The worldwide transformation of the healthcare industry is being driven by ever-increasing costs and an ageing population. The global population is estimated to reach 8.1 billion by 2025, out of which 2.1 billion people will be at or above 50 years of age. (1,2) Organisations like WHO and UN confirm that by 2025, 70% of illnesses will consist of chronic conditions. All this contributes to the need of necessary change, since the global healthcare expenditure will expectedly reach USD 18.3 trillion by 2030. (3)
Robust data analytics are significantly important in several tasks from managing rising healthcare costs and clinical outcomes to providing a deep understanding of the current trends or issues and solutions to deal with them. The efficacy of analytics can help guide important and timely decisions, such as patient interventions to choose in order to have the greatest impact on outcomes and costs. It also helps determine the plausible success of different clinical initiatives. This is the power behind predictive analytics (PA). (4)
Furthermore, physicians can’t possibly commit to have all the knowledge for every situation, at their fingertips. Also, they do need time and expertise for analysis of massive amounts of data on various treatment outcomes to further combine it with the patient’s medical profile. However, this kind of in-depth research and statistical analysis is beyond the scope of a physician’s work. Therefore, more and more physicians as well as insurance companies are using PA. (5)
Predictive analytics is the process of learning from historical data in order to make predictions about the future (or any unknown), by applying technology and statistical analyses. (6) For health care, PA will enable the best decisions to be made, allowing for care to be personalised to each individual. It includes data ranging from past treatment outcomes to the latest medical research published in peer-reviewed journals and databases.(5)
In addition, the growing interest in and excitement around PA has been set off by big data and algorithm production. The industry is witnessing an explosion of health care data; be it new technologies to sequence our DNA, collecting continuous monitoring data, or patient-reported social media data. The healthcare data is expected to grow up to 25,000 petabytes by 2020. Fortunately, new technologies have also been emerging over the past few years, including many open-source ones, to process and manage all this healthcare data. (6)
Nonetheless, many challenges exist while implementing PA in healthcare, such as issues regarding data protection that involve multiple procedures and regulations. But, fret not! The industry is now coming up with key performance indicators (KPIs) to meet those challenges. According to a recent report from the International Institute for Analytics, many healthcare analytics personnel are using PA to improve patient engagement, public health, overall quality of care and life and other areas. (7)
Building a solid PA strategy in healthcare involves setting up competitive objectives of patient care. Here, data and the capacity to extract relevant information from that data can be a key strategic asset. Factors such as People, Process, and Technology are critical while building an efficient strategic team for PA. Strict alignment with clinical and business stakeholders is a prerequisite for putting up a lasting advanced capability of analytics. (2) Models and analytic projects can help manage the complexities of integrating health care and public health. (8)
Health information technology offers numerous opportunities to improve preparedness, response, and resilience. The introduction of Affordable Care Act (ACA) (9) has facilitated more health information exchanges (HIEs) to enable data sharing. Also, with plentiful health data and possibilities for sharing, predictive modeling and analytics are expected to grow in order to support decision making for authorities during public health emergencies, especially a pandemic or emergency requiring rapid medication distribution. For instance, the ACA suggests improvement in the uptake of EHRs and consequently, the ability to use the data in them. This is to improve data monitoring and application for improved models, analysis, and decision making; and to improve service delivery through public and private partnerships. (8)
There’s enormous potential for PA to facilitate care and dramatically reduce waste in the healthcare system, efficiently addressing issues in over-treatment, care delivery, and care coordination; thereby improving the overall preparedness. The only problem is for healthcare industry to acknowledge the value of PA and implement it in critical cases.
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- Siegel E. Predictive analytics: The power to predict who will click, buy, lie or die. March, 2013. Wiley.
- Bartley A. Predictive analytics in healthcare. White paper: Intel.
- Elton J. Healthcare disrupted: Next generation business models and strategies. February, 2016. Wiley, 1st edition.
- Take action: How predictive analytics can help you improve healthcare value. 3M Health Information Systems.
- Winters-Miner LA. Seven ways predictive analytics can improve healthcare- Medical predictive analytics have the potential to revolutionize healthcare around the world. Elsevier Connect. October 6th, 2014.
- Gandhi M, et al. The future of personalized healthcare: Predictive analytics. Rock Health.
- Driving clinical and operational performance through analytics. International Institute for Analytics.
- Forum on Medical and Public Health Preparedness for Catastrophic Events; Board on Health Sciences Policy; Board on Health Care Services; Institute of Medicine. The Impacts of the Affordable Care Act on Preparedness Resources and Programs: Workshop Summary. Washington (DC): National Academies Press (US); August 27th, 2014. 6, Potential Opportunities to Enhance Preparedness Through Health Information Exchanges and Predictive Analytics.
- Affordable Care Act (ACA). HealthCare.gov.