Business intelligence (BI) tools have been around for a long time, and these "decision support systems" have only grown more popular and more effective over the years. BI software is so commonplace that buyers can find options ranging from generic tools for any type of business to incredibly niche systems geared toward specific markets. That's where healthcare BI software comes in.
In this Buyer's Guide, we'll go over everything you need to know to gain a fundamental understanding of healthcare-specific business intelligence software. Here's what we'll cover:
Before going into more detail about business intelligence software made exclusively for healthcare providers, it will help to understand what BI software is in a more general sense. Speaking broadly, BI tools are designed to organize and analyze data to provide useful insights to businesses when making any decisions.
The type of data being analyzed can vary widely depending on the needs of the user—whether it's internal key performance indicators, external feedback from customers or social media or marketing campaigns. Regardless of the type of information being collected, BI technology works to make it understandable and actionable.
The difference between generic BI software and healthcare-specific BI software is the functionality. Most healthcare BI systems offer integrations for existing medical software, such as electronic medical records (EMR), patient portals, medical accounting and patient engagement. By linking a BI program to all of these existing sources of data, practices can see every piece of information and how each data point fits together, all in one place. Practices can then use all of that information to do things such as:
And that's just to name a few benefits. Obviously, the applications of this software depend on the features practices elect to pay for with their BI system, which leads to the next topic of discussion.
Data management encompases all the prep work that happens before data can be interpreted, such as collecting information, optimizing databases to house and organize it and cleaning the data.
Data discovery is the analysis process. This is where the data that was collected gets compiled in order to produce the easy-to-understand and actionable insights necessary for decision making.
Reporting features present the analyzed data in ways that are easy to comprehend, such as dashboards or charts and graphs.
Here are some of the more specific features to look for that fall within these three categories:
|Big data collection||Connects BI systems to big data sources (such as diagnostic information gathered from EMRs) to help medical professionals see and understand national or global health trends to provide better care for patients.|
|Data quality management||Users can standardize data that's pulled from different sources so the information collected is automatically uniform. This feature is important to ensuring future analysis fits the ideal scope so it will be useful.|
|Extract, transform and load (ETL)||Enables BI software to gather data from diverse sources before converting it to fit user specifications and storing it in the correct database.|
|Data cleaning||Sifts through collected data to filter out or flag anything that does not fit the standard. This process ensures all of the information gathered is free of errors—such as validation failures, typographical mistakes or incomplete data.|
|Data mining||Automatically sorts through cleaned data to pick out any type of pattern. This is typically the first step to analysis, without which other data discovery steps couldn't happen.|
|Predictive analytics||Compares data collected in the past to current data sets in order to make informed predictions for the future.|
|Data visualization||Allows users to create customizable charts, graphs or diagrams to represent data sets in digestible, interactive visuals.|
|Dashboards||Think of this as the home-base for BI software and data interpretation. From a dashboard, users are granted an overarching view of all the available information in one place.|
|Scorecards||Assigns a number or grade to data sets indicating whether or not metrics are successful. One example of this is year over year performance evaluations that allow users to see progress compared against an existing benchmark.|
Data visualization tools in Sisense
When these BI features are deployed in the healthcare arena, they can be used to analyze things such as clinic or practice data (e.g., diagnostics), patient feedback (e.g., reviews), finances (e.g., billing time) and patient care (e.g., treatment progress).
Having access to all of the information BI software can collect and provide is a good thing. Just a few of the myriad advantages to using this software include:
Access to more data. It's true, BI software will give users a broader view of their own practices—but it won't stop there. These tools can also help interpret medical research from a variety of publications or other sources to shed light on cutting edge technology and new lines of thought within the medical community.
More informed decisions made faster. Whether it's triage in a hospital emergency room or deciding which prescription to go with, knowing the data can make those decisions much easier.
More effective treatment. Making better decisions at the point of care will lead to treatment options that work, which will in turn lead to fewer repeat visits and happier patients.
The biggest benefit to tracking these types of things, though, is the ability to mark progress and improve patient experiences and outcomes, which is the number one goal of every healthcare provider. Once medical professionals begin making their patients happier, a lot of other good things will follow.
Naturally, pricing for healthcare BI software will vary depending on the functionality and deployment. When researching and working up a budget for new software, it's always a good idea to get price quotes from specific vendors.
That said, a look at some of the top vendors in this space shows that buyers can expect a few things. First, the deployment options for this software are either going to be on-premise or cloud-based. Neither is necessarily less expensive than the other.
The next thing buyers should look at is pricing models. For those vendors that offer licensing options, cost usually depends on number of users. This typically ranges from $350 to $5,000 per named user.
If a subscription model is more appealing, the choice for buyers becomes feature options typically set up in tiers, such as "Starter," "Professional" or "Enterprise." These systems sometimes offer the lowest tier of service for free, but buyers can expect to spend between $600 and $3,000 for more robust packages. Again, pricing depends on number of users, so that's a good thing to know before jumping into this research.
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