Monday, May 23, 2016

Big Data in Healthcare – Reducing Health Care Costs

Health care providers face many obstacles while analyzing health care data and realizing the costs involved. Securing, sharing, and organizing sensitive data with the help of BI tools and data warehousing is key.

Health care data is diverse, disparate and distributed into hard-to-penetrate silos owned by a multitude of stakeholders. Each stakeholder has different interests and business incentives while still being closely intertwined.

Healthcare analytics connects the technologies to help deliver insights into the complex data and clinical mutuality that drive medical outcomes, costs, and oversight. These follow the disease models, which help the bridge the gap between researchers and clinicians resulting in better outcomes, saved lives and resources.

Fully integrated data is needed to harness ability to identify the area of waste and opportunities of healthcare cost reduction. Healthcare data warehousing is the best method to organize a health system’s financial, clinical, administrative and patient satisfaction information. Once the aggregation of all such data occurs in one place, it helps categorize and identify the sources of waste reducing the involved costs with improved outcomes. 

The Increased Dependence Of Big Data In Hospitals

The hospital big data contains billions of data points collected from different sources within the healthcare facility. These data files are too large, complex to capture, store and manage for any data tool. Big data applications are rapidly transforming the healthcare industry by quickly transforming loads of such disparate and unstructured data into instantly available and highly actionable information.This data improves population health management, care delivery, performance reporting, and resource utilization.

Big data software applications represent the single most effective solution to addressing the multitude of problems faced by healthcare facilities, including the reduction of avoidable hospital readmissions.

A ‘hospital readmission’ is admitting a patient to the hospital within 30 days of discharge from a prior hospitalization. With the help of big data, hospitals can track the patients who are at a high risk of readmissions. There are various reasons of unplanned readmission such as surgical wound infections.

Big data applications aid evidence-based medicine, which involve making use of all clinical data available to improve patient outcomes. This includes improved ability to detect and diagnose diseases in their early stages, before clinical signs are present. This patient level care helps save lives and reduce suffering along with waste of resources.

Healthcare organizations also need to be able to detect fraud based on analysis of irregularities in billing data or patient records. Big data applications can analyze patient records and billing to detect over utilization of resources and services in a hospital.

How Is Big Data Instrumental In Improving Healthcare?

Over the past one-decade or so, hospitals and healthcare centers have witnessed a huge amount of data generation. Big data is the intersection of these changes. In healthcare, it provides profits and reduction on waste overheads. It is benefiting the healthcare industry in multiple ways.

Barriers for using big data
The big data phenomenon has great value and is yet to face several challenges. Specifically, it has two major drawbacks in the healthcare field:
  • Requirement of technical expertise
  • Lack of robustness

Security of patient data
Security of every patient record or data is primary. Big data is not able to manage an integrated amount of data. For this reason, hospitals require data scientists to take some steps to confirm better security of data. Big data acts on an open source technology with non-compatible security technology. To avoid problems, organizations should select original big data vendors to implement.

Structure of healthcare and predictive analytics

Predictive analytics is one of the most discussed topics in healthcare today.Meaningful analysis of the data in healthcare can improve patient care and chronic disease management based on predictions. However, making predictions would be a waste of time if they do not get transformed into consequential actions.

Enterprise data warehouse is essential for the management of patient records and data. With EDW, systematic integration of data is made easy using the analytics approach.This step helps prioritize resources within the health system focusing on cost reduction and revenue enhancement while improving patient outcomes.