Healthcare provider solutions for data management challenges

Aug. 5, 2010

Good news, you are not alone.   

Healthcare providers across the United States are inundated with data and suffer from poor data management policies and techniques. Regardless of the number of beds, revenue and payer mix, healthcare provider organizations have evolved their reporting immensely in the last 30 years, but still are hamstrung by their inefficiencies in data management, lack of analytic capabilities and information distribution challenges.

Now for the bad news: It’s about to get worse … a lot worse.

Healthcare data management challenges will multiply in the next three to five years, placing organizations at great financial risk. Numerous healthcare initiatives will spawn a healthcare data evolution, the likes of which the industry has never seen. With the healthcare industry experiencing a paradigm shift in reimbursement, whereby provider organizations will be reimbursed based on the quality and efficiency of care provided, it’s essential that provider organizations address their existing data management challenges and understand the data implications of upcoming health IT projects.

Several health IT initiatives and programs are now underway, which will generate volumes of critical healthcare data. Electronic medical records (EMRs), health information exchanges (HIEs) and meaningful use reporting are three health IT initiatives that will not only exacerbate the existing healthcare data management challenges, but will shine a spotlight on the disconnect between data management, analytics and information distribution. To compound the problems, the various health IT projects and initiatives will generate a tidal wave of reporting challenges and data integration, management and governance issues.   

The underlying challenge starts with how to manage and integrate data from disparate systems that typically contain data in different formats and are stored in proprietary databases, reports or spreadsheets. Questions often arise regarding who owns the data and therefore maintains the “single source of the truth” when data is replicated and contained in multiple source systems. Despite the logistical nuances and challenges of data management, there are numerous software tools, techniques and resources that can assist healthcare organizations unlock the value of their data assets. Without a doubt, healthcare organizations faced with limited budgets, financial risks and constantly evolving needs often forget to step back and create a roadmap for how analytics can be used within their organizations. Despite the glaring spotlight on the healthcare industry today, healthcare organizations continually miss the opportunity to define a vision and plan for both their short and long-term reporting and analytic needs.  

A significant problem for healthcare organizations is that valuable data is locked in disparate clinical, financial, and operational information systems. To unlock and capitalize on this critical data, organizations have been taking two distinct approaches to address their data management, analytic and information distribution challenges. Each approach has its pros, cons, cost implications and risks, but is valid in the data management challenge. Whether at the enterprise or departmental level, data integration and analytic reporting capabilities are required, but future needs are constantly evolving. Two widely adopted approaches are:

  • Enterprise data warehouse (EDW) – The EDW integrates data from several disparate data sources into a common data model and is used in conjunction with business intelligence (BI) software solutions to develop reports, key performance indicators (KPIs) and dashboards. The EDW approach provides a single source of the truth and comprehensive analytic possibilities for healthcare organizations because it integrates clinical, financial and operational data.
  • Report mining – Utilizes existing reports (e.g., revenue cycle) as the data source and allows end users to integrate disparate data (e.g., reports, databases and spreadsheets).   This approach provides an alternative method for data management and allows for a newly integrated view of the data to be consumed by BI software for development of metrics, KPIs and dashboards.

Despite the differences, the healthcare organizations that benefit most from tackling their data management issues have utilized both approaches in conjunction. Data management solutions range in complexity, cost, performance and the required level of support. Although complex, data management is just a stepping stone to developing the analytic capabilities required to support healthcare organizations. The value in healthcare data can only be truly realized when analytic capabilities are developed via reports, metrics and KPIs that allow end users to identify and act upon trends and patterns within their data. 

Whether you’re trying to measure financial performance, clinical quality or operational efficiency, healthcare organizations often develop reporting and analytic capabilities that are limited in two ways. First, the reporting and analytic capabilities are laser focused on a particular subject area and therefore do not allow for the incorporation of disparate data, which can provide insightful views into financial, clinical and operational performance. Second, reporting and analytic capabilities are often built without defining the information distribution requirements. As a result, end users are left frustrated because they are still unable to interact and receive their data in a meaningful and timely manner. 

Moving forward, the healthcare industry and its various initiatives will only further compound the data management, analytical and information distribution challenges. Successful healthcare organizations will realize the relationship between data management, analytics and information distribution and will only develop or implement solutions that incorporate the requirements from each area.

Contributed by: Tom Callahan, healthcare product manager, Datawatch