The Importance of Clinical Analytics

Posted onCategoriesMedical

Clinical Analytics is when a healthcare organization utilizes stored and managed data in order to provide data-driven decisions. This may be decisions within a specific clinical setting or to produce information as to the organization’s bottom line or inner workings. The common factor is putting the data that has been collected to work instead of just generating reports with facts and figures.

image courtesy of freedigitalphotos.net/bluebay
image courtesy of freedigitalphotos.net/bluebay

With the introduction of electronic medical records (EMR), and other data warehousing options, the healthcare industry has gained, organized and recorded a huge amount of essential statistics. Storing this data is one thing, but finding actionable information is other story altogether.

Part of what clinical analytics can provide is a picture how past events can have many similarities with current patients, thus yielding information to better treat ailments. Also, tell-tell signs of future issues can be identified sooner and make it possible to avoid negative outcomes due to preventative measures.

Clinical analytics are important for healthcare organizations to have right now. Some aspects of analytics may already be implemented within certain systems, however, having fully integrated software that works with all types of information being received and has the ability to produce meaningful and useful reports is much more effective. Unfortunately, not all clinical analytic software on the market offers the ability to prioritize and increase the value from within.

As in any industry, technology and advancements are coming fast and furious. Healthcare had been one of the last businesses to seek out and utilize many of the data collection, storage, management and analysis systems to improve care and function. However, they have all but made up for the lateness to the party by taking very seriously the produced outputs to change the way we receive care and moderate costs.

image courtesy of flickr.com/DougWaldron
image courtesy of flickr.com/DougWaldron

Think of how far EMR and data analytics has come in just 5 years. How much further will it go in the next 5 years? 10 years? 20 years? Clinical analytics are not on a downward trend, but the use and usability are just beginning to be understood. As more data is collected, and we know this will happen because we all see our doctors or visit hospitals, more information will be gleaned and better analytics will reveal more insights.

The healthcare industry and its professionals have a great tool in their arsenal to help provide better treatment at a lower cost for everyone involved. This is the pathway that is being focused upon to help patient and expert alike. Clinical analytics are not a flash in the pan, but a true answer to many questions that we haven’t even begun to ask.

Obstacles in Collecting Healthcare Data

Posted onCategoriesMedical

Collecting healthcare data is difficult for a variety of reasons. Where the industry is right now, healthcare data is “owned” by a variety of people, and in a variety of ways. The necessity to make changes to the industry as a whole requires changes that aren’t simple and won’t be met with open arms by all those involved with inputting of data.

image courtesy of freedigitalphotos.net/photostock
image courtesy of freedigitalphotos.net/photostock

In electronic medical records and other forms of healthcare databases, data is recorded both numerically and textually. However, much of the data has a long history of being written out. Converting this data, which is inaccessible to most healthcare professionals due to being located a physical site, requires the time, effort and dedication to transfer into digital form.

To understand better these two different forms of data, numerical data is defined as anything containing a simple number, such as date of birth, age, height, weight, temperature, blood pressure and amount of medicine being administered. Textual data is data in all other forms, including all written information, MRI images, x-rays, etc. This sort of data is much more difficult to delineate into specific areas or categories because it isn’t always an absolute, like numbers are.

This is where we introduce systems like the International Classification of Diseases (ICD), which assigns numbers and codes to different textual data in the healthcare industry. For example, the current ICD-10 code for the Ebola virus is A98.4.

This coding system will certainly help to define healthcare data more precisely, and allow it to all be readable by any system. This is the type of uniformity that is crucial to produce a readable and analytical system. However, making sure that all healthcare professionals are on the same page can be a different story altogether, and adds to the obstacles that make collection of data difficult.

In addition to the two major forms of data (which are hopefully more easily defined by the ICD), data can also come in the form of doctor’s or nurse’s opinion, which cannot always be categorized into the coding system. There certainly are standards in healthcare practices, but there is also variation in the ways doctors might diagnose something or note specifications of a patient. One hopes for consistency across the board, but that can’t happen when from practice to practice, from doctor to doctor, or even from patient to patient there are personalities and uniquenesses that make this impossible.

On top of the obstacle of data collection and varietals, there is also the legality in collecting healthcare data. Different providers “own” their own data. The patient has gone to that doctor and agreed that that doctor/practice/hospital can have their data, but not anyone else. Confidentiality agreements are signed, and the data cannot be shared unless the patient agrees to it.

image courtesy of freedigitalphotos.net/hywards
image courtesy of freedigitalphotos.net/hywards

Undergoing this data transformation is a huge undertaking for everyone in the healthcare field. It means learning new systems to track this data, learning new codes for doing so, and learning how to use and measure this data. While it is certainly a part of the solution, it means doctors, nurses and other healthcare experts will need to learn what is virtually an entire new language to record their data. This will be a challenge, but one that has the potential to benefit both patient and professional.

Though the current system of data collection for healthcare information is not ready for the diversity in how facts and opinions are recorded, technology specifically focused on this classification of data is moving forward and may one day allow for the variety of textual data and still produce a plethora of usable analytics. Until then, finding standardization within the system is necessary and will turn out very useful information that can be beneficial to everyone involved.