilised to lower health care costs. It can reduce patient costs by preventing unnecessary care concerning hospitalisation when there is no real need for it, controlling hospital expenses on drugs and supplies, and predicting hospital staffing requirements.

10. Prevent Human Errors

The consequences of human errors in health care could potentially be deadly. Fortunately, with the help of real-time and accurate insights that guide medical practitioners’ actions, data can help in flagging these possible errors and, together with their respective checks, can act to avert major catastrophes.

Use of Predictive Analytics in Healthcare

The healthcare industry generates a tremendous amount of data but struggles to convert that data into useful insights to improve patient outcomes. Data analytics in healthcare is intended to be applied to every aspect of patient care and operations management. It is used to investigate methods of improving patient care, predicting disease outbreaks, reducing the cost of treatment, and so much more. At a business level, with the help of analytics, healthcare organisations can simplify internal operations, polish the utilisation of their resources, and improve care teams’ coordination and efficiency. The ability of data analytics to transform raw healthcare data into actionable insights has a significant impact in the following healthcare areas:

1. Clinical research 
2. Development of new treatments 
3. Discovery of new drugs
4. Prediction and prevention of diseases
5. Clinical decision support 
6. Quicker, more accurate diagnosis of medical conditions
7. High success rates of surgeries and medications 
8. Automation of hospital administrative processes
9. More accurate calculation of health insurance rates