Predictive Analysis in the health care industry

Running Head: PREDICTIVE ANALYAIS IN THE HEALTH CARE INDUSTRY
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Predictive Analysis in the health care industry
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PREDICTIVE ANALYAIS IN THE HEALTH CARE INDUSTRY
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Predictive Analysis in the health care industry
The predictive analysis encompasses statistical techniques from machine learning,
predictive modeling, and data mining. It analyzes historical and current facts to predict future
unknown events. Healthcare organizations are developing sophisticated capabilities of big data
analytics [1]. They are moving from basic descriptive analytics to predictive insights realm.
Predictive analytics does not only present past information but it also estimates the future
outcome. Predictive analysis alerts clinicians, administrative staff and financial experts about the
future potential event. It allows them to make informed choices before uncertainties. It becomes
more useful when the knowledge is transferred into action. Predictions for making predictions
only are a waste of money and time. Willingness to intervene harnesses the power real-time and
historical data. To add value and efficacy in healthcare, there should be the integration of the
intervention and predictor.
How to start using predictive analytics in the health care industry
It is important first to establish fundamental analytic and data infrastructure. Afterward,
move the organization up the levels of the healthcare analytics adoption model [2]. It starts with
an enterprise data warehouse in combination with discovery and foundational analytic
applications.
Enterprise Data Warehouse
Healthcare organizations require enterprise data warehouse platform to manage patient
populations. It is the central platform for building a scalable analytics approach to make sense of
data and integrate it systematically. Catalysts in healthcare deploy a late-binding data warehouse
to automate aggregation, integration, and extraction of clinical, patient experience, financial and

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administrative data [3]. They apply advanced analytics to measure and organize patient
satisfaction, safety, and outcomes, cost, and clinical processes.
Predictive modeling three basic steps
Source:https://downloads.healthcatalyst.com/wp-content/uploads/2013/10/predictive-
modeling.png
To be effective in predictive analysis, lean practitioners should understand the actual
workflow, type of data, the targeted audience and actions to be prompted after the prediction.
First, health care analysts should define the problem, gather initial data and evaluate the available
algorithm approaches [4]. Second, they select the best models and use a separate data set to
validate the process. The third step is to apply the model in a real-world setting.
Predictive analytics includes evidence, actions, and recommendations for the predicted
outcome. They should link to measurable events such as patient outcomes, clinical protocols, and
clinical priorities. There are many options to stratify patient risk and develop predictive

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algorithms. Healthcare providers should partner with people with commercial tools and leading
academic knowledge to develop more appropriate prediction models.
When adopting predictive analytics healthcare organizations should not confuse more
insight with more data. They should not overestimate their ability in data interpretation. They
should not confuse value with insight [5]. They should not underestimate the implementation
challenges. Subsequent intervention and clinical should be clinician and content-driven. The
overall goal of predictive analysis is to improve patient outcomes by use of their historical data.
Predictive analysis use cases in healthcare
Predictive analysis in healthcare improves chronic disease management, supply chain
efficiencies, patient care, and hospital administration. Across the care continuum, predictive
analysis supports health management, better outcomes, and financial success. Healthcare
organizations use predictive analysis in the following ways:
Chronic diseases risk scoring
Healthcare organizations use predictive analysis to identify people at risk of having
chronic conditions early to help them avoid those health problems. They use lab tests, claim,
biometric data, health social determinants, and patient-generated health data to create risk scores.
The risk scores give healthcare providers insights to enhance wellness activities and services.
Management, stratification, and identification of patients at high risks improve cost and quality
outcomes.
Avoidance of hospital readmissions in 30 days