Predictive analytics is
a part of advanced analytics with applications to predict future events that are not known.
In healthcare, predictive analytics is used to detect early signs of deteriorating health
conditions of a patient in an intensive care unit (ICU) or a general ward.
It is also used to identify at-risk patients in their homes to prevent hospital readmissions
and ward off avoidable downtime of medical equipment.
Healthcare service providers can use predictive analytics tools to enhance healthcare by improving
patient outcomes, assisting in decision-making, and providing relief for the healthcare staff.
Moreover, with modelling and forecasting techniques, predictive analytics helps determine the high
probability of events occurring in the future.
Doctors, scientists, pharmaceutical players, medical speciality societies, and healthcare
stakeholders can use these predictions to offer the best possible care for individual
patients.
The technology aims to inform clinicians and caregivers of the probability of events and
outcomes in advance.
This will help in the prevention and cure of diseases.
Predictive analytics is helping the healthcare industry in shifting from treating a patient
as an average to treating a patient as an individual.
The process can improve overall patient care in terms of cost, efficiency, quality, and
patient satisfaction.
Predictive analytics also helps in a more definitive patient diagnosis, followed by
the suitable treatment of the identified indication(s).
Role of predictive analytics in healthcare Developing precision medicine and innovative
therapies
As the precision medicine and genomics in the healthcare sector is coming into force,
healthcare providers and scientists are turning to analytics to supplement traditional clinical
studies and drug discovery techniques.
Moreover, in silico testing (experimentation performed by the computer) is a promising
way to reduce the requirement of patient recruitment for complex and expensive clinical trials
while accelerating the evaluation of innovative therapies.
Bolstering patient engagement and satisfaction
In addition to supporting the management of chronic disease strategies, cutting wait times,
and targeting therapies, predictive analytics can also keep patients engaged in other care
features.
Furthermore, customer relationship management has become a crucial skill for both providers
as well as insurance companies that are looking to encourage wellness and lessen long-term
spending.
Strong data protection
Predictive analytics and artificial intelligence (AI) are also expected to play a vital role
in cybersecurity, especially as the sophistication of attacks continues to rise.
With the use of analytics tools to monitor patterns in data access, sharing, and utilisation,
organisations can get an early warning if something unusual happens, especially when
those variations show that an intruder may have penetrated the network.
Supply chain management
The supply chain is one of a provider’s largest cost centres and signifies one of
the most significant opportunities for healthcare companies to reduce unnecessary spending and
improve efficiency.
The predictive tools are in great demand among hospital administrators considering lower
variation and obtaining more actionable insights into the supply utilisation.
Prediction of patient utilisation patterns
Predictive analytics can also give providers the information when the clinic is about to
get over busy.
The care sites operating without fixed schedules, like emergency departments and urgent care
centres, must alter their staffing levels to account for fluctuations in patient flow.
Moreover, inpatient wards must have the availability of beds for patients who need to be admitted.
At the same time, outpatient clinics and physician offices are responsible for keeping wait times
low for patients.
The use of analytics to foresee the utilisation patterns can help ensure optimal staffing
levels while minimising waiting periods and increasing patient satisfaction.
Identification of at-risk patient cohorts
Predictive analytics can help identify the specific risk factors for several populations.
For instance, it can identify diabetic patients with the greatest likelihood of hospitalisation
based on age, coexisting chronic indications, medication adherence, and past patterns of
care.
Based on this analysis, predictions on the disease probability and chronic illness can
produce early interventions aiming to decrease emergency room visits and reduce hospital
readmission rates.