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Predictive analytics in healthcare is changing the way the data have been perceived so far in the medical industry. Predictive analytics in healthcare has gained a heavy momentum in the recent years, with large volume of data inflow in the medical space. Although the predictive analytics adoption rate in the other sectors such as retail, banking, etc. is yet higher to that of healthcare, there is a lot more to explore and gain over the coming period.


Beyond the various reasons, driving the demand for predictive analytics in healthcare, such as increasing readmission rate, patient satisfaction, optimized care, etc., the fiscal concerns due to rising healthcare expenses influenced the deployment of predictive analytics in healthcare the most. The increasing healthcare expenditure is a major concern at a global level affecting the overall GDP, and both the government and private entities are taking measures to curtail these rising costs. In this scenario, the healthcare sector is significantly embracing predictive analytics to improve the functioning of the entire industry by minimizing costs, reducing readmissions, improving care facility, etc.


Digital health industry, which attained a revenue of USD 51.3 billion in 2015, and is slated to register a double-digit growth over the coming years, is also providing a major push for the adoption of predictive analytics in healthcare. The payers and providers have digitized the records of the patients and this data is being leveraged by the predictive modelling/mining to derive actionable insights to enable better patient outcomes.


Predictive analytics in healthcare is also becoming critical, owing to the increasing readmission rate and regulations and measures to prevent the same. For instance, the federal Healthcare reform addresses the well-being of a patient after being discharged as a key element of its process, and ensures the patient care in a way that he/she is not readmitted to the hospital within 30 days of the discharge. In addition to this, the U.S. centers for Medicare and Medicaid services are needed to reduce the payment to the hospitals with higher frequency of readmissions.


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Electronic health record or EHR deployment has witnessed a rapid surge over the past few years, with the heavy adoption by the payers and providers. According to Global Market Insights, Inc., EHR market is anticipated to surpass a revenue of USD 116 billion by 2024. As per the US HITECH Act, USD 20 billion was raised to support the installation of EHRs. The huge data base from these electronic records are analyzed via predictive modelling to achieve an accurate and optimized operational, financial, and clinical data management.


The evolving customer-centric approach is further proving to be a key factor influencing the adoption of predictive analytics in healthcare. Predictive analytics technology helps the organizations to obtain accurate answers to the critical questions pertaining to the patients. Example- the number of days a patient may need to be kept under treatment, which will also provide answers to the costing and medicines required by the patient. It also effectively manages the supply chain, which is one of the major expense incurring concerns addressed by the providers. Moreover, the personalized or precision medicine market will also witness a radical change, with the adoption of predictive analytics in healthcare.


US is estimated to be the leading region witnessing the deployment of predictive analytics in healthcare, with huge adoption of EHR and GPS enabled systems in the medical space. As per the estimates, more than 75% of the healthcare providers in North America use EHR system. Moreover, the presence of leading service providers such as IBM, SAS, Oracle across this region will also support healthcare predictive analytics in US.


Eric Siegel, the Executive Director of Predictive Analytics World, summarized the applications of predictive analytics in the healthcare industry in three broad areas: Clinical, which further covers outcome prediction, diagnosis, and treatment decision-making; marketing; and insurance coverage. Eric also cited how predictive analytics is revolutionizing the businesses worldwide. In his upcoming events- Predictive Analytics World for Business San Francisco and Predictive Analytics World for Business Chicago, scheduled for May 14- May 18, 2017 and June 19 – June 22, 2017 respectively, Siegel will throw a light on how predictive analytics is influencing the business approach of the leading players such as Google, LinkedIn, Cisco, Verizon, Lenovo, SAP, etc.


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**Please use the following code while registering for a PAW event to get a discount of 15% off the price of registration for 2 Day and Combo passes:  GMSBP17


Payers, the term used to refer the insurance companies, third party sponsors, etc. are likely to be the dominant end-users of the healthcare predictive analytics market.  With a large pool of data available in the industry, the payers can use the predictions to construct the most suitable medical policy for the patients, based on their old health record analysis. Moreover, payers would also play a major role by altering their reimbursement policies in case of readmissions of the patients. The payers are also associating with the pharmaceutical companies in terms of reimbursement, as per the drug’s ability to improve the patient’s health.


However, privacy concerns pertaining to the private information of the patients may serve as a roadblock for the growth of predictive analytics in healthcare industry over the coming years. Despite this, the growing demand for personalized medicines, clinical decision support, enhanced patient care, and most importantly the need to curtail global healthcare expenditure will boost the adoption of predictive analytics in healthcare substantially over the coming years. Predictive Analytics still has a wide set of untapped potential in the healthcare space to explore, evolve, and transform.


Explore all reports by Global Market Insights, Inc. on healthcare and medical devices markets at .


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