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As a result of more physicians using EHRs than ever before, more patient information is accessible in digital formats. This data is a new opportunity for physicians and data analysts to extract meaningful insights about their patient populations. What began several years ago as the practice of reporting and analyzing historical data is progressing into more intelligent data analytics. This evolution of analytics capabilities gives physicians more than just a view of the past; it enables more capabilities related to the present and future.
The current implementations of business intelligence (BI) vary significantly from organization to organization. Some hospitals have the BI maturity level in which they manage a data warehouse, and apply advanced algorithms, analytics tools and machine learning to discover insights into the stored data. Other organizations are still only using basic reporting capabilities and are limiting themselves to analyzing data to learn from past performance. These two extremes are caused by some or all of the following challenges that present themselves when implementing BI in healthcare facilities.
Barriers to BI in healthcare
Complex systems: Historically, many analytics platforms have been difficult to use because they required data scientists to develop data models and build algorithms to interpret data. Many midsize and small hospitals have not accumulated the funding needed to carry out a BI initiative. Also, the results of BI implementations in hospitals have varied and the return on investment has been difficult to measure.
Leadership buy-in: When it comes to using analytics tools and data visualization tools, some executives are challenged to justify the costs associated with the platforms. Over time, it's been shown that when BI is applied in the appropriate areas of a hospital, it can lead to cost savings and patient outcome improvements.
Understanding what BI can do: Using data to support business decisions is widely used in numerous areas in healthcare facilities, such as administration, scheduling and finance. However, it has not been fully embraced on the clinical side of things. Due, in part, to the lack of data interoperability, identifying clinical use cases where BI can be applied has been limited.
Where to use BI in healthcare
However, some of the past burdens that complicated the use of BI in healthcare have gone away. With the advancements in computing and analytics tools, healthcare is experiencing a new era of BI that brings far more than just dashboards and view of the past. Today's healthcare BI tools allow hospitals to see into the future with predictive models, and help them reduce costs, improve outcomes and enhance the patient experience. The following is a list of examples of how hospitals are using modern BI capabilities.
Patient care in hospitals: More hospitals are mining their data to identify what treatment plans are best-suited for each individual patient. These advanced analytics enable hospitals to provide patients with more targeted treatment and reduce the length of their hospital stays. Hospital systems can also identify high-risk patients by detecting specific data elements related to their conditions. These alerts help ensure these patients are monitored closely in the hospital and after their discharge to reduce the chance of readmission.
Resource management: Predictive analytics can be tremendously valuable in helping hospitals identify the amounts of staff or beds needed simply by evaluating existing data. Some integrated delivery networks are able to have just the right amount of staff to meet the demand of their operating rooms and patients. The same analytics processes can be applied to manage the availability of other hospital assets by projecting the volume of patients. The costs savings are significant for those providers that use this side of BI in healthcare.
Financial optimizations: When it comes to patient collections or claims payments, hospitals have traditionally relied on industry best practices or used outside agencies to collect payments. With some of the new financial analytics tools, many hospitals are able to predict upfront what the outcome of an account may be by looking at the historical payment behavior of the patient, as well as claim type. These indicators help the billing team decide on an appropriate course of action before the patient even leaves the hospital.
Predictive analytics is one area of BI in healthcare that organizations must take seriously. While the traditional BI stack may take longer to adopt, healthcare IT executives cannot afford to wait any longer. Staying competitive with today's shrinking reimbursements and changing policies requires healthcare executives to be nimble and more efficient. Adopt technologies that have a direct effect on patient care, and costs should be part of their strategies.
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