The use of clinical data analytics holds much promise for the health care industry. Providers can use health care data analytics to learn about patient populations, enhance
How can data analytics improve a hospital's bottom line?
Every entity in the health care space wants to lower costs. Enhancing supply chain management is one method to support a hospital's revenue stream. For example, data analytics makes it easier to keep tabs on hospital materials and inventory since the process is performed electronically.
When it comes to patient registration, the use of data analytics can help reduce man-hours previously needed for manual processes. Tasks such as determining a patient’s insurance eligibility, obtaining demographics and estimating treatment costs can be done in a more timely fashion with analytics software.
How do decision support systems rely on health care data analytics?
Making informed medical decisions is the bedrock principle for physicians in treating patients at the point of care. Clinical data analytics helps physicians search through past data on a larger patient population to make informed decisions on one particular condition.
While the data might be robust, a type of data mining called "forecasting" helps providers make reasonable predictions about patient care and illnesses. These predictions can prepare providers for patients' conditions and, through the use of preventive care, help them try to prevent illnesses from reoccurring or worsening.
How can health care data analytics be used to improve preventive care?
Patient populations in heath care play a valuable role in understanding where patients are being admitted. Health care data analytics applications encompass deep search tools that can help health care professionals spot trends -- and prepare treatment beforehand -- within those demographic areas.
Finding trends within particular demographic areas can be achieved through text analytics tools. These tools, which can sort through limitless amounts of text-based data, are beneficial in searching through information in emails, social media and electronic documents. Providers can search documentation from both past and present patients to improve treatment. Text analytics makes it easier -- and faster -- to find related cases as they pertain to particular illnesses.
Lastly, many hospitals track quality initiatives. Data analytics helps check the progress of these initiatives, which range from reducing hospital-acquired illnesses to guaranteeing access to real-time patient charts, without relying on paper processes and administrative man-hours.
What is streaming analytics software, and how can health care providers benefit from it?
Streaming analytics software lets providers examine vast volumes of data as they are made available. It holds much promise in detecting medical patterns to minimize fatal conditions such as strokes and heart attacks.
One such example comes from Columbia University Medical Center, where researchers are using IBM streaming analytics software to identify brain aneurysms in patients before they occur.
Another example from Intermountain Medical Center in Murray, Utah features a risk measurement tool that can be used to predict survival rates in patients with implanted defibrillators. This helps providers determine how to continue with care options. The "Risk Score" tool can decipher -- through age, gender and routine blood tests -- whether a patient may develop cardiovascular troubles.
What is a clinical data warehouse?
A clinical data warehouse, or data mart, is a collection of data that reflects all aspects of hospital operations. Instead of backing up and archiving both operational and informational data on a computer or server, a data warehouse lets users find subject-oriented information on demand. Data is obtained in clear groups, such as patients or providers. What's more, operational and informational data are stored separately within the data mart.
For large hospitals seeking meaningful use incentives, implementing a data warehouse is a viable option, as it could optimize and standardize patient and population data before it is sent to disparate locations through the health information exchange process.
For the most part, health care data analytics applications connect to data warehouses in large organizations. Smaller practices and specialty clinics often lack the resources and capital to host such large-scale technology.
Let us know what you think about the FAQ; email Craig Byer, Assistant Editor
This was first published in October 2011