Hospitals are always crowded with people in need and those helping them. As patients come into the healthcare system, their data starts to be retrieved and stored. This information it’s not static, and adds many layers of information regarding the patient’s community and environment too.
With such a huge amount of information, the data collected inside a hospital is always growing at high speed and overflowing with the most unexpected details and inputs that can change for good the lives of thousands of patients.
That’s why every health institution must develop its practices to deal with big data. For example, with a managed service provider in Minneapolis, hospitals can get support in IT services that would allow them to automatize various big data processes.
Here you will learn a little more about three trendy big data tools that are the most effective in healthcare systems, from a metropolitan hospital to a small local clinic, these tools powered by advanced technologies are increasingly popular.
What Is Big Data?
Big data is a term that has been around for quite a while, and today it refers to complex and copious datasets that are too gigantic to be analyzed by traditional data-processing tools.
A dataset’s volume, the speed at which it gathers and makes additional information available, and the range of both categorized and uncategorized data are the three main dimensions of big data expansion.
Although often described as “unmanageable,” big data can be reorganized or discovered with new tools that are enhanced by artificial intelligence (AI) and machine learning (ML) to grasp insights and patterns from a vast ocean of information.
Big Data and Healthcare
The healthcare system is a multidimensional structure that has many levels and sublevels with health professionals, providers and patients operating at each of them.
Over time the complexification of the healthcare system started to produce an amount of datasets that were seen as chaotic before the big data trend kicked in.
With the advancements in technology, the exponential growth of medical data had to be monitored for the patient’s sake. These tools are currently the most popular in the healthcare sector thanks to their speed, user-friendliness and networking features. Here are three of them:
Electronic Health Records (EHRs)
These computerized medical records are key to predicting the patient’s future health based on information gathered from the past and current state of their physical and mental condition. Today, EHRs aim to not only be a digital version of a patient’s health history but also provide real-time, patient-centered information with a fast data retrieval speed.
EHRs compile a large set of information, comparing and contrasting different aspects of health in one chart that might contain a patient’s demographics, medical histories, progress notes, medications, lab and test results, and administrative and billing data, among others.
The use of EHRs brings benefits such as better decisions and coordinated care of the patient between the different hospital professionals and according to prescribed treatment, reducing logistics mistakes and delays. EHRs now are collecting new variables like medical imaging, and socio-behavioral and environmental data to understand and predict patterns both in the public health sector and at the individual level.
The consideration of these variables will improve the tracking of existing and developing medical conditions in certain populations subject to these studies.
Medical Practice Management Software (MPM)
Keeping a hospital schedule tidy and organized is a herculean task that needs constant attention to detail, daily monitoring and note-keeping. That’s why medical practice management software (MPM) comes to the rescue. MPMs are here to deal with the hassle of appointments, reminders, calendars and everything that has to do with scheduling.
Medical practice management software also serves as a support for the EHRs, storing them as files that are easily accessible by physicians and surgeons, among other qualified health workers who can handle sensitive data. Patients can also access their information stored in MPMs, having all their medical records in one place. In case of wanting to change health providers, MPMs make it possible to switch from one health institution to another quickly.
Regarding finances, the use of MPMs also facilitates the finance section of a hospital, reducing or eliminating billing and claims management issues due to their automated registration of appointments and invoices. MPMs also have features regarding reporting, which allow to create or pre-save reports to compile a patient’s information.
This reporting is also important for healthcare providers regarding inventory management, making it possible to streamline inventory, track orders and create reports in an integrated system.
Predictive Analytics
With this amount of complied information, it doesn’t come as a surprise that predictive analytics are trendy in the healthcare system.
Powered by machine learning, predicting trends and probabilities it’s a way to look further into a patient’s medical history or annotate patterns in a community’s rate of infection per month. By extracting information from EHR datasets, predictive analytics can automate many diagnosis decisions or reject treatments based on patients’ adverse reactions.
Predictive analytics can grasp logistics information patterns too by simply analyzing the stored data from MPMs. The creation of risk scores, for example, stems from the information that predictive analytics can harvest to support these indicators’ effectiveness in preventing health issues.
Helping to make better-informed decisions and uncovering hidden information makes predictive analytics an indispensable tool in today’s medicine. Handling the schedule can also be supported by predictive analytics, which can fill a gap or foresee possible cancelations of appointments based on a patient’s frequency of attendance.
Conclusion
Any structure can benefit from big data, especially the ones that integrate many different subsystems with complex and fast dynamics in movement. Big data in healthcare systems has contributed to the increasing development of a database that doesn’t discriminate but integrates categorized and uncategorized information.
This openness to information has allowed health workers and providers to predict, prevent and improve the lives of thousands of patients, as well as polish the workflows inside a health institution. Big data trends in healthcare are going to keep growing while nurturing each other from every niche: as EHRs are the unit that is stored by the MPMs, predictive analytics can create impressive outcomes thanks to these records.