The healthcare industry generates a massive amount of data each year and the amount of data tracked and recorded shows clear signs of increasing as healthcare decisions rely on more and more metrics and parameters to provide personalized patient care.

With ongoing scientific breakthroughs, medical care aims to achieve a holistic understanding of patient needs, which means collecting and incorporating research and data about various aspects of health and well-being.

The data in question can relate to both the personal care of each individual patient (medical history, lab results, lifestyle) and the healthcare industry as a whole (public health records, available medicine, billing data, etc.).

This data-driven approach requires a new strategy for managing information and bringing it all into one place, where it can be used by healthcare professional and assist them in their daily workflow.

What is Causing Data Explosion in Healthcare?

This explosion of data in the healthcare industry is fueled by many factors that have been in play for years and have only been accelerated by the COVID-19 pandemic.

Patient records have become digitalized, and medical imaging techniques (X-rays, MRI, nuclear medicine, etc.) are discovering new ways to monitor the processes in the body.

There are also various types of wearable devices that help track, analyze, and transmit medical data about a person. They are equipped with a sensor that can identify and collect information about a person's activity level, as well as biometric and environmental data.

All of this information comes from different sources, and healthcare workers often face the challenge of collecting data from different sources like patients' medical history, symptoms, vital signs, lab reports, and imaging scans.

How do Healthcare Workers Gather Data?

In their daily task of collecting all the needed information, healthcare workers turn to many different data sources and a variety of tools. First, electronic health records (EHRs) alone contain a lot of data under different categories, such as patient demographics, medical histories, diagnoses, medications, allergies, laboratory results, imaging reports, and clinical notes.

Then, you have PACS (Picture Archiving and Communication System), which is used to manage and share diagnostic images, and telehealth platforms, which have seen a huge growth in popularity lately.

The list doesn’t stop here because healthcare providers also rely on clinical decision support systems (CDSS) and population health analytics platforms for data analysis and deeper insights.

All things considered, managing data in healthcare is no simple undertaking, and it's no wonder that many in the healthcare industry have started implementing various solutions that combine data searching with AI and NLP.

How Can Enterprise Search Help in the Healthcare Industry?

With the amount of data generated, simple keyword searching and database indexing just aren’t enough anymore.

Enterprise search has also advanced from simple information retrieval, establishing itself as a SaaS that offers additional features that help with organizing and using information. These additional features vary from vendor to vendor, but data-heavy industries like healthcare often require enterprise search with advanced AI and machine learning capabilities that allow for in-depth analysis, predictive analytics, and automation.

Enterprise search essentially collects all the healthcare data from the different sources we discussed to make it an easily searchable and unified database from which it is possible to extract informed insights about patients and medical trends.

Enterprise search essentially makes it much easier to collect all the relevant information, analyze it, and offer a perspective that is based on a holistic overview, which, when coupled with AI, can aid in making all sorts of necessary correlations.

Examples of Using Enterprise Search in Healthcare

When it comes to using enterprise search in the healthcare industry, let's try to paint the picture with a few examples.

For example, a clinician might type "patient's latest lab results" into an ES tool integrated with the hospital's Electronic Health Record (EHR) system.  The search system is then able to answer this query by retrieving and summarising relevant lab results. This means that it is no longer necessary to navigate specific databases and manually search through records.

AI can further assist in this process when it's used to analyze patient data. For instance, AI algorithms have so far been successfully used to predict patient readmission risks for conditions like heart failure by taking into account both clinical and social factors. There are also AI-powered ECG analysis systems that assist cardiologists in identifying arrhythmias with high accuracy, known for being able to catch subtle patterns that can be easily overlooked manually.

Last but not least, AI helps healthcare workers automate routine tasks, which are common in categorizing, organizing, and researching data. Some examples include automated image analysis for radiology, predicting disease progression, and generating follow-up recommendations from radiology reports. 

Conclusion

Overall, enterprise search in healthcare transforms data retrieval and analysis. It brings the features of generative AI together with critical patient information to allow predictive insights and real-time decision support, as well as enable healthcare workers to make decisions with all the relevant data that are easily accessible.

Security is a major concern in the healthcare industry, so it's important to ensure that your enterprise search vendor follows the latest security measures and protocols.

There are quite a few studies that show that AI significantly enhances diagnostic precision and patient outcomes in healthcare.

This is to be expected, as it becomes clear that making information easily accessible is one major predictor of success in any field, and coupled with the latest features of generative AI and NLP that allow users to creatively manipulate data in natural language, possibilities to utilize this information become endless.