Healthcare business intelligence is the applied analytics of big data. Data analytics is conducted on structured and unstructured data, giving insights into preventative care, treatment outcomes, costs, and other areas. 

Traditional business intelligence relies on historical models and data, requiring the creation of dashboards and reports (typically with the help of data scientists and business analysts). The insights are presented through raw numbers, healthcare BI reports, and data visualization via healthcare dashboards, representing the most comprehensive evidence-based conclusions available.

Embedded business intelligence takes BI capabilities a step further. Embedded BI integrates analytic capabilities and data visualizations (such as real-time reports and interactive dashboards) directly into applications that users are already familiar with.  

Utilizing embedded business intelligence, healthcare professionals can still glean insights from data, but they can interact with that data as part of their daily workflows. Utilizing embedded business intelligence tools have direct benefits for everyone in clinical healthcare, ranging from solo practices to hospital networks.

Embedded BI tools are also beneficial for pharmacies, pharmaceutical companies, medical schools, clinical trial researchers, insurers (both health and malpractice insurers), and others tangentially connected with the healthcare field. 

Analyzing organization-wide treatment recommendations allows providers to identify their high-value treatments and ones that are potentially overlooked. Being aware of high-value treatments improves the bottom line and financial viability.

Showing providers where treatment recommendations are being overlooked simultaneously gives patients additional treatment options and increases revenues. Being aware of high-value and overlooked treatment options helps manage the business side of a healthcare organization.

Healthcare business intelligence tools can also analyze data from a provider perspective, showing how efficient various providers are and even which treatments providers are most likely and least likely to recommend. Having provider-specific data available is necessary to hold individual medical professionals accountable, but the usefulness of this data extends beyond individual management. With provider-specific data, organizations can identify what diagnoses and treatments providers at different levels can effectively handle. 

Physician assistants and nurses have long-managed cases that medical doctors don't necessarily need to see directly. Data insights make it possible to direct these types of delegations more effectively. Definitive data allows organizations to delegate specific categories of cases to mid-level medical providers, knowing that those providers are fully capable of handling these cases.

The data also will show where cases are beyond physician assistants' and nurses' capabilities and should first be seen directly by a doctor. The former helps reduce doctors' caseloads, while the latter prevents unnecessary duplication due to assigning mid-level providers cases that doctors ultimately must treat.

Insurance companies are steadily transitioning to more outcome-based reimbursement models rather than service-based models. While not the primary purpose, this will increasingly be a significant financial reason for organizations to give the most promising treatment recommendations. As shown, healthcare business intelligence tools are essential to making this improvement.

Where can the organization improve revenues without sacrificing patient care? Identifying high-value treatments and overlooked treatments shows where an organization can maintain or increase revenues.

Pairing that financial data with treatment outcomes ensures that revenues are never pursued at the expense of patient care, but are improved in concert with maintaining and improving patient care. This can cut costs for patients by reducing unnecessary care of hospitalization when it is not needed.