Why Preparation Is Crucial to Success in Data Analytics

Value-based care and the associated requirements have put providers under enormous pressure to create efficiencies from EHR investments. In the article, "Why Preparation is Crucial to Success in Data Analytics," published by Health Data Management, Robert Barras, VP of Health Solutions, and John Walton, Client Solution Executive, discuss the need for a viable enterprise information management strategy.  

Read the full article below.

Why preparation is crucial to success in data analytics

Preparation is critically important to any initiative, but let’s be clear about something—it is not easy. We have all participated in projects or initiatives that lacked proper planning or preparation, and we’ve experienced the fallout from those poorly planned initiatives.

Value-based care, and the associated requirements, has wrung all of the wiggle room out of strategic initiatives. Provider organizations are under enormous pressure to create efficiencies from EHR investments in preparation for value-based care. These organizations are about as well prepared to take on the risk of patient care as they are to start a casino.

Organizations may have a deluge of data, but without having data structured or related to care-based decisions, organizations are, in essence, flying blind.

In the meantime, healthcare IT has become inundated by analytics solutions and vendors. Many of these platforms and tools have matured in their ability to provide insight to guide providers in their journey to accepting additional risk. And the timing couldn’t be better, because Medicare Advantage (MA) already provides an opportunity for prepared organizations to effectively and efficiently manage their MA populations.So, why hasn’t there been progress, especially around MA? Perhaps healthcare organizations haven’t successfully prepared to effectively move the ball down the value-based care field. To be clear, there are many moving parts in the transition from fee-for-service to value-based reimbursement. However, the tools are available and maturing rapidly.

Perhaps the issue then lies in the need for a strong foundation from which these tools can be leveraged—this is where enterprise information management (EIM) comes in.

EIM is , by definition, the preparation for analytics and intelligence. It establishes the technology frameworks, business processes, content management and governance structures that enable organizations to rapidly consume data from multiple, highly complex data sources and transform these “raw materials” into strategic information assets. Many healthcare organizations now are turning to third-party analytic solutions to support their mission-critical analytic needs, such as population health management. Unfortunately, providing high-quality data to these analytic solutions in a timely manner can seem like an overwhelming challenge.

A planned three-month analytic solution implementation can quickly become a nine-month, or longer, ordeal if data quality and conformance has not been proactively identified and addressed.Data provisioning, at its core, is the process of making data available in a structured and secure way to enterprise users, developers and systems. It addresses data quality and conformance, resulting in trusted and timely data delivery.

An enterprise-level approach to data provisioning must begin with agreement that the organization needs to drive toward data being available to support processes so that they can be reliably executed, and an understanding of the true cost of data ownership.

Organizations that begin with these agreements in place and then utilize a platform-agnostic data provisioning framework to integrate high-value data assets experience a much cleaner, accurate and timely implementation that presents the opportunity for substantial return on investment.

The successful implementation of analytic solutions requires first preparing a data provisioning framework, as well as having a mature data governance program to oversee the entire analytic lifecycle —from data sourcing to data cleansing and conformance. Business processes and accountability must be established for data quality assurance, in addition to the creation and approval of data definitions and business rules. Incorporating these responsibilities into position descriptions further ensures that an organization’s data governance is fully operationalized.

A viable EIM strategy prepares the organization for analytics by establishing the people, processes and technologies necessary for data consumption, conformance, provisioning, and governance. Spending the time upfront to assess your current EIM “ecosystem” and determine your readiness to implement an analytic solution can pay significant downstream dividends."