by John Lewis
O ver the past few years, there has been an explosion of data from electronic medical records, doctor notes, patient surveys, medical images, pharmacy records, research data, and hospital enterprise resource planning (ERP) systems. Managing the data from all of these sources will require major changes in the role of healthcare providers’ IT organizations. And putting the data into a useable format will allow providers to transform how they currently deliver healthcare to their patients, and reduce costs.
Managing a large amount of information is one thing; managing information intelligently to reach a goal is another. Intelligent use of data is the future in healthcare, because healthcare reform in the United States is causing a shift from fee-for-service-based models to value-based models where payment or compensation is based on the quality of care. This model change is transforming provider organizations at a fundamental level.
In the past, a hospital provided a patient with services and charged her account. If the patient returned and received the same service, her account was charged again. The hospital IT department needed cost and billing systems to record the cost of each service and the patient to charge.
In today’s post healthcare reform world, the hospital still needs a cost and billing system, but it now needs to track if a patient is readmitted within the next 30 days for the same service, as well as the reasons for readmission, in order to prevent a repeat visit for the same issue. The hospital has to have the tools to track readmissions within a timeframe, and to compare the reasons for service. It then needs to understand the cause for the incident reoccurrence in order to make process improvements. The inability to manage this type of problem can cause a hospital to be penalized in its funding received from federal and other payers, leading to a decrease in revenue, but without affecting the cost of service.
Intelligent use of data is the future in healthcare, because healthcare reform in the United States is causing a shift from fee-for-service-based models to value-based models.”
This scenario shows two important points to the provider industry:
The key to transforming any organization through the use of analysis for big data management relies on working in three areas: people, process, and technology.People
A cultural change needs to happen in an organization, starting with senior management. It begins with a top-down sponsorship of the use of analytics for big data management, and empowering the organization to make improvements based on accurate analytics data. The change can be aided with the development of a center of excellence around analytics in the organization focuses on defining key data elements, shifting to standard reporting, cleaning up underlying data definitions, delivering common horizontal metrics across service areas, and adopting a framework for providing vertical specific key performance indicators and insight by service area.Process
Providers need to employ data management best practices. Key items in setting best practices for data management are to define all quality requirements, centralize data sources, and establish a data governance group. When providers define quality requirements, they must start with developing training programs with quality assessment and continuing education. Next, they can develop a data quality plan or a section in a data management plan and encourage and collect feedback from everyone involved with the plan. Then, they can define data quality checks (type and frequency) as well as resolution procedures, and finally, define a data transfer schedule and procedure.
As providers centralize data sources, they will decrease the resources required to maintain data. This will also help to adopt industry data standards and increase the accessibility of data to all in the organization by preventing it from becoming siloed and technology-dependent. Lastly, providers should establish a centralized data governance group. This group manages data quality for data coming from all work streams, and defines key data quality responsibilities and the metrics to measure improvement in data quality and timeliness.
Solutions are available and constantly improving to provide the necessary infrastructure for a transformation to managing big data and analytics. Some keys to making the transition easier and faster are using state-of-the-art extraction, transformation, and loading (ETL) tools and creating an operational data warehouse which deploys department-specific, pre-built business intelligence applications, where possible.
The transformation healthcare providers and payers will experience in the next few years has the potential to be more profitable in a faster and easier manner to their organizations if they recognize the people and process changes are as important as the implementation of technology.
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