BI for Healthcare

Posted on by By Nikhilesh, in Business Intelligence | 0

BI for healthcare

 Healthcare organizations are going through a technology and data revolution. Pressure from a wide range of sources are forcing both providers and health plans to look at their data and technology investments in new and innovative ways to gain competitive advantage. Healthcare business intelligence can provide organizations the ability to use their data to improve quality of care, increase financial efficiency and operational effectiveness, conduct innovative research and satisfy regulatory requirements.

What is driving the use of analytical tool?

  • Desire to create better clinical outcomes
  • Improving patient care through understanding trends
  • Desire to improve business efficiency
  • Federal healthcare mandates such as HIPAA
  • Track quality indicators across electronic health record (EHR) systems
  • Organization’s self analysis that might require during emergencies like department staffing etc.
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Implementing Analytics

Address Complex Task

For a successful implementation of BI in a patient-centric environment, there are many complex tasks that need to be addressed. Some are related to showing value and engaging the leadership to back the initiative, and others relate to all the technical requirements for a successful install and deployment. But whether you’re focusing on logistics, ER, A/R, or nursing dashboards, all of these areas have meaningful data that will help ensure the organization’s goals are met at all levels and everyone is in line with the objectives.

 Get right data at right time

It is very critical to ensure that there is sufficient data available to plot your progress in certain areas. This would require

  • Analyzing the reports and trends — and looking beyond meaningful use when doing so.
  • Identify how often the data will need to be reported for each department within the organization.

Define where the data is

The majority of the analysis will be applied against existing data that has been collected through the hospital system and internal applications. But there are a few data sets that will need to come from third parties and/or other public or federal groups. As an example, when reviewing the suggested measures that will assess theappropriate use of medications based on standards of care for applicable conditions, realize that professionals are comparing data collected within the hospital against standards that are outside the IDN. This clearly indicates the importance of knowing the origin of the other data collected.

 Share value with rest of the group

It would be beneficial to share with all management and executive team members what are the capabilities and value of BI within your organization’s context. Whether the team needs to apply it to A/R analysis, the revenue cycle, quality metrics, general charge capture, patient satisfaction or marketing, there are numerous areas that can benefit from the analysis of the overall data

 Define Reporting & Analysis Intervals

For the data to be properly analyzed and meaningful, it must query from the production system at different frequencies. This will store it at different processing data repositories. In order to avoid performance issues, schedule data extraction and processing ahead of time. For example if we are looking at reviewing the clinical services and perform a comparative analysis over a period of five years, then this is a job to be performed during non-peak hours and would most likely take many hours. But if you are looking to review a ticker in real-time that displays patient admissions or discharges then a simple query can be executed periodically throughout the hour.

Are you looking for Best business intelligence tool for healthcare industry
Get started with Helical Insight. Helical Insight is best business intelligence tool for for healthcare industry

Get your 30 Days Trail Version

 Select right BI tool

There are a variety of BI tools, from spreadsheets, to online analytical processing (OLAP), and reporting tools. The tools that need to be implemented would depend on several things: overall data size, current platform, in-house skill set, physical architecture, mobile app capabilities and support.


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