When it comes to adoption of technology, the healthcare industry is often decades behind other industries, this is because physicians prefer more traditional ways of operating. This led to a new branch of data analytics known as Health Analytics. With the increasing rate of reimbursement for value-based services, rather than traditional fee for service payment, physicians and the healthcare community as a whole have begun to rely on technology to provide solutions that would result in better outcomes through the use of Health Analytics.
Health Analytics is a broad term that is used to describe the branch of analytics that focuses on the provision and interpretation of data which would be useful in offering solutions to healthcare problems. Health Analytics provide insight into decision making by collecting data from four major areas of the healthcare sector. These data collected include Clinical data, Claims and Cost data, Pharmaceutical and Research data, and lastly Patient data.
Different Applications of Health Analytics
As stated above; health analytics tools find their applications in the four major areas of the healthcare where they perform specific functions. The particular type of analytics model deployed would depend on the area it’s being used, and the information needed. Below are some examples of how analytics tools are been used in healthcare;
Electronic Health Records
Analytics tools such as Electronic Health Records (EHR) is used to identify patients who are at risk of certain diseases and help physicians carry out interventions to prevent their manifestation, or manage their symptoms if they manifest.
Remote monitoring of patients has now become possible with the introduction of various health apps and sophisticated smart devices (some of which are wearable), which allows patients input their vitals, symptoms and other health data from any location and transmit to their provider. Providers can detect any decline in health and take immediate actions before complications arise.
Health Analytics can help in the identification of patterns of disease management and costs across the care continuum using a system of interconnected EHR. This provides physicians with detailed information that can help them make comparisons, reduce unnecessary care and reduce costs.
Uses in the Pharmaceutical Industry
In the pharmaceutical industry, health analytics can be used for identifying and evaluating strategic opportunities, market forecasting, determination of product pricing and even the monitoring of competing businesses. Still in the area of pharmaceutical research, analytics are being used to supplement traditional clinical trials and drug discovery techniques. Modeling and simulation methods are used to predict clinical outcomes, inform clinical design, support effectiveness, safety, as well as evaluate adverse effects.
How Can Healthcare Organizations Adopt Health Analytics
It has been mentioned that healthcare officers are more traditional in their method of care, so adopting the use of analytics may seem like a huge deal. The trick to conquering this fear is to start small.
First, begin by creating an information database; a simple spreadsheet tool such as Excel can be used to achieve this. Patients biodata and clinical history are recording as well as existing or preexisting conditions. This information database is basic enough to be interpreted by any member of the healthcare team. From this stage, a specialized analytics solution which would use this information as well as other variables to map out best practices can be introduced. An example of such a module is the Electronic Health Records.
The next step would be to assemble a team of data analysts and health informaticians, who would help translate the data into actionable plans. It is one thing to get the required data, and another to interpret it; the data analysts transforms the data into formats which the healthcare team can understand. In the ever-changing world of technology, however, it is also important for members of the healthcare team to have average knowledge of data analysis and analytics tools so as to grasp new concepts and ideas more easily.
Last but not least, with the use of the given analytics tool organizations can begin to make informed decisions and carry out the best, quality cost-effective healthcare practices. It should be noted that an organization could have more than one analytics module depending on the field of use; so for instance, a hospital facility that employs the use of EHR for its overall data storage could also have another analytics module for its oncology unit.
The Importance of Health Analytics
The importance of health analytics can be found in its applications in the various aspects of the healthcare system where it is being used. The most significant importance of health analytics systems is the fact that they have revolutionized the healthcare system by increasing the overall wellness of patient populace while saving costs at the same time.
Challenges of Health Analytics
Data analytics may be a revolution in the healthcare sector, but as with any system, it comes with its own disadvantages. Common challenges encountered in health analytics are;
The high cost of deployment. Health Analytics modules are expensive to deploy, costing providers millions of dollars to acquire and maintain. Also, data analysts are needed to man these modules, either as permanent or temporary staff and their services don’t come cheap.
Variations in standards across the care continuum. Since there are no policies guiding how IT stakeholders interpret and implement clinical data, some stakeholders are usually reluctant to share and collaborate with others to foster better engagements and user outcomes.
The reluctance of healthcare stakeholders (patients, physicians, insurance providers) to adopt data analytics. Some of them lack confidence in the analytics tools and modules and do not feel confident that IT products and services offered are secure, safe and useful in practice.
Difficulty in the standardization of certain clinical data. For the analytics system to effectively provide insights and reports, the data fed into it would have to be in a format that can easily be interpreted by the system. While some clinical data such as laboratory results is easy to input, others such as prescriber notes may be more difficult to standardize.
In summary, data analytics in healthcare has revolutionized the system in the last decade, bringing the industry up to speed with cutting edge technology. With the increasing adoption of IT in the industry, it is safe to expect greater positive changes in the future of healthcare.